Analytics Engineering Without The Friction Of Complex Pipeline Development With Optimus and dbt
Data Engineering Podcast
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Is Data Traceability a Common Best Practice?
In terms of your predictions or forward-looking assessment for the evolving target of what constitutes best practices for analytics projects, I'm wondering what you see as some of the, I guess, influences that might impact change. With this kind of technology, becoming more and more predominant, best practice expectations like traceability is one thing that will become common practice because everything is in tables,. It's easy to capture the lineage using tools like Informatica and others. And data quality is also something that is used and applied in e-mail pipelines. So again, with tools like this, you know, and data quality is making more and more easy to apply and more common as well.
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Summary
One of the most impactful technologies for data analytics in recent years has been dbt. It’s hard to have a conversation about data engineering or analysis without mentioning it. Despite its widespread adoption there are still rough edges in its workflow that cause friction for data analysts. To help simplify the adoption and management of dbt projects Nandam Karthik helped create Optimus. In this episode he shares his experiences working with organizations to adopt analytics engineering patterns and the ways that Optimus and dbt were combined to let data analysts deliver insights without the roadblocks of complex pipeline management.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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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 Nandam Karthik about his experiences building analytics projects with dbt and Optimus for his clients at Sigmoid.
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Sigmoid is and the types of projects that you are involved in?
- What are some of the core challenges that your clients are facing when they start working with you?
- An ELT workflow with dbt as the transformation utility has become a popular pattern for building analytics systems. Can you share some examples of projects that you have built with this approach?
- What are some of the ways that this pattern becomes bespoke as you start exploring a project more deeply?
- What are the sharp edges/white spaces that you encountered across those projects?
- Can you describe what Optimus is?
- How does Optimus improve the user experience of teams working in dbt?
- What are some of the tactical/organizational practices that you have found most helpful when building with dbt and Optimus?
- What are the most interesting, innovative, or unexpected ways that you have seen Optimus/dbt used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on dbt/Optimus projects?
- When is Optimus/dbt the wrong choice?
- What are your predictions for how "best practices" for analytics projects will change/evolve in the near/medium term?
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:
- Datafold:  Datafold helps you deal with data quality in your pull request. It provides automated regression testing throughout your schema and pipelines so you can address quality issues before they affect production. No more shipping and praying, you can now know exactly what will change in your database ahead of time. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI, so in a few minutes you can get from 0 to automated testing of your analytical code. Visit our site at [dataengineeringpodcast.com/datafold](https://www.dataengineeringpodcast.com/datafold) today to book a demo with Datafold.
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