The scale and complexity of the systems that we build to satisfy business requirements is increasing as the available tools become more sophisticated. In order to bridge the gap between legacy infrastructure and evolving use cases it is necessary to create a unifying set of components. In this episode Dipti Borkar explains how the emerging category of data orchestration tools fills this need, some of the existing projects that fit in this space, and some of the ways that they can work together to simplify projects such as cloud migration and hybrid cloud environments. It is always useful to get a broad view of new trends in the industry and this was a helpful perspective on the need to provide mechanisms to decouple physical storage from computing capacity.
Datacoral is this week’s Data Engineering Podcast sponsor. Datacoral provides an AWS-native, serverless, data infrastructure that installs in your VPC. Datacoral helps data engineers build and manage the flow of data pipelines without having to construct its infrastructure. Datacoral’s customers report that their data engineers are able to spend 80% of their work time invested in data transformations, rather than pipeline maintenance. Raghu Murthy, founder and CEO of Datacoral built data infrastructures at Yahoo! and Facebook, scaling from mere terabytes to petabytes of analytic data. He started Datacoral with the goal to make SQL the universal data programming language. Visit dataengineeringpodcast.com/datacoral for more information.
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!
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- This week’s episode is also sponsored by Datacoral, an AWS-native, serverless, data infrastructure that installs in your VPC. Datacoral helps data engineers build and manage the flow of data pipelines without having to manage any infrastructure, meaning you can spend your time invested in data transformations and business needs, rather than pipeline maintenance. Raghu Murthy, founder and CEO of Datacoral built data infrastructures at Yahoo! and Facebook, scaling from terabytes to petabytes of analytic data. He started Datacoral with the goal to make SQL the universal data programming language. Visit dataengineeringpodcast.com/datacoral today to find out more.
- You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today.
- Your host is Tobias Macey and today I’m interviewing Dipti Borkark about data orchestration and how it helps in migrating data workloads to the cloud
- How did you get involved in the area of data management?
- Can you start by describing what you mean by the term "Data Orchestration"?
- How does it compare to the concept of "Data Virtualization"?
- What are some of the tools and platforms that fit under that umbrella?
- What are some of the motivations for organizations to use the cloud for their data oriented workloads?
- What are they giving up by using cloud resources in place of on-premises compute?
- For businesses that have invested heavily in their own datacenters, what are some ways that they can begin to replicate some of the benefits of cloud environments?
- What are some of the common patterns for cloud migration projects and what challenges do they present?
- Do you have advice on useful metrics to track for determining project completion or success criteria?
- How do businesses approach employee education for designing and implementing effective systems for achieving their migration goals?
- Can you talk through some of the ways that different data orchestration tools can be composed together for a cloud migration effort?
- What are some of the common pain points that organizations encounter when working on hybrid implementations?
- What are some of the missing pieces in the data orchestration landscape?
- Are there any efforts that you are aware of that are aiming to fill those gaps?
- Where is the data orchestration market heading, and what are some industry trends that are driving it?
- What projects are you most interested in or excited by?
- For someone who wants to learn more about data orchestration and the benefits the technologies can provide, what are some resources that you would recommend?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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- UC San Diego
- Spark SQL
- Data Orchestration
- Data Virtualization
- Rook storage orchestration
- Parquet Files
- ORC Files
- Hive Metastore
- Iceberg Table Format
- Data Orchestration Summit
- Star Schema
- Snowflake Schema
- Data Warehouse
- Data Lake