What is Data Integration?
Data integration is the process of achieving consistent access and delivery for all types of data in the enterprise. All departments in an organization collect large data volumes with varying structures, formats, and functions. Data integration includes architectural techniques, tools, and practices that unify this disparate data for analytics. As a result, organizations can fully view their data for high-value business intelligence and insights.
Why is data integration important?
Modern organizations typically have multiple tools, technologies, and services that collect and store data. Fragmented data leads to silos and creates access challenges.
For example, a business intelligence application requires marketing and financial data to improve advertising strategies. However, both datasets are in diverse formats. Hence, an external system has to clean, filter, and reformat both datasets before analysis. In addition, data engineers may perform specific preprocessing tasks manually, causing further delays. Despite this effort, the application may miss out on a critical dataset because the analytics team was unaware of its existence.
Data integration aims to solve these challenges through different methods of consistent access. For example, all data analysts and business intelligence applications use a single, unified platform to access siloed data from different business processes. Here are some benefits of data integration:
- Improved data management efficiency and utilization
- Better data quality and integrity
- Faster, meaningful insights from accurate and relevant data
What are the use cases of data integration?
Companies use data integration solutions for several key use cases. We go into more detail below.
Machine learning
Machine learning involves training artificial intelligence (AI) software with large amounts of accurate data. Data integration pools the data into a centralized location and prepares it in formats that support machine learning. For example, Mortar Data provides companies with modern data technologies to train machine learning models by consolidating data on Amazon RedShift.
Predictive analytics
Predictive analytics is an approach to forecasting a particular trend using the latest historical data. For example, companies use predictive analytics to schedule equipment maintenance before a breakdown occurs. They analyze historical operational data to spot abnormal trends and take mitigative actions.
Cloud migration
Companies use data integration technologies to ensure a seamless shift to cloud computing. Moving all legacy databases to the cloud is complicated and might disrupt business operations. Instead, companies use data integration strategies such as middleware integration to gradually transfer data to a cloud data warehouse while ensuring the business remains operational.
How does data integration work?
Data integration is a complex field with different tools and solutions that take diverse approaches to the challenge. In the past, solutions focused on physical data storage. Data was physically transformed and moved to a central repository in a unified format. Over time, virtual solutions were developed. A central system integrated and presented a unified view of all the data without changing the underlying physical data. Recently, the focus has shifted to federated solutions like data mesh. Every business unit manages its data independently but presents it to others in a centrally defined format.
Data integration solutions in the market also use various approaches. You will still find several tools that use modern technologies to make traditional techniques more efficient. Unfortunately, the existing fragmentation of solutions in the market has led to a fragmented approach within large enterprises. Different teams use different tools to meet their specific requirements. Large organizations typically have legacy and modern data integration systems that coexist with overlap and redundancy.
What are the approaches to data integration?
Data architects use these approaches in their data integration efforts.
Data consolidation
Data consolidation uses tools to extract, cleanse, and store physical data in a final storage location. It eliminates data silos and reduces data infrastructure costs. There are two main types of tools used in data consolidation.
ETL
ETL stands for extract, transform, and load. First, the ETL tool extracts the data from different sources. Next, it changes the data according to specific business rules, formats, and conventions. For example, the ETL tool could convert all transaction values to US dollars, even if the sales were in other currencies. Finally, it loads the transformed data to the target system, such as a data warehouse.
ELT
ELT stands for extract, load, and transform. It is similar to ETL, except that ELT switches the final two data processes on the sequence. All the data is loaded in an unstructured data system, like a data lake, and transformed only when required. ELT takes advantage of cloud computing’s processing power and scalability to provide real-time data integration capabilities.
Data replication
Data replication, or data propagation, creates duplicate copies of data instead of moving data physically from one system to another. This technique works well for small and medium businesses with few data sources. For example, a retail hardware business could use enterprise data replication to copy specific tables from its inventory to its sales database.
Data virtualization
Data virtualization does not move data between systems but creates a virtual unified view that integrates all the data sources. The storage systems do not transfer data between databases during data virtualization. Instead, it populates the dashboard with data from multiple sources after receiving a query.
Data federation
Data federation involves creating a virtual database on top of multiple data sources. It works similarly to data virtualization, except that data federation doesn’t integrate the data sources. Instead, when receiving a query, the system fetches data from the respective sources and organizes them with a standard data model in real time.
What is the difference between data integration and application integration?
Application integration is a process that allows two or more software applications to communicate with each other. This involves creating a common communication framework or API that allows one application to access another application’s function. An API is an intermediary software that allows software programs to talk to each other.
Application integration expands an existing software program’s features by integrating it with another program. For example, you could integrate an email autoresponder with a customer relationship management (CRM) application. Meanwhile, data integration extracts, combines, and loads all customer data from multiple source systems into a cloud data repository.
How does AWS help with data integration?
Analytics on AWS provides all the infrastructure you need for complex data integration solutions. We provide the broadest selection of analytics services to build your customized data integration applications at the best price performance, scalability, and lowest cost.
For an out-of-the-box solution, AWS Glue is a data integration tool that allows companies to extract, cleanse, and consolidate data at scale. It allows data architects to integrate data with different methods, such as extract, transform, and load (ETL); extract, load, and transform (ELT); batch; and streaming.
- AWS Glue Data Catalog allows data scientists to query data efficiently and observe how data changes over time
- AWS Glue DataBrew offers a visual interface that allows data analysts to transform data without writing code
- AWS Glue Sensitive Data Detection automatically identifies, processes, and masks sensitive data
- AWS Glue DevOps allows developers to track, test, and deploy data integration jobs more consistently
Get started with data integration on AWS by signing up for an AWS account today.
