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Understanding the Differences Between Data Lakes and Data Warehouses

Smart Data Collective

Data lakes and data warehouses are probably the two most widely used structures for storing data. Data Warehouses and Data Lakes in a Nutshell. A data warehouse is used as a central storage space for large amounts of structured data coming from various sources. Data Type and Processing.

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Look Out: Computer Vision in AI is Coming Into Sight

David Menninger's Analyst Perspectives

Unstructured data has been a significant factor in data lakes and analytics for some time. Twelve years ago, nearly a third of enterprises were working with large amounts of unstructured data. As I’ve pointed out previously , unstructured data is really a misnomer.

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

AWS Big Data

With data becoming the driving force behind many industries today, having a modern data architecture is pivotal for organizations to be successful. In this post, we describe Orca’s journey building a transactional data lake using Amazon Simple Storage Service (Amazon S3), Apache Iceberg, and AWS Analytics.

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Navigating Data Entities, BYOD, and Data Lakes in Microsoft Dynamics

Jet Global

There is an established body of practice around creating, managing, and accessing OLAP data (known as “cubes”). Data Lakes. There has been a lot of talk over the past year or two in the D365F&SCM world about “data lakes.” There are virtually no rules about what such data looks like. It is unstructured.

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Complexity Drives Costs: A Look Inside BYOD and Azure Data Lakes

Jet Global

Option 3: Azure Data Lakes. This leads us to Microsoft’s apparent long-term strategy for D365 F&SCM reporting: Azure Data Lakes. Azure Data Lakes are highly complex and designed with a different fundamental purpose in mind than financial and operational reporting. Data lakes are not a mature technology.

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Choosing an open table format for your transactional data lake on AWS

AWS Big Data

A modern data architecture enables companies to ingest virtually any type of data through automated pipelines into a data lake, which provides highly durable and cost-effective object storage at petabyte or exabyte scale.

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Build a serverless transactional data lake with Apache Iceberg, Amazon EMR Serverless, and Amazon Athena

AWS Big Data

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

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