Select Star Data Discovery Platform - The Biggest Challenge With Modern Data Systems

Summary

Data has permeated every aspect of our lives and the products that we interact with. As a result, end users and customers have come to expect interactions and updates with services and analytics to be fast and up to date. In this episode Shruti Bhat gives her view on the state of the ecosystem for real-time data and the work that she and her team at Rockset is doing to make it easier for engineers to build those experiences.

Announcements

• Hello and welcome to the Data Engineering Podcast, the show about modern data management
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• The biggest challenge with modern data systems is understanding what data you have, where it is located, and who is using it. Select Star’s data discovery platform solves that out of the box, with an automated catalog that includes lineage from where the data originated, all the way to which dashboards rely on it and who is viewing them every day. Just connect it to your database/data warehouse/data lakehouse/whatever you’re using and let them do the rest. Go to dataengineeringpodcast.com/selectstar today to double the length of your free trial and get a swag package when you convert to a paid plan.
• 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 Shruti Bhat about the growth of real-time data applications and the systems required to support them

Interview

• Introduction
• How did you get involved in the area of data management?
• Can you describe what is driving the adoption of real-time analytics?
• architectural patterns for real-time analytics
• sources of latency in the path from data creation to end-user
• end-user/customer expectations for time to insight
  • differing expectations between internal and external consumers
• scales of data that are reasonable for real-time vs. batch
• What are the most interesting, innovative, or unexpected ways that you have seen real-time architectures implemented?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on Rockset?
• When is Rockset the wrong choice?
• What do you have planned for the future of Rockset?

Contact Info

• LinkedIn
• @shrutibhat on Twitter

Parting Question

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

Links

• Rockset
  • Podcast Episode
• Embedded Analytics
• Confluent
• Kafka
• AWS Kinesis
• Lambda Architecture
• Data Observability
• Data Mesh
• DynamoDB Streams
• MongoDB Change Streams
• Bigeye
• Monte Carlo Data

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