Remove Data Processing Remove Data Quality Remove Data Warehouse
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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.

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The DataOps Vendor Landscape, 2021

DataKitchen

RightData – A self-service suite of applications that help you achieve Data Quality Assurance, Data Integrity Audit and Continuous Data Quality Control with automated validation and reconciliation capabilities. QuerySurge – Continuously detect data issues in your delivery pipelines. Data breaks.

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The future of data: A 5-pillar approach to modern data management

CIO Business Intelligence

Manish Limaye Pillar #1: Data platform The data platform pillar comprises tools, frameworks and processing and hosting technologies that enable an organization to process large volumes of data, both in batch and streaming modes. The choice of vendors should align with the broader cloud or on-premises strategy.

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Take Your SQL Skills To The Next Level With These Popular SQL Books

datapine

With a MySQL dashboard builder , for example, you can connect all the data with a few clicks. A host of notable brands and retailers with colossal inventories and multiple site pages use SQL to enhance their site’s structure functionality and MySQL reporting processes. It is a must-read for understanding data warehouse design.

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Set up advanced rules to validate quality of multiple datasets with AWS Glue Data Quality

AWS Big Data

Poor-quality data can lead to incorrect insights, bad decisions, and lost opportunities. AWS Glue Data Quality measures and monitors the quality of your dataset. It supports both data quality at rest and data quality in AWS Glue extract, transform, and load (ETL) pipelines.

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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

Domain ownership recognizes that the teams generating the data have the deepest understanding of it and are therefore best suited to manage, govern, and share it effectively. This principle makes sure data accountability remains close to the source, fostering higher data quality and relevance.

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Common Business Intelligence Challenges Facing Entrepreneurs

datapine

These benefits include cost efficiency, the optimization of inventory levels, the reduction of information waste, enhanced marketing communications, and better internal communication – among a host of other business-boosting improvements. 7) Dealing with the impact of poor data quality.