Decoupling Data Operations From Data Infrastructure Using Nexla - Episode 215

Summary

The technological and social ecosystem of data engineering and data management has been reaching a stage of maturity recently. As part of this stage in our collective journey the focus has been shifting toward operation and automation of the infrastructure and workflows that power our analytical workloads. It is an encouraging sign for the industry, but it is still a complex and challenging undertaking. In order to make this world of DataOps more accessible and manageable the team at Nexla has built a platform that decouples the logical unit of data from the underlying mechanisms so that you can focus on the problems that really matter to your business. In this episode Saket Saurabh (CEO) and Avinash Shahdadpuri (CTO) share the story behind the Nexla platform, discuss the technical underpinnings, and describe how their concept of a Nexset simplifies the work of building data products for sharing within and between organizations.

Your data platform needs to be scalable, fault tolerant, and performant, which means that you need the same from your cloud provider. Linode has been powering production systems for over 17 years, and now they’ve launched a fully managed Kubernetes platform. With the combined power of the Kubernetes engine for flexible and scalable deployments, and features like dedicated CPU instances, GPU instances, and object storage you’ve got everything you need to build a bulletproof data pipeline. If you go to dataengineeringpodcast.com/linode today you’ll even get a $100 credit to use on building your own cluster, or object storage, or reliable backups, or… And while you’re there don’t forget to thank them for being a long-time supporter of the Data Engineering Podcast!


Census LogoCensus is the operational analytics platform that syncs your cloud warehouse with all the SaaS applications used by your Sales, Marketing & Success teams. If you need to get your company data into Salesforce, Marketo, Hubspot, Intercom, Zendesk, and other tools, Census is the easiest way to do so. Just write SQL (or plug in your dbt models), set up the sync frequencies, and voila, your data is now available to be used by all of your teams.  No need to worry about incremental sync, backfilling, API quota management, API versioning, monitoring, and maintaining custom scripts. Just SQL. Start your free 14-day trial now.


Databand LogoDataband.ai is a unified Data Observability Platform that helps DataOps teams catch and solve data health issues fast. Databand.ai’s platform helps data engineers pinpoint pipeline issues and quickly identify their root cause so DataOps can begin working on a resolution before bad data is delivered. Whether you’re using Apache Spark, Apache Airflow, Databricks, Amazon S3, self-hosted python scripts, or combinations of these, Databand.ai allows you to monitor data health along every step of its journey. Powerful integrations to 20+ tools gives you full visibility of your stack. Our mission is to help businesses trust their data with the most powerful Data Observability Platform. Experience unified observability with a free trial today: www.databand.ai


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 managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
  • Schema changes, missing data, and volume anomalies caused by your data sources can happen without any advanced notice if you lack visibility into your data-in-motion. That leaves DataOps reactive to data quality issues and can make your consumers lose confidence in your data. By connecting to your pipeline orchestrator like Apache Airflow and centralizing your end-to-end metadata, Databand.ai lets you identify data quality issues and their root causes from a single dashboard. With Databand.ai, you’ll know whether the data moving from your sources to your warehouse will be available, accurate, and usable when it arrives. Go to dataengineeringpodcast.com/databand to sign up for a free 30-day trial of Databand.ai and take control of your data quality today.
  • We’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to dataengineeringpodcast.com/census today to get a free 14-day trial.
  • Your host is Tobias Macey and today I’m interviewing Saket Saurabh and Avinash Shahdadpuri about Nexla, a platform for powering data operations and sharing within and across businesses

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you describe what Nexla is and the story behind it?
  • What are the major problems that Nexla is aiming to solve?
    • What are the components of a data platform that Nexla might replace?
  • What are the use cases and benefits of being able to publish data sets for use outside and across organizations?
  • What are the different elements involved in implementing DataOps?
  • How is the Nexla platform implemented?
    • What have been the most comple engineering challenges?
    • How has the architecture changed or evolved since you first began working on it?
    • What are some of the assumptions that you had at the start which have been challenged or invalidated?
  • What are some of the heuristics that you have found most useful in generating logical units of data in an automated fashion?
  • Once a Nexset has been created, what are some of the ways that they can be used or further processed?
  • What are the attributes of a Nexset? (e.g. access control policies, lineage, etc.)
    • How do you handle storage and sharing of a Nexset?
  • What are some of your grand hopes and ambitions for the Nexla platform and the potential for data exchanges?
  • What are the most interesting, innovative, or unexpected ways that you have seen Nexla used?
  • What are the most interesting, unexpected, or challenging lessons that you have learned while working on Nexla?
  • When is Nexla the wrong choice?
  • What do you have planned for the future of Nexla?

Contact Info

Parting Question

  • From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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