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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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Bridging the gap between mainframe data and hybrid cloud environments

CIO Business Intelligence

A high hurdle many enterprises have yet to overcome is accessing mainframe data via the cloud. Mainframes hold an enormous amount of critical and sensitive business data including transactional information, healthcare records, customer data, and inventory metrics. Four key challenges prevent them from doing so: 1.

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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 to Implement Data Lineage Mapping Techniques

Octopai

Look for the Metadata. In order to perform accurate data lineage mapping, every process in the system that transforms or touches the data must be recorded. This metadata (read: data about your data) is key to tracking your data. Data Lineage by Tagging or Self-Contained Data Lineage.

Metadata 133
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Modernize your ETL platform with AWS Glue Studio: A case study from BMS

AWS Big Data

In addition to using native managed AWS services that BMS didn’t need to worry about upgrading, BMS was looking to offer an ETL service to non-technical business users that could visually compose data transformation workflows and seamlessly run them on the AWS Glue Apache Spark-based serverless data integration engine.

Metadata 111
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How to Build a Successful Metadata Management Framework

Alation

This is where metadata, or the data about data, comes into play. Having a data catalog is the cornerstone of your data governance strategy, but what supports your data catalog? Your metadata management framework provides the underlying structure that makes your data accessible and manageable.

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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

This person (or group of individuals) ensures that the theory behind data quality is communicated to the development team. 2 – Data profiling. Data profiling is an essential process in the DQM lifecycle. This means there are no unintended data errors, and it corresponds to its appropriate designation (e.g.,