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Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Datalakes are notoriously complex. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data.
Summary A data lakehouse is intended to combine the benefits of datalakes (cost effective, scalable storage and compute) and data warehouses (user friendly SQL interface). Datalakes are notoriously complex. Visit [dataengineeringpodcast.com/data-council]([link] and use code *depod20* to register today!
Datalakes are notoriously complex. For data engineers who battle to build and scale highqualitydataworkflows on the datalake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics.
Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Datalakes are notoriously complex. Datalakes in various forms have been gaining significant popularity as a unified interface to an organization's analytics. When is Fabric the wrong choice?
Datalakes are notoriously complex. For data engineers who battle to build and scale highqualitydataworkflows on the datalake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics.
In this episode Kevin Liu shares some of the interesting features that they have built by combining those technologies, as well as the challenges that they face in supporting the myriad workloads that are thrown at this layer of their data platform.
Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management This episode is brought to you by Datafold – a testing automation platform for data engineers that prevents dataquality issues from entering every part of your dataworkflow, from migration to dbt deployment.
In this episode Yingjun Wu explains how it is architected to power analytical workflows on continuous data flows, and the challenges of making it responsive and scalable. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Datalakes are notoriously complex.
Datalakes are notoriously complex. For data engineers who battle to build and scale highqualitydataworkflows on the datalake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics.
He highlights the role of data teams in modern organizations and how Synq is empowering them to achieve this. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Datalakes are notoriously complex.
Datalakes are notoriously complex. Starburst Logo]([link] This episode is brought to you by Starburst - an end-to-end data lakehouse platform for data engineers who are battling to build and scale highqualitydata pipelines on the datalake. Datalakes are notoriously complex.
Datalakes are notoriously complex. Starburst Logo]([link] This episode is brought to you by Starburst - an end-to-end data lakehouse platform for data engineers who are battling to build and scale highqualitydata pipelines on the datalake. Datalakes are notoriously complex.
Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management This episode is brought to you by Datafold – a testing automation platform for data engineers that prevents dataquality issues from entering every part of your dataworkflow, from migration to dbt deployment.
In this episode she shares the practical steps to implementing a data governance practice in your organization, and the pitfalls to avoid. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Datalakes are notoriously complex.
Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Datalakes are notoriously complex. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data.
Datalakes are notoriously complex. For data engineers who battle to build and scale highqualitydataworkflows on the datalake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics.
Datalakes are notoriously complex. For data engineers who battle to build and scale highqualitydataworkflows on the datalake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics.
Datalakes are notoriously complex. For data engineers who battle to build and scale highqualitydataworkflows on the datalake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics.
In this episode Andrew Jefferson explains the complexities of building a robust system for data sharing, the techno-social considerations, and how the Bobsled platform that he is building aims to simplify the process.
In this episode Tasso Argyros, CEO of ActionIQ, gives a summary of the major epochs in database technologies and how he is applying the capabilities of cloud data warehouses to the challenge of building more comprehensive experiences for end-users through a modern customer data platform (CDP).
Summary A significant portion of dataworkflows involve storing and processing information in database engines. In this episode Gleb Mezhanskiy, founder and CEO of Datafold, discusses the different error conditions and solutions that you need to know about to ensure the accuracy of your data. Datalakes are notoriously complex.
Datalakes are notoriously complex. For data engineers who battle to build and scale highqualitydataworkflows on the datalake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics.
Summary Data lakehouse architectures are gaining popularity due to the flexibility and cost effectiveness that they offer. The link that bridges the gap between datalake and warehouse capabilities is the catalog. Datalakes are notoriously complex. What is involved in integrating Nessie into a given data stack?
Datalakes are notoriously complex. For data engineers who battle to build and scale highqualitydataworkflows on the datalake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics.
Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Datalakes are notoriously complex. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data.
Datalakes are notoriously complex. For data engineers who battle to build and scale highqualitydataworkflows on the datalake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics.
Datalakes are notoriously complex. For data engineers who battle to build and scale highqualitydataworkflows on the datalake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics.
This episode is brought to you by Datafold – a testing automation platform for data engineers that finds dataquality issues for every part of your dataworkflow, from migration to deployment. Datafold has recently launched a 3-in-1 product experience to support accelerated data migrations. Datafold : .
During data ingestion, raw data is extracted from sources and ferried to either a staging server for transformation or directly into the storage level of your data stack—usually in the form of a data warehouse or datalake. There are two primary types of raw data.
Datalakes are notoriously complex. Starburst Logo]([link] This episode is brought to you by Starburst - an end-to-end data lakehouse platform for data engineers who are battling to build and scale highqualitydata pipelines on the datalake. Datalakes are notoriously complex.
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