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Do the Benefits of Cloud Outweigh the Costs?

Jet Global

But the constant noise around the topic – from cost benefit analyses to sales pitches to technical overviews – has led to information overload. Auto-generation technology speeds up the process of populating reporting tools and worksheets with the business content necessary to gain meaningful business intelligence in less time and cost.

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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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Four Use Cases Proving the Benefits of Metadata-Driven Automation

erwin

Organization’s cannot hope to make the most out of a data-driven strategy, without at least some degree of metadata-driven automation. Metadata-Driven Automation in the BFSI Industry. Metadata-Driven Automation in the Pharmaceutical Industry. Metadata-Driven Automation in the Insurance Industry.

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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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Achieve your AI goals with an open data lakehouse approach

IBM Big Data Hub

Typically, on their own, data warehouses can be restricted by high storage costs that limit AI and ML model collaboration and deployments, while data lakes can result in low-performing data science workloads. Also, a lakehouse can introduce definitional metadata to ensure clarity and consistency, which enables more trustworthy, governed data.

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Do I Need a Data Catalog?

erwin

Organizations with particularly deep data stores might need a data catalog with advanced capabilities, such as automated metadata harvesting to speed up the data preparation process. Three Types of Metadata in a Data Catalog. The metadata provides information about the asset that makes it easier to locate, understand and evaluate.

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Materialized Views in Hive for Iceberg Table Format

Cloudera

The snapshotId of the source tables involved in the materialized view are also maintained in the metadata. Thus, the scans and joins of the three tables in the original query are not needed and this can improve performance significantly due to both I/O cost saving and the CPU cost saving of computing the joins and aggregations.