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Reference guide to build inventory management and forecasting solutions on AWS

AWS Big Data

ElastiCache manages the real-time application data caching, allowing your customers to experience microsecond response times while supporting high-throughput handling of hundreds of millions of operations per second. In the inventory management and forecasting solution, AWS Glue is recommended for data transformation.

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Ensuring Data Transformation Results with Great Expectations

Wayne Yaddow

Data quality rules are codified into structured Expectation Suites by Great Expectations instead of relying on ad-hoc scripts or manual checks. The framework ensures that your data transformations comply with rigorous specifications from the moment they are created through every iteration of your pipeline.

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From Raw Inputs to Polished Outputs: The Art of Testing Data Transformations

Wayne Yaddow

The goal is to examine five major methods of verifying and validating data transformations in data pipelines with an eye toward high-quality data deployment. First, we look at how unit and integration tests uncover transformation errors at an early stage.

Testing 52
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Accelerate your data workflows with Amazon Redshift Data API persistent sessions

AWS Big Data

You can create temporary tables once and reference them throughout, without having to constantly refresh database connections and restart from scratch. Please refer to Redshift Quotas and Limits here. After 24 hours the session is forcibly closed, and in-progress queries are terminated.

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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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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

Reporting being part of an effective DQM, we will also go through some data quality metrics examples you can use to assess your efforts in the matter. But first, let’s define what data quality actually is. What is the definition of data quality? Why Do You Need Data Quality Management? date, month, and year).

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Functional Gaps in Your Data Transformation Testing Tools?

Wayne Yaddow

Managing tests of complex data transformations when automated data testing tools lack important features? Photo by Marvin Meyer on Unsplash Introduction Data transformations are at the core of modern business intelligence, blending and converting disparate datasets into coherent, reliable outputs.

Testing 52