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Real-Time Processing

Improve decision-making and operational efficiency with real-time data processing. This technology quickly transforms data into actionable insights, enabling timely strategic decisions across multiple industries, including life sciences. 
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How to implement real-time data processing?

We must select robust data acquisition tools that can handle fast data streams from various sources. Then, stream processing architectures are crucial for real-time analysis and event detection. These architectures allow us to process and analyze data as it arrives, providing immediate insight and supporting timely decision-making. Managing the speed and volume of data is essential, as real-time systems need to acquire, process, and analyze large streams of data without delay. Also, real-time data processing requires continuous validation and cleaning of incoming data to maintain accuracy and reliability.

Immediate data-driven decisions 

Use real-time processing to make quick, informed decisions based on current and relevant information, improving responsiveness to market changes and new opportunities.

Supply chain resiliency

Improve supply chain resiliency with real-time visibility into inventory, logistics, and supplier dynamics to ensure consistent product availability, even in the face of supply chain disruptions.

Operational efficiency and cost savings 

Increase efficiency and reduce costs by using real-time data to optimize operations and drive continuous improvement.

Responsive marketing strategies 

Create dynamic marketing strategies on the fly with real-time processing that quickly adapts to changing market trends and consumer behavior for optimal effectiveness.

Overview

Real-time processing is essential in the age of big data. This technology allows you to process data from continuous data streams and use it for real-time analytics. Unlike batch processing, real-time stream processing makes continuous data streams immediately available so you can analyze data as it’s being created. With immediate data analysis, you can make responsive, timely, and strategic decisions like never before. Processing data in real-time isn’t an easy feat and requires continuous validation and cleaning to maintain accuracy and reliability of incoming data. With our solutions, you can harness the power of your real-time data to make immediate data-driven decisions, enhance operational efficiency, improve supply chain resiliency, and quickly adapt to changing consumