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Q: Netbeans attached debugger won't stop on breakpoint in jsp page *Please see EDIT 2 at the end of the post.
I'm able to debug my .java classes with no problem. I attached the debugger using socket (SocketAttach), the host is localhost and the port when I'm in debug mode is 8000.
But I'm trying to debug a function in a jsp page such as
<%
...
ArrayList list = vb.getStuff();
...
Iterator it = list.iterator();
while (it.hasNext()){
...
//I show my list items in a table
...
}
%>

When I put a breakpoint on
ArrayList list = vb.getStuff();

It won't stop on it, even though I see my breakpoint on the right line.
In my Debugger Console, I see
JspLineBreakpoint myPath/mylist.jsp : 48 partially submitted - not into all classes, reason: No executable location available at line 48 in class org.apache.jsp.portal_jsp.

But the breakpoint stays there.
I have one single project in Netbeans, so it doesn't get confuse with other project...
Any help is appreciated, thanks a lot.
P.S: Here's some stuff that can be useful ->
Product Version: NetBeans IDE 8.0.2 (Build 201411181905)
Updates: NetBeans IDE is updated to version NetBeans 8.0.2 Patch 2
Java: 1.8.0_65; Java HotSpot(TM) 64-Bit Server VM 25.65-b01
Runtime: Java(TM) SE Runtime Environment 1.8.0_65-b17
System: Windows 7 version 6.1 running on amd64; Cp1252;
EDIT: I forgot to say that I'm using tomcat 7
EDIT 2: I just realized that I can debug the function called from there written in the jsp page..

When I put a breakpoint on
ArrayList list = vb.getStuff();

It won't stop on it, even though I see my breakpoint on the right
  line.

However, if I put a breakpoint at the first line of that function (which is also in the jsp page), I'll be able to debug it. I guess it's because it is in a function... The previous code with the ArrayList is not. So the debugger will skip the code executed at first which is not contain in a fucntion...
Any explication on why? Does it think it's part of the "html code" and just skip it, but when it sees I called the function, it debugs it?

A: This was probably due to the code in your project not matching the code running on the server, though it could also be due to the server type you're using (Weblogic for instance auto gens a servlet class by compiling the jsp whenever it changes).  This type of mismatch can make debugging "fun", since some of the code will line up, but other parts won't, making setting and breaking on breakpoints iffy.  The best thing to do is rebuild and redeploy, or in debug Sources, set the directory where jsp sources are deployed as a source and remove the project code from Sources.  I'm pretty sure this can work when the server is running with a port and definition setup so that you can attach your debugger (as in this case), as opposed to running the server from the IDE.  Hope this helped someone (someday...)

A: I was having a similar trouble with Netbeans 8.2 and Glassfish 4, but only in some web projects.
The failing project had Web Pages Folder set to ../other/web2 instead of just web (it was needed to organize the teamwork).
After copying the resources to web/ I additionally had to clear the Netbeans cache folder to make it work again.


