Remove Data Lake Remove Data Strategy Remove Data Warehouse
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Differences Between Data Lake and Data Warehouses

TDAN

Data lake is a newer IT term created for a new category of data store. But just what is a data lake? According to IBM, “a data lake is a storage repository that holds an enormous amount of raw or refined data in native format until it is accessed.” That makes sense. I think the […].

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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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How Cloudinary transformed their petabyte scale streaming data lake with Apache Iceberg and AWS Analytics

AWS Big Data

A modern data strategy redefines and enables sharing data across the enterprise and allows for both reading and writing of a singular instance of the data using an open table format. Cloudinary realized early in the process that different queries and usage types can potentially benefit from different runtime engines.

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Build an Amazon Redshift data warehouse using an Amazon DynamoDB single-table design

AWS Big Data

A key pillar of AWS’s modern data strategy is the use of purpose-built data stores for specific use cases to achieve performance, cost, and scale. These types of queries are suited for a data warehouse. Amazon Redshift is fully managed, scalable, cloud data warehouse.

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The Unexpected Cost of Data Copies

An organization’s data is copied for many reasons, namely ingesting datasets into data warehouses, creating performance-optimized copies, and building BI extracts for analysis. Read this whitepaper to learn: Why organizations frequently end up with unnecessary data copies.

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Deriving Value from Data Lakes with AI

Sisense

AI and ML are the only ways to derive value from massive data lakes, cloud-native data warehouses, and other huge stores of information. Once your data is prepared for analysis, the next question is: how else can AI help you?

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The rise of the data lakehouse: A new era of data value

CIO Business Intelligence

Previously, Walgreens was attempting to perform that task with its data lake but faced two significant obstacles: cost and time. Those challenges are well-known to many organizations as they have sought to obtain analytical knowledge from their vast amounts of data. Lakehouses redeem the failures of some data lakes.

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