Remove Data Lake Remove Data Processing Remove Testing Remove Unstructured Data
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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. Open AWS Glue Studio. Choose ETL Jobs.

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FINRA CIO Steve Randich pushes the public cloud forward

CIO Business Intelligence

But for two years, we were testing limits within the public cloud.” While managing unstructured data remains a challenge for 36% of organizations, according to the 2022 Foundry Data and Analytics Research survey, many IT leaders are actively seeking ways of harnessing all types of data stored in data lakes.

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Access Amazon Athena in your applications using the WebSocket API

AWS Big Data

Many organizations are building data lakes to store and analyze large volumes of structured, semi-structured, and unstructured data. In addition, many teams are moving towards a data mesh architecture, which requires them to expose their data sets as easily consumable data products.

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How The Cloud Made ‘Data-Driven Culture’ Possible | Part 1

BizAcuity

2007: Amazon launches SimpleDB, a non-relational (NoSQL) database that allows businesses to cheaply process vast amounts of data with minimal effort. The platform is built on S3 and EC2 using a hosted Hadoop framework. An efficient big data management and storage solution that AWS quickly took advantage of.

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Quantitative and Qualitative Data: A Vital Combination

Sisense

Additionally, quantitative data forms the basis on which you can confidently infer, estimate, and project future performance, using techniques such as regression analysis, hypothesis testing, and Monte Carlo simulations. Despite its many uses, quantitative data presents two main challenges for a data-driven organization.

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How Cloudera Data Flow Enables Successful Data Mesh Architectures

Cloudera

Those decentralization efforts appeared under different monikers through time, e.g., data marts versus data warehousing implementations (a popular architectural debate in the era of structured data) then enterprise-wide data lakes versus smaller, typically BU-Specific, “data ponds”.

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