Data integration is a common industry term referring to the requirement to combine data from multiple separate business systems into a single unified view, often called a single view of the truth. This unified view is typically stored in a central data repository known as a data warehouse.
For example, customer data integration involves the extraction of information about each individual customer from disparate business systems such as sales, accounts, and marketing, which is then combined into a single view of the customer to be used for customer service, reporting and analysis.
There are a number of different data integration approaches which can be used to populate a data warehouse, two of the most common are:
The trade-off with ELT is that storage space and CPU usage within the database can be relatively expensive. Using ETL to perform the transformation step outside of the database reduces the storage and CPU load on the data warehouse, making ETL a more cost-effective approach for many applications.
Three major data integration criteria to consider when building a data warehouse are:
These criteria will affect the performance and storage requirements for the data warehouse, and hence the cost of the hardware and software required for the project.
In the beginning, we had Extract, Transform, and Load (ETL). Soon after, as data volumes grew, people were quick to point out the value that databases could bring to data integration, so we had Extract, Load, and Transform (ELT).
The problem is that databases were never designed for ELT and, in fact, can create roadblocks that prevent organisations from addressing their needs.
Learn how to end to the ETL vs. ELT debate once and for all and discover a smarter approach that overcomes the limitations of conventional data integration tools.
With this guide you will learn:
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