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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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Important Considerations When Migrating to a Data Lake

Smart Data Collective

Azure Data Lake Storage Gen2 is based on Azure Blob storage and offers a suite of big data analytics features. If you don’t understand the concept, you might want to check out our previous article on the difference between data lakes and data warehouses. Then, move your data.

Data Lake 100
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Query your Apache Hive metastore with AWS Lake Formation permissions

AWS Big Data

The Hive metastore is a repository of metadata about the SQL tables, such as database names, table names, schema, serialization and deserialization information, data location, and partition details of each table. Therefore, organizations have come to host huge volumes of metadata of their structured datasets in the Hive metastore.

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Build a pseudonymization service on AWS to protect sensitive data: Part 2

AWS Big Data

The account on the right hosts the pseudonymization service, which you can deploy using the instructions provided in the Part 1 of this series. Amazon EMR empowers you to create, operate, and scale big data frameworks such as Apache Spark quickly and cost-effectively. AWS Glue, and Athena. deployment_scripts/deploy_1.sh

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Implement alerts in Amazon OpenSearch Service with PagerDuty

AWS Big Data

For Host , enter events.PagerDuty.com. Choose Send test message and test to make sure you receive an alert on the PagerDuty service. This notification can be safely acknowledged and resolved from PagerDuty because this is was a test. Vivek Shrivastava is a Principal Data Architect, Data Lake in AWS Professional Services.

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

AWS Big Data

For the past 5 years, BMS has used a custom framework called Enterprise Data Lake Services (EDLS) to create ETL jobs for business users. BMS’s EDLS platform hosts over 5,000 jobs and is growing at 15% YoY (year over year). About the authors Sivaprasad Mahamkali is a Senior Streaming Data Engineer at AWS Professional Services.

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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.