Remove Data Transformation Remove Data Warehouse Remove IT
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MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

Why: Data Makes It Different. In contrast, a defining feature of ML-powered applications is that they are directly exposed to a large amount of messy, real-world data which is too complex to be understood and modeled by hand. However, the concept is quite abstract. Can’t we just fold it into existing DevOps best practices?

IT 364
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From data lakes to insights: dbt adapter for Amazon Athena now supported in dbt Cloud

AWS Big Data

The need for streamlined data transformations As organizations increasingly adopt cloud-based data lakes and warehouses, the demand for efficient data transformation tools has grown. Using Athena and the dbt adapter, you can transform raw data in Amazon S3 into well-structured tables suitable for analytics.

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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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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. Redshift Data API provides a secure HTTP endpoint and integration with AWS SDKs. In the next step, copy data from Amazon Simple Storage Service (Amazon S3) to the temporary table.

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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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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. The following video provides a full demonstration of the experience with AWS Glue Studio.

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Ensuring Data Transformation Results with Great Expectations

Wayne Yaddow

The framework ensures that your data transformations comply with rigorous specifications from the moment they are created through every iteration of your pipeline. Great Expectations can enable a wide range of data transformations and conversion operations.