The Internet of Things has been around for a few years but has hit an all time high for buzzword status. Is IoT important for Data Engineers and Machine Learning Engineers to understand? By 2020 Gartner predicts there to be over 21 Billion connected devices world wide. The data from these devices will be included in current and emerging big data work flows. Data Engineers & Machine Learning Engineers will need to understand how to quickly process this data data merge with existing data sources. Learn why Data Engineers should care about IoT in this episode of Big Data Big Questions.
Hi folks, Thomas Henson here, with thomashenson.com. Today is another episode of Big Data Big Questions. Today, I want to tackle IoT for data engineers. I'm going to explain why IoT, or the Internet of Things, matters for data engineers, and how it's going to affect our careers, how it's going to affect our day-to-day jobs, and honestly, just the data that we're going to manage. Find out more, right after this.
Today's question is, what is IoT, and how does that affect the data engineers? We've probably seen the buzz word, or the concept of the Internet of Things, but what does that really mean? Is it just these little dash buttons that we have? Is this? Wait a minute. Is that ordering something?
Is this what IoT is, or is it the whole ecosystem and concept around it? First things first. IoT, or the Internet of Things, is the concept of all these connected devices, right? It's not something that is, I will say, brand new. Something that's been out there for a while, and when we really think about it, getting down to it, it is a sensor. We have these sensors, these cheap sensors.
We've had them for a long time, but what we haven't had is all these devices connected with an IP address to the Internet, that can send the data. That's the big part of the concept. It's not just about the sensor, it's about being able to move the data from the sensor.
Also, a sensor could be inside the refrigerator, and tell you if something's going wrong. Maybe the ice maker is blocked. Maybe you need a new water filter in your refrigerator, and the refrigerator knows that, has a sensor into it. It can send information to wherever, to be able to order that water filter for you and send it to your home, so you don't even have to go in, and remember, "Hey, has it been 90 days? Or was it 60 days? Is it time? Is it time to change it?" Then, you're going to forget. You're going to let it go over, but now, you can have this sensor that's going to tell you, and it's going to order that for you. That's the concept. It's not just about the sensor. It's about that ecosystem.
It's about being able to move the data. For data engineers, what does this mean? Why do we care?
There are a lot of predictions out there about IoT and where it's going. One of the big ones is, Gardner has a prediction that by 2020 we will have 20 billion, over 20 billion, of these devices. Not just the dash buttons, but just think of all these sensors, all these things with IP addresses connected to the Internet. What does that mean, from a data perspective? Some numbers that I've seen are 44 zettabytes of data are some of the predictions that I've seen, that's going to be contributed to new data that's coming in and the data that we have that's already existing. Think about it. What is a zettabyte? It's not a petabyte. It's bigger than a petabyte.
How are we going to manage all these data, when right now we're still managing terabytes and petabytes of data, and being like, "Man! This is a lot of data!" That's why it's important for data engineers, is that's contributing to this deluge of data. How does all that affect us, as far as what are some of the concepts? When we start talking about IoT, and sensors, and having these data on the edge, being able to pull information back, but also being able to push the information out. What does that start to say?
As we've talked more and more about real-time analytics, this is where we're really going to start to see real-time analytics really taking hold. As soon as we can get that data, and be able to analyze it and push information back out, that's what's going to help us. Think about it with automated cars, with a lot of the things that are going on outside in the physical world, where we have sensors, and devices talking to devices, streaming analytics is going to be huge in IoT.
The question becomes, if you're looking to get involved in IoT, what are some of the projects? What are some of the things you can do to contribute and be a part of this IoT revolution? I would look into some of the messaging queues. Look at Pravega, look at Kafka, even look at RabbitMQ, and some of the other messaging queues, because think about it. As 20 billion devices, maybe more, by 2020. As these devices come in, they have to come into a queue. They have to be stored somewhere before they can be processed and before we can analyze them. I would look into the storage aspect of that.
Also, know how to do the processing. Look at some of your streaming processing, whether it be Apache Beam, whether it be Flink, or whether it be Spark. I would look into those, if you're looking to get involved in IoT. If you have any questions, make sure you submit those in the comments section here below, or go to thomashenson.com/big-questions. Submit your questions, and I'll try to answer them on here.
Until next time, see you again.
A lot of the world's economy is shifting to freelance/contracting economy or what Seth Godin terms as a "gig" economy. For Data Engineers heavy on the development side of Hadoop projects that is an easy transition. Software development projects have a a natural flow of a starting and end point. How does that work for the data engineers who are Hadoop Admins? Traditionally Operations or OPs roles are full time with no end in sight. In fact most have on call hours where Administrators have to be available 24/7. How can these types of Data Engineers find Freelance Hadoop Admin Roles? Find my thoughts on what a Freelance Hadoop Admin role looks like and where to look in the video below.
Hi, folks! Thomas Henson here with thomashenson.com. Today is another episode of Big Data Big Questions. In today's episode, I'm going to tackle questions about, what are some of the projects freelance Hadoop engineers can do, or Hadoop administrators? What are some projects in the freelance world that are going to translate and be good targets if you're looking to be able to grab some kind of freelance Hadoop administrative job?
Find out more, right after this.
Welcome back. Before we jump into today's question, I just want to remind you. If you have any questions, submit them in the comments section here, below, and then also, make sure you subscribe to my channel, so that you never miss an episode. I will answer as many of these questions as I can get to. I just need you to keep coming in, and submitting the questions, and giving me feedback on the types of content that you'd like to see.
Thank you everyone for subscribing. I really appreciate it, and now let's jump into today's Big Data Big Question. My question comes in from a YouTube comment. What freelance projects can be done by Hadoop administrators?
This one's a little bit tougher, I believe, than when we talk about data engineers and we talk about the development side. I feel like those projects are a little bit easier to find as far as being able to bid for them, but also new projects come in on the development side a lot. When you think about the administrative side, so think about continuous development, continuously holding up that operations side for Hadoop. You've got a Hadoop cluster. You're continually adding new clusters. You're patching it. You're doing the day-to-day operations, so, those are more permanent roles.
It's a little bit harder to find a freelance position for a couple months or something like that for a project on the development side versus in the administrative side. However, I will say, if you're looking to fill those roles, I think you're going to find more of a short-term contract with those, and so by those I don't mean a development. You'll have a project that comes in. It may take you two weeks, it may take you two months. I believe the Hadoop administrator roles, they're going to be a little longer if you're looking for a contract position. A lot of those are going to be more consultive type, so think of new emerging companies. They're starting up their Hadoop environment, jumping into the Hadoop ecosystem. They don't really have a basis for how they're going to do it, and so they're looking for people to really come in and be those knowledge experts to help them get off the ground.
That kind of engagement is probably going to be at least three months, probably six, maybe even a little bit longer. These are more long-term contracts in my opinion, that you're going to be able to find. The cool thing about these roles is, if you have a background or you have a desire to be able to be a trainer, and be able to help lead and teach other people, that's one of my passions, these are the kind of roles that you're going to be able to do. Not only do you get to be hands-on and be technical, you get to help a team that is brand new to the Hadoop ecosystem, maybe has a ton of experience in other areas, but you get to draw on that experience and help them build out their first Hadoop cluster, start working on some of their first use cases, and it's something that, it can be very rewarding.
If you're looking for these roles, and that's probably what the question is referring to, I would look for companies that are just starting to dive into the Hadoop ecosystem. This is probably going to be a little more, you're looking for people that are just going into that role, so look to see who's hiring Hadoop administrators and some of those other roles, and then just reach out and content those companies. Let them know. Give them your background, talk to them a little bit about some of the technologies that they're working on. If you have anything that you've been contributing to or working with in the open source community, around HDFS, or Ambari, or any of the administrative things like Zookeeper, those are where you can really shine, and say, "Hey, look. I'm involved in this community, here. I'd love to come in, have a conversation, talk to you about how you're setting up your Hadoop cluster, where some of the troubleshooting issues are going to come up. What are some of the things that I've seen with my experience, that I think you should look out for, and I can kind of help with?
Those are going to be amazing roles. Like I said, it's going to be harder, probably, than the data engineer who focuses more on the development side, but it's going to be, in my opinion, could be a lot more rewarding, because of the fact that, you're probably going to be more of a consultant, and you're going to be more running a team. You're still hands on in the tech, but you're actually being able to train and communicate to others how they're going to have this system up and running long time after your engagement ends.
Thanks for the question, and make sure you subscribe to the channel, and then we will see you on the next episode of Big Data Big Questions.
Data Engineers and Big Data Developers spend a lot of type developing their skills in both Hadoop and Spark. For years Hadoop's MapReduce was King of the processing portion for Big Data Applications. However for the last few years Spark has emerged as the go to for processing Big Data sets. Still it can be unclear what the differences are between Spark & Hadoop. In this video I'll breakdown the differences every Data Engineer should know between Spark & Hadoop.
Make sure to watch the video below to find out the differences and subscribe to never miss an episode of Big Data Big Questions.
Hi, folks, Thomas Henson here with thomashenson.com, and today is another episode of Big Data Big Questions. And so, today's question is one I've been wanting to tackle for a long time, I'm not sure why I haven't gotten to it, but I'm ready to it. So, it's the ultimate showdown. What's the difference between Hadoop and Spark, and which one will win in the fight. So, find out how I'll answer that question right after this.
Welcome back. So, today's question comes in from a user. It came on through a YouTube comment section. So, post your question down here below. You can actually go to my website and go to Big Questions. So, thomashenson.com/bigquestions. Put it out on Twitter. Use the hashtag Big questions. I'll look it up, try to answer those questions.
So, that's an amazing question. It's one that we hear all the time. So, Hadoop, very sure technology, it's been out there. Really, it's associated with a lot of things. There are going on in the Big Data community and a lot of things you talk about, you say big data, it's almost anonymous, synonymous that you're going to say Hadoop as well. But with Hadoop being over 10 years, maybe 13 years old, just depending on how you look at it, a lot of people are calling for its death, and Spark is the one that's going to do that.
But there's a little bit of difference. Like I said, we say that Hadoop is this all-encompassing thing. You hear me say it all the time, the Hadoop Ecosystem. So, I call it an ecosystem because a lot of things get pulled into the Hadoop Ecosystem. A lot of people say things, like assuming that Hadoop runs and does all the processing, and has all the functionality for your applications, or if you're running it. But in a lot of data centers, you can run big data clusters and not be using Hadoop or not be using MapReduce.
And so, let me explain a little bit what I mean really by the true definition for Hadoop and then we'll talk a little bit about Spark. So, Hadoop is built of two components. So, we separated it out into two different components. And so, the first one we're going to break down is MapReduce. So, you've probably heard of MapReduce that's what started that being able to process large datasets, and so it's an indexing, somewhat of an indexing way to do data. So, if you have a cluster, you're able to run your mapper and your reducer jobs, and be able to process data that way, and that functionality is called MapReduce. That's one portion of Hadoop.
Another part of Hadoop, the really cool, the part that I've been involved with a ton is called the Hadoop Distributed File System or HDFS. And so, HDFS is the way that all the data is stored. And so, we have our MapReduce that's controlling how the data is going to be processed, but HDFS is how we store that data. And, so many applications whether they're in the Hadoop Ecosystem or new to just data processing or even just scripting, uses that Hadoop or HDFS to be able to pull data and be able to use your data as a file system.
And so, you have those two pieces right there and those two components.
When they talk about Hadoop being old, or Hadoop being slow, or portions of Hadoop that people aren't interested in, most of the time they're talking about the MapReduce portion. And so, there's been a lot of things that have come out. So, there's been MapReduce 1, and then MapReduce version 2, and Tez, and just different components around to compete with MapReduce, and Spark is one of those technologies as well.
And so, Spark is a framework. It's called lightning fast, but it's a framework for processing data. And so, you can still process your data that exist in HDFS, that exist in S3. There's other places that your data can exist and be processed by Spark, but predominantly, most of the data centers, they still have their data in HDFS. So, things were built upon HDFS. HDFS is where your data is housed and so you process it whether you're using Spark, whether you're using Tez, whether you're using any new way of processing the data, or you still may be using MapReduce, but you can have all that in HDFS. So, when you think about it, the two do compete, they do compete, but primarily from a processing engine.
And so, I've got a couple blog post out there that I'll link to here in the show in the show notes, but you can go out and see where I break down the difference between batch and streaming, and some of those different workloads. And so, Spark really came on whenever we started talking about being able to stream data and being able to process data faster as it comes in.
And so, that's why you see a lot of people that are talking about Hadoop being the past technology and then Spark's, the newer technology that's going to take over the world. There's still going to be components from the traditional Hadoop like we talked about with HDFS. That's probably still going to be used I don't think for a long time. Like I said, there's still a ton of people and a ton of developers still using MapReduce. And so, MapReduce has its functionalities for when we talk about batch workloads and there's still development going on with MapReduce 1, and then Tez and some other platforms that are encompassed in the Hadoop community.
So, I would say, if you're looking at it from a learning perspective, all right, which one do I want to learn, do I want to learn Hadoop, or do I want to learn Spark, and thinking that it's all or nothing. I would say it's not. I would focus mainly depending on what you're looking to do, but I would definitely focus and learn HDFS, and so understand how the file system works and how you can compress, and how you can make those calls because chances are you're going to be using HDFS and you're also going to be using Spark, and Tez, and HBase, and Pig, and Hive, and a lot of different other tools in the ecosystem.
And so, I would say, it's not an either or, you're not going to pick and say, "I'm only going to do Spark," or, "I'm only going to do Hadoop." You're more than likely going to be using a lot, using Spark too for your streaming applications and for your processing of your data, but you're still using Hadoop, and the things in Hadoop with HDFS and being able to manage your data maybe with the [INAUDIBLE 00:06:28], and some of the other functionalities that are in that ecosystem. So, it's not an all or nothing thing. And so, learning one is not going to stop you from getting your job or is going to stop you or prevent you from having to not learn another one. So, it's not an either-or thing, but if you're asking who will win in the future, I would say they both win.
Well, that's all I have for today. Make sure to subscribe to the channel so you never miss an episode. We've got a lot of things that we're working on, so we got some Isilon quick tips that are still rolling out. We've got some book reviews starting to get some interviews, so you can see some interviews that [INAUDIBLE 00:07:03] in the past, and then also these Big Data Big Questions. And so, anything that you want to see, just pop here in the comment section and I'll try to answer it or try to tackle at the best I can. Thanks again.
In today's episode of Big Data Big Questions we tackle what the skills are needed for Big Data Administrators. Data Engineers wear many hats in Data Analytic workflows, one part software engineer and one part systems administrators. The Big Data Administrators are responsible for keeping Hadoop, Kafka, Ambari, and other frameworks running. Find out what other skills Big Data Administrators need in the video below.
Make sure to subscribe to my YouTube channel to never miss an episode of Big Data Big Questions.
Hi, folks! Thomas Henson here, with thomashenson.com, and today is another episode of Big Data Big Questions. Today, I'm going to answer a user question about data administration, or in big data, what is that big data administrator's role?
What are some of the tools that they use? How can you get involved? Find out more, right after this.
Welcome back. Today's question is going to revolve all around the big data administrator, what that role is, what are some of the tools that they use? This question came in from my website. You can do Big Data Big Questions, go to thomashenson.com, click on Big Questions, submit a question there. Put them in the comments section here below, and then always, make sure that you're subscribing to this YouTube channel, so that you'll never miss an episode. These are great tips. These are great ways for me to answer any questions that you have. If you have those questions, ask them, but also make sure you're subscribing to the channel.
Today's question comes in from Jarvis. He says he has a dilemma on Python for big data. We answered a number of questions around Python and big data, and then do you have to know Java? But, this one is a little bit different. It's going to cover the data administrator.
Hi Thomas, a big fan of yours.
Thanks for watching. Thanks for sending in the question.
I had a question related to IT careers and skills in big data. I wanted to know if Python is required only by data administrators, or can all things done by Java on big data be implemented using Python as well?
This question is really good. Like I said, we've talked a little bit about, do you have to know Java in order to be able to be a big data admin, be involved in big data, be a data engineer?
The answer is no. You can do things in Python, but I want to tackle the question from the perspective of, you're asking about data administration, and so there are two different roles. We've talked about the data engineer versus the data scientists. The data engineer is the one who's setting up the cluster, maybe doing some of the software development, running your Hive jobs, maybe even just the software developer, from if you're writing Java jobs, if you're writing your Spark jobs, but your data administrator, that's a different role inside of that. We have two pieces of the spectrum. This side over here, this is more software development side generated, and on this side over here, let's say that this is more of the administrator, or our systems engineer, the person who's setting up and running the cluster. Maybe not doing the day-to-day coding but doing the administrating and running of the system. Think of that as your full stack developer.
Think about when you split up your systems admin, who's setting up the stack, making sure the database is running, doing those tasks versus who's running the… Whether it be PHP code or .NET code. What skills does a data administrator have to have?
I would say that, if we're talking about being able to be involved in the community, and be involved in big data, you're going to keying on HTFS, Ambari, Hive, Flume, and you're going to have a lot of Linux skills. If you're asking me, you want to get into data administration, you want to be an awesome data administrator in the big data ecosystem, do you have to know Java? No. Can everything be implemented in Python? Maybe, but you're probably going to be doing more administrative tasks as far as setting up the cluster, understanding the operating system that Hadoop's running on.
You're maintaining more that Linux level, and the Hadoop ecosystem level, so if you're using Hortonworks or you're using Cloudera, how all those tools are integrating and talking to each other. I would focus more on not even so much the coding part, but as far as being able to set up that cluster. It's going to vary, too. It's going to vary in the role.
Some places, especially when you're just starting out on big data, and you have a small team in your company, you're going to be the software engineer and the data administrator, right? You might need to have a little more code.
If you're going to a more seasoned team or a bigger team, you can actually have that role where you're running the administration. My answer is, I wouldn't worry so much about Python and Java, if that's the role that you're wanting.
