Meaning of Data Integration:

The term "Data Integration" refers to the process of combining data from a variety of sources into a single dataset that can then be put to use for analytical or business intelligence purposes.

This is a pretty straightforward explanation for a subject that has become more complicated throughout the course of its history of thirty years. Examining the processes involved in data integration is the first step in gaining an understanding of how data integration has evolved from a back-end, retrospective activity into an essential component of the real-time infrastructure.

A uniform fact base for analytics may be created through the process of data integration, which involves combining different kinds and formats of data from across an organization's numerous sources into a data lake or data warehouse. Working off of this one data set enables organizations to improve their decision-making processes, better aligns departments to work together and propels improvements in the quality of the customer experience.

How do you integrate data?

To move data from one system to another, you need a data pipeline that understands the structure and meaning of the data and defines the path it will take through the technical systems.

Data ingestion is a fairly simple and common type of data integration in which data from one system is regularly added to another system. Data Integration may also include cleaning, sorting, enriching, and other steps to get the data ready for use at its final destination. Sometimes this happens before the data is saved, and the process is called ETL (extract, transform, load).

Sometimes it's better to store the data first and then get it ready to use. This is called ELT (extract, load, transform). In other cases, data is changed and made to fit where it is stored without actually moving it.

How has Data Integration evolved?

When businesses first started implementing data warehouses in the early 1990s to aggregate data from numerous systems in order to feed analysis, there were neither cell phones nor online shopping available. There was no such thing as Salesforce, and "Software as a Service" was not yet a recognized category. Amazon has not yet made a single book sale, much less a transaction involving on-demand computing. On-premises applications, software as a service (SaaS) applications, databases, and data warehouses all began to see the emergence of a set of technologies designed to integrate their data with one another. During that time:

• The data originated from several business applications and operational databases and were stored in a structured way that enabled them to be mapped to the structure that was necessary for analysis.
• The data would arrive in batches, which would then be processed. This would result in the creation of time capsules that would be kept in data warehouses or data marts.
• Data was utilized for the purposes of financial reporting, sales dashboards, supply chain analytics, and several other critical business operations.

ETL developers were largely responsible for data integration. These developers created ETL mappings and tasks by either manual coding or the use of specialist tools. They honed specific abilities in relation to the source and destination systems they integrated in order to construct ETL mappings that would operate appropriately with the complexities of those systems. This was done in order to integrate the systems successfully.

Transformation into Data Engineering

There has been a rise in the importance of Data Engineers on the data platform team. They are technical experts that know the value of data to business analysts and data scientists and how to construct data pipelines to ensure the timely delivery of clean, accurate data. The most effective data engineers have a keen understanding of business demands, a keen eye for emerging technologies, and the ability to manage a dynamic data infrastructure.

With the correct resources, a competent data engineer may help tens of ETL developers, who in turn help hundreds of data scientists. Hence, a Data Nami analysis from 2020 states that the need for data engineers is up 50%, making it one of the fastest-expanding occupations in the USA.

Data Engineering from Data Integration

Data integration "how" has become nearly unknown as data kinds, processing types, and infrastructures grow. The boardroom IT infrastructure map is dead. A single individual or organization cannot map and track it. In such a large, linked, unknown system, every change to the data structure, semantics, or infrastructure is a possible failure or opportunity.

This is why new data platforms for data engineers use smart data pipelines to abstract away the "how" of implementation so you can focus on the data's what, who, and where. The Stream Sets Data Engineering platform builds smart data pipelines for DataOps across hybrid and multi-cloud systems. Freely design your first data pipeline using StreamSets Data Collector.