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Comprehensive data management for AI: The next-gen data management engine that will drive AI to new heights

CIO Business Intelligence

Managing the lifecycle of AI data, from ingestion to processing to storage, requires sophisticated data management solutions that can manage the complexity and volume of unstructured data. As the leader in unstructured data storage, customers trust NetApp with their most valuable data assets.

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Monitoring Apache Iceberg metadata layer using AWS Lambda, AWS Glue, and AWS CloudWatch

AWS Big Data

In the era of big data, data lakes have emerged as a cornerstone for storing vast amounts of raw data in its native format. They support structured, semi-structured, and unstructured data, offering a flexible and scalable environment for data ingestion from multiple sources.

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Migrate an existing data lake to a transactional data lake using Apache Iceberg

AWS Big Data

A data lake is a centralized repository that you can use to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data and then run different types of analytics for better business insights. Supported formats are Avro, Parquet, and ORC.

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Chose Both: Data Fabric and Data Lakehouse

Cloudera

Organizations don’t know what they have anymore and so can’t fully capitalize on it — the majority of data generated goes unused in decision making. And second, for the data that is used, 80% is semi- or unstructured. Both obstacles can be overcome using modern data architectures, specifically data fabric and data lakehouse.

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Orca Security’s journey to a petabyte-scale data lake with Apache Iceberg and AWS Analytics

AWS Big Data

To overcome these issues, Orca decided to build a data lake. A data lake is a centralized data repository that enables organizations to store and manage large volumes of structured and unstructured data, eliminating data silos and facilitating advanced analytics and ML on the entire data.

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Ensuring Data Transformation Quality with dbt Core

Wayne Yaddow

Instead of relying on one-off scripts or unstructured transformation logic, dbt Core structures transformations as models, linking them through a Directed Acyclic Graph (DAG) that automatically handles dependencies. The following categories of transformations pose significant limitations for dbt Cloud and dbtCore : 1.

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Discover Efficient Data Extraction Through Replication With Angles Enterprise for Oracle

Jet Global

This growth is caused, in part, by the increasing use of cloud platforms for data storage and processing. But it is also a result of the surge in multimedia content in cloud repositories that requires tools and methods for extracting insights from rich, unstructured data formats.