Be Confident In Your Data Integration By Quickly Validating Matching Records With data-diff
Data Engineering Podcast
00:00
Data Engineering Podcast - Ascend Data Automation Cloud
Data teams are increasingly under pressure to deliver according to a recent survey by s and dot io, 95 % reported being at or over capacity. 85% of data team's plan on investing an automation in the next 12 months. The ascend data automation cloud provides a unified platform for data andgestion transformation, orchestration and observability. Ascend users love its declarative pipe lines, powerful s dk, elegant u i and extensible plugin architecture. Go to data engineering podcast slash ascend and sign up for a free trial.
Transcript
Play full episode
Transcript
Episode notes
Summary
The perennial challenge of data engineers is ensuring that information is integrated reliably. While it is straightforward to know whether a synchronization process succeeded, it is not always clear whether every record was copied correctly. In order to quickly identify if and how two data systems are out of sync Gleb Mezhanskiy and Simon Eskildsen partnered to create the open source data-diff utility. In this episode they explain how the utility is implemented to run quickly and how you can start using it in your own data workflows to ensure that your data warehouse isn’t missing any records from your source systems.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their new managed database service you can launch a production ready MySQL, Postgres, or MongoDB cluster in minutes, with automated backups, 40 Gbps connections from your application hosts, and high throughput SSDs. Go to dataengineeringpodcast.com/linode today and get a $100 credit to launch a database, create a Kubernetes cluster, or take advantage of all of their other services. And don’t forget to thank them for their continued support of this show!
- Random data doesn’t do it — and production data is not safe (or legal) for developers to use. What if you could mimic your entire production database to create a realistic dataset with zero sensitive data? Tonic.ai does exactly that. With Tonic, you can generate fake data that looks, acts, and behaves like production because it’s made from production. Using universal data connectors and a flexible API, Tonic integrates seamlessly into your existing pipelines and allows you to shape and size your data to the scale, realism, and degree of privacy that you need. The platform offers advanced subsetting, secure de-identification, and ML-driven data synthesis to create targeted test data for all of your pre-production environments. Your newly mimicked datasets are safe to share with developers, QA, data scientists—heck, even distributed teams around the world. Shorten development cycles, eliminate the need for cumbersome data pipeline work, and mathematically guarantee the privacy of your data, with Tonic.ai. Data Engineering Podcast listeners can sign up for a free 2-week sandbox account, go to dataengineeringpodcast.com/tonic today to give it a try!
- 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.
- RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their state-of-the-art reverse ETL pipelines enable you to send enriched data to any cloud tool. Sign up free… or just get the free t-shirt for being a listener of the Data Engineering Podcast at dataengineeringpodcast.com/rudder.
- Your host is Tobias Macey and today I’m interviewing Gleb Mezhanskiy and Simon Eskildsen about their work to open source the data diff utility that they have been building at Datafold
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what the data diff tool is and the story behind it?
- What was your motivation for going through the process of releasing your data diff functionality as an open source utility?
- What are some of the ways that data-diff composes with other data quality tools? (e.g. Great Expectations, Soda SQL, etc.)
- Can you describe how data-diff is implemented?
- Given the target of having a performant and scalable utility how did you approach the question of language selection?
- What are some of the ways that you have seen data-diff incorporated in the workflow of data teams?
- What were the steps that you needed to do to get the project cleaned up and separated from your internal implementation for release as open source?
- What are the most interesting, innovative, or unexpected ways that you have seen data-diff used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on data-diff?
- When is data-diff the wrong choice?
- What do you have planned for the future of data-diff?
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.
- To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers
Links
- Datafold
- data-diff
- Autodesk
- Airbyte
- Debezium
- Napkin Math newsletter
- Airflow
- Dagster
- Great Expectations
- dbt
- Trino
- Preql
- Erez Shinan
- Fivetran
- md5
- CRC32
- Merkle Tree
- Locally Optimistic
- Presto
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Special Guest: Gleb Mezhanskiy.
Remember Everything You Learn from Podcasts
Save insights instantly, chat with episodes, and build lasting knowledge - all powered by AI.
