5 Things to Look for in Your Next Data Integration Platform
Not all data integration platforms are created equal.
If your existing data integration solution can’t meet your company’s requirements for handling extracting, transforming, filtering, aggregating, modeling, and synchronizing data faster and in larger volumes, then it’s time to look for one that does.
However, choosing the right data integration platform isn’t always a cakewalk.
To ensure an effective and efficient data integration process, you’ll need to consider many critical factors, such as out-of-the-box connectors, master data management capabilities, and even big data trends.
This guide dives into the five essential things you should consider when choosing your next data integration platform.
1. Data sources and destinations
Even if this isn’t your first rodeo with data integration platforms, it’s still crucial to know if the next solution supports the data types in your data environment.
The platform should connect to your sources to pull data and seamlessly store your formatted and cleaned results in the intended destination.
Consider these other factors to ensure the data integration platform allows seamless data flow between your sources and destinations.
- Check whether the data integration platform includes connectors to your database, data warehouse, and other reliable data management tools. Assess whether the connectors are native or generic, such as Open Database Connectivity (ODBC) or Java Database Connectivity (JDBC).
The platform’s method to connect to a database can impact the setup’s complexity and connection performance. This, in turn, affects data flow timeliness across your environment, making it crucial to choose a platform with the right connectors.
- Learn how the data integration solution tunnels through your environment’s security protocols to allow data to move freely. It’s vital for data stored in your network, such as the cloud and on and off-premises environments.
Also, understand how the platform will work with firewalls and your other perimeter security measures to help you integrate your data assets seamlessly into a unified, consistent view.
- Determine if the data integration platform integrates both your structured and unstructured data sources. This includes document repository systems, spreadsheets, webhooks using various authentication methods, and exposed APIs.
The platform should also have the capabilities to integrate alternative forms of your structured data such as NoSQL, columnar store, key-value, and in-memory databases.
As data demands grow in critical aspects of business operations, so will your number of sources.
This makes it vital to identify where your data comes from, such as your reliable multi-channel ecommerce software and the transformations it undergoes throughout the data integration process.
This is where data integration platforms with built-in (or third-party app) metadata management tools come in handy.
Metadata management tools help you understand where your data comes from by letting you trace the technical and business rules in place during the data transformation process.
This can include match and merge procedures, data cleansing processes, and certain business logic to prepare your data for downstream analysis.
Reliable data integration platforms with robust metadata management capabilities help you seamlessly audit data changes over time. This allows you to get durable, accurate, and consistent data critical for making key business decisions.
3. Bulk and Batch Data Movement
Moving volumes of data in one go is crucial for integrating data efficiently. This makes it vital to find a data integration platform that lets you schedule and move your data in predefined batches.
Highly technical data integration teams, for instance, are better off with a platform with more technical configurations that use the command line or scripting.
Assess whether your data integration platform offers a clean user interface for monitoring and scheduling batch jobs.
Also, check the platform’s batch data movement logging, access, and alert options, so you can trace and remedy issues in case something goes wrong when moving and processing data.
For instance, some modern logging platforms can direct every log and event into a central system, then query the platform. This allows you to see the exact events and logs you want.
However, this can come with some complexities since the more flexible your data integration platform captures events and logs in the system, the more effort it requires to extract the data in your preferred, usable format.
It’s also critical to assess how your prospective data integration platform manages bulk data processing. This includes the mechanisms limiting the amount of data moving through your data integration process (at any given time).
Also, check whether the platform offers a control mechanism based on source integrated timestamp data or one you can use for more complex forms of Change Data Capture (CDC).
These control mechanisms help ensure correct data flow without overloading the system. Overloading can occur when the system copies data multiple times from the source to the destination, causing performance inefficiencies and duplicates.
Modern data environments require streaming data access to streamline batch processing.
Discover how your prospective data integration platform works with streaming data, such as queues and messages, since these are critical components of real-time data environments.
Evaluate the platform’s streaming capabilities, including whether it allows parallelized processing since this lets you increase your capacity to scale to accommodate growing data loads.
You’ll also need to know the platform’s limitations and capabilities associated with stream data inbound processing.
Doing so helps you understand the types of transformation that occur during streaming and the kind of transformation necessary post load in the succeeding batch process (event-driven or scheduled).
Additionally, ensure the platform’s message queuing includes reliable recoverability and high availability features. This helps ensure that issues with your data mid-stream can get replayed and won’t corrupt the target destination.
5. Data Virtualization
Data virtualization features in a data integration platform allow you to get real-time, unified views of your multiple data sources without removing the data from the source system.
It’s also useful when there are regulatory (or business) reasons associated with limitations on where you can copy the data.
Learn the real-time data transformation limitations during data federation to determine impacts to performance since transformation requirements generally become more complex.
Determine whether your data management projects include a combination of data marts, a data lake, and a data warehouse.
Each one might require specific data environment requirements, including the type of data that goes through your environment temporarily and those pulled from a secondary source in real time.
Knowing these can help you assess and analyze the data virtualization requirements you need from a data integration platform.
Wrapping up the key features to consider in a reliable data integration platform
While it can be overwhelming to find the right data integration platform with all the requirements, integration capabilities, and other essential factors to consider, you can start with the features and tips in this guide.
Find the perfect data integration platform for your current and future data integration requirements by putting in some hard work and diligence.
Drill down on your existing integration and sort the use cases. Next, reverse engineer the formats, destination points, data sources, triggering conditions, and transformations of your requirements.
Then, qualify the operating requirements, including security and data validation requirements, compliance needs, and service-level objectives.
You can also add emerging or new high business importance use cases with specific requirements different from your existing data integrations.
With the right platform, you can seamlessly integrate and translate your data to fulfill your requirements and streamline making strategic business decisions.
This also allows you to optimize your data integration platform and keep it from becoming ineffective, unused, and costly shelfware.