Remove Data Remove Data Warehouse Remove Metadata
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How Apache Iceberg Is Changing the Face of Data Lakes

Snowflake

Data storage has been evolving, from databases to data warehouses and expansive data lakes, with each architecture responding to different business and data needs. Traditional databases excelled at structured data and transactional workloads but struggled with performance at scale as data volumes grew.

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Data News — Week 24.11

Christophe Blefari

Saying mainly that " Sora is a tool to extend creativity " Last point Mira has been mocked and criticised online because as a CTO she wasn't able to say on which public / licensed data Sora has been trained on. This is related to Paris testing automated video surveillance during Olympics. This is Croissant.

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How Meta discovers data flows via lineage at scale

Engineering at Meta

Data lineage is an instrumental part of Metas Privacy Aware Infrastructure (PAI) initiative, a suite of technologies that efficiently protect user privacy. It is a critical and powerful tool for scalable discovery of relevant data and data flows, which supports privacy controls across Metas systems.

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How to get started with dbt

Christophe Blefari

dbt Core is an open-source framework that helps you organise data warehouse SQL transformation. dbt was born out of the analysis that more and more companies were switching from on-premise Hadoop data infrastructure to cloud data warehouses. This switch has been lead by modern data stack vision.

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Cloudera Data Warehouse outperforms Azure HDInsight in TPC-DS benchmark

Cloudera

Performance is one of the key, if not the most important deciding criterion, in choosing a Cloud Data Warehouse service. In today’s fast changing world, enterprises have to make data driven decisions quickly and for that they rely heavily on their data warehouse service. . Cloudera Data Warehouse vs HDInsight.

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Announcing New Innovations for Data Warehouse, Data Lake, and Data Lakehouse in the Data Cloud 

Snowflake

Over the years, the technology landscape for data management has given rise to various architecture patterns, each thoughtfully designed to cater to specific use cases and requirements. These patterns include both centralized storage patterns like data warehouse , data lake and data lakehouse , and distributed patterns such as data mesh.

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Eliminate Friction In Your Data Platform Through Unified Metadata Using OpenMetadata

Data Engineering Podcast

Summary A significant source of friction and wasted effort in building and integrating data management systems is the fragmentation of metadata across various tools. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform.

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