Data integration is the process of combining data from different sources into a single, unified view. This empowers you to connect the dots between virtually all your different structured and unstructured data sources, whether it’s a social media platform data, app information, payment tools, CRM, ERP reports, etc. so you can make smarter business decisions — a must in a competitive landscape.
It’s estimated that the world will generate 181 zettabytes of data by 2025.1 Given that, chances are your data will interact with several other applications, services and systems. Especially in today’s evolving economy, data integration cannot be an afterthought.
Data sources are no longer limited to mainframes and applications; they are also being increasingly distributed on multi-cloud and hybrid cloud environments. Cloud data integration can help by bringing together data of virtually any type, from one or more sources, into a single destination to produce useful business information or enable new business processes.
And serverless integration takes cloud data integration a step further. It makes data integration easier since IT teams do not have to manage any servers, virtual machines or containers. They also don’t incur costs when an application sits idle. With the cloud, IT teams can deploy solutions in several different ways, enabling them to offload IT infrastructure maintenance in steps. Serverless integration also enables auto-tuning and auto-scaling for effortless data pipeline processing.
Integrating diverse data from various sources into one location can enable more-informed data-driven decisions for improved customer experience, productivity and growth — must-haves in a demanding economy.
Now that we know what data integration is, let’s review how it works.
Because every business has different data goals, there’s no one size fits all method for data integration. Still, data integration tools and solutions are usually comprised of a few standard building blocks:
Users who need data
A primary server
A disparate or interconnected system of external and internal data sources plugged into the primary server
The process itself is straightforward: Users request data from the main server. And then the main server intakes, aggregates, enhances and combines the requested data from the various data sources into a unified set of data that’s delivered to the user.
Overall, data integration helps to transfer and sync data from different systems, types and formats between systems and applications. It’s not a one-and-done event, but a continuous process that keeps evolving as business requirements, technologies and frameworks change. As organizations generate more data, it provides an opportunity for better business insights. Your data integration strategy determines how much value you can get out of your data, and which data integration type will work best for your use cases and initiatives.
One way of categorizing data integration is based on frequency and latency of dataflow. There are two main ways to do this: batch data integration and real-time data integration. Your data integration strategy should help guide you in selecting the best way to integrate your data. Regardless of which data integration system you use, cost-effectiveness and speed will play into your decision.
-Batch data integration. Just as the name implies, it involves processing data in batches. A group of data is collected and stored until a specified amount is gathered, then that data is processed all at once as a batch. Batch data integration is still widely used today when companies don’t have a need to gather and analyze data in real-time.
Batching is an efficient way to process data. It lets you schedule data integration at regular intervals, optimize resource allocation, and improve performance for high-volume data transformation and transfer.
-Real-time data integration. A newer way to integrate data, real-time integration is triggered every time new data is available, bringing the latency to almost zero.
Given the demanding market, companies are relying on real-time data integration and data processing more than ever to capitalize on up-to-date insights and to help improve customer experience.
For example, artificial intelligence (AI) technology company SparkCognition wanted to enable real-time AI use cases for financial services customers. They did this with cloud application integration and cloud data integration capabilities to capture streaming data for use in machine learning (ML) models. This helps them to incorporate more data sources, add more features and generate useful results from their models so they can improve their fraud detection efforts. Many companies like SparkCognition rely on streaming analytics platforms to achieve real-time data integration and data processing.
See “Streaming Analytics: What It Is and How it Benefits Your Business” for an in-depth overview.
Below lists the most common architectural patterns of data integration.
ETL (extract, transform, load). ETL is the most common pattern. However, relatively new patterns are gaining momentum as pushdown techniques get smarter. In a diverse IT landscape spanning multiple clouds and on-premises environments with several data sources and targets, it might make sense to process data locally with ETL and then send the transformed data to downstream applications and datastores.
ELT (extract, load, transform). ELT, on the other hand, is efficient if you have your data source and target are in the same ecosystem. For example, for transformations within a single cloud data warehouse, ELT can be effective from both a cost and performance standpoint.
Reverse ETL. In reverse ETL, cleaned and processed data from a source like a data lake or data warehouse is ingested back into a business application like Workday, Marketo or Salesforce. From there it can be used for business operations and forecasting.
Data ingestion. Data ingestion moves and replicates source data into a target landing or raw zone (like a cloud data lake) with minimal transformation. This pattern works well for real-time streaming, ELT, data replication and data migration use cases. It requires minimal data transformation. Once data is ingested into the landing zone, you can parse, filter and transform it. This makes it available for advanced analytics and AI usage.
Now let’s review the most common features of a data integration tool.
While data integration tools have evolved over time, the basic feature set remains the same. All quality data integration tools should be able to:
Write data to target systems. This feature copies data from the source application, whether located in the cloud or on-premises, then saves the transformed version of the data into the target applications, systems and services. Without the transformation step, data integration isn’t happening.
Access data from a mix of sources. Data integration is designed to enable both the transfer of data as well as the ability to combine or collate data from different sources and present it in a standardized format to any target applications or users.
