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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

1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data. 10) Data Quality Solutions: Key Attributes.

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How Your Finance Team Can Lead Your Enterprise Data Transformation

Alation

Building a Data Culture Within a Finance Department. Our finance users tell us that their first exposure to the Alation Data Catalog often comes soon after the launch of organization-wide data transformation efforts. After all, finance is one of the greatest consumers of data within a business.

Finance 52
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Create a modern data platform using the Data Build Tool (dbt) in the AWS Cloud

AWS Big Data

In this post, we delve into a case study for a retail use case, exploring how the Data Build Tool (dbt) was used effectively within an AWS environment to build a high-performing, efficient, and modern data platform. It does this by helping teams handle the T in ETL (extract, transform, and load) processes.

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Enable advanced search capabilities for Amazon Keyspaces data by integrating with Amazon OpenSearch Service

AWS Big Data

You simply configure your data sources to send information to OpenSearch Ingestion, which then automatically delivers the data to your specified destination. Additionally, you can configure OpenSearch Ingestion to apply data transformations before delivery.

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Cloudera DataFlow Designer: The Key to Agile Data Pipeline Development

Cloudera

A critical feature for every developer however is to get instantaneous feedback like configuration validations or performance metrics, as well as previewing data transformations for each step of their data flow. To learn more, take the product tour or check out the DataFlow Designer documentation.

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Smart Factories: Artificial Intelligence and Automation for Reduced OPEX in Manufacturing

DataRobot Blog

The first step in building a model that can predict machine failure and even recommend the next best course of action is to aggregate, clean, and prepare data to train against. This task may require complex joins, aggregations, filtering, window functions, and many other data transformations against extremely large-scale data sets.

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AI, the Power of Knowledge and the Future Ahead: An Interview with Head of Ontotext’s R&I Milena Yankova

Ontotext

Within a large enterprise, there is a huge amount of data accumulated over the years – many decisions have been made and different methods have been tested. We translate their documents, presentations, tables, etc. They have different metrics for judging whether some content is interesting or not.