Maintain Your Data Engineers' Sanity By Embracing Automation
Data Engineering Podcast
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The Data Mashing Approach
Wepe had a hierarchy of data products. We called it the canonical data representation, the c d r. And that's where you spend the most amount of effort doing the checks on the day to warehouse. monitoring health is really important and we didn't get that sticated at wepe but one could imagine assigning some monitoring health score to each table and topic or scheme. One project has in its trying to assign a sort of scheancata based on your kind of driving force. There is looking at, from a graph perspective, how interconnected other stuff is.
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Summary
Building and maintaining reliable data assets is the prime directive for data engineers. While it is easy to say, it is endlessly complex to implement, requiring data professionals to be experts in a wide range of disparate topics while designing and implementing complex topologies of information workflows. In order to make this a tractable problem it is essential that engineers embrace automation at every opportunity. In this episode Chris Riccomini shares his experiences building and scaling data operations at WePay and LinkedIn, as well as the lessons he has learned working with other teams as they automated their own systems.
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 Chris Riccomini about building awareness of data usage into CI/CD pipelines for application development
Interview
- Introduction
- How did you get involved in the area of data management?
- What are the pieces of data platforms and processing that have been most difficult to scale in an organizational sense?
- What are the opportunities for automation to alleviate some of the toil that data and analytics engineers get caught up in?
- The application delivery ecosystem has been going through ongoing transformation in the form of CI/CD, infrastructure as code, etc. What are the parallels in the data ecosystem that are still nascent?
- What are the principles that still need to be translated for data practitioners? Which are subject to impedance mismatch and may never make sense to translate?
- As someone with a software engineering background and extensive experience working in data, what are the missing links to make those teams/objectives work together more seamlessly?
- How can tooling and automation help in that endeavor?
- A key factor in the adoption of automation for application delivery is automated tests. What are some of the strategies you find useful for identifying scope and targets for testing/monitoring of data products?
- As data usage and capabilities grow and evolve in an organization, what are the junction points that are in greatest need of well-defined data contracts?
- How can automation aid in enforcing and alerting on those contracts in a continuous fashion?
- What are the most interesting, innovative, or unexpected ways that you have seen automation of data operations used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on automation for data systems?
- When is automation the wrong choice?
- What does the future of data engineering look like?
Contact Info
- Website
- @criccomini on Twitter
- criccomini on GitHub
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.
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Links
- WePay
- Enterprise Service Bus
- The Missing README
- Hadoop
- Confluent Schema Registry
- Avro
- CDC == Change Data Capture
- Debezium
- Data Mesh
- What the heck is a data mesh? blog post
- SRE == Site Reliability Engineer
- Terraform
- Chef configuration management tool
- Puppet configuration management tool
- Ansible configuration management tool
- BigQuery
- Airflow
- Pulumi
- Monte Carlo
- Bigeye
- Anomalo
- Great Expectations
- Schemata
- Data Engineering Weekly newsletter
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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