Building Data Pipelines That Run From Source To Analysis And Activation With Hevo Data

Summary

Any business that wants to understand their operations and customers through data requires some form of pipeline. Building reliable data pipelines is a complex and costly undertaking with many layered requirements. In order to reduce the amount of time and effort required to build pipelines that power critical insights Manish Jethani co-founded Hevo Data. In this episode he shares his journey from building a consumer product to launching a data pipeline service and how his frustrations as a product owner have informed his work at Hevo Data.

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!
• Data stacks are becoming more and more complex. This brings infinite possibilities for data pipelines to break and a host of other issues, severely deteriorating the quality of the data and causing teams to lose trust. Sifflet solves this problem by acting as an overseeing layer to the data stack – observing data and ensuring it’s reliable from ingestion all the way to consumption. Whether the data is in transit or at rest, Sifflet can detect data quality anomalies, assess business impact, identify the root cause, and alert data teams’ on their preferred channels. All thanks to 50+ quality checks, extensive column-level lineage, and 20+ connectors across the Data Stack. In addition, data discovery is made easy through Sifflet’s information-rich data catalog with a powerful search engine and real-time health statuses. Listeners of the podcast will get $2000 to use as platform credits when signing up to use Sifflet. Sifflet also offers a 2-week free trial. Find out more at dataengineeringpodcast.com/sifflet today!
• 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.
• Data stacks are becoming more and more complex. This brings infinite possibilities for data pipelines to break and a host of other issues, severely deteriorating the quality of the data and causing teams to lose trust. Sifflet solves this problem by acting as an overseeing layer to the data stack – observing data and ensuring it’s reliable from ingestion all the way to consumption. Whether the data is in transit or at rest, Sifflet can detect data quality anomalies, assess business impact, identify the root cause, and alert data teams’ on their preferred channels. All thanks to 50+ quality checks, extensive column-level lineage, and 20+ connectors across the Data Stack. In addition, data discovery is made easy through Sifflet’s information-rich data catalog with a powerful search engine and real-time health statuses. Listeners of the podcast will get $2000 to use as platform credits when signing up to use Sifflet. Sifflet also offers a 2-week free trial. Find out more at dataengineeringpodcast.com/sifflet today!
• 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 Manish Jethani about Hevo Data’s experiences navigating the modern data stack and the role of ELT in data workflows

Interview

• Introduction
• How did you get involved in the area of data management?
• Can you describe what Hevo Data is and the story behind it?
• What is the core problem that you are trying to solve with the Hevo platform?
  • What are the target personas of who will bring Hevo into a company and who will be using/interacting with it for their day-to-day?
• What are some of the lessons that you learned building a product that relied on data to function which you have carried into your work at Hevo, providing the utilities that enable other businesses and products?
• There are numerous commercial and open source options for collecting, transforming, and integrating data. What are the differentiating features of Hevo?
  • What are your views on the benefits of a vertically integrated platform for data flows in the world of the disaggregated "modern data stack"?
• Can you describe how the Hevo platform is implemented?
  • What are some of the optimizations that you have invested in to support the aggregate load from your customers?
• The predominant pattern in recent years for collecting and processing data is ELT. In your work at Hevo, what are some of the nuance and exceptions to that "best practice" that you have encountered?
  • How have you factored those learnings back into the product?
• mechanics of schema mapping
  • edge cases that require human intervention
    • how to surface those in a timely fashion
• What is the process for onboarding onto the Hevo platform?
  • Once an organization has adopted Hevo, can you describe the workflow of building/maintaining/evolving data pipelines?
• What are the most interesting, innovative, or unexpected ways that you have seen Hevo used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on Hevo?
• When is Hevo the wrong choice?
• What do you have planned for the future of Hevo?

Contact Info

• LinkedIn
• @ManishJethani on Twitter

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.
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Links

• Hevo Data
• Kafka
• MongoDB

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