The data administrator, I would worry about being able to integrate the tools. Be familiar with the tools, be familiar with how to set up, how to add notes, how to take notes down. How to set up secondary name nodes, so, being able to make sure that, when one name node goes down, the second, you can flip over to the second name node. Being able to back up the data. Making sure that we're taking snapshots. All the kind of tasks that go into running the system, versus being able to write a MapReduce job. If you're really keen on being a big data administrator, which, those are great roles, those are a lot of fun, you're still hands on, but you're not really having to write the jobs.
A lot of cool things that you can do in that role. Most of them aren't going to involve coding, so you're not really going to have to worry about Java, you're not going to have to worry about Python, as much as you would in the traditional data engineer, where you're looking at being more of a software engineer.
I hope I answered your question. If anybody else has any questions, put them in the comments section here below. Make sure to follow me here, so click subscribe, and then I'll see you next time.
What is a Machine Learning Engineer?
Move over Data Scientist the Machine Learning Engineer is now the best role in Big Data Analytics. The Machine Learning Engineer is a hybrid mix of half Data Engineer and half Data Scientist, who can implement the data models and even make recommendation for new data sets. Find out why the Machine Learning Engineer is getting a lot of attention in 2018 by watching the video below.
Hi, folks! Thomas Henson here, with thomashenson.com. Today is another episode of Big Data Big Questions. Today's question comes in from a user, and this all are going to be about the machine learning engineer. What is a machine learning engineer? How does it differ from a data engineer or data scientist? We're going to jump into all that right after this.
Welcome back. Today's question comes in from a user, so before we jump into the question, make sure that you go and click on the subscribe, so that you never miss an episode. Also, if you have a question and you would like for me to answer it, about data engineering, about books, about business, anything around IT and specifically probably data analytics, make sure you put those in the comments section here below. Go to my website, thomashenson.com/bigquestions or use the hashtag #BigDataBigQuestions on Twitter. I will try my best to answer those as quickly as I can.
I've been getting a lot of questions in, and I'm really thankful for all the questions, and I am working through them as well. Today's question comes in from a user. From the comments section on YouTube, Andrew Wiley [Phonetic]. He says, "Is it possible to learn both data science and data engineering?" This question stems off of the Cloudera certification. I've answered some questions around what is a data engineer, what is a data scientist, but this question is specifically, "Okay, is there a blended of two?" Is there one position that's a blend of two?
I'll say, for a while, there's been a lot of confusion around, "Okay, if you're a data scientist, you know how to stand up a Hadoop cluster, or if you know how to stand up a Hadoop cluster, you must be a data scientist. You're a wizard, right?" This question is about, what about the blending of the two skills? Think about it from a web development perspective. For a long time, we had our web developers, and we had our back-end developers, and then we had the full-stack web developer. Now, we have a full-stack data engineer, and those are called machine learning engineers.
On a recent podcast out there, that O'Reilly did at Strata, they had a couple quests on talking about the rise of the machine learning engineer, and so I would say that if you're looking to have skills with data science and data engineering, that position is going to be called a machine learning engineer. My view on how the machine learning engineer has come to fruition is in two parts. If you're working in a small development or small analytics shop, most likely the data engineer, the person who's putting together the code and running the system, there's going to be one or two people on that. It's going to be a really small team, who are going to be filling that role of a data scientist.
There's a lot. There's a big skills gap for data engineers and even more so with data scientists, too. You might be able to go through and look at some of the prescribed analytics and machine learning algorithms that you want to use, and you, as the data engineer, will understand how to use those. It's not just willy-nilly, like, "Hey, I'm just going to pull this one down and have it." You need to have a background in statistics, and probability, and heavy on math. One of the things, one of my gaps in skills that I've been working on is the math part.
You can follow along as, watch me learn how machine learning… The machine learning course, with Andrew Ng's course, and you can see some of the things, especially if you're a data engineer, that you need to shore up, so that you can fit into that machine learning engineer.
Think of the machine learning engineer in the small shop as, you're the full-stack developer, you're the full-stack engineer. It's kind of doing everything. Then, in larger corporations, what you're going to have is, like I said, we've got it on both sides of the spectrum. You've got your data engineer, that are really good at setting up, administrating an environment, maybe even doing the software development, running Hive, creating the MapReduce jobs or the Spark jobs, but then you have your data scientists who are, maybe have some SQL skills, really good at math, but not really good at the technical. The machine learning engineer is that person in the middle, to kind of bridge the gap. In bigger shops, you're going to have your machine learning engineer who's working with your data scientist, and then starts to be able to pick up on, "Okay, this is the way that we like to do some of the things here, and you're really owning that part of the stack, and so, you're not so much worried about developing and doing what I would call the Hadoop administration, or even the Hadoop development.
When I say Hadoop, remember, we're just talking about anything in that ecosystem. Your machine learning engineer is your specialization of that. I did a little research, too, just to look at it. Just pulling it up, just some preliminary research, just looking for jobs out there. A lot of times, we'll say, "Yeah, this is, you're an Excel guru, and you say, 'Excel guru?'" You go look, and there's nobody with a job title excel guru. You're giving it to yourself.
Looking at machine learning engineer, quick search on Google for jobs, there are a lot of different postings from companies all the way from IBM to Facebook, Lyft, a lot of different postings out there, just in my quick search. Also, looking at Glassdoor, and some of the other places, the salary ranges are right there with what a data engineer is, so anywhere from the low 80s, which I wouldn't think that, that's probably not really a true machine learning engineer, or maybe it's in a different part of the country, all the way up to the 160s. That's salary range per year. I thought that was pretty good mix, there.
Really fit in line with what we see as the data engineer and the data scientist, so those roles are out there. If you're excited to go out and learn those, remember what I was saying. Want to have a solid background as a data engineer with understanding how the Hadoop administration works. Also, the workflows, and some of the development skills. Want to be able to implement, if you're using Mahout, if you're using TensorFlow, any of those frameworks, you want to be able to implement those, but then you also want to have the math portion too, so make sure you understand the algorithms from a math level, and how to tweak, and how to tune those.
That's all for today. Hope I answered your question. If you have any questions, anybody out there, make sure that you first go and subscribe, and then ask your question. I'll try to answer them here. Have a good day.
The Data Scientist has been one of the top trending careers choices for the past 3-4 years but where is the love of the Data Engineers? In reality I think more people are confused about the roles in Big Data. Both Data Scientist and Data Engineers are used interchangeably but the roles require different skills sets. In this video I will break down the differences between the Data Engineer vs. Data Scientist.
Thomas: Hi, folks. I'm Thomas Henson with thomashenson.com, and today is another episode of Big Data, Big Questions. And so today, we're going to be tackling the differences between a data scientist and a data engineer. Fine out more right after this.
Thomas: So, do you want to become a data engineer, or do you want to become a data scientist? So, this is a question…this is something we see a lot about is all about the data scientists, and big data, and then data engineering. But what's the different between the two roles, and why is it that since 2012 data scientists have been the sexiest career in IT? And there's been a lot of publication about it. There's been a lot of information about it. We're all talking about machine learning, and smart cars, and the battle between Tesla and Apple for machine learning engineers, data scientists, and how they can battle that out. But what about the data engineer, too? And kind of what are the differences? Well, I'll tell you. Recently, Information Weekly had a survey out there for 2018 and the highest paying salaries in IT. Data engineer came in at number five. The only other roles that were above the data engineer were all C-level positions. So, think of your CIO, your CSO, your CTO. So, data engineers are getting a lot of love, too. But what are the differences between those two roles? So, we'll break it down first jumping into what a data scientist does.
And so the data scientist, what they'll do is they'll evaluate all those data points and tell you which ones are going to be the most relevant. And they'll do that by using algorithms. So, they'll look, and maybe they'll use SVD to find out, okay, which variables are going to make the most sense for us to have more engaging content, have a web application that makes users want to stay and engage with it longer. And so that's kind of where their role is. Now, they're not going to be the ones that are writing the MapReduce jobs or doing some of the Spark jobs. We really want them just evaluating the data and helping us build different data models that are going to give us the results we're looking for.
So, if we can just increase our user retention time or increase our engagement of our content, our web application is going to be more popular. So, in our example, that's what we want. We want our data scientists that are really evaluating…finding correlations between data and also eliminating correlations. So, this variable that we predicted that we thought was going to be very key to engaging for our web application for our users, it really doesn't make a difference. And so it gives our developers, and our engineers, and our product and marketing team things for them to look at and say, "Hey, these are the variables that we need to focus on, and this is what's going to make our web application…give us the desired results we're looking for and increase that user retention time, increase the engagement for our users in our web application. So, that's our data scientist.
Now, on the flip side, what is our data engineer going to do? So, our data engineer, they're the ones that are going to say, "We've got this data here. We're moving the data maybe into our Hadoop cluster. So, we're moving it into our Hadoop cluster or wherever we're storing it for analytics." And so they're the ones that are really moving that data there. They're also writing those MapReduce jobs or Spark jobs. They're doing the development portion of big data. So, our data scientists are over here saying this is the data that I need. The data engineer is saying, "Oh, we have the data. What kind of format…? How should we clean the data? How fast do you need the data, too?" So, how much speed is a concern for some of these variables and being able to fetter out some of the details, and being able to give…maybe improve that product a little bit faster to get it to the users.
And so that's where you're going to see the data engineer. They're also going to be the ones that are managing and configuring our Hive and HBase deployments and doing some of the technical debt work that we've talked about before with making sure that we have a strategy for backup, making sure we have a strategy for high availability. So, this product that we've got here for our web application, we want to make sure that we're still feeding our data in, and our data models are feeding our data back to our data scientists. But then we're also pushing out those results from what the data scientists have given us, too. So, you kind of see two distinct roles.
So, our data engineer, they're going to be involved in the tech. They're going to be the ones that are really building those systems out. Where our data scientists, they're involved with the data. They're involved with the technology as far as how to use the…what tools are going to help them be able to [INAUDIBLE 00:05:20], use different algorithms, and be able to say, "This data point really makes a different where this other data point may not be making as much of a difference, and so it's going to…" They're going to be using those tools for that. But basically what they're doing is they're involved in the data. And you see the data engineers involved in the technology, and implementing, and kind of using that strategy.
So, I'm not saying one is better than the other one, but I may be a little bit biased because I'm a data engineer and like data engineering. But two different skillsets, two very important skillsets, two amazingly great career choices right now in IT, two of the probably highest paying individual contributor roles in IT right now. So, you can't go wrong either way. If you're looking for more tips and more information about being a data engineer, make sure you subscribe to this channel and find out more information about data engineers. We explore how to do different things. If you have any questions like this, make sure you submit them. Big Data, big questions, using the #bigdatabigquestions on Twitter, go to my website, thomashenson.com, Big Questions, submit your questions there. Put it in the comments below here. I'll answer it on YouTube the best I can. Any questions like that, just get in touch with me. I'll answer them on here. Make sure you subscribe. Thanks again for tuning in, and I'll see you next time.
What are the options for using Python in Hadoop?
Python developers are looking to transition their Python Skills in the Hadoop Ecosystem. In a recent episode of Big Data Big Questions I answered question about using Python on Hadoop. Let's take a deeper look at how to use Python in the Hadoop Ecosystem by building a Hadoop Python Example.
Python consistently ranks in the top 5 programming languages. The market is certainly red hot for Python developers; a quick search on Indeed results in 45K open roles in the US. Python is used heavily in the Data Science world, so crossing over Python for Data Engineers is a natural move. Hadoop Ecosystem tools are quick to add support for Python with the Data Science talent pool available to take advantage of Big Data. Python is also a easy language to pick up and allows for new Data Engineers to write their first Map Reduce or Spark Job faster than learning Java.
Why Not Just Use Java?
Java is still topping the list for popularity so why not just use Java? The first reason is it's fairly complicated to setup and run your Java development environment. On the flip side with Python it's as easy as installing Python and running the interactive shell. The second reason is once you have your code you have to compile it then run it versus Python write your script then run from the command line. The last reason is the amount of code you have to write in Java is a lot more than Python or other languages. Checkout how the amount of code to run a word count for Java versus Python.
Roughly 57 lines of code versus 15. Which code base would you rather maintain?
How Does Hadoop Support Python?
If Java is more complex than Python and Spark supports Python, does Hadoop have a Python option? Yes! Python developers have 4 options for using Python on Hadoop. Let's dive into each of those options.
Hadoop streaming is one of the most popular ways to write Python on Hadoop. Streaming is built into Hadoop Distribution and offers the ability to pass script in the stdin. If you are using Hadoop then you already have Streaming built into the package. Using Streaming a developer can pass in Python script or other languages for Mapper jobs. The functionality works similar to how User Defined Functions work in Pig Latin. Developers use Java as a wrapper to pass in their Python (other languages) scripts, then at run time the job is executed in Java.
Hadoopy is an extension of Hadoop Streaming and uses Cython for Python Map Reduce jobs. I'm a huge fan of Hadoopy's documentation, extremely developer friendly. However, be aware if you are looking to use the Hadoopy package it might not be compatible with latest Hadoop versions. The project hasn't been update since the end of 2012.
Pydoop allows for developers to write Python scripts natively then interface directly with data in Hadoop cluster. It's possible by using Pydoop's HDFS API for reading and writing data into HDFS from Python. Basically developer writes taking advantage of the full Python library then wraps the script with Mappers and Reducers. Pydoop can be installed using pip pydoop.
MRJob is a Python package that helps write and read Hadoop Streaming jobs. Heavily used and incubated out of Yelp, MRJob supports EMR, Native Hadoop, and Google's Cloud Dataproc. The setup is similar to Pydoop using pip to install but the project is still very active. The documentation is up to date as well supporting the different implementations of MRJob. The project continues to be supported. For the Python Hadoop example below I will use MRJob.
The example used below is a word count application provided by the examples located on Yelp's Github for MRJob. For data you can use any text file to parse over and count the number of words. Also you will need have a running a Hadoop cluster and Python is installed with Pip.
Install the MRJob library using Pip.
Open your favorite text editor (VIM) and use the following script and save following script as word-count.py.
Now that we have our MRJob word-count script we can turn it loose on our files in Hadoop. To run the script prefix with Python then script name (word-count.py), next the file location (hdfs:url/user/file.txt) and finally -r hadoop.
Congratulations you just ran your first Hadoop Python job using the Python Package MRJob! Now if you want to go further and start experimenting with other examples, be sure to checkout more Python examples by downloading the MRJobs Examples. Also make sure to subscribe to my news letter focused on Building Stronger Data Engineers.
Big Data Big Questions: Learning to Become a Data Engineer?
Data Scientist for the past few years has been named the sexiest job in IT. However the Data Engineer is a huge part of the Big Data movement. The Data Engineer is one the top paying jobs in IT. On average the Data Engineer can make anywhere from 90K – 150K a year.
Data Engineers are responsible for moving large amounts of data, administering the Hadoop/Streaming/Analytics Cluster, and writing MapReduce/Spark/Flink/Scala/etc. jobs.
With all this excitement for Data Analytics and Data Engineers, how can you get involved in this community?
Ready to learn tips to becoming a Data Engineer? Checkout this video for tips to becoming a Data Engineer.
Hi Folks, I'm Thomas Henson, with thomashenson.com, and welcome back to another episode of Big Data, Big Questions. Today's question is: What are some tips for learning to become a better data engineer? Find out more right after this.
So, today's episode is all about tips for learning to become a better data engineer. So, if you're watching this, you're probably concerned with, one, how can I start out becoming a data engineer? What are some ways that I can learn to become better? Or maybe you're just looking to answer one specific question. But all those are encompassed in what we call the data engineer.
A data engineer is somebody who's concerned with moving data in and out of Hadoop ecosystem, being able to give status scientists and data analysts better views into the data. So, we're involved with the day-to-day interactions of how that data is coming in. Is it in how we're ingesting that data? How are we creating those applications and tuning those applications so that the data comes in faster? All to support those business analysts, those business decisions, and data scientists in creating better models and having just more data to put their hands on.
And so, a lot of times what we're always doing is we're asked to take on a couple terabytes of data here, maybe implement and do all the configuration for your hives. You know, your hive implementation or HBase or anything that's in that big data ecosystem. Some of the tips that I've found for just getting started, so if you're brand new to this and you don't know where to start, the first thing I would recommend is, go out and just download the sandboxes.
So, download Cloudera's sandbox, or download Hortonworks' sandbox and just start playing with it. Go through some of the tutorials. Stand up on your local machine in a VM environment, and just start playing with moving some of the data around. Find some sample data, so go to data.world. Also, I have a post and a video on where to find some data sets, so take those data sets in, start ingesting those. I have a ton of resources and a ton of material on just some simple examples that you can walk through with Pig, and some around Hive. So, go there and find some of those. But, basically what I'm saying is, just get hands-on. Start creating applications. Start trying to do some simple things like, ingest some data in, put it into Hive, and be able to create a table and pull some of that data out, and just maybe some simple Hive queries. And do the same thing with Pig, and just kind of go around to some of those applications that you're curious about, and start playing with them.
Another thing is, is once you start playing, and sampling, and testing that data, get involved. By getting involved, just ask some questions, create a blog post, try to find a way that you can contribute back to the community. I mean, that's what I did when I was first starting out. I started off with a sandbox, and what I did was, I took and made sure that every day for 30 minutes, I was learning something new in the Hadoop ecosystem. And so, that's another tip for you too, is to take and try to do this 30 minutes a day, every day. Even Saturdays, Sundays. Don't take a day off. And it's only 30 minutes. And if it's something that you're passionate about, and you like doing, that time is just going to fly by. But over time, that's just really going to give you more and more time in the Hadoop ecosystem. So, whether you're doing this for a project at work, whether you're already in the ecosystem and you're just trying to improve, that 30 minutes a day is really going to help. And it's something that I've continued to do, and continued to do, now, even though I've been in part of the community for three or four years now. It's how I just continue to learn, so I make sure I'm always kind of pushing.


