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

Precisely

And then a wide variety of business intelligence (BI) tools popped up to provide last mile visibility with much easier end user access to insights housed in these DWs and data marts. But those end users werent always clear on which data they should use for which reports, as the data definitions were often unclear or conflicting.

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Data Version Control for Data Lakes: Handling the Changes in Large Scale

ODSC - Open Data Science

In the ever-evolving world of big data, managing vast amounts of information efficiently has become a critical challenge for businesses across the globe. As data lakes gain prominence as a preferred solution for storing and processing enormous datasets, the need for effective data version control mechanisms becomes increasingly evident.

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How to modernize data lakes with a data lakehouse architecture

IBM Journey to AI blog

Data Lakes have been around for well over a decade now, supporting the analytic operations of some of the largest world corporations. Such data volumes are not easy to move, migrate or modernize. The challenges of a monolithic data lake architecture Data lakes are, at a high level, single repositories of data at scale.

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Data Cataloging in the Data Lake: Alation + Kylo

Alation

Architecturally the introduction of Hadoop, a file system designed to store massive amounts of data, radically affected the cost model of data. Organizationally the innovation of self-service analytics, pioneered by Tableau and Qlik, fundamentally transformed the user model for data analysis. Disruptive Trend #1: Hadoop.

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Retrieval-Augmented Generation with LangChain, Amazon SageMaker JumpStart, and MongoDB Atlas semantic search

Flipboard

The vector field should be represented as an array of numbers (BSON int32, int64, or double data types only). Query the vector data store You can query the vector data store using the Vector Search aggregation pipeline. It uses the Vector Search index and performs a semantic search on the vector data store.

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Data platform trinity: Competitive or complementary?

IBM Journey to AI blog

In another decade, the internet and mobile started the generate data of unforeseen volume, variety and velocity. It required a different data platform solution. Hence, Data Lake emerged, which handles unstructured and structured data with huge volume. All phases of the data-information lifecycle.

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10 Best Data Engineering Books [Beginners to Advanced]

Pickl AI

Key Components of Data Engineering Data Ingestion : Gathering data from various sources, such as databases, APIs, files, and streaming platforms, and bringing it into the data infrastructure. Data Processing: Performing computations, aggregations, and other data operations to generate valuable insights from the data.