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Modernize your ETL platform with AWS Glue Studio: A case study from BMS

AWS Big Data

In addition to using native managed AWS services that BMS didn’t need to worry about upgrading, BMS was looking to offer an ETL service to non-technical business users that could visually compose data transformation workflows and seamlessly run them on the AWS Glue Apache Spark-based serverless data integration engine.

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How HR&A uses Amazon Redshift spatial analytics on Amazon Redshift Serverless to measure digital equity in states across the US

AWS Big Data

This dynamic tool, powered by AWS and CARTO, provided robust visualizations of which regions and populations were interacting with our survey, enabling us to zoom in quickly and address gaps in coverage. Figure 1: Workflow illustrating data ingesting, transformation, and visualization using Redshift and CARTO.

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Optimize data layout by bucketing with Amazon Athena and AWS Glue to accelerate downstream queries

AWS Big Data

In the era of data, organizations are increasingly using data lakes to store and analyze vast amounts of structured and unstructured data. Data lakes provide a centralized repository for data from various sources, enabling organizations to unlock valuable insights and drive data-driven decision-making.

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Introducing Apache Hudi support with AWS Glue crawlers

AWS Big Data

Apache Hudi is an open table format that brings database and data warehouse capabilities to data lakes. Apache Hudi helps data engineers manage complex challenges, such as managing continuously evolving datasets with transactions while maintaining query performance.

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How Cargotec uses metadata replication to enable cross-account data sharing

AWS Big Data

Cargotec captures terabytes of IoT telemetry data from their machinery operated by numerous customers across the globe. This data needs to be ingested into a data lake, transformed, and made available for analytics, machine learning (ML), and visualization.

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Themes and Conferences per Pacoid, Episode 8

Domino Data Lab

Data governance shows up as the fourth-most-popular kind of solution that enterprise teams were adopting or evaluating during 2019. That’s a lot of priorities – especially when you group together closely related items such as data lineage and metadata management which rank nearby. Definition and Descriptions.

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Data Cataloging in the Data Lake: Alation + Kylo

Alation

The result, as many industry observers have put it, is that many data lakes become data swamps. New data visualization user interfaces from Tableau and Qlik proved that any business user can analyze their own data. Disruptive Trend #2: Self-Service Analytics.