Susan Simpson designs cabinets, architectural elements, kiosks and sculptural objects that open or reveal their contents in surprising ways.
These objects all invite interaction and close viewing.
A model for street library pods designed by Susan Simpson (a project currently in development ). These will be stand alone street libraries with book shelves and peephole scenes depicting a moment in the history of the Silver Lake neighborhood of Los Angeles. The design is inspired by egg shaped phone booths and pre-cinematic viewing devices. This project was commissioned by the Silver Lake neighborhood council. Pods will be installed throughout the neighborhood in 2019.
Over the past few years I have lead three projects for the Huntington Library, Art Galleries and Botanical Gardens. The first was as an Artist in Residence working with local high school students to create a 700ft photo mural. The mural stretched around a construction site for two years during the completion of the new Huntington facilities. The second was to design an outdoor mobile display unit to display art work done by students from schools partnering with the Huntington. The third was creating an illustrated map of the Huntington using drawings and thank-you letters from young visitors.
Two folding outdoor display cases designed for The Huntington's Education Center in 2015. These are portable units for the purpose of displaying student art work as well as the ephemera related to their art making process.
Display cases with the first exhibit of student work.
Mobile display cases with the first student exhibit.
A multi-panel three dimensional map of the grounds created for the mobile display cases designed in 2015. The map was illustrated with children's drawings of Huntington artwork and gardens. It is part of a campaign to raise funds to provide transportation for field trips to The Huntington.
Detail from the Save the World One Bus at a Time display.
Detail from the Save the World One Bus at a Time display designed by Susan Simpson.
A 700ft photomural created with students from three high schools that partner with The Huntington. The students were all photographed holding photos that they took at the Huntington and in and around their neighborhood of East Los Angeles. The mural stretched around the construction site for two years during the construction of the new Huntington facilities.
This kiosk was a design and performance project for Automata. It was both puppet theater and display case. It housed an "artist in residence", the puppet known as Augustine September, who painted pictures and made performance art incorporating objects and imagery from around the plaza. The display cases featured Augustine's work that strung together a fictional narrative inspired by the goings on about the plaza. The peepholes offered magnified views of objects collected in the vicinity.
Kiosk design and built around a pre-existing vendor kiosk in The Far East Plaza, Chinatown, Los Angeles. The kiosk included windows for viewing performance, display cases and peepshows.
A cabinet of dioramas and peepshows created by Susan Simpson, a public artwork commissioned by The City of Inglewood. The Infinite Library was installed in April of 2017 and is on permanent display at The Crenshaw Imperial Library.
Toy theater design for The Last Act of Lilka Kadison, by Abbie Phillips and Directed by Dan Bonnell at the Falcon Theater in Los Angeles, in April 2014. Susan Simpson designed and built the toy theater as well as the shows that were performed inside of it. Included here are concept drawings, the cabinet and the images of the puppet show contained within.
Reading Ours, founded by Robby Herbst and Kimberly Varella, is a micro-gallery celebrating the book, published objects, the history of publication, and the reader. It's a tiny space for big ideas in Los Angeles.
Phase II will give the space expanded exhibition area, with built in shelving and personal reading station.


