Making The Open Data Lakehouse Affordable Without The Overhead At Iomete

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. 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Boasting more than 150 out-of-the-box connectors that can be set up in minutes, Hevo also allows you to monitor and control your pipelines. You get: real-time data flow visibility, fail-safe mechanisms, and alerts if anything breaks; preload transformations and auto-schema mapping precisely control how data lands in your destination; models and workflows to transform data for analytics; and reverse-ETL capability to move the transformed data back to your business software to inspire timely action. All of this, plus its transparent pricing and 24*7 live support, makes it consistently voted by users as the Leader in the Data Pipeline category on review platforms like G2. Go to dataengineeringpodcast.com/hevodata and sign up for a free 14-day trial that also comes with 24×7 support. 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 LinkedIn 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. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links Iomete Fivetran Podcast Episode Airbyte Podcast Episode Snowflake Podcast Episode Databricks Collibra Podcast Episode Talend Parquet Trino Spark Presto Snowpark Iceberg Podcast Episode Iomete dbt adapter Singer Meltano Podcast Episode AWS Interface Gateway Apache Hudi Podcast Episode Delta Lake Podcast Episode Amundsen Podcast Episode AWS EMR AWS Athena The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast