The outlook and results of businesses and big companies have changed over the years. The volume of information needed for a successful venture needs to be collected, analyzed, and processed into meaningful data. And this is where data integration and data engineering come in. Before diving into the differences between data integration and data engineering, it is important to have an idea of what they both entail.

What is Data Integration?

Data integration involves the act of compiling data from several sources into one view. That is, making complex data from several sources into a single piece of information. The ingestion process, ETL mapping, and cleansing are among the steps involved in data integration. Data integration involves the compilation of big data into simple or centralized data. Data from big companies such as Amazon, Google, and Facebook are generally referred to as big data. Data integration helps to make these complex data easier to process.

Importance of Data Integration

There are several benefits of data integration, the most important one is the compilation of numerous data from several sources into one view. Data integration helps to improve the unification and collaboration of work among employees in an organization because all the data required can be found in one place. It also helps to conserve resources and increase time efficiency. Time and resources saved from data integration can be put to use in other aspects of the company which can make the organization more competitive in the market. Data integration reduces error and makes numerous valuable data available.

What is Data Engineering?

Data engineering involves creating, building, and designing special systems which collect, store and analyze data on a large scale. Data engineering is very useful and important across all fields and industries. It is also applicable to all industries of the world. Data engineers like develop systems that mine and process raw data into useful and refined data for interpretation by business analysts and data scientists. Data engineering seeks to make data available for easy evaluation and optimization of performance. 

Importance of Data Engineering

Data engineering helps to mine information that suits the business or organizational needs. It also helps to build systems that will then convert data into important information. Data engineering also helps in the development of new data analysis tools and validation methods. Data engineering involves programming and problem-solving skills which help to ease access to data and the subsequent ease of work for data scientists, analysts, and decision-makers.

Comparison Between Data Integration and Engineering

Below are some of the key differences between data integration and data engineering:

  • Data integration involves the ingestion, transformation, and delivery of structured data into a large-scale data warehouse structure. While data engineering involves the development of structured and unstructured data for analytical modeling.
  • Data integration converts structured and unstructured data and integrates both sets of data into a data warehouse. While data engineering gathers data from several sources (traditional and nontraditional sources) and blends the collected data for further analysis.
  • Data integration seeks to make data into a cohesive meaningful unit of information of good quality and compliance considerations from several disjointed sources. While data engineering develops systems that help to process data required by an organization and upgrade the systems to run more perfectly.
  • An upgraded data integration solution mines reliable data from myriads of sources. While data engineering makes data available for the exploitation and discovery of information.
  • Data integration does not only convert data, it also involves the transformation and cleansing of the said data during the extraction process. Data engineering makes use of complex algorithms to map out data and compiles the result for analytics.
  • Data integration is not domain-specific while data engineering is domain-specific. This means that understanding the niche of the data involved in a business is necessary for data engineering.
  • Data integration makes use of the traditional ETL tools and methodologies to comb for data. While data engineering makes use of ELT tools to enhance data that will be processed by further analytics. The ELT involves Extract, Load, and Transform techniques. ETL is Extract, Transform and Load tools for data mining and processing. An example of data integration is converting a variable of “personal data” into “name”, “contact address”, “marital status”, “age”, and “occupation” fields.


The understanding of the meaning, importance, and differences between data integration and data engineering is extremely valuable in companies and organizations nowadays. World-class organizations are heavily investing in data integration and engineering to exploit the hidden information these data tend to uncover.

Data scientists and data engineers all over the world are in high demand in every industry of the world including finance, law, sports, medicine, and many more. The importance of data integration and engineering is not limited to the ones mentioned above, as new applications of these concepts arise daily.