Remove Data Mining Remove ETL Remove Metadata
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Tackling AI’s data challenges with IBM databases on AWS

IBM Journey to AI blog

This involves unifying and sharing a single copy of data and metadata across IBM® watsonx.data ™, IBM® Db2 ®, IBM® Db2® Warehouse and IBM® Netezza ®, using native integrations and supporting open formats, all without the need for migration or recataloging.

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A Beginner’s Guide to Data Warehousing

Unite.AI

These can include structured databases, log files, CSV files, transaction tables, third-party business tools, sensor data, etc. The pipeline ensures correct, complete, and consistent data. Metadata: Metadata is data about the data. Metadata: Metadata is data about the data.

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A brief history of Data Engineering: From IDS to Real-Time streaming

Artificial Corner

The advent of relational databases and data warehouses in the 1970s and 1980s set the stage for the next wave of advancements in data engineering, including the development of data mining techniques, the rise of big data, and the evolution of data storage and processing technologies.

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Exploring the Power of Data Warehouse Functionality

Pickl AI

Let’s delve into the key components that form the backbone of a data warehouse: Source Systems These are the operational databases, CRM systems, and other applications that generate the raw data feeding the data warehouse. Data Extraction, Transformation, and Loading (ETL) This is the workhorse of architecture.

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