Astronomer kubernetes8/21/2023 ![]() In turn, they allow anyone in your organization to understand your canonical set of data assets and allow you to reproduce them at any time. These asset definitions are version-controlled through git and inspectable via tooling. This concept starts by using code to define the data assets that you want to exist. It does this by creating software defined assets. Instead of a more jobs or tasks based approach, Dagster is putting together a software-defined approach to data pipelines. The goal of Dagster is to take a different approach to data pipelines compared to Airflow and some other orchestrators. For those who haven’t worked with or heard of these tools here is a quick overview.Īsset graph from re-bundling the data platformĭagster is developed by Elementl which was founded by Nick Schrock. Prefect and Dagster have been two other well-funded start-ups in the data space. Of course, Airflow is now facing at least two other competitors that are picking up steam. Since Luigi has often been used as a comparison to Airflow, it does seem like Airflow might have won this battle. I do love that engineering teams are so transparent about their technology these days as it’s great to see what is occurring under the hood and why. Is decoupled from the rest of our ecosystem, enabling mixing and matching in the service layer according to our strategy. Has a thin client SDK by moving more to the backend: this makes the maintenance of the overall platform much simpler than our existing offering with two libraries (Python, Java) both holding the logic. Uses Task as a first-class citizen, making it easy for engineers to share and reuse tasks/workflows. Has a similar entity model and nomenclature as Luigi and Flo, making the user experience and migration easier. Here were some of the reasons they listed on their site why they decided to go with Flyte: Let’s shift away from Airflow because, on the flip-side, Luigi, the orchestration tool managed by Spotify seems to have been fully replaced by Flyte according to a recent article on Spotify's engineering site. Truthfully, I believe that the data space is going to get a lot more interesting in the next 2-3 years. But, I can foresee the need for tool consolidation not only for the sanity of us data engineers but also to increase the distribution of tools that are more difficult to sell as a one-off solution. Of course, this might be a little difficult with most of the top players being valued at billions of dollars. Perhaps re-bundling will be the way of the future for data infrastructure. ![]() There seem to be clear goals here to take Astronomer and build a lot more functionality on top of a managed Airflow service. We’ve learned how companies are using it, and we are getting ready to launch a product and start scaling field teams, so there is a big opportunity out there.” “Now we are working with them to take Airflow to the next level. This move makes a lot of sense as Astronomer looks to continue to grow its offerings to so much more than just managed Airflow.Īs stated by Astronomer’s CEO Joe Otto, “For the last couple of years, we focused on Airflow and working with the people who created it,” he added. Meaning that when a number looks off in a dashboard or there is a failure in a pipelines step, you can find out why fast. Another way to put this is that Datakin helps data engineers trace relationships between datasets. What I found more interesting with this raise was the acquisition of Datakin.ĭatakin helps data engineers and data scientists better track the lineage of their data pipelines. Morgan, K5 Global, Sutter Hill Ventures, Venrock, and Sierra Ventures.īut billion-dollar valuations are nothing new in the data space at this point. This was led by Insight Ventures and was joined by Meritech Capital, Salesforce Ventures, J.P. In case you missed it, Astronomer raised $213 million a few weeks back. ![]()
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