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Data Integrity for AI: What’s Old is New Again

Precisely

The goal of this post is to understand how data integrity best practices have been embraced time and time again, no matter the technology underpinning. In the beginning, there was a data warehouse The data warehouse (DW) was an approach to data architecture and structured data management that really hit its stride in the early 1990s.

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How Marriott Modernized Their Data Architecture with Snowflake

Snowflake

More than 50% of data leaders recently surveyed by BCG said the complexity of their data architecture is a significant pain point in their enterprise. As a result,” says BCG, “many companies find themselves at a tipping point, at risk of drowning in a deluge of data, overburdened with complexity and costs.”

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Straining Your Data Lake Through A Data Mesh

Data Engineering Podcast

Summary The current trend in data management is to centralize the responsibilities of storing and curating the organization’s information to a data engineering team. This organizational pattern is reinforced by the architectural pattern of data lakes as a solution for managing storage and access.

Data Lake 100
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Maintaining Your Data Lake At Scale With Spark

Data Engineering Podcast

Summary Building and maintaining a data lake is a choose your own adventure of tools, services, and evolving best practices. The flexibility and freedom that data lakes provide allows for generating significant value, but it can also lead to anti-patterns and inconsistent quality in your analytics.

Data Lake 100
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Simplify Your Data Architecture With The Presto Distributed SQL Engine

Data Engineering Podcast

In this episode he explains how it is designed to allow for querying and combining data where it resides, the use cases that such an architecture unlocks, and the innovative ways that it is being employed at companies across the world.

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Why Open Table Format Architecture is Essential for Modern Data Systems

phData: Data Engineering

Note : Cloud Data warehouses like Snowflake and Big Query already have a default time travel feature. However, this feature becomes an absolute must-have if you are operating your analytics on top of your data lake or lakehouse. It can also be integrated into major data platforms like Snowflake.

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

Monte Carlo

Key Differences Between AI Data Engineers and Traditional Data Engineers While traditional data engineers and AI data engineers have similar responsibilities, they ultimately differ in where they focus their efforts. Data Storage Solutions As we all know, data can be stored in a variety of ways.