The Analytics Edge Data Operations for Cloud Data Warehouses with Sanjay Agrawal, Co-Founder and CEO at Revefi
This episode of The Analytics Edge (sponsored by NetSpring), features Sanjay Agrawal, Co-Founder and CEO of Revefi. Revefi's data operations cloud offers a zero-touch data quality, spend, usage, and performance co-pilot for monitoring and optimizing cloud data warehouses. With Revefi, one customer reduced warehouse spend by 30% and their data team saw zero escalations from the business for data quality related issues, despite data adoption increasing 35%. Throughout the conversation Sanjay explores the continuing challenges in managing data quality, the emergence of zero-touch observability enabled by AI, and the need to control data warehouse costs despite the anticipated cost reductions with the cloud.
Throughout the episode, Sanjay discusses the rapidly evolving field of data observability. He delves into the challenges and costs of data quality, emphasizing the importance of the right data at the right time and cost. Sanjay explores the concept of zero-touch data observability, likening it to level 4 automation in autonomous vehicles. He touches on the role of AI and ML in this context. The conversation also veers towards the new emerging dilemma where even though the cloud was supposed to reduce cost, businesses now find themselves seeking innovative ways to control costs within their cloud data warehouses..
Bio:
Sanjay Agrawal is a two-time co-founder of Revefi and ThoughtSpot. Sanjay has spent over 2 decades building foundational databases, technologies, SQL optimizers, and automating performances for entire warehouses. His latest endeavor, Revefi, offers a zero-touch, 360-degree data observability and monitoring solution for cloud data warehouses. At ThoughtSpot, he was instrumental in building a self-managing, distributed in-memory ACID compliant data warehouse capable of operating at 100 nanoseconds per input table.
Key Quote:
“Cloud data warehouses like Snowflake, RedShift, BigQuery, Databricks, and Azure have become the de facto place where businesses pull data out and use it for a business purpose. So the more compute you push on the cloud data warehouse, the closer it stays to the ecosystem and the easier it is for anyone to even consume such a system.”- Sanjay Agrawal
Episode Timestamps
(Segment 1) Challenges
(1:25) Motivations as a two-time founder
(2:37) Defining data observability
(5:32) Quantifying impact of poor data quality
(8:47) Understanding the problem of bad data
(13:08) Organizational responsibilities for data quality
(15:30) Data quality and/or analytics
(Segment 2) Solutions
(18:17) Challenges to zero-touch data observability
(21:15) Data observability in centralized warehouses
(23:52) Managing cloud data warehouse costs
(29:07) Leveraging AI/ML for data quality
(32:06) Building a non-invasive observability platform
(Segment 3) Business Opportunities
(34:39) Product vision for data observability
(Segment 4) (37:56) Takeaways