Top 5 emerging practices for real-time data integration

Real-time data integration is nothing but different databases and applications connected in a way that if data in one system changes it simultaneously updated in all others.

The company needs timely accurate and relevant information to navigate there, passionate business situations and to add value to products business partners and customer relationships. The more agile and responsive an organization is, there better it responses to mergers, growth, fierce competition, globalization, customer expectations, and demands for faster product delivery. Further, to achieve agility and speed interactions between systems, effective response to the powerful market forces and integration of data in real time is a must.

Not only large organizations but several SMEs also operate multiple applications, which run variously in the cloud, on-premise and other locations or data centers.

Economical & Efficient Transfer of Data – Real-time data integration moves single record and not an entire batch it is more efficient and can save money in cloud environments. Usually, charges are applicable on the amount of data pulled from the systems.

Good Customer Engagement – For example, after the purchase is complete, a real-time integrated CRM can be reported to trigger the email given to the customer immediately.

Better visibility and insight into your business – Real-time dashboards ease monitoring key areas such as customer purchases, operations, the supply chain, inventory levels, and staff are provided with a constant flow of insight to improve performance and make better decisions.

Superior Customer Service – Providing real-time information on available stock, account balances, customer’s purchase history, and loyalty rewards can take customer service to new levels by the customer service representatives.

Best practices required to follow when developing real-time data integration strategies:

Real-time needs to stop the former batch-oriented ETL apps. Often the older manual systems involve real-time according to accelerate the organization, so the company gets worse than ever before.

Here is an instance: To rebuild the gate agent’s app, the airport’s management makes use of real-time data integration to provide the agent with real-time data, but it doesn’t create any new value. Instead, real-time monitors can be provided to the passengers to check the flight status on their own devices. Real-time data applications are deployed across the world today.

When high-volume and high-speed is present in the system, it creates a tough problem as they not exactly designed for these kinds of challenges. To handle these data streams, a critical design method has to operate a highly parallel fashion, using the co-ordinated ingestion engines and multiple parallels that can shrink elastically and scale to meet all the processing requirements of the data. The recent advancements in parallel processing concepts and execution make handling today’s high-speed data streams easy.

Proprietary platforms and open source are invented that built the processing engines which enables the applications developed to further run in the configurations that highly parallel. Starting with one of these platforms is best for companies developing any new app for real-time computation.

For example, to analyze omnichannel communications and the subscriber base, a company normalizes messages via all channels, sends them via a single server analytics application. The hardware cannot keep up with the increase in data load as the system designed do not work in parallel and had no way to scale up. Thus, it has to be redesign for smooth operation in parallel execution.

More testing and up-front simulation required in real-time data integration than traditional data integration. Here is an example earlier for real-time data a few algorithmic trading desks on Wall Street builds a new trading algorithm test its logic and start trading. It works sometimes, but real-time cuts both the ways, a huge capital was lost in less than 40min due to a bug.

Component failure planning

A major challenge in real-time data integration is the failure of a component in a few sections of the pipeline. Improper designing may lead to a stale unorganized data system outage or data loss. Establishing resiliency in each phase and decoupling of every phase in the pipeline makes the system run smoothly.

For better insights always consider package streams

Real-time data streams produce business value only when the developers can implement this data to new applications. Untapped streams of data give rich businesses data, but poor information if you can’t pull actionable insights from this data. To solve these issues, organizations must have clear visibility on how their systems, devices, and applications are interacting and where their data resides.

In today’s world of competition, real-time data integration capabilities are essential for companies to keep growing on the digital platform and to maintain a larger competition. In this article, experienced Java programmer describes the top 5 emerging practice also real-time data integration methods are now affordable and are becoming a professional requirement to compete effectively in any market.