Data helps businesses make better decisions, provide a better customer experience, and increase efficiency. But today, data is distributed across countless sources, bringing new complexities for businesses large and small. Learn what data integration is, how it works, major benefits, and how to choose the best data integration system.
Data integration is the process of combining data from different systems into one, unified view to share information, gain meaningful insights, and actionable intelligence.
A data integration system works by aggregating all disparate data regardless of its type, structure, or volume. It is an integral part of a data pipeline, encompassing data ingestion, data processing, transformation, and storage for easy retrieval.
Organizations are moving to become more data-driven, yet data sources are more distributed and fragmented than ever before. By connecting systems that contain valuable data and integrating them across departments and locations, organizations are able to achieve one-point data storage and access, data availability, and data quality.
Integrated data unlocks a layer of connectivity that businesses need if they want to compete in today’s economy. By connecting systems that contain valuable data and integrating them across departments and locations, organizations are able to achieve data continuity and seamless knowledge transfer. This benefits the company as a whole, not just a team or individual, promoting intersystem cooperation.
When systems are properly integrated, collecting data and converting it into its final, usable format takes less time and allows organizations to make better choices based on deeper understanding of their business data.
Ultimately, data integration allows for a full overview of business processes and performance - from sales, marketing, customer service, website activity, and analytics, to IT systems, applications, and software, providing intersystem cooperation, actionable insights, and operational efficiency.
To explain how data integration works, we'll bring a real life example of how a medium-sized business would integrate data.
Typically, businesses large and small use numerous disparate systems to run its operations. Combining that data could include integrating user profiles, sales, marketing, accounting, and application or software data to get a full overview of their business. For example, one small business could use:
Because each data storage system is different, the data integration process includes data ingestion, cleansing/transforming data, and unifying it into a single data store. A complete data integration solution would not only integrate data, it’d allow this data to be readily available while maintaining data integrity and quality for reliable insights and better collaboration.
In this next example, we'll delve into enterprise data integration by using a Fortune 10 company - Walmart. Seamlessly integrating data across a large, enterprise retailer with 20,000 brick-and-mortar store locations, a massive online website, millions of items in inventory, mobile apps, global data, and 3rd party resellers becomes yet another level of complexity.
Not only do they need to collect data across every customer, store, warehouse, website, and application, they need real-time data integration in order to function properly at scale.
Each one of these systems stores its own repository of information related to the company’s operations. Because each data storage system is different, the data integration process includes data ingestion, cleansing/transforming data, and unifying it into one seamless stream of data.
Due to Walmart’s need for reliable, real-time data integration on mass scale, they turned to Apache Kafka to integrate data across globally distributed systems, process, analyze, and stream data in real-time to ensure accurate, real-time tracking, inventory management, analytics, and machine learning.
Learn more about target="_blank">Walmart's real-time data integration use case.
There are several data integration tools and applications that work in a variety of ways.
Creating a data warehouse: Data warehouses allow you to integrate different sources of data into a master relational database. By doing this, you can run queries across integrated data sources, compile reports drawing from all integrated data sources, and analyze and collect data in a uniform, usable format from across all integrated data sources.
When all of an organization’s critical data is collected, stored and easily available, it’s much easier to assess micro and macro processes, assess client/customer behavior/preferences, manage operations and make strategic decisions based on this business intelligence.
In this case, data integration works by providing a cohesive and centralized look at the entirety of an organization’s information, streamlining the process of gaining business intelligence insights. To achieve this, the managed service provider would a process called ETL.
ETL (Extract, Transform, Load): ETL is the process of sending data from source systems an organization possesses to the data warehouse where this information will be viewed and used. Most data integration systems involve one or more ETL pipelines, which make data integration easier, simpler, and quicker.
Building Data Pipelines: There are several ways to prepare an ETL pipeline – by writing manual code, which is a complex and inefficient task or by making use of enterprise-grade data integration platforms, such as Apache Kafka.
These data integration solutions offer significant benefits as they come with a variety of built-in data connectors (for data ingestion), pre-defined transformations, and built-in job scheduler for automating the ETL pipeline. Such tools make data integration easier, faster, and more cost effective by reducing the dependency on your IT team.
One way to achieve hassle-free, real-time data pipelines is by using Kafka Connect – a framework to stream data into and out of Apache Kafka®. You can stream data to or from commonly used systems such as relational databases or HDFS. In order to efficiently discuss the inner workings of Kafka Connect, it is helpful to establish a few major concepts.
As an open source framework for connecting Kafka (or, in our case – OSS) with external sources Kafka Connect facilitates integration with things like object stores, databases, key-value stores, etc.
Streamlining data from a database (MySQL) into Apache Kafka® offers significant benefits as they come with a variety of built-in data connectors (for ingestion), pre-defined transformations, and built-in job scheduler for automating the process. Such tools make data integration easier, simpler, and quicker, while reducing the dependency on your IT team.
Confluent is a full-scale data platform capable of not just integrating data, but storage and real-time data aggregation, processing, and analytics. You can seamlessly connect data across applications, big data systems, traditional databases and modern, distributed architectures.
With over 100+ built-in data connectors, it it removes the need for multiple integrations or complex code. All data sources are aggregated into a single platform, regardless of where your data sits, decreasing latency, delivering big data quickly, and in real time.