Interact with sources and targets. Data integration is also a method of communication between source and target systems. There are many ways to set up communication methods: one-to-one communication, one source to one target or one-to-many and many-to-one channels among various data sources and targets. One such way to connect data is an integration hub where sources publish data to the hub and the targets, or users, subscribe to that data as and when they need it.
Transform data. The main component of data integration is the ability to transform data for consumption by a target application. This is what differentiates data integration from data transfer or data ingestion.
Design the data flow. A basic data integration tool feature is the ability to create a data pipeline using a combination of targets, sources and transformations. In fact, you can set up the process regardless of whether it’s ETL or ELT and automate it.
Support efficient operations. Data integration tools are designed to make interconnective systems efficient by removing the effort of hand coding. With automation and monitoring, the management of data pipelines becomes easy, timely and less error prone.
Monitor the data pipeline. Designing a perfect data pipeline is one thing but executing it without a major breakdown is another. That’s where a monitoring and alert system can help troubleshoot issues so the pipeline runs smoothly.
Let’s switch gears and explore how data integration and application integration are different.
The Difference Between Data Integration and Application Integration
On the surface, data integration and application integration may look the same. They’re both cloud-based and give you all the benefits of cloud computing. They both take different data sources and translate and convert them into a new data set. But the big difference is when and how they’re used.
Application integration is preferred when business processes are automated and operational data is shared between applications in real time.
Data integration is mostly used for consolidating data for analytical purposes. Generally, data integration is taken into consideration when the normalization, transformation and reusability of data sets is required.
Organizations rely on both application and data integration to automate processes and share real-time and batch data. These are two distinct but essential capabilities: the yin and yang that businesses need to achieve digital transformation.
Application programming interfaces (APIs) act as a window to enable interaction and sharing of data among applications, systems or services. Due to growing cloud and web-based products and applications, API integration has gained momentum. There is greater control over APIs when it comes to security, monitoring and limiting access. You can custom-build private or public APIs and open your data for innovation and monetization opportunities. Companies now opt for data integration using API technology.
Companies are constantly challenged by the four “Vs” of data: velocity, volume, variety and veracity. A data integration platform helps to standardize, automate and scale data connectivity as these variables grow or change. Some top benefits include the ability to:
Boost productivity. Team productivity improves when you automate data workflows and reuse frameworks and templates to accommodate new data types and use cases.
Improve data quality. A common data integration use case is consolidating data from multiple sources to a data warehouse or data lake. Data is standardized and its quality is ensured before it’s made available to any consuming applications, systems or services.
Gain a holistic business view. The opportunity to combine the right sets of data irrespective of the sources empowers stakeholders to make fast business decisions while keeping multiple perspectives in mind. The success of data science and advanced analytics projects depends on how much employees can trust and access the data they need. Timely access to valuable business insights can give you the competitive advantage to stay ahead of the curve.
Enhance data flows. With the right data integration platform, it’s easier to govern the data flow. Data integration solutions help ensure a secure and easy data flow between systems. The visibility of end-to-end data lineage helps ensure data governance and maintain compliance according to the corporate policies.
Democratize data. In today’s data-driven culture, virtually every user must have the ability to access data to help them make everyday business decisions. The effectiveness of data democratization initiatives hinges on the connectivity of the IT landscape and the ease of data accessibility.
Enable AI and analytics. Companies are dependent on business insights to stay relevant. For AI and analytics to function well, teams need access to the right data irrespective of the format, source and type. Data integration plays a crucial role in bringing that data to these analytical engines.
It’s clear that a data integration platform can help serve as a backbone in an evolving environment so you can better understand your business and deliver data-driven insights.
Like anything, there can be hurdles as well when it comes to connecting your data. See “Overcoming Data Integration Challenges” so you can rectify hiccups as soon as possible.
Finding the ideal data integration tool for your business goals depends on identifying which product features are a must-have. Here are some advanced features you should look for:
Real-time data integration. Data volume and complexity are expanding. And the demand to get insights from that data as soon as it’s generated is growing too. You need tools to ingest and process this data quickly and accurately. Look for solutions that offer seamless scalability and real-time, high-performance processing.
Pushdown optimization. A must-have feature to look for in a data integration tool is pushdown optimization for the ELT operations. This feature can help you save costs by using data warehouse and ecosystem resources more effectively.
Job scheduling and automation. These two features are a lifesaver! Automating data integration tasks gives you a faster and easier way to extract data for insights and analytics. Job scheduling lets you easily schedule virtually any of your data integration tasks.
Data pipeline error handling. Sometimes data pipelines break. This can be for a variety of reasons ranging from conditional logic errors to corrupt data. Find a tool that helps ensure data availability, consistency and accuracy by easily and efficiently managing errors.
Cost optimization. No matter what data integration solution you buy, cost is going to be a critical factor. Look for tools that can leverage artificial intelligence (AI) and machine learning (ML) to recommend the most cost-effective option for your workload. Also look for tools that offer flexible, consumption-based pricing.
