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AWS Glue-Unleashing the Power of Serverless ETL Effortlessly

ProjectPro

Let us dive deeper into this data integration solution by AWS and understand how and why big data professionals leverage it in their data engineering projects. When Glue receives a trigger, it collects the data, transforms it using code that Glue generates automatically, and then loads it into Amazon S3 or Amazon Redshift.

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Top Data Catalog Tools

Monte Carlo

A data catalog is a constantly updated inventory of the universe of data assets within an organization. It uses metadata to create a picture of the data, as well as the relationships between data assets of diverse sources, and the processing that takes place as data moves through systems.

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Monte Carlo + Databricks Doubles Mutual Customer Count—and We’re Just Getting Started

Monte Carlo

But for a data lake to be truly effective for modern data teams, there are a lot of components and technologies that need to work together to ensure that your pipelines are reliable across all endpoints. Delta Lake Delta Lake is the key to storing data and tables within the Databricks Lakehouse Platform.

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Implementing the Netflix Media Database

Netflix Tech

In the previous blog posts in this series, we introduced the N etflix M edia D ata B ase ( NMDB ) and its salient “Media Document” data model. In this post we will provide details of the NMDB system architecture beginning with the system requirements?—?these key value stores generally allow storing any data under a key).

Media 94
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Hands-On Introduction to Delta Lake with (py)Spark

Towards Data Science

In this context, data management in an organization is a key point for the success of its projects involving data. One of the main aspects of correct data management is the definition of a data architecture. The data became useless. The Lakehouse architecture was one of them. What is Delta Lake?

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More Editorial Content, please.

Zalando Engineering

Also, it was based on Zalando's "Mosaic" system architecture, which was being phased out in favour of the newer Interface Framework. So the team decided to build a new tool to replace the old, overcome the feature and scalability related shortcomings, on top of this new architecture. The main entry is the landing page itself.

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From Patchwork to Platform: The Rise of the Post-Modern Data Stack

Ascend.io

Stage 3 begins as these early adopters collaborate formally and informally, identifying and documenting best practices and patterns in the form of “reference architectures”. In our case, data ingestion, transformation, orchestration, reverse ETL, and observability. This is the modern data stack as we know it today.