Data sources are numerous today, from cloud data warehouses to data lakes to streaming platforms, graph databases and others. The existence of these different data sources means that for meaningful analytics to occur, data integration must take place. Data integration collates data from numerous sources into a single location to grant users and analysts a comprehensive view of data for operational purposes.
Data integration, however, involves multiple subprocesses and requires careful consideration to design a process that’s efficient, scalable, adaptable, and cost-effective, hence the need for a data integration strategy to carefully develop the process. Your data integration strategy considers your business goals, budget, and data needs to select an integration technique and the required integration tools to oversee the process.
This article discusses the importance of aligning your integration strategy with your strategic business goals.
The Subprocesses of Data Integration
The primary goal of every data integration process is to grant users, data professionals, and business analysts easier access to a single, consistent, comprehensive, and accurate view of data for use in analysis, visualizations, decision-making, or other data use cases.
The end use case for the integration process helps inform the different subprocesses and integration techniques; for example, an organization dealing with large volumes of streaming data for operations might include Change Data Capture (CDC) in its integration process because CDC helps conserve resources and reduces operational latency. Other high-level functions standard with data integration include:
- Data consolidation involves collating and cleaning data from disparate sources into a single storage location like a data warehouse or data lake. Data consolidation usually involves ETL processes that help ingest, transform, and load this data into target destinations. Consolidating data helps provide a 360-view of organizational data, granting users more access and eliminating data silos.
- Data virtualization is a virtual approach that helps eliminate the complexity of replicating and consolidating data in a central repository. It involves a virtual layer integrating siloed enterprise data regardless of format, location, and latency from various data source systems for easier access and management.
- Data replication: Data replication involves making copies of data from a source’s location to other locations to ensure synchronization after data integration or for backup purposes. Data replication can occur in batches, in bulk, or in real-time.
Aligning Data Integration Methodology With Strategy
It’s vital that your data integration strategy not only improves your IT and team’s productivity but also aligns with your business interests. Fulfilling business needs like increased revenue, customer satisfaction, and data quality relies heavily on your chosen integration method.
Therefore, organizations must make sure that their strategy enforces an integration method that makes it easier to achieve the integration objective. For example, implementing manual integration techniques in a large organization with multiple data sources and plans to grow would encounter scaling and efficiency issues. These issues arise because while the manual integration method grants an organization or data team more control over the integration method, it’s difficult to scale, prone to risks, and challenging to implement as data sources increase. Therefore, a large organization with multiple data sources and scaling plans may choose another integration method or use the manual technique with a more scalable integration technique.
Organizations looking to map out and decide on a technique for their integration strategy should mind the following factors:
- Data use cases: Your chosen method should make your data processes proceed more efficiently. The standard storage technique, for example, is the most common integration technique used by large enterprises because of its ability to handle multiple and sophisticated queries.
- Data types: Most organizational data today comes in a variety of formats — structured, unstructured, or semi-structured. Your chosen integration method and tools must accommodate the various data types from your sources.
- Data sources and destinations: A crucial aspect of integrating data involves the compatibility between the source and target destinations. Increased compatibility between source and target systems produces a more efficient and scalable integrated system. On the other hand, limited compatibility breeds may result in data loss and an inefficient process. For example, the middleware integration system has limited compatibility with systems, so organizations dealing with multiple host systems are better off with another technique.
- Project budget: Some integration techniques are more expensive than others; hence, your chosen approach should align with your budget. For instance, while an effective solution, common storage techniques can be costly. Businesses with limited resources can adopt a more cost-effective method.
The Application-Based Data Integration Technique
Application-based integration involves automated processes where software applications help locate, collect, clean, and integrate data from multiple sources. This integration involves more accessible communication between data sources and the destination and more direct information exchange. Also, engineers and data managers have time to focus on more tasks because it is automated. However, the complexity involved with setting up this technique requires skilled personnel and tools, which means more cost. Also, this technique is not a one-size-fits-all, as the choice of service depends on the service provider. This technique is common with applications in hybrid cloud environments.
The Uniform Access Data Integration Method
This technique connects and presents a single unified data view without moving the data from its original locations. Every data request pools data from their respective locations to create a comprehensive view for the data user. This method means more strain on the host systems, which can cause system failures. There is no storage requirement for this technique, hence its suitability for businesses with limited resources, but having to access multiple data points each time may reduce data consistency and integrity.
The Manual Data Integration Method
In this case, one or more data engineers handle the integration process via custom code. This integration technique opens systems to manual errors. Additionally, as business needs change and scale, the integration process becomes too demanding for data teams to bear. Therefore, the manual integration method is suitable for one-off integrations or integrations with few data sources.
The Common Storage Data Integration Method
This method is the most popular data integration technique and the basis for most data warehouse and lake integrations. It is similar to uniform access, but this technique requires creating a central data repository in a storage location like a data warehouse. In addition, this technique usually involves an ETL process involving cleaning and transforming data. Also, the data repository can handle multiple sophisticated queries, improving time to analytics. However, this technique calls for increased storage and maintenance costs as the technical expertise and tools required to maintain and ensure peak performance are high.
The Blended Data Integration Method
Sometimes, a single data integration technique will not work for your specific data use case. Then, you will need to blend multiple integration techniques. A blended integration technique combines two or more techniques to create a robust integration technique. For example, most organizations running applications in the cloud might incorporate application integration and common storage to power their applications.
Data Integration Strategic Alignment Summarized
| Approach | Ideal Use Case |
| Application-based Data Integration | Software applications do the work of collating and integrating the data in a central location. Ideal for hybrid cloud environments |
| Uniform Access Data Integration | Organizations that need access to multiple disparate systems but cannot afford the storage and maintenance costs involved with common storage. |
| Manual Data Integration | Ideal for one-off situations or integrations with small data sources with no plan to add data sources or scale |
| Common Storage Data Integration | Data is consolidated in a central repository like a data warehouse. Best for large enterprises with enough resources and that need to run sophisticated queries. |
| Blended Data Integration | IoT applications where data flows continuously from the devices. The applications integrate the data into a data warehouse or data lake. |
The StreamSets Data Integration Technique
StreamSets supports a blended data integration strategy with its intuitive visual interface, extensive library of connectors, and support for structured and unstructured data.
By leveraging StreamSets data integration and transformation platform, data teams can easily orchestrate, transform, and route data across various platforms, applications, and formats.
With StreamSets, you can confidently tackle complex integration scenarios, adapt to evolving data requirements, and drive better business outcomes by delivering accurate and on-time actionable insights. Start building with us today.
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