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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.

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

AWS Big Data

These improvements are available through the Amazon Q chat experience on the AWS Management Console , and the Amazon SageMaker Unified Studio (preview) visual ETL and notebook interfaces. The DataFrame code generation now extends beyond AWS Glue DynamicFrame to support a broader range of data processing scenarios.

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Scaling RISE with SAP data and AWS Glue

AWS Big Data

Customers often want to augment and enrich SAP source data with other non-SAP source data. Such analytic use cases can be enabled by building a data warehouse or data lake. Customers can now use the AWS Glue SAP OData connector to extract data from SAP. Choose Visual ETL to create a job in the Visual Editor.

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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. About the Authors Dave Horne is a Sr.

Data Lake 101
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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

In addition to real-time analytics and visualization, the data needs to be shared for long-term data analytics and machine learning applications. The applications are hosted in dedicated AWS accounts and require a BI dashboard and reporting services based on Tableau. datazone_env_twinsimsilverdata"."cycle_end";')

IoT 101
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Create an Apache Hudi-based near-real-time transactional data lake using AWS DMS, Amazon Kinesis, AWS Glue streaming ETL, and data visualization using Amazon QuickSight

AWS Big Data

Data analytics on operational data at near-real time is becoming a common need. Due to the exponential growth of data volume, it has become common practice to replace read replicas with data lakes to have better scalability and performance. For more information, see Changing the default settings for your data lake.

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Introducing a new unified data connection experience with Amazon SageMaker Lakehouse unified data connectivity

AWS Big Data

On your project, in the navigation pane, choose Data. For Add data source , choose Add connection. For Host , enter your host name of your Aurora PostgreSQL database cluster. format(connection_properties["HOST"],connection_properties["PORT"],connection_properties["DATABASE"]) df.write.format("jdbc").option("url",