text:
1969 Liberal Party of Australia leadership spill
Liberal Party of Australia
leadership spill, 1969
← 1968 7 November 1969 1971 →
John Gorton William McMahon David Fairbairn
First Ballot
34–40 (est.) 20–25 (est.) 5–6 (est.)
Leader before election
Elected Leader
The Liberal Party of Australia held a leadership spill on 7 November 1969, following the party's poor performance at the federal election on 25 October. Prime Minister John Gorton was re-elected as the party's leader, defeating challengers William McMahon and David Fairbairn.
2 Election
The Liberal–Country coalition lost a combined 16 seats at the 1969 federal election, and the Labor Party (under Gough Whitlam) won the two-party-preferred vote. On 2 November, National Development Minister David Fairbairn announced his intention to challenge Gorton for the leadership of the Liberal Party.[1] He was joined the following day by Treasurer William McMahon, who had been deputy leader since 1966.[2] Deputy Prime Minister John McEwen, the leader of the Country Party, announced that his party was willing to work with any of the three candidates; this lifted the veto he had applied to McMahon at the previous leadership ballot in January 1968.[3]
Election[edit]
The election on 7 November was set for 10 a.m., but delayed by an hour as five MPs travelling from Melbourne were delayed by a faulty aircraft. With Speaker William Aston presiding, the 65 members of the Liberal partyroom took 49 minutes to elect a leader. Gorton won an absolute majority on the first ballot, but the final results were kept secret, with the ballot papers burnt immediately after being tallied.[4] Alan Reid of The Daily Telegraph estimated Gorton had won 34 votes,[3] while The Canberra Times estimated 38 votes;[4] Gorton's supporters claimed up to 40 votes.[3] The deputy leadership was also declared vacant, and McMahon was re-elected over Immigration Minister Billy Snedden and Postmaster-General Alan Hulme with about 35 votes.[4]
Aftermath[edit]
After the challenge, Fairbairn resigned from cabinet and McMahon was demoted to Minister for External Affairs. McMahon challenged Gorton again in March 1971, and was successful.[3]
^ Struggle for PM's post sharpens, The Canberra Times, 3 November 1969.
^ McMahon in race for P.M., The Canberra Times, 4 November 1969.
^ a b c d Summer of '69: lessons from a Liberal spill, Crikey, 2 February 2015. Retrieved 5 November 2017.
^ a b c Result a secret, The Canberra Times, 8 November 1969.
Interview with John Gorton the day after the leadership challenge
William McMahon
Billy Snedden
Andrew Peacock
John Hewson
Alexander Downer
Brendan Nelson
Deputy Leaders
Eric Harrison
Phillip Lynch
Neil Brown
Fred Chaney
Peter Reith
Michael Wooldridge
Peter Costello
Julie Bishop
Josh Frydenberg
Menzies 4
Gorton 1
Fraser 1
Howard 1
Turnbull 1
Morrison 1
Shadow cabinets
State divisions
Northern Territory (Country Liberal Party)
Queensland (Liberal National Party)
Party Related Organisations
Australian Liberal Students' Federation
Cormack Foundation
Young Liberals
Young LNP
Menzies Research Centre
Ginger Group (defunct)
Lyons Forum (defunct)
Liberal Party of Australia (Queensland Division)
Protectionist Party
Free Trade Party
Commonwealth Liberal Party
National Labor Party
Nationalist Party
Liberal Union
Australian Party
United Australia Party
United Australia Party – Queensland
Citizens' Municipal Organisation
Leadership contests
2015 (Feb)
2015 (Sep)
Leadership spills and motions in Australia
1977 (May)
1977 (Dec)
2013 (Mar)
2013 (Oct)
Retrieved from "https://en.wikipedia.org/w/index.php?title=1969_Liberal_Party_of_Australia_leadership_spill&oldid=968266129"
Liberal Party of Australia leadership spills
November 1969 events in Oceania
1969 elections in Australia

