Remove Data Transformation Remove Metadata Remove Testing
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Enriching metadata for accurate text-to-SQL generation for Amazon Athena

AWS Big Data

These data processing and analytical services support Structured Query Language (SQL) to interact with the data. Writing SQL queries requires not just remembering the SQL syntax rules, but also knowledge of the tables metadata, which is data about table schemas, relationships among the tables, and possible column values.

Metadata 105
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Introducing simplified interaction with the Airflow REST API in Amazon MWAA

AWS Big Data

It’s a set of HTTP endpoints to perform operations such as invoking Directed Acyclic Graphs (DAGs), checking task statuses, retrieving metadata about workflows, managing connections and variables, and even initiating dataset-related events, without directly accessing the Airflow web interface or command line tools. Creating a test variable.

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Amazon OpenSearch Service launches flow builder to empower rapid AI search innovation

AWS Big Data

This middleware consists of custom code that runs data flows to stitch data transformations, search queries, and AI enrichments in varying combinations tailored to use cases, datasets, and requirements. Ingest flows are created to enrich data as its added to an index. Flows are a pipeline of processor resources.

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Available Now! Automated Testing for Data Transformations

Wayne Yaddow

Selecting the strategies and tools for validating data transformations and data conversions in your data pipelines. Introduction Data transformations and data conversions are crucial to ensure that raw data is organized, processed, and ready for useful analysis.

Testing 52
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From Raw Inputs to Polished Outputs: The Art of Testing Data Transformations

Wayne Yaddow

In this post, well see the fundamental procedures, tools, and techniques that data engineers, data scientists, and QA/testing teams use to ensure high-quality data as soon as its deployed. First, we look at how unit and integration tests uncover transformation errors at an early stage. PyTest, JUnit,NUnit).

Testing 52
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Expanding data analysis and visualization options: Amazon DataZone now integrates with Tableau, Power BI, and more

AWS Big Data

With this launch of JDBC connectivity, Amazon DataZone expands its support for data users, including analysts and scientists, allowing them to work in their preferred environments—whether it’s SQL Workbench, Domino, or Amazon-native solutions—while ensuring secure, governed access within Amazon DataZone. Choose Test connection.

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Data’s dark secret: Why poor quality cripples AI and growth

CIO Business Intelligence

We also examine how centralized, hybrid and decentralized data architectures support scalable, trustworthy ecosystems. As data-centric AI, automated metadata management and privacy-aware data sharing mature, the opportunity to embed data quality into the enterprises core has never been more significant.