How much time do you spend maintaining your data pipeline? How much end user value does that provide? Raghu Murthy founded DataCoral as a way to abstract the low level details of ETL so that you can focus on the actual problem that you are trying to solve. In this episode he explains his motivation for building the DataCoral platform, how it is leveraging serverless computing, the challenges of delivering software as a service to customer environments, and the architecture that he has designed to make batch data management easier to work with. This was a fascinating conversation with someone who has spent his entire career working on simplifying complex data problems.
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- Alluxio is an open source, distributed data orchestration layer that makes it easier to scale your compute and your storage independently. By transparently pulling data from underlying silos, Alluxio unlocks the value of your data and allows for modern computation-intensive workloads to become truly elastic and flexible for the cloud. With Alluxio, companies like Barclays, JD.com, Tencent, and Two Sigma can manage data efficiently, accelerate business analytics, and ease the adoption of any cloud. Go to dataengineeringpodcast.com/alluxio today to learn more and thank them for their support.
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- Your host is Tobias Macey and today I’m interviewing Raghu Murthy about DataCoral, a platform that offers a fully managed and secure stack in your own cloud that delivers data to where you need it
- How did you get involved in the area of data management?
- Can you start by explaining what DataCoral is and your motivation for founding it?
- How does the data-centric approach of DataCoral differ from the way that other platforms think about processing information?
- Can you describe how the DataCoral platform is designed and implemented, and how it has evolved since you first began working on it?
- How does the concept of a data slice play into the overall architecture of your platform?
- How do you manage transformations of data schemas and formats as they traverse different slices in your platform?
- On your site it mentions that you have the ability to automatically adjust to changes in external APIs, can you discuss how that manifests?
- What has been your experience, both positive and negative, in building on top of serverless components?
- Can you discuss the customer experience of onboarding onto Datacoral and how it differs between existing data platforms and greenfield projects?
- What are some of the slices that have proven to be the most challenging to implement?
- Are there any that you are currently building that you are most excited for?
- How much effort do you anticipate if and/or when you begin to support other cloud providers?
- When is Datacoral the wrong choice?
- What do you have planned for the future of Datacoral, both from a technical and business perspective?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Apache Hive
- Relational Algebra
- Social Capital
- EIR == Entrepreneur In Residence
- AWS Lambda
- DAG == Directed Acyclic Graph
- AWS Redshift
- AWS Athena
- AWS Glue
- Noisy Neighbor Problem
- DataBricks Delta
- AWS Sagemaker