Automate Your Pipeline Creation For Streaming Data Transformations With SQLake
Data Engineering Podcast
00:00
Architecting Upsovere Differently
Upsovere stores all the data you can just on a data lake on a timeline. Each task is going to be defined by some kind of time period. So I don't need to ask the users to define the tasks for me. Sharding is something that's hard for the users to really get to do well. How many shards do I need for every pipeline? In what case and getting everything to work with auto sharding was hard to do.
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Summary
Managing end-to-end data flows becomes complex and unwieldy as the scale of data and its variety of applications in an organization grows. Part of this complexity is due to the transformation and orchestration of data living in disparate systems. The team at Upsolver is taking aim at this problem with the latest iteration of their platform in the form of SQLake. In this episode Ori Rafael explains how they are automating the creation and scheduling of orchestration flows and their related transforations in a unified SQL interface.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- Data and analytics leaders, 2023 is your year to sharpen your leadership skills, refine your strategies and lead with purpose. Join your peers at Gartner Data & Analytics Summit, March 20 – 22 in Orlando, FL for 3 days of expert guidance, peer networking and collaboration. Listeners can save $375 off standard rates with code GARTNERDA. Go to dataengineeringpodcast.com/gartnerda today to find out more.
- Truly leveraging and benefiting from streaming data is hard - the data stack is costly, difficult to use and still has limitations. Materialize breaks down those barriers with a true cloud-native streaming database - not simply a database that connects to streaming systems. With a PostgreSQL-compatible interface, you can now work with real-time data using ANSI SQL including the ability to perform multi-way complex joins, which support stream-to-stream, stream-to-table, table-to-table, and more, all in standard SQL. Go to dataengineeringpodcast.com/materialize today and sign up for early access to get started. If you like what you see and want to help make it better, they're hiring across all functions!
- Struggling with broken pipelines? Stale dashboards? Missing data? If this resonates with you, you’re not alone. Data engineers struggling with unreliable data need look no further than Monte Carlo, the leading end-to-end Data Observability Platform! Trusted by the data teams at Fox, JetBlue, and PagerDuty, Monte Carlo solves the costly problem of broken data pipelines. Monte Carlo monitors and alerts for data issues across your data warehouses, data lakes, dbt models, Airflow jobs, and business intelligence tools, reducing time to detection and resolution from weeks to just minutes. Monte Carlo also gives you a holistic picture of data health with automatic, end-to-end lineage from ingestion to the BI layer directly out of the box. Start trusting your data with Monte Carlo today! Visit dataengineeringpodcast.com/montecarlo to learn more.
- Your host is Tobias Macey and today I'm interviewing Ori Rafael about the SQLake feature for the Upsolver platform that automatically generates pipelines from your queries
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what the SQLake product is and the story behind it?
- What is the core problem that you are trying to solve?
- What are some of the anti-patterns that you have seen teams adopt when designing and implementing DAGs in a tool such as Airlow?
- What are the benefits of merging the logic for transformation and orchestration into the same interface and dialect (SQL)?
- Can you describe the technical implementation of the SQLake feature?
- What does the workflow look like for designing and deploying pipelines in SQLake?
- What are the opportunities for using utilities such as dbt for managing logical complexity as the number of pipelines scales?
- SQL has traditionally been challenging to compose. How did that factor into your design process for how to structure the dialect extensions for job scheduling?
- What are some of the complexities that you have had to address in your orchestration system to be able to manage timeliness of operations as volume and complexity of the data scales?
- What are some of the edge cases that you have had to provide escape hatches for?
- What are the most interesting, innovative, or unexpected ways that you have seen SQLake used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on SQLake?
- When is SQLake the wrong choice?
- What do you have planned for the future of SQLake?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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Links
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Sponsored By:
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- MonteCarlo:  Struggling with broken pipelines? Stale dashboards? Missing data? If this resonates with you, you’re not alone. Data engineers struggling with unreliable data need look no further than Monte Carlo, the leading end-to-end Data Observability Platform! Trusted by the data teams at Fox, JetBlue, and PagerDuty, Monte Carlo solves the costly problem of broken data pipelines. Monte Carlo monitors and alerts for data issues across your data warehouses, data lakes, dbt models, Airflow jobs, and business intelligence tools, reducing time to detection and resolution from weeks to just minutes. Monte Carlo also gives you a holistic picture of data health with automatic, end-to-end lineage from ingestion to the BI layer directly out of the box. Start trusting your data with Monte Carlo today! Visit [dataengineeringpodcast.com/montecarlo](https://www.dataengineeringpodcast.com/montecarlo) to learn more.
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