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.
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Ascend.io, the Data Automation Cloud, provides the most advanced automation for data and analytics engineering workloads. Ascend.io unifies the core capabilities of data engineering—data ingestion, transformation, delivery, orchestration, and observability—into a single platform so that data teams deliver 10x faster. With 95% of data teams already at or over capacity, engineering productivity is a top priority for enterprises. Ascend’s Flex-code user interface empowers any member of the data team—from data engineers to data scientists to data analysts—to quickly and easily build and deliver on the data and analytics workloads they need. And with Ascend’s DataAware™ intelligence, data teams no longer spend hours carefully orchestrating brittle data workloads and instead rely on advanced automation to optimize the entire data lifecycle. Ascend.io runs natively on data lakes and warehouses and in AWS, Google Cloud and Microsoft Azure.
Go to dataengineeringpodcast.com/ascend to find out more.
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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
- 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?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- 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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