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Ingest data from Google Analytics 4 and Google Sheets to Amazon Redshift using Amazon AppFlow

AWS Big Data

With Amazon AppFlow, you can run data flows at nearly any scale and at the frequency you chooseon a schedule, in response to a business event, or on demand. You can configure data transformation capabilities such as filtering and validation to generate rich, ready-to-use data as part of the flow itself, without additional steps.

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Accelerate your data workflows with Amazon Redshift Data API persistent sessions

AWS Big Data

Amazon Redshift is a fast, scalable, secure, and fully managed cloud data warehouse that you can use to analyze your data at scale. You can create temporary tables once and reference them throughout, without having to constantly refresh database connections and restart from scratch.

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

AWS Big Data

Your generated jobs can use a variety of data transformations, including filters, projections, unions, joins, and aggregations, giving you the flexibility to handle complex data processing requirements. To learn more, refer to Amazon Q data integration in AWS Glue.

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Unlocking near real-time analytics with petabytes of transaction data using Amazon Aurora Zero-ETL integration with Amazon Redshift and dbt Cloud

AWS Big Data

While customers can perform some basic analysis within their operational or transactional databases, many still need to build custom data pipelines that use batch or streaming jobs to extract, transform, and load (ETL) data into their data warehouse for more comprehensive analysis.

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How DeNA Co., Ltd. accelerated anonymized data quality tests up to 100 times faster using Amazon Redshift Serverless and dbt

AWS Big Data

dbt provides a SQL-first templating engine for repeatable and extensible data transformations, including a data tests feature, which allows verifying data models and tables against expected rules and conditions using SQL. Solution overview DeNA designed the following architecture using AWS serverless services.

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Harnessing the Power of Nested Materialized Views and exploring Cascading Refresh

AWS Big Data

Materialized views store precomputed query results that future similar queries can utilize, offering a powerful solution for data warehouse environments where applications often need to execute resource-intensive queries against large tables. Let’s explore how to implement this powerful feature in your data warehouse environment.

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Enriching metadata for accurate text-to-SQL generation for Amazon Athena

AWS Big Data

Enterprise data is brought into data lakes and data warehouses to carry out analytical, reporting, and data science use cases using AWS analytical services like Amazon Athena , Amazon Redshift , Amazon EMR , and so on. Maintaining lists of possible values for the columns requires continuous updates.