With the development of remote work and online businesses in 2022, the importance of data has grown significantly with it. For organizations using big data to conduct real-time operations, a real-time data integration system is a necessity. In addition, you may want to find one that has Name Matching features.
There’s a distinction between having an integration system and managing it effectively. Let’s understand what real-time data integration is and why you need to upgrade your systems to integrate real-time data from batch data. And then we shall discuss 9 best practices for managing real-time data integration.
What Is Real-Time Data Integration
Real-time data is one that is readily available to users as soon as it’s created and obtained. Rather than storing data in physical warehouses, it’s forwarded to the users whenever they request it. Real-time data integration is crucial for live decision-making that empowers technologies like GPS and bank transactions.
Data is one of the most important elements for a business. Upon having easier access to big data and analytics, organizations now focus more on accelerating the process by employing real-time data integration technologies. Although not all businesses require having real-time data, it helps to provide better customer service and optimize management decisions for most businesses.
Difference Between Batch Data Integration and Real-Time Data Integration
Batch data integration is a less sensitive process than real-time data integration that takes hours or even days to collect, extract, and store data. The fundamental difference between batch data and real-time data is the collection period. Real-time integration continuously collects data to be analyzed, but batch integration collects the data over a set period.
If you already have considered real-time data integration, you must follow these 9 best practices to manage real-time data integration.
1. Simulate More Testing
As real-time data integration is based on fast extraction and automation, having an effective and robust test cycle helps a lot. There have been instances where one bad algorithm wiped out $440 million from the market.
These kinds of events are rare in 2022 due to the automation testing approaches that organizations take to ensure that the real-time data integrations don’t malfunction due to a system bug.
It’s for the best that you work with sample data as much as you can to train the ETL or ELT software that is handling your real-time operations.
2. Take the Steaming-First Approach
Batch data collection can’t ever match the pace of real-time integration due to the higher latency that’s introduced by processing them in batch or micro-batch. If your system collects data in batch and proceeds with batch data processing in real-time, it doesn’t matter what kind of system you have. To attain the maximum benefit of real-time data integration, having a real-time data collection system is also a must. This is called the teaming-first approach that accelerates the data collection first before proceeding to execute the algorithms.
3. Look for Centralized Solutions
ETL systems were designed to abolish the decentralized solutions that have been present for decades. It’s just not practical anymore to develop functions for each of the operations that ETL systems execute without the hassle of programming. They also incorporate graphical means to the software for faster and easier integration.
A centralized solution also helps you keep a custom dashboard that outputs only the data that’s required.
4. Target Simplicity
A simple solution doesn’t need to be less powerful. Target a simplified system with more graphical elements and less complicated customization options. Ask for free trials before integrating real-time solutions into your system. Enterprise solutions are often complicated and need a lot of training before they can be used. If you don’t need a solution that can handle thousands of requests in a second, don’t go for one.
5. Consider Performance Over Features
A well-designed real-time data solution should be able to offer its best performance when in need. An integration tool that trembles down under the pressure of multiple requests isn’t the best alternative you can have, despite getting a bargain. As the online market is booming, IT teams should be prepared to handle any traffic surge at any given time. If your solution buckles down under pressure, it’s worth giving others a try.
6. Make Room for Growth
Use your data to predict data. If your current data trend indicates that your business is growing or might grow rapidly in the future, having the flexibility to collect real-time data from a larger data pool will help. Your real-time data integration framework should grow quickly if your business needs it to be.
7. Improve Latency by Minimizing Disk I/O
The entire point of having a real-time data integration system is to handle a huge amount of data with minimal latency. If your system is reading and writing to physical disks at every step of the operation, the latency time might even be increased to an inoperable extent. Disk I/O should be used rarely when an intermediary system needs to be employed.
8. Practice Continuous Data Operation
Real-time data integration needs to be processing data and executing algorithms continuously for years at a time. Administering these operations and understanding the status of the pipeline needs to be addressed by the IT team. Although some latest data processing technologies integrate self-healing measures in their solutions, it’s better to continuously validate the data movement from source to target and look for anomalies in data formats.
9. Think About Future-Proofing Your Solution
As your data will evolve constantly, having an outdated source and target system can slow the whole architecture down. Opt for real-time data integration solutions that offer flexibility to upgrade in the future where you need it the most. Ensure that your solution can meet your requirements and can perform well in the future as you grow.
The Bottom Line
Managing a real-time data integration system calls for the best in-house practices. Before integration, test vigorously for bugs, ensure that the solution is future-proof, and look for a centralized option. After integration, keep an eye on the performance, latency, and flexibility. While managing real-time data integration solutions, monitor the pipelines continuously for any anomalies and malfunctions.