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What is Data Integration?

Big data, the Internet of Things (IoT), software as a service (SaaS), cloud activity, and more created an explosion in the number of data sources and the sheer volume of data existing in the world. Historically most of this data has been collected and stored in stand-alone silos or separate data stores. Data integration is the process of discovering, moving, and combining data from multiple sources to drive insights and power machine learning and advanced analytics.  

Data integration is especially important as your business pursues digital transformation strategies, since your ability to improve operations, boost customer satisfaction, and compete in an increasingly digital world requires insight from all your data.

Google Cloud's data integration solution is a suite of loosely coupled but tightly integrated services that include:

  • Cloud Data Fusion: a fully managed, cloud-native data integration service that helps users efficiently build and manage ETL/ELT data pipelines
  • Cloud Composer: a fully managed workflow orchestration service built on Apache Airflow to manage and orchestrate the end-to-end data and process life cycle
  • Datastream: a serverlees and easy-to-use change data capture and replication service
  • Dataplex: an intelligent data fabric to discover, manage, monitor and govern distributed data at scale
  • Dataflow: a fully managed streaming analytics service that minimizes latency, processing time, and cost
  • Pub/Sub: an asynchronous and scalable messaging service used for streaming analytics and data integration pipelines
  • Dataproc: a fully managed Spark and Hadoop service for batch processing, querying, streaming, and machine learning

Data integration defined

Data integration is the process of bringing together data from different sources to gain a unified and more valuable view of it, so that your business can make faster and better decisions.  

Data integration can consolidate all kinds of data—structured, unstructured, batch, and streaming—to do everything from basic querying of inventory databases to complex predictive analytics.

What are the challenges of data integration?

Difficulty of using data integration platforms

Experienced data professionals are difficult to find—and expensive—and are generally required to deploy most data integration platforms. Business analysts who need access to data to make business decisions are often dependent on these experts. Typical time for integrating data from enterprise sources takes 6 months, which slows down time to value of data analytics.

Data management at scale is difficult

Organizations are struggling to make high quality data easily discoverable and accessible for analytics. As data sources and data silos grow, organizations are forced to make tradeoffs between moving and duplicating data across silos to enable advanced analytics or leave their data distributed but limit agility.

Integrating data through multiple delivery styles

There is an increased need from customers for multiple delivery styles like batch, streaming, and event in a single platform. As more aspects of business create digital traces, organizations are looking to make use of real-time data integration and analysis to drive better outcomes for their businesses. 

Data semantic issues

Multiple versions of data that mean the same thing can be organized or formatted differently. For example, dates can be stored numerically as dd/mm/yy or as month, day, year. The “transform” element of ETL and master data management tools address this challenge.

High capex and opex of data integration infrastructure

Both capital and operational expenses add up when procuring, deploying, maintaining, and managing the necessary infrastructure for an enterprise-class data integration initiative. Cloud-based data integration as a managed service addresses this cost issue directly.

Data that’s tightly coupled with applications

Previously, data was so tied to and dependent on specific applications that you couldn’t retrieve and use it elsewhere in your business. Today, we’re seeing application and data layers being decoupled so your data can be used more flexibly.

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What are data integration tools?

Data integration platforms generally include many of the following tools:

  • Data ingestion tools: These tools allow you to obtain and import data, to use immediately or to store for later use
  • ETL tools: ETL stands for extract, transform, and load—the most common data integration method 
  • Data catalogs: These help businesses find and inventory data assets scattered through multiple data silos
  • Data governance tools: Tools that ensure the availability, security, usability, and integrity of data
  • Data cleansing tools: Tools that clean up dirty data by replacing, modifying, or deleting it
  • Data migration tools: These tools move data between computers, storage systems, or application formats
  • Master data management tools: Tools that help businesses adhere to common data definitions and achieve a single source of truth  
  • Data connectors: These tools move data from one database to another and can also perform transformations

What is data integration used for?

Data integration is commonly used to do the following:

Artificial intelligence (AI) and machine learning (ML)

Data integration serves as the foundation for AI and ML by providing the combined, high quality data necessary to power ML models. 

Data warehousing

Data integration combines data from various sources into a data warehouse to analyze for business purposes. 

Data lake development

Data integration moves data from siloed on-premises platforms into data lakes in order to easily extract value by performing advanced analytics and AI on the data.

Cloud migration and database replication

Data integration is a central part of ensuring a smooth transition to the cloud. Data transfer services, data connectors, CDC tools, and ETL tools all provide different options for organizations to move to the cloud while maintaining business continuity.   


Data integration helps collect data from multiple IoT sources into a single place so that you can get value from it.

Real-time intelligence

Data integration capabilities such as streaming and event ingestion activate use cases such as real-time predictions and recommendations.  

Google has removed one of the biggest barriers to data integration, which is that data integration tools have historically required technical teams skilled in data mining, merging, cleansing, and analyzing in order to produce valuable data products like a data lake or data warehouse.

Code-free development of ETL/ELT data pipelines is available with Cloud Data Fusion, a managed, cloud-native data ingestion and integration service that can bring the capabilities of a seasoned data engineer to any team—whether they know a little code or none at all.