Launch HN: DAGWorks – ML platform for data science teams
As a long-time fan of DAG-oriented tools, congrats on the launch. Maybe you can get added here <a href="https:&#x2F;&#x2F;github.com&#x2F;pditommaso&#x2F;awesome-pipeline">https:&#x2F;&#x2F;github.com&#x2F;pditommaso&#x2F;awesome-pipeline</a> now or in the future...<p>This is a problem space I&#x27;ve worked in and been thinking about for a very, very long time. I&#x27;ve extensively used Airflow (bad), DBT (good-ish), Luigi (good), drake (abandoned), tested many more, and written two of my own.<p>It&#x27;s important to remember that DAG tools exist to solve two primary problems, that arise from one underlying cause. Those problems are 1) getting parallelism and execution ordering automatically (i.e. declaratively) based on the structure  of dependencies, and 2) being able to resume a partially-failed run. The underlying cause is: data processing jobs take significant wall-clock time (minutes, hours, even days), so we want to use resources efficiently, and avoid re-computing things.<p>Any DAG tool that doesn&#x27;t solve these problems is unlikely to be useful. From your docs, I don&#x27;t see anything on either of those topics, so not off to a strong start. Perhaps you have that functionality but haven&#x27;t documented it yet? I can imagine the parallelism piece being there but just not stated, but the &quot;resumption from partial failure&quot; piece needs to be spelled out. Anyway, something to consider.<p>A couple more things...<p>It looks like you&#x27;ve gone the route of expressing dependencies only &quot;locally&quot;. That is, when I define a computation, I indicate what it depends on <i>there</i>, right next to the definition. DBT and Luigi work this way also. Airflow, by contrast, defines dependencies centrally, as you add task instances to a DAG object. There is no right answer here, only tradeoffs. One thing to be aware of is that when using the &quot;local&quot; style, as a project grows big (<i>glances at 380-model DBT project...</i>), understanding its execution flow at a high level becomes a struggle, and is often only solvable through visualization tools. I see you have Graphviz output which is great. I recommend investing heavily in visualization tooling (DBT&#x27;s graph browser, for example).<p>I don&#x27;t see any mention of development workflow. As a few examples, DBT has rich model selection features that let you run one model, all its ancestors, all its descendants, all models with a tag, etc etc. Luigi lets you invoke any task as a terminal task, using a handy auto-generated CLI. Airflow lets you... run a single task, and that&#x27;s it. This makes a BIG DIFFERENCE. Developers -- be they scientists or engineers -- will need to run arbitrary subgraphs while they fiddle with stuff, and the easier you make that, the more they will love your tool.<p>Another thing I notice is that it seems like your model is oriented around flowing data through the program, as arguments &#x2F; return values (similar to Prefect, and of course Spark). This is fine as far as it goes, but consider that much of what we deal with in data is 1) far too big for this to work and&#x2F;or 2) processed elsewhere e.g. a SQL query. You should think about, and document, how you handle dependencies that exist in the World State rather than in memory. This intersects with how you model and keep track of task state. Airflow keeps task state in a database. DBT keeps task state in memory. Luigi track task state through Targets which typically live in the World State. Again there&#x27;s no right or wrong here only tradeoffs, but leaning on <i>durable</i> records of task state directly facilitates &quot;resumption from partial failure&quot; as mentioned above.<p>Best of luck.