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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. Migrate data, workloads, and applications.

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Expanding data analysis and visualization options: Amazon DataZone now integrates with Tableau, Power BI, and more

AWS Big Data

Amazon DataZone now launched authentication supports through the Amazon Athena JDBC driver, allowing data users to seamlessly query their subscribed data lake assets via popular business intelligence (BI) and analytics tools like Tableau, Power BI, Excel, SQL Workbench, DBeaver, and more. Choose Test connection.

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Eight Top DataOps Trends for 2022

DataKitchen

In 2022, data organizations will institute robust automated processes around their AI systems to make them more accountable to stakeholders. Model developers will test for AI bias as part of their pre-deployment testing. Quality test suites will enforce “equity,” like any other performance metric.

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Design a data mesh pattern for Amazon EMR-based data lakes using AWS Lake Formation with Hive metastore federation

AWS Big Data

Use cases for Hive metastore federation for Amazon EMR Hive metastore federation for Amazon EMR is applicable to the following use cases: Governance of Amazon EMR-based data lakes – Producers generate data within their AWS accounts using an Amazon EMR-based data lake supported by EMRFS on Amazon Simple Storage Service (Amazon S3)and HBase.

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Enrich your serverless data lake with Amazon Bedrock

AWS Big Data

For many organizations, this centralized data store follows a data lake architecture. Although data lakes provide a centralized repository, making sense of this data and extracting valuable insights can be challenging. We recommend testing your use case and data with different models.

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Amazon Q data integration adds DataFrame support and in-prompt context-aware job creation

AWS Big Data

The DataFrame code generation now extends beyond AWS Glue DynamicFrame to support a broader range of data processing scenarios. Next, the merged data is filtered to include only a specific geographic region. Then the transformed output data is saved to Amazon S3 for further processing in future.