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Using Trino And Iceberg As The Foundation Of Your Data Lakehouse

Data Engineering Podcast

Summary A data lakehouse is intended to combine the benefits of data lakes (cost effective, scalable storage and compute) and data warehouses (user friendly SQL interface). Data lakes are notoriously complex. Multiple open source projects and vendors have been working together to make this vision a reality.

Data Lake 262
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Tackling Real Time Streaming Data With SQL Using RisingWave

Data Engineering Podcast

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. Dagster offers a new approach to building and running data platforms and data pipelines. Starburst : ![Starburst

SQL 173
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What is an AI Data Engineer? 4 Important Skills, Responsibilities, & Tools

Monte Carlo

AI data engineers are data engineers that are responsible for developing and managing data pipelines that support AI and GenAI data products. Essential Skills for AI Data Engineers Expertise in Data Pipelines and ETL Processes A foundational skill for data engineers?

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X-Ray Vision For Your Flink Stream Processing With Datorios

Data Engineering Podcast

Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake.

Process 147
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Modern Customer Data Platform Principles

Data Engineering Podcast

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).

Data Lake 147
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Data Engineering Weekly #206

Data Engineering Weekly

Shifting left involves moving data processing upstream, closer to the source, enabling broader access to high-quality data through well-defined data products and contracts, thus reducing duplication, enhancing data integrity, and bridging the gap between operational and analytical data domains.

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Data Pipelines in the Healthcare Industry

DareData

With these points in mind, I argue that the biggest hurdle to the widespread adoption of these advanced techniques in the healthcare industry is not intrinsic to the industry itself, or in any way related to its practitioners or patients, but simply the current lack of high-quality data pipelines.