Making The Open Data Lakehouse Affordable Without The Overhead At Iomete
Data Engineering Podcast
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IOMIT Platform - Build Versus Buy?
The IOMIT platform was built from the ground up. It's designed to be a cloud-based governance and transformation tool for big data companies like Facebook, Google or Uber. The company decided to go in-house rather than buy an open source version of the system. They say it makes sense because they can save money by building their own solution.
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Summary
The core of any data platform is the centralized storage and processing layer. For many that is a data warehouse, but in order to support a diverse and constantly changing set of uses and technologies the data lakehouse is a paradigm that offers a useful balance of scale and cost, with performance and ease of use. In order to make the data lakehouse available to a wider audience the team at Iomete built an all-in-one service that handles management and integration of the various technologies so that you can worry about answering important business questions. In this episode Vusal Dadalov explains how the platform is implemented, the motivation for a truly open architecture, and how they have invested in integrating with the broader ecosystem to make it easy for you to get started.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I’m interviewing Vusal Dadalov about Iomete, an open and affordable lakehouse platform
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Iomete is and the story behind it?
- The selection of the storage/query layer is the most impactful decision in the implementation of a data platform. What do you see as the most significant factors that are leading people to Iomete/lakehouse structures rather than a more traditional db/warehouse?
- The principle of the Lakehouse architecture has been gaining popularity recently. What are some of the complexities/missing pieces that make its implementation a challenge?
- What are the hidden difficulties/incompatibilities that come up for teams who are investing in data lake/lakehouse technologies?
- What are some of the shortcomings of lakehouse architectures?
- What are the fundamental capabilities that are necessary to run a fully functional lakehouse?
- Can you describe how the Iomete platform is implemented?
- What was your process for deciding which elements to adopt off the shelf vs. building from scratch?
- What do you see as the strengths of Spark as the query/execution engine as compared to e.g. Presto/Trino or Dremio?
- What are the integrations and ecosystem investments that you have had to prioritize to simplify adoption of Iomete?
- What have been the most challenging aspects of building a competitive business in such an active product category?
- What are the most interesting, innovative, or unexpected ways that you have seen Iomete used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Iomete?
- When is Iomete the wrong choice?
- What do you have planned for the future of Iomete?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- Thank you for listening! Don’t forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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Links
- Iomete
- Fivetran
- Airbyte
- Snowflake
- Databricks
- Collibra
- Talend
- Parquet
- Trino
- Spark
- Presto
- Snowpark
- Iceberg
- Iomete dbt adapter
- Singer
- Meltano
- AWS Interface Gateway
- Apache Hudi
- Delta Lake
- Amundsen
- AWS EMR
- AWS Athena
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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