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Presto Powered Cloud Data Lakes At Speed Made Easy With Ahana
Podcast |
Data Engineering Podcast
Publisher |
Tobias Macey
Media Type |
audio
Podknife tags |
Data Science, 
Interview, 
Technology
Categories Via RSS |
Technology
Publication Date |
Sep 02, 2021
Episode Duration |
01:00:30

Summary

The Presto project has become the de facto option for building scalable open source analytics in SQL for the data lake. In recent months the community has focused their efforts on making it the fastest possible option for running your analytics in the cloud. In this episode Dipti Borkar discusses the work that she and her team are doing at Ahana to simplify the work of running your own PrestoDB environment in the cloud. She explains how they are optimizin the runtime to reduce latency and increase query throughput, the ways that they are contributing back to the open source community, and the exciting improvements that are in the works to make Presto an even more powerful option for all of your analytics.

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 managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
  • Schema changes, missing data, and volume anomalies caused by your data sources can happen without any advanced notice if you lack visibility into your data-in-motion. That leaves DataOps reactive to data quality issues and can make your consumers lose confidence in your data. By connecting to your pipeline orchestrator like Apache Airflow and centralizing your end-to-end metadata, Databand.ai lets you identify data quality issues and their root causes from a single dashboard. With Databand.ai, you’ll know whether the data moving from your sources to your warehouse will be available, accurate, and usable when it arrives. Go to dataengineeringpodcast.com/databand to sign up for a free 30-day trial of Databand.ai and take control of your data quality today.
  • Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription
  • Your host is Tobias Macey and today I’m interviewing Dipti Borkar, cofounder Ahana about Presto and Ahana, SaaS managed service for Presto

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you describe what Ahana is and the story behind it?
  • There has been a lot of recent activity in the Presto community. Can you give an overview of the options that are available for someone wanting to use its SQL engine for querying their data?
    • What is Ahana’s role in the community/ecosystem?
    • (happy to skip this question if it’s too contentious) What are some of the notable differences that have emerged over the past couple of years between the Trino (formerly PrestoSQL) and PrestoDB projects?
  • Another area that has been seeing a lot of activity is data lakes and projects to make them more manageable and feature complete (e.g. Hudi, Delta Lake, Iceberg, Nessie, LakeFS, etc.). How has that influenced your product focus and capabilities?
    • How does this activity change the calculus for organizations who are deciding on a lake or warehouse for their data architecture?
  • Can you describe how the Ahana Cloud platform is architected?
    • What are the additional systems that you have built to manage deployment, scaling, and multi-tenancy?
  • Beyond the storage and processing, what are the other notable tools and projects that have become part of the overall stack for supporting open analytics?
  • What are some areas of ongoing activity that you are keeping an eye on as you build out the Ahana offerings?
  • What are the most interesting, innovative, or unexpected ways that you have seen Ahana/Presto used?
  • What are the most interesting, unexpected, or challenging lessons that you have learned while working on Ahana?
  • When is Ahana the wrong choice?
  • What do you have planned for the future of Ahana?

Contact Info

Parting Question

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

Links

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

Summary

The Presto project has become the de facto option for building scalable open source analytics in SQL for the data lake. In recent months the community has focused their efforts on making it the fastest possible option for running your analytics in the cloud. In this episode Dipti Borkar discusses the work that she and her team are doing at Ahana to simplify the work of running your own PrestoDB environment in the cloud. She explains how they are optimizin the runtime to reduce latency and increase query throughput, the ways that they are contributing back to the open source community, and the exciting improvements that are in the works to make Presto an even more powerful option for all of your analytics.

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 managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
  • Schema changes, missing data, and volume anomalies caused by your data sources can happen without any advanced notice if you lack visibility into your data-in-motion. That leaves DataOps reactive to data quality issues and can make your consumers lose confidence in your data. By connecting to your pipeline orchestrator like Apache Airflow and centralizing your end-to-end metadata, Databand.ai lets you identify data quality issues and their root causes from a single dashboard. With Databand.ai, you’ll know whether the data moving from your sources to your warehouse will be available, accurate, and usable when it arrives. Go to dataengineeringpodcast.com/databand to sign up for a free 30-day trial of Databand.ai and take control of your data quality today.
  • Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription
  • Your host is Tobias Macey and today I’m interviewing Dipti Borkar, cofounder Ahana about Presto and Ahana, SaaS managed service for Presto

