Remove Cost-Benefit Remove Data Integration Remove Data Warehouse Remove Metadata
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How Metadata Makes Data Meaningful

erwin

Metadata is an important part of data governance, and as a result, most nascent data governance programs are rife with project plans for assessing and documenting metadata. But in many scenarios, it seems that the underlying driver of metadata collection projects is that it’s just something you do for data governance.

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How Metadata Makes Data Meaningful

erwin

Metadata is an important part of data governance, and as a result, most nascent data governance programs are rife with project plans for assessing and documenting metadata. But in many scenarios, it seems that the underlying driver of metadata collection projects is that it’s just something you do for data governance.

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Use Apache Iceberg in your data lake with Amazon S3, AWS Glue, and Snowflake

AWS Big Data

Data engineers use Apache Iceberg because it’s fast, efficient, and reliable at any scale and keeps records of how datasets change over time. Apache Iceberg offers integrations with popular data processing frameworks such as Apache Spark, Apache Flink, Apache Hive, Presto, and more.

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Dive deep into security management: The Data on EKS Platform

AWS Big Data

Addressing big data challenges – Big data comes with unique challenges, like managing large volumes of rapidly evolving data across multiple platforms. Effective permission management helps tackle these challenges by controlling how data is accessed and used, providing data integrity and minimizing the risk of data breaches.

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Accenture’s Smart Data Transition Toolkit Now Available for Cloudera Data Platform

Cloudera

Cloudera and Accenture demonstrate strength in their relationship with an accelerator called the Smart Data Transition Toolkit for migration of legacy data warehouses into Cloudera Data Platform. Accenture’s Smart Data Transition Toolkit . Are you looking for your data warehouse to support the hybrid multi-cloud?

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Data architecture strategy for data quality

IBM Big Data Hub

Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues. Several factors determine the quality of your enterprise data like accuracy, completeness, consistency, to name a few.

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Data Modeling 101: OLTP data modeling, design, and normalization for the cloud

erwin

Data modeling is a serious scientific method with many rules and best practices. One must also capture the vast quantity of metadata around the OLTP business requirements that must be reflected. Data modeling helps you right-size cloud migrations for cost savings. Figure 1: Amazon Cloud cost calculator.