We have a good understanding of what to look for when evaluating options. Now let’s look at how data integration is commonly used across different industries.
Data integration plays a big role in how an industry innovates and addresses its data-driven use cases. Let’s take a quick look at some examples.
Healthcare. The right data integration strategy will ensure that today’s health systems provide valuable insights about each patient while keeping data safe and confidential. Data integration plays a critical role in combining real-time patient data from IoT or mobile apps with historical medical data to provide personalized care and mitigate risk.
Financial services. Fighting fraud, ensuring compliance and running complex analytics are some of the priorities that financial services institutions need to consider when choosing data integration solutions. Data governance, industry regulations and privacy issues must be considered before data is made available for consumption. Financial services companies are using the cloud with proven cloud data integration solutions.
Public sector. Government organizations are modernizing their data infrastructure to achieve mission-critical outcomes while complying with regulatory mandates. Integrating trusted data can help agencies gain real-time insights and improve decision-making. A modern data integration platform paves the way for the public sector to transform and stay more connected to the people they serve.
Manufacturing. Manufacturing companies are going through a series of automations fueled by the intelligence derived from the data they generate. Sensor data integration helps with real-time monitoring of the equipment in plants, boosting performance and ensuring production quality. Automation and integration with the whole supplier ecosystem enable transparent transactions. Inventory and warehouse management gets easier with data integration. The orchestration of orders and deliveries helps optimize resource allocation and remove inefficiencies.
Retail. Customer experience is a brand differentiator in the retail industry. Online stores are nothing new, and organizations are going above and beyond to provide a seamless digital customer experience. This includes using data from their online behavior, social media interactions, preferences, purchase history or other data sources to help provide targeted marketing.
Now that we have a good idea of common scenarios for data integration, let’s review a few success stories.
Paycor, an HR technology company, wanted to standardize analytics across product, service, sales and finance. They centralized data extraction using a Snowflake-based SQL data lake and utilized Informatica cloud data integration capabilities for unified pipelines. This resulted in substantial ROI, saving 36,000+ analyst hours and shifting to daily reporting for enhanced visibility.
The University of New Orleans (UNO) wanted to analyze student and employee data from multiple on-premises and cloud sources in a centralized data warehouse. UNO also wanted to improve efficiency and reduce costs by automating manual ETL processes. The university used cloud data integration to extract, transform and load data from Salesforce, SQL Server and Workday into a cloud data warehouse. They also used cloud connectors for Snowflake and Workday to connect on-premises and cloud systems without hand coding. This streamlined approach has helped enhance program analytics, which in turn has enabled the university to improve student enrollment and retention and reduce reporting time and costs.
To deliver more personalized experiences to its members, the National Roads and Motorists’ Association (NRMA) needed to unify its very disparate and siloed data environment. They adopted Informatica cloud data integration, which facilitates data availability for analysis. Several disparate data sources — including multiple CRM environments, financial tools and marketing solutions — are now reliably brought into Google Cloud. With cloud data integration, they can generate mappings, which loads data into Google Cloud, for instant analysis through Google BigQuery.
Healthcare system Franciscan Alliance needed to meet emerging needs, including reporting and patient satisfaction analytics. By utilizing cloud data integration, Microsoft Azure and PowerCenter tools, they consolidated data from multiple sources — internal and external — into data marts. They can now easily comply with government reporting requirements in days rather than weeks or months by using auditable, automated tools.
According to Nucleus research, Informatica Cloud Data Integration, a service of Intelligent Data Management Cloud (IDMC), has proven to increase data processing, accelerate KPI development and achieve an average ROI of 328%.2 It also provides the critical capabilities necessary to centralize data in your cloud data warehouse and cloud data lake with:
Rapid data ingestion, replication and integration using the intuitive GUI of Informatica Cloud Mass Ingestion
Pre-built cloud-native connectivity to virtually any type of enterprise data, whether multi-cloud or on-premises with Informatica Connectors
Critical optimization capabilities of SQL ELT for efficient data processing
Serverless Spark-based processing for scalability and capacity on demand with Informatica Advanced Cloud Data Integration-Elastic
Plus, Informatica IDMC is consumption-driven, so you can choose a range of cloud services at any time across the platform as your business requirements change.
To realize the benefits of cloud data integration in terms of productivity, performance and time to market, we recommend you start small. The simpler the tool, the faster you can get started. Cloud Data Integration-Free is the industry’s only free, AI-powered solution to easily load, transform and integrate data. It’s fast, flexible and helps you turn data into insights in minutes. And it scales with your changing business needs.
If your workload exceeds the monthly ELT processing limit, you can easily transition (in the same platform) to Cloud Data Integration-PayGo. Then, when you reach the cut-off, you just pay for only what you have consumed.
These innovative tools eliminate budget concerns with the free option, unburden your IT teams with a wizard-driven experience and help jumpstart your data integration journey.
And down the road, when your needs go beyond simple data integration — like monitoring and improving data quality, governing data access, maintaining a data catalog or setting up a data marketplace — you can leverage Informatica Cloud Data Integration services to experience a broad range of capabilities.