Don't use Hadoop when your data isn't that big (2013)
I have seen so so so many projects get bogged down by the need  to use a &quot;big data&quot; stack.<p>I think my favorite example, was a team that spent six months trying to build a system to take in files, parse them, and store them. Files came through a little less than one per second, which translated to about 100kb. This translated to about 2.5Gb a day of data. The data only needed to be stored for a year, and could easily be compressed.<p>They felt the need to setup a cluster, with 1Tb of RAM to handle processing the documents, they had a 25 Kafka instances, etc. It was just insane.<p>I just pulled out a python script, and combine that with Postgres and within an afternoon I had completed the project (albeit not production ready). This is so typical within companies it makes me gag. They were easily spending $100k a month just on infrastructure, my solution cost ~$400 ($1200 with replication).<p>The sad part, is that convincing management to use my solution was the hardest part. Basically, I had to explain how my system was more robust, faster, cheaper, etc. Even side-by-side comparisons didn&#x27;t seem to convince them, they just felt the other solution was better some how... Eventually, I convinced them, after about a month of debates, and an endless stream of proof.