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you describe what Ahana is and the story behind it?
  • There has been a lot of recent activity in the Presto community. Can you give an overview of the options that are available for someone wanting to use its SQL engine for querying their data?
    • What is Ahana’s role in the community/ecosystem?
    • (happy to skip this question if it’s too contentious) What are some of the notable differences that have emerged over the past couple of years between the Trino (formerly PrestoSQL) and PrestoDB projects?
  • Another area that has been seeing a lot of activity is data lakes and projects to make them more manageable and feature complete (e.g. Hudi, Delta Lake, Iceberg, Nessie, LakeFS, etc.). How has that influenced your product focus and capabilities?
    • How does this activity change the calculus for organizations who are deciding on a lake or warehouse for their data architecture?
  • Can you describe how the Ahana Cloud platform is architected?
    • What are the additional systems that you have built to manage deployment, scaling, and multi-tenancy?
  • Beyond the storage and processing, what are the other notable tools and projects that have become part of the overall stack for supporting open analytics?
  • What are some areas of ongoing activity that you are keeping an eye on as you build out the Ahana offerings?
  • What are the most interesting, innovative, or unexpected ways that you have seen Ahana/Presto used?
  • What are the most interesting, unexpected, or challenging lessons that you have learned while working on Ahana?
  • When is Ahana the wrong choice?
  • What do you have planned for the future of Ahana?

Contact Info

Parting Question

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

Links

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

Support Data Engineering Podcast

Summary

The Presto project has become the de facto option for building scalable open source analytics in SQL for the data lake. In recent months the community has focused their efforts on making it the fastest possible option for running your analytics in the cloud. In this episode Dipti Borkar discusses the work that she and her team are doing at Ahana to simplify the work of running your own PrestoDB environment in the cloud. She explains how they are optimizin the runtime to reduce latency and increase query throughput, the ways that they are contributing back to the open source community, and the exciting improvements that are in the works to make Presto an even more powerful option for all of your analytics.

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 managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
  • Schema changes, missing data, and volume anomalies caused by your data sources can happen without any advanced notice if you lack visibility into your data-in-motion. That leaves DataOps reactive to data quality issues and can make your consumers lose confidence in your data. By connecting to your pipeline orchestrator like Apache Airflow and centralizing your end-to-end metadata, Databand.ai lets you identify data quality issues and their root causes from a single dashboard. With Databand.ai, you’ll know whether the data moving from your sources to your warehouse will be available, accurate, and usable when it arrives. Go to dataengineeringpodcast.com/databand to sign up for a free 30-day trial of Databand.ai and take control of your data quality today.
  • Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription
  • Your host is Tobias Macey and today I’m interviewing Dipti Borkar, cofounder Ahana about Presto and Ahana, SaaS managed service for Presto

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you describe what Ahana is and the story behind it?
  • There has been a lot of recent activity in the Presto community. Can you give an overview of the options that are available for someone wanting to use its SQL engine for querying their data?
    • What is Ahana’s role in the community/ecosystem?
    • (happy to skip this question if it’s too contentious) What are some of the notable differences that have emerged over the past couple of years between the Trino (formerly PrestoSQL) and PrestoDB projects?
  • Another area that has been seeing a lot of activity is data lakes and projects to make them more manageable and feature complete (e.g. Hudi, Delta Lake, Iceberg, Nessie, LakeFS, etc.). How has that influenced your product focus and capabilities?
    • How does this activity change the calculus for organizations who are deciding on a lake or warehouse for their data architecture?
  • Can you describe how the Ahana Cloud platform is architected?
    • What are the additional systems that you have built to manage deployment, scaling, and multi-tenancy?
  • Beyond the storage and processing, what are the other notable tools and projects that have become part of the overall stack for supporting open analytics?
  • What are some areas of ongoing activity that you are keeping an eye on as you build out the Ahana offerings?
  • What are the most interesting, innovative, or unexpected ways that you have seen Ahana/Presto used?
  • What are the most interesting, unexpected, or challenging lessons that you have learned while working on Ahana?
  • When is Ahana the wrong choice?
  • What do you have planned for the future of Ahana?

Contact Info

Parting Question

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

Links

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

Support Data Engineering Podcast

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