Learn the differences between data integration, application integration and ETL.
Data integration is the data engineering process of combining data from disparate sources into a single unified view of the data.
The process begins with data ingestion from different source systems. This includes data extraction from disparate sources, data transformations or cleaning, and loading the data into a single repository - anything from Excel data sets to Enterprise data stores. Within the single view, data is modeled to provide a (star or snowflake) schema that unifies disparate data into a single company-wide view.
Integrating data is often confused with other data engineering initiatives, such as application integration and ETL/ELT. How do the three approaches differ?
Data integration is often mistaken for application integration or ETL (extract, transform. load) data pipelines. What’s the difference?
Data integration is focused on reconciling disparate data sources and data sets into a single view of data shared across the company.
The goal of application integration, on the other hand, is not the reconciliation of different data sources into a coherent and shared data model. Instead, the goal of application integration is to share crucial data between different apps. For example, stock information data from the backend SQL database of retailers with a mobile app used by customers to shop for shoes. Here the main goal is not to get a holistic view of all (financial, marketing, support, …) datasets. Instead, operational efficiency is the goal, to provide (near) real-time data to the app and keep business processes running.
Finally, ETL stands for extract, transform, load, a tripartite process in which data is extracted (collected) from the raw sources, transformed (cleaned), and loaded (or saved) to a data destination. The ETL data pipeline is an umbrella term for all data movement and cleaning, and it also includes piping data into non-unified data sources.
The difference between the three data engineering approaches might seem abstract, so let us look at a concrete example.
Imagine you are building a data integration system for a chocolate-selling e-commerce store called SweatDreams Inc. SweatDreams has multiple and disparate data sources:
The data integration system that you are building would collect the data from each source, apply data transformation rules (e.g. join duplicate sales records, align marketing spend on Google, Linkedin and Facebook ads, connect the user ids from the MySQL database to the relevant customers in the Shopify store, etc.), and load all the data into a normalized schema within a data warehouse such as Redshift on AWS.
Why is integration necessary? If you want to compute interesting metrics, you have to have a single unified view of data. Let us look at some examples:
In other words, data integration allows you to build a big picture view of your data that helps you address crucial business needs. But that is not the only advantage of integrating data.
Let us look at 5 different areas in which data integration helps companies grow.
Properly formatted and modeled customer data gives you a 360 holistic view that can help you address business needs. A customer 360 view joins in one place all the information you have on a customer. This can be used by operatives in Sales and Support to quickly gather business intelligence on the person they are talking with to help them sell faster and resolve issues quicker.
Data warehousing is both a goal and an outcome of data integration. Centralizing enterprise data into a singular and clearly-organized data store allows for self-service data analytics and empowers your data scientists and business intelligence analysts to perform advanced analytics without the need to rely on data engineers. The centralized data warehouse could be on-premise or provisioned by the main cloud service providers (AWS, GCP, Azure). Irrespective of where you deploy it, the data warehouse serves as a tool that empowers everyone in your company to answer data by themselves.
Data quality assurance is part of the data management umbrella process in which metadata management, semantic constraint imposition, and master data management appear. The principle is simple. You validate your data so it is coherent from a business perspective and impeccable from a regulatory perspective. The data integration process is properly configured with extensive tests that guarantee data reliability and regulatory compliance as well as data replication processes that guarantee data availability in case of failure. Building data integration with extensive testing and failover guarantees is not a necessary condition. But it is a best practice of properly engineered data integration systems. So if you build it right, the data quality management will follow.
Departments often find it hard to collaborate because of the data silos between them. When the marketing and sales departments cannot agree on how to measure “new customers”, their common growth initiatives are stopped before they even start. This is often due to legacy systems that hold data in silos and prevent alignment on basic metrics. Building a data integration system that unifies data across all silos allows you to bridge the gap between different departments and fosters collaboration by unifying different stakeholders on one source of truth.
Machine learning, data science, and artificial intelligence are proving critical for the fast growth of companies. The growth of these new fields was fostered by the rise in the volume, velocity, and variety of (big) data. Interestingly enough, big data by its nature causes the problems data integration is set to solve. Big data is characterized by multiple different types of data - from structured, unstructured, to semistructured - that need to be cleaned and joined onto a common denominator before your data scientists can jump into the data lake and analyze that data. Similarly, big data is often produced in different sources from the traditional data sources: 3rd Party APIs, Internet of Things (IoT) telemetry, streaming real-time inflow of data, etc. All those different data sources need to be integrated by data integration solutions if we want to make them ready for advanced analytics and algorithms.
But big data integrations also pointed out a new issue with data integration. It can be hard to do it manually. As the data ingestion grows, we need data integration solutions that scale with the velocity, volume, and variety spurs.
The data integration process can be hard to do manually. This is why successful companies rely on data integration tools to automate data integration for them.
Keboola is an integration platform that can help you set up data integration pipelines in a couple of clicks and scale them with seamless automation.
As an end-to-end data integration platform, Keboola offers more than 250 integrations between disparate sources and databases, data warehouses, and data lakes. With its intuitive UI, you can set up integration pipelines that extract, transform and load your data from a myriad of heterogeneous sources into a single centralized data storage.
Try it out. Keboola offers a no-questions-asked, always-free tier, so you can play around and build your data integration pipelines with a couple of clicks.
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