redpajama_set_name: RedPajamaCommonCrawl


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This 35' long slip and slide is a perfect addition to any outdoor summer event and a great way to combat the heat! The double lane ensures everybody gets a turn as two riders slide down at the same time and race to the bottom, which has an inflatable bed and end barrier for a safe and splashy stop!
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Our Photo Booth is an entertaining way to take lasting memories. Our booth is a modern, fully automated kiosks that add excitement to your special day! Our booth has a touch screen, can print in black and white or color, produces high quality pictures, comes with fun, colorful props, unlimited photos during your event, a party attendant and we can create a customized logo or message on your pictures!
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Grand River Inflatables & Party Rentals brings you the best in Bouncy Castle Rentals and Party Rentals. Grand River Inflatables & Party Rentals is a fully licensed and insured event rentals company in the Tri-City area. Our continual focus is on safe, quality equipment as well as exceeding customer expectations. What started out as a hobby has now expanded to provide an extensive selection of inflatables, climbing walls, carnival games, sports games, fun food products, casino games and much more! With everything you need in a 'one stop shop' we are able to cater to all events whether it be Corporate, Festivals, Fun Fairs, Schools, Churches, Community Groups, Backyard Parties, and anything else to celebrate! We are the Festival and Events Ontario Service Excellence Award Winner for 2013, 2014, 2017 & 2018!


Our third annual SQUAD WARS has begun! Last night, almost 50 students came out sporting their squad color, chanting their squad's anthem, and completing the obstacle course challenge.
Check out our Instagram page for event details and pictures!
Our mystery edition of SQUAD WARS was one for the books! The competition was fierce as we threw down FAMILY FEUD style. And let's not forget our amazing host! Pastor Javin wore a different hat...or should we say bright green blazer...as he took over the night's shenanigans! The BLUE SIRENS came out on top, dominating the Fast Money finale round!
NEXT SQUAD WARS EVENT IS NOVEMBER 5TH.
COME READY FOR AN EPIC SCAVENGER HUNT!


This fun frock is the Centaurée Dress from Deer and Doe. The Centaurée is a sundress with double spaghetti straps (made from bias binding) and a geometric pieced bodice. I made Version A that has a skirt that hits above the knees. If you want to make this a bit dressier (bwah ha ha – bad pun!) then Version B would be a winner – it features a high/low hem with some extra length.
The fabric is a Pima cotton I picked up at the Gamarra markets in Lima while on my Peru odyssey last year. It presses like a dream, feels amazing to wear and has a nice amount of body. If you look closely the little flecks are birds in flight and are actually a very pale pink. Perfection! Though every time I look at this fabric all I can think of is "put a bird on it" from Portlandia.
I did end up making a couple of adjustments to the pattern – I lined the bodice, shaped the waist by 5cm on each side and added pockets. Because every lady needs pockets in her frocks – where else would she hide emergency chocolate bars? Oh, wait – is that just me?
The bodice details. I haven't seen any other dress bodice like this and it is a fab change from the normal princess seam or four dart details.
The bias binding straps. This is pretty much the epitome of sundress features!
Bodice fit. Even though I did a toile of this bodice the fit is off on this finished piece.
Okay, so let's real talk. While I had a blast making this dress I am not feeling it. There are a number of issues with this bad boy: the fit in the bodice is off, the bodice seam is in a weird place and the bodice shaping is just too boxy for me. On top of that, I loathe strapless bras and the cute spaghetti straps of the Centaurée deserve one.
So what does that mean? This frock is going in for a refashion. I am going to remove the skirt and see if there is enough fabric to piece together this cute peplum top. I think for someone with more patience to get the bodice fit right the Centaurée is a winner – and there are some gorgeous versions out there. But my sewing time is too precious and this pattern doesn't light a fire in my belly so for the first time I'm breaking up with a Deer and Doe pattern. Eep! But before I go through the break up let's dance party again!
This dress is sooo cute on you! I want one after seeing how good you look in it. I think sometimes we as sewists are our own worst critics, particularly on fit. I think maybe what might be throwing you in the bodice is the strapless bra – have you tried it on with one of your regular bras and seen if that helps? I regularly wear spaghetti straps with standard strapped bras – so long as the colors don't clash much no one even notices and it's much more comfortable. Or you could even make little bra strap covers out of the remaining fabric.
Anyway, just something to try before chopping a (super cute) dress up!
Aaah such a shame you're not happier with it! Interesting seaming doesn't always translate to awesome fit does it? Good luck with the refashion!
Some dresses are like that & I like that rather than hanging on, you are moving on. Good luck with Take 2.
A cute summer dress and while you may not have much time to wear it this year there is always next year!
Your bodice seams are fab! And looks great on but it's how you feel in it that matters, hope you are able to make it into something wearable.
Hasn't it just cooled down suddenly in Canberra? (love!) I can imagine that fitting the bust with those seams lines would be trickier than most adjustments, though I'm dreaming of a multicoloured patch work bodice. Great seam matching, and ALL dresses (clothes) need pockets. I didn't add pockets once, I love the dress but always feel grumpy with no pockets.


Alexandrea and Dan gave me the wonderful pleasure of capturing their beautiful wedding last Saturday, and I could not be happier with how the photos came out! I was so eager to work on them that I pumped out 243 favorites within 24-hours of their wedding. After reviewing their photos with them today, not only were they in love with the favorites I had picked out for their Story, but they were ecstatic about the remaining 1300 images from their 6 hour wedding!


Surprised to see Mens Health in the Comercial Break section is a Mau Mau review!..
Uk Show. Opens: 7.5.15. West Bank Gallery.
Big ups to the Glasto sk8 crew, A 3rd Foot, Rum Shack, Lyrix Organix, and all mi breddas an sistas i partied with an blazed with inna the mud n sunshine!.
Mission: Impossible !!! Devon wall MauMau Town.
MauMau, Alex face, Bon, Brick Lane E.London, barbeque Roa stork..
Street Art Tour - Shoreditch, East London..
Mau Mau interviewed on the topic of the Olympic Legacy murals...Click here to read the article.
Printin Snub nose prints for The Brooklynite Gallery..
David and Goliath...Grants Pen, Kingston, Jamaica.
More fire on Monsanto is now on sale.
The plan is to auction off the artwork at the end of the shows to raise the money for cause, for a crew from Brighton to go to Gaza for a month in December this year, meeting up with a group of skate activists called the Bedouins to help give out boards and teach kids how to ride.
Bideford Bridge, River Torridge, Devon.
Bairro Alto, Lisbon Portugal, 2012.


