Remove Data Transformation Remove Data Warehouse Remove Metadata
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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. Table metadata is fetched from AWS Glue. The generated Athena SQL query is run.

Metadata 105
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SAP Datasphere Powers Business at the Speed of Data

Rocket-Powered Data Science

In fact, by putting a single label like AI on all the steps of a data-driven business process, we have effectively not only blurred the process, but we have also blurred the particular characteristics that make each step separately distinct, uniquely critical, and ultimately dependent on specialized, specific technologies at each step.

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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

An extract, transform, and load (ETL) process using AWS Glue is triggered once a day to extract the required data and transform it into the required format and quality, following the data product principle of data mesh architectures. This process is shown in the following figure.

IoT 111
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How ANZ Institutional Division built a federated data platform to enable their domain teams to build data products to support business outcomes

AWS Big Data

These nodes can implement analytical platforms like data lake houses, data warehouses, or data marts, all united by producing data products. The Institutional Data & AI platform adopts a federated approach to data while centralizing the metadata to facilitate simpler discovery and sharing of data products.

Metadata 105
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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.

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

datapine

With quality data at their disposal, organizations can form data warehouses for the purposes of examining trends and establishing future-facing strategies. Industry-wide, the positive ROI on quality data is well understood. 2 – Data profiling. Data profiling is an essential process in the DQM lifecycle.