Data lineage is something that has grown from a convenient feature to a critical need as data systems have grown in scale, complexity, and centrality to business. Alvin is a platform that aims to provide a low effort solution for data lineage capabilities focused on simplifying the work of data engineers. In this episode co-founder Martin Sahlen explains the impact that easy access to lineage information can have on the work of data engineers and analysts, and how he and his team have designed their platform to offer that information to engineers and stakeholders in the places that they interact with data.
Ascend.io, the Data Automation Cloud, provides the most advanced automation for data and analytics engineering workloads. Ascend.io unifies the core capabilities of data engineering—data ingestion, transformation, delivery, orchestration, and observability—into a single platform so that data teams deliver 10x faster. With 95% of data teams already at or over capacity, engineering productivity is a top priority for enterprises. Ascend’s Flex-code user interface empowers any member of the data team—from data engineers to data scientists to data analysts—to quickly and easily build and deliver on the data and analytics workloads they need. And with Ascend’s DataAware™ intelligence, data teams no longer spend hours carefully orchestrating brittle data workloads and instead rely on advanced automation to optimize the entire data lifecycle. Ascend.io runs natively on data lakes and warehouses and in AWS, Google Cloud and Microsoft Azure.
Go to dataengineeringpodcast.com/ascend to find out more.
“Metaplane is the most exciting tool in our data stack. It’s the command-and-control center where we have a comprehensive view of the health of our data.” — Head of BI and Analytics Engineering at Vendr.
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Go to dataengineeringpodcast.com/metaplane today and sign up for a free-forever plan at Metaplane.dev, or try out our most advanced features with a 14-day free trial. Mention the podcast to get a free In Data We Trust World Tour t-shirt.
Your data platform needs to be scalable, fault tolerant, and performant, which means that you need the same from your cloud provider. Linode has been powering production systems for over 17 years, and now they’ve launched a fully managed Kubernetes platform. With the combined power of the Kubernetes engine for flexible and scalable deployments, and features like dedicated CPU instances, GPU instances, and object storage you’ve got everything you need to build a bulletproof data pipeline. If you go to dataengineeringpodcast.com/linode today you’ll even get a $100 credit to use on building your own cluster, or object storage, or reliable backups, or… And while you’re there don’t forget to thank them for being a long-time supporter of the Data Engineering Podcast!
RudderStack provides all your customer data pipelines in one platform. You can collect, transform, and route data across your entire stack with its event streaming, ETL, and reverse ETL pipelines.
RudderStack’s warehouse-first approach means it does not store sensitive information, and it allows you to leverage your existing data warehouse/data lake infrastructure to build a single source of truth for every team.
RudderStack also supports real-time use cases. You can Implement RudderStack SDKs once, then automatically send events to your warehouse and 150+ business tools, and you’ll never have to worry about API changes again.
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- You wake up to a Slack message from your CEO, who’s upset because the company’s revenue dashboard is broken. You’re told to fix it before this morning’s board meeting, which is just minutes away. Enter Metaplane, the industry’s only self-serve data observability tool. In just a few clicks, you identify the issue’s root cause, conduct an impact analysis—and save the day. Data leaders at Imperfect Foods, Drift, and Vendr love Metaplane because it helps them catch, investigate, and fix data quality issues before their stakeholders ever notice they exist. Setup takes 30 minutes. You can literally get up and running with Metaplane by the end of this podcast. Sign up for a free-forever plan at dataengineeringpodcast.com/metaplane, or try out their most advanced features with a 14-day free trial. Mention the podcast to get a free "In Data We Trust World Tour" t-shirt.
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- Data teams are increasingly under pressure to deliver. According to a recent survey by Ascend.io, 95% in fact reported being at or over capacity. With 72% of data experts reporting demands on their team going up faster than they can hire, it’s no surprise they are increasingly turning to automation. In fact, while only 3.5% report having current investments in automation, 85% of data teams plan on investing in automation in the next 12 months. 85%!!! That’s where our friends at Ascend.io come in. The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability. Ascend users love its declarative pipelines, powerful SDK, elegant UI, and extensible plug-in architecture, as well as its support for Python, SQL, Scala, and Java. Ascend automates workloads on Snowflake, Databricks, BigQuery, and open source Spark, and can be deployed in AWS, Azure, or GCP. Go to dataengineeringpodcast.com/ascend and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $5,000 when you become a customer.
- Your host is Tobias Macey and today I’m interviewing Martin Sahlen about his work on data lineage at Alvin and how it factors into the day-to-day work of data engineers
- How did you get involved in the area of data management?
- Can you describe what Alvin is and the story behind it?
- What is the core problem that you are trying to solve at Alvin?
- Data lineage has quickly become an overloaded term. What are the elements of lineage that you are focused on addressing?
- What are some of the other sources/pieces of information that you integrate into the lineage graph?
- How does data lineage show up in the work of data engineers?
- In what ways does your focus on data engineers inform the way that you model the lineage information?
- As with every data asset/product, the lineage graph is only as useful as the data that it stores. What are some of the ways that you focus on establishing and ensuring a complete view of lineage?
- How do you account for assets (e.g. tables, dashboards, exports, etc.) that are created outside of the "officially supported" methods? (e.g. someone manually runs a SQL create statement, etc.)
- Can you describe how you have implemented the Alvin platform?
- How have the design and goals shifted from when you first started exploring the problem?
- What are the types of data systems/assets that you are focused on supporting? (e.g. data warehouses vs. lakes, structured vs. unstructured, which BI tools, etc.)
- How does Alvin fit into the workflow of data engineers and their downstream customers/collaborators?
- What are some of the design choices (both visual and functional) that you focused on to avoid friction in the data engineer’s workflow?
- What are some of the open questions/areas for investigation/improvement in the space of data lineage?
- What are the factors that contribute to the difficulty of a truly holistic and complete view of lineage across an organization?
- What are the most interesting, innovative, or unexpected ways that you have seen Alvin used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Alvin?
- When is Alvin the wrong choice?
- What do you have planned for the future of Alvin?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
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