Data Engineering Podcast Adding Anomaly Detection And Observability To Your dbt Projects Is Elementary
Exploring the importance of observability in dbt projects, with focus on enhancing testing capabilities and anomaly detection. The conversation delves into the challenges faced by data engineers in building trust in data accuracy and the approach taken by Elementary to embed observability into the workflow.
50:44
Early Passion for Data Quality
- Mayan Salom began working with data as a child fascinated by sports statistics and databases, which sparked her passion for data quality.
- Her experience in cybersecurity incident response highlighted the need for accurate and trusted data pipelines under pressure.
Three Pillars of DBT Observability
- Observability in dbt projects requires focus on data validation, operational monitoring of individual pipeline steps, and leveraging metadata to understand pipeline context.
- Combining data, operational metrics, and metadata results in comprehensive monitoring and governance.
Beware DIY Observability Limits
- Small teams often parse dbt logs and manifests manually, sending output to familiar tools or BI dashboards for visibility.
- However, DIY setups can require heavy maintenance and may not scale well with project growth or complexity.
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Introduction
00:00 • 2min
Discussion on Incorporating Observability into DBT Workflow
01:34 • 3min
Enhancing Observability in DBT Projects
04:28 • 15min
Designing User-friendly Product with Familiar Tools and Technologies
19:56 • 2min
Innovative Utilization of the Elementary Tool Chain in the dbt Ecosystem
21:59 • 18min
Navigating the Ever-changing Landscape of Data Observability
39:34 • 8min
Challenges in Data Management and Communication
48:01 • 3min
Summary
Working with data is a complicated process, with numerous chances for something to go wrong. Identifying and accounting for those errors is a critical piece of building trust in the organization that your data is accurate and up to date. While there are numerous products available to provide that visibility, they all have different technologies and workflows that they focus on. To bring observability to dbt projects the team at Elementary embedded themselves into the workflow. In this episode Maayan Salom explores the approach that she has taken to bring observability, enhanced testing capabilities, and anomaly detection into every step of the dbt developer experience.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino.
- Dagster offers a new approach to building and running data platforms and data pipelines. It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. Your team can get up and running in minutes thanks to Dagster Cloud, an enterprise-class hosted solution that offers serverless and hybrid deployments, enhanced security, and on-demand ephemeral test deployments. Go to dataengineeringpodcast.com/dagster today to get started. Your first 30 days are free!
- This episode is brought to you by Datafold – a testing automation platform for data engineers that prevents data quality issues from entering every part of your data workflow, from migration to dbt deployment. Datafold has recently launched data replication testing, providing ongoing validation for source-to-target replication. Leverage Datafold's fast cross-database data diffing and Monitoring to test your replication pipelines automatically and continuously. Validate consistency between source and target at any scale, and receive alerts about any discrepancies. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold.
- Your host is Tobias Macey and today I'm interviewing Maayan Salom about how to incorporate observability into a dbt-oriented workflow and how Elementary can help
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you start by outlining what elements of observability are most relevant for dbt projects?
- What are some of the common ad-hoc/DIY methods that teams develop to acquire those insights?
- What are the challenges/shortcomings associated with those approaches?
- Over the past ~3 years there were numerous data observability systems/products created. What are some of the ways that the specifics of dbt workflows are not covered by those generalized tools?
- What are the insights that can be more easily generated by embedding into the dbt toolchain and development cycle?
- Can you describe what Elementary is and how it is designed to enhance the development and maintenance work in dbt projects?
- How is Elementary designed/implemented?
- How have the scope and goals of the project changed since you started working on it?
- What are the engineering challenges/frustrations that you have dealt with in the creation and evolution of Elementary?
- Can you talk us through the setup and workflow for teams adopting Elementary in their dbt projects?
- How does the incorporation of Elementary change the development habits of the teams who are using it?
- What are the most interesting, innovative, or unexpected ways that you have seen Elementary used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Elementary?
- When is Elementary the wrong choice?
- What do you have planned for the future of Elementary?
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.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
- If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story.
Links
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Sponsored By:
- Starburst:  This episode is brought to you by Starburst - a data lake analytics platform for data engineers who are battling to build and scale high quality data pipelines on the data lake. Powered by Trino, Starburst runs petabyte-scale SQL analytics fast at a fraction of the cost of traditional methods, helping you meet all your data needs ranging from AI/ML workloads to data applications to complete analytics. Trusted by the teams at Comcast and Doordash, Starburst delivers the adaptability and flexibility a lakehouse ecosystem promises, while providing a single point of access for your data and all your data governance allowing you to discover, transform, govern, and secure all in one place. Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Try Starburst Galaxy today, the easiest and fastest way to get started using Trino, and get $500 of credits free. [dataengineeringpodcast.com/starburst](https://www.dataengineeringpodcast.com/starburst)
- Datafold:  This episode is brought to you by Datafold – a testing automation platform for data engineers that prevents data quality issues from entering every part of your data workflow, from migration to dbt deployment. Datafold has recently launched data replication testing, providing ongoing validation for source-to-target replication. Leverage Datafold's fast cross-database data diffing and Monitoring to test your replication pipelines automatically and continuously. Validate consistency between source and target at any scale, and receive alerts about any discrepancies. Learn more about Datafold by visiting https://get.datafold.com/replication-de-podcast.
- Dagster:  Data teams are tasked with helping organizations deliver on the premise of data, and with ML and AI maturing rapidly, expectations have never been this high. However data engineers are challenged by both technical complexity and organizational complexity, with heterogeneous technologies to adopt, multiple data disciplines converging, legacy systems to support, and costs to manage. Dagster is an open-source orchestration solution that helps data teams reign in this complexity and build data platforms that provide unparalleled observability, and testability, all while fostering collaboration across the enterprise. With enterprise-grade hosting on Dagster Cloud, you gain even more capabilities, adding cost management, security, and CI support to further boost your teams' productivity. Go to [dagster.io](https://dagster.io/lp/dagster-cloud-trial?source=data-eng-podcast) today to get your first 30 days free!