Data Quality Data Transformation Management Metadata 
datapine
SEPTEMBER 29, 2022
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.
AWS Big Data
DECEMBER 13, 2023
In collaboration with AWS, BMS identified a business need to migrate and modernize their custom extract, transform, and load (ETL) platform to a native AWS solution to reduce complexities, resources, and investment to upgrade when new Spark, Python, or AWS Glue versions are released.
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CIO Business Intelligence
MARCH 4, 2024
With its emphasis on decentralized, domain-oriented data ownership and architecture, data mesh provides a potential answer for overmatched, out-manned businesses. Despite these benefits, the core problems that data centralization so often fails to address are the pragmatic realities of many enterprise data ecosystems.
erwin
JANUARY 11, 2019
Organizations have spent a lot of time and money trying to harmonize data across diverse platforms , including cleansing, uploading metadata, converting code, defining business glossaries, tracking data transformations and so on. So questions linger about whether transformed data can be trusted.
erwin
APRIL 29, 2020
An understanding of the data’s origins and history helps answer questions about the origin of data in a Key Performance Indicator (KPI) reports, including: How the report tables and columns are defined in the metadata? Who are the data owners? What are the transformation rules? Data Governance.
Alation
MARCH 22, 2022
As the latest iteration in this pursuit of high-quality data sharing, DataOps combines a range of disciplines. It synthesizes all we’ve learned about agile, data quality , and ETL/ELT. In fact, DataOps places an outsized burden on data management teams. DataOps has emerged as an exciting solution.
erwin
SEPTEMBER 17, 2020
Here are six benefits of automating end-to-end data lineage: Reduced Errors and Operational Costs. Data quality is crucial to every organization. Automated data capture can significantly reduce errors when compared to manual entry. Automating data capture frees up resources to focus on more strategic and useful tasks.
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