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Q: How to get "all" (or the MAIN/LAUNCHER) android intent i want receive all start app intents (i think MAIN/LAUNCHER, see below) to log how often i used the application. So i do not want create an activity... I tried several receiver blocks, but for now nothing works:
with, without priority, only the action, both, only the category and so on..
<receiver android:name=".Receiver" android:enabled="true"
    android:process=".e">
    <intent-filter priority="100000" android:priority="100000">
        <action android:name="android.intent.action.MAIN" />
        <category android:name="android.intent.category.LAUNCHER" />

    </intent-filter>
</receiver>

Have anyone an idea?
Thanks

A:
I want receive all start app intents

You cannot do that, sorry.

redpajama_set_name: RedPajamaStackExchange


There you are at lunch—chomping down on another salad—when all of a sudden it hits you: a brilliant idea to improve the patient experience in your organization. It's a fantastic idea. One which will certainly transform the way staff and management at your hospital interacts with patients. It's genius—and you're a genius for thinking of it. You pause. You panic. Now what? Well, now comes the fun part: you have to convince other people just how great your idea is—or rather you have to get buy-in. But how?
An often overlooked first step is: assessing your own buy-in. So, start by asking yourself whether you're truly committed to the initiative you are proposing. If the answer is yes, then you will be able to stand up to the naysayers who will try to derail your proposal.
Now that you have your own buy-in, you need to decide what role you want to play. Will it be the champion who leads the initiative and makes the effort to get it approved? The cheerleader, who encourages others to lead and adopt the initiative? Or the instigator, who plants the seed of the idea with lots of people in order to build a groundswell of support until a champion can emerge.
No matter which role you choose, you need to do your homework. Research and talk to others who have done something similar—become an expert on the subject. The more you know, the more confident you'll be. The more you read, the more credible your arguments will be.
Read part 2: getting buy-in from your peers now!


KAB was founded in 1953 as a private organization with the purpose of building and sustaining a national cleanliness ethic. The Boy Scouts of America has been affiliated with KAB for more than thirty years as a member of KAB's national advisory council, a program advisory body composed of more than eighty public service organizations and federal agencies.
Keep America Beautiful Day, which began in 1971 as "Scouting Keep America Beautiful Day," was originally cosponsored by KAB and Boy Scouts of America as a massive national cleanup and recycling program. Millions of Scouts still turn out annually to participate in similar activities during what is now called Keep America Beautiful Week.
KAB Week developed from eleven consecutive years' observances of KAB Day. It originated in 1971 as "Scouting Keep America Beautiful Day." KAB Day was eventually extended to a weeklong observance (usually the fourth week of April). Millions of citizens across the nation participate each year.
Scouts must complete the Hometown U.S.A. Award application form and present it to their Scoutmaster or troop advancement chairman. To qualify for the award, Scouts must have successfully completed the requirements as outlined. The troop advancement chairman will know which merit badges each Scout needs in order to qualify. It is suggested that the community service project be under the overall direction of a natural resources professional or other qualified adult supervisor. After completing the project, the adult supervisor should sign a statement that the project has been completed and meets the requirements of the project sponsor.
The following is a list of organizations and the free brochures that may be obtained to assist Boy Scouts in earning the required merit badges included in the Hometown U.S.A. Award.
Write: Keep America Beautiful Inc.
The National Gardening Association (NGA) also hosts a grant program which presents more than 300 grants to qualified youth gardening projects. Grant packages include approximately $750 worth of tools, seeds, NGA manuals and other gardening equipment and supplies. Materials about this program include National Gardening Grant Applications. The application deadline is November 1.


Biting insects typically transmit EIA, also known as swamp fever, which is a blood-borne illness.
After years of providing in-depth coverage of equine disease alerts when they happen, we're trying something new. It's our goal to provide you with details you won't find elsewhere about each case, although you'll find the information in a central location by month.
Your feedback is welcome and encouraged during this time.
A veterinarian sampled blood from a Saskatchewan horse confirming a positive case of equine infectious anemia (EIA).
Canadian officials report that the Great Bend horse traveled frequently throughout the province competing. The veterinarian didn't note any clinical signs of the highly contagious disease.
The Canadian Food Inspection Agency (CFIA) is investigating. Additionally, it quarantined the premises and any on-premises contact animals. It remains in effect until the disease response activities are completed, which includes the euthanasia of positive horses, and any trace-out activities to additional locations.
Biting insects typically transmit EIA which is a blood-borne illness. The use of infected needles can also transmit EIA from horse to horse.
To help prevent the disease, veterinarians recommend insect control, good sanitation, testing new horses with a Coggins test before bringing them onto your property, and quarantining new horses for 45 days.
EIA is also called swamp fever. There is no cure or vaccine.
Equine Infectious Anemia (EIA) proves deadly for two more Texas horses bringing the count to 9 since March.
Texas Animal Health Commission (TAHC) officials confirm veterinarians euthanized a Standardbred in Dallas County and a Quarter Horse in McLennan County. Now, both premises are quarantined as the state helps monitor the premises' horses and implement biosecurity.
The often fatal disease affects horses, donkeys, and mules. No other animals or humans can contract the virus.
Symptoms include fever, depression, weight loss, swelling, and anemia.
A Coggins test is utilized to determine the presence of EIA. Under Texas law, any horse over 8 months old traveling to an event, being sold or entering the state must have a negative Coggins within the last 12 months.
Maryland agriculture officials confirm a horse stabled in Montgomery County tested positive for Equine Infectious Anemia (EIA).
A veterinarian's routine wellness exam that included a blood draw for a Coggins test confirmed the diagnosis by the National Veterinary Services Lab in Ames, Iowa. The veterinarian euthanized the EIA infected horse.
There is no cure or vaccine to prevent EIA.
Although the infected horse did not display any clinical symptoms it was a carrier of the disease.
The state veterinarian placed the farm on a 60-day investigational hold order. Veterinarians are testing the remaining 42 stablemates of the index horse now and again in 60-days.
To help prevent the disease, veterinarians recommend insect control, good horse facility sanitation, testing new equines with a Coggins test before bringing them onto your property, and quarantining new equines for 45 days.
There are no treatment options for infected horses so the United States Department of Agriculture requires euthanasia or strict lifelong quarantine for horses testing positive for EIA. Studies show that a 200-yard separation between an isolated positive EIA and other horses prevents transmission of the disease.
Nebraska's State Veterinary office reports a neurologic horse located in Buffalo County tested positive for equine herpesvirus (EHV-1) leading to its euthanasia.
EHV-1 is highly contagious among horses. Dennis Hughes State Veterinarian reminds horse owners to always use biosecurity practices.
Additionally, the facility voluntarily quarantined itself and put biosecurity measures in place.
An Oklahoma horse rescue may feel the financial sting after bringing a horse into the state without a Coggins.
Furthermore, the agency states the horse tested positive for equine infectious anemia (EIA) and was traveling without a Health Certificate.
Due to the positive blood pull, a veterinarian euthanized the 16-year-old Quarter Horse mare that originated from Alabama. Oklahoma agriculture officials tell us they don't know the name of the "horse sale barn" or if the horse came from a kill pen. It is also unclear if a professional horse transporter or individual hauled the horse into the state.
The Oklahoma Department of Agriculture quarantined the horse rescue. When asked, the state said it cannot divulge the rescue's name due to state law. There are a couple of exceptions including if the state veterinarian deems it is necessary to protect the well-being of the public or domestic animals or by a court order.
This is the first case of EIA in Oklahoma in 2018.
The Canadian Food Inspection Agency's (CFIA) national lab confirms an Alberta horse tested positive for Equine Infectious Anemia (EIA).
A veterinarian pulled blood from the horse located in Ponoka County to comply with the United State's import requirements. The horse was asymptomatic.
The agency's investigation includes a quarantine of the premises, follow-up testing, and the euthanization of all positive horses. CFIA's actions may include additional premises due to trace-out procedures.


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Q: Can I use GRUB only when I want to use ubuntu I've installed windows 10 already, but the time to install ubuntu is coming.
I usually don't need ubuntu, as I just use Ubuntu to use Tensorflow..
so, the 'GRUB' is very annoying thing to me.
I don't want to see GRUB everytime I push my power butten.
I want to use GRUB when I want to use ubuntu.
When I don't need ubuntu, I want to boot windows 10 without seeing grub.
help me..
p.s
Thanks for your help!!
after I read your answers and comments, I think i should have written more info about my computer.
I use two disk.
ssd : windows 10 drive
hhd : drive for data storage(and ubuntu will be here. I'll shrink this volume to install ubuntu)

A: Yes you can.  When you want to boot to Windows, make the change in your computer's BIOS for Windows to be the primary Boot option.
When you want to boot to Ubuntu or have the choice of Windows or Ubuntu, then make Ubuntu your primary boot option in you BIOS.
Using Ubuntu to presented with the Choice is most likely the best way to go.
Also, there are many ways you can customize the way Grub Looks and how it behaves.  But the first thing you see is a black screen with a choice of which OS you want to go into.
Update:
After the comments below, I find that Windows Boot Manager can boot Linux.  Ubuntu will have to be on a separate drive for this to work, but you can take a look at this answer:
Is it possible to boot Ubuntu using the Windows bootloader?
After installing Ubuntu on a different drive, use EasyBCD to add the functionally of as they say on their site Dual-boot awith anything you want.

A: I can't give you a straight answer because there's a fundamental issue with the propositions implied in your question (see below).
What you can do:

*

*Use different boot loader than Grub. Some possible alternatives are the one included with your BIOS or Windows.

*Have Grub default to one boot menu entry after some time when you don't press any keys until then. You can even hide the boot menu and only show a black (or whatever colour you want) screen unless you press Shift while Grub waits.
What you cannot do:

*

*Have the boot loader "know" which boot entry you want to choose unless you happen to always want to boot the same entry. The only way to not have to choose is to restrict the set options to one.


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Tolkien Podcast: Books vs. Movies
Last week I was invited to be a guest on a podcast debating the works of JRR Tolkien vs. the movies that Peter Jackson made based on them. It was an invigorating discussion filled with nerd rage (on both sides), fun personalities, and lots of geeky info!
Moderator: Jim Ryan of Crucible of Realms and much more!
Richard Rohlin with Grapple Gun Publishing
Denise Lhamon of the Accessories Not Included blog
Kimi of The Golden Lasso and Happy Jack's RPG Podcast
Jon of Crucible of Realms
Thanks for inviting me to be part of the fun, Jim!
Happy Birthday, Professor Tolkien!
Known as the father of "high fantasy," Tolkien has been on of the most influential figures in many geeks' lives. Even if you aren't an elf-obsessed Tolkien fangirl (like me) your favorite RPG, comic book, movie, or fantasy series probably was heavily influenced by the works of Tolkien. Hell, with the release of The Hobbit, even your Denny's breakfast is Tolkien themed. But who was he, and why are crazy people people so crazy for his books?
I am a scholar (read: overzealous fangirl) of Tolkien's Middle Earth, a fantasy history of our own world which laid the foundation for all fantasy works that have followed. The enormity and depth of Middle Earth is astonishing, and it includes countless maps, illustrations, histories, races and species of beings, and multiple native languages which are completely flushed out and translatable. For every story that was published during his lifetime, there are countless other tales that he wrote only to give his published world a back story. It's overwhelming and inspiring. So, here is a handy guide to my favorite of Tolkien's Middle Earth writings.
A children's tale about a funny little creature that lived in a hole and was tricked by a wizard into going on a great adventure. It's a classic, and I have my class read it every year. I love it because it introduces the reader to the lighthearted side of Tolkien's writing. There is a great joy in the simple adventure story, but there is a definite disconnect with the feel of Tolkien's other writings. This book was written to be a stand-alone piece, and was actually retconned to make Gollum more evil in a later publishing to fit with Lord of the Rings. Still, it's a great introduction to Middle Earth, and is a great piece of literature on it's own. Everyone should read it at least once.
Tolkien's largest work, and the only other book besides The Hobbit that was published during his lifetime, takes a much darker look at Middle Earth. Tolkien captures the weight of time beautifully in this story. The reader enters a world that is ruled by the pale echos of long forgotten civilizations, being abandoned by the elves who have protected it since before time began. Humans, the youngest and arguably least qualified of the great races, are left to defend the world against the horrors of an ancient demi-god. History and the reoccurring problems of the world are a huge theme in this book. There are no "happy endings" in history, there are good times and bad times, and Tolkien mirrors that in Middle Earth. The people who face the hard times do what they can, and sometimes they succeed against insurmountable odds, even if they are only a hobbit.
The Silmarillion
This is my favorite of Tolkien's works, and was actually not meant for publication. It was compiled from Tolkein's notes after his death. This is basically the bible of Middle Earth and documents the history of the world and the elves. I love the different tales of the elves because they do everything on a much more epic scale than humans. They are wise beings by the time of the Lord of the Rings, but they earned it through ages of betrayals, mistakes, love, and loss. My favorite tale is the story of Beren and Luthien, the elven princess who fell in love with a mortal man. Arwen and Aragorn are but a pale shadow of this story. If Arwen and Aragorn went hand in hand to face Sauron alone (and Arwen was a bad ass fighter and magic wielder), you might have some idea of the epic scale of this love story. It defies the ages and death itself, and Tolkien even had the names Beren and Luthien engraved on the headstone that he shared with his wife of fifty years.
Many people cannot make it through The Silmarillion, but I encourage people to look at it like they would a history book or bible. It's not really meant to be read through to tell a singular liner story. Think of it as a collection of stories, and if you find one you aren't interested in, skip it. There are some great parts and some very dry parts.
The Book of Lost Tales I & II
These are the earliest versions of the tales that eventually became the other stories based in Middle Earth. They are a fascinating read for any Tolkien fan(atic), but might not appeal to the general audience. I love reading these stories and comparing them with the final versions that appear in the published books. The names are different, and he even called the elves "gnomes" in these early drafts, until realizing that the term had very different meaning for most readers. These books were also compiled and edited by Christopher Tolkien after his father's death.
The Children of Húrin
This story might be the most tragic in the entire history of Middle Earth. It tells the tale of Húrin Thalion and his offspring. Húrin was tortured by Morgoth himself (that's Sauron's boss for LOTR fans) to reveal the location of a hidden elven city. Húrin refuses, and his line is cursed by Morgoth for all time. He is placed on a mountain top and is magically able to witness the sorrows that befall his wife and children. If you're a fan of Romeo and Juliet, or Othello, this story could be right up your alley.
This book has nothing to do with Middle Earth, but it is a truly magical book. These are a collection of letters and pictures that were created between 1920 and 1942 for his children. They are written from Santa or his trusty elf secretary and tell enchanting stories about life at the North Pole. It also is a wonderful collection of Tolkien's original artwork, which is very charming and impressive and too often left out of modern publications of his work. It's a favorite of mine each Christmas.
In the wake of the modern popularity of films based on Tolkien's work, I highly encourage everyone to read the original stories. It's easy to lose the magic of his genius in the commercialized hype. To forget that it's the characters that make the tales meaningful, rather than the overblown special effects.
Happy 121st Birthday, Professor Tolkien. Don't worry, we won't tell the Sackville-Bagginses.
The New Hobbit Trailer
The new trailer for The Hobbit movie was released last week, just in time for the 75th anniversary of The Hobbit. I'm currently reading the book with my middle school English classes, so I've been spending even more time than usual contemplating Middle Earth. We just finished the "Riddles in the Dark" chapter, and the kids are loving it. The first trailer was wonderful and the dwarven music gave me goosebumps. This time, I was only left with worry lines in my forehead. Maybe it's my students' new-found delight in these characters as they are written, or perhaps it is just that the story is so fresh in my mind, but the trailer seemed to stray from the book a bit too much for my liking.
While The Hobbit is a much more lighthearted book than The Lord of the Rings, it worries me that some of the scenes with the dwarves seem to be inspired by The Three Stooges. They are the principle heroes in The Hobbit, and I really hope they are treated with the respect they deserve. It's bad enough that Gimli ended up being comic relief for much of the trilogy, do all the dwarves have to be goofy? It seems that only Thorin is treated with any respect. These are capable warriors from a noble race of artisans, and while there should be comedic moments in The Hobbit, they shouldn't simply be comic relief.
Another thing that is worrying me is the amount of obvious CGI in the trailer. I realize that it was a hug part of the LOTR movies too, but when combined with miniatures it was usually very seamless (other than a few very annoying green screen shots). Some of the shots in both trailers look very fake. The one moment that made me cringe in the first trailer was when Bilbo held up Sting, and it was very obviously a CGI sword instead of a real one. Perhaps they are not finalized effects yet, or maybe they will look better when they are all in context. A girl can hope, right?
The amount of content that has been added has always been something I was worried about. There were a lot of scenes that seemed to be drastically different than the book, or flat out created for the movie in spite of the book. I realize that some adjustments have to be made when a book is brought to life on film, but I've been nervous since I heard they were going to turn it into three movies. They brought all three books of The Lord of the Rings to life in three movies. Why are they taking three movies to recreate The Hobbit when it's only one book and a more simple story?
I'm holding out hope and reminding myself of a bit of wisdom from the man himself. "Do not be too eager to deal out death in judgement. For even the very wise cannot see all ends." ― J.R.R. Tolkien, The Fellowship of the Ring