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Eliminate The Overhead In Your Data Integration With The Open Source dlt Library

Data Engineering Podcast

In this episode Adrian Brudaru explains how it works, the benefits that it provides over other data integration solutions, and how you can start building pipelines today. The obvious comparison is with systems like Singer/Meltano/Airbyte in the open source space, or Fivetran/Matillion/etc. Who is the target audience?

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Why Isolated Test Environments Are Valuable

Meltano

An isolated test environment is a set of computing resources in the form of hardware, software, and configuration files working together—but set apart from the production environment—to execute test cases and ascertain certain system behavior. Testing in isolation ensures that dependencies are working before being shipped to production.

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How to Build a Custom Extractor with Meltano

Meltano

Most scripts/tools work with known data formats like JSON and CSV. In this article, you will learn about custom extractors in ELT pipelines and how you can implement a custom extractor for extracting data from a JSON placeholder API to a JSONL file using Meltano. What Are Custom Extractors?

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What Is Data Observability? Everything You Need To Know

Meltano

Data observability can help, but what is it and how does it work? See How Meltano Supports Data Observability We’ll explain all you need to know about data observability, including the pillars that drive it and how a DataOps platform can enable observability for your organization. Interested in Meltano?

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Data Exploration For Business Users Powered By Analytics Engineering With Lightdash

Data Engineering Podcast

In this episode Oliver Laslett describes why dashboards aren’t sufficient for business analytics, how Lightdash promotes the work that you are already doing in your data warehouse modeling with dbt, and how they are focusing on bridging the divide between data teams and business teams and the requirements that they have for data workflows.

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Laying The Foundation Of Your Data Platform For The Era Of Big Complexity With Dagster

Data Engineering Podcast

Summary The technology for scaling storage and processing of data has gone through massive evolution over the past decade, leaving us with the ability to work with massive datasets at the cost of massive complexity. How has the project and community changed/evolved since we last spoke 2 years ago? No more scripts, just SQL.

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Insights And Advice On Building A Data Lake Platform From Someone Who Learned The Hard Way

Data Engineering Podcast

In this episode he shares his insights and advice on how to approach such an undertaking in your own organization. How has the ecosystem for building maintainable and usable data lakes matured over the past few years? How did your experiences at Yelp inform your current architectural approach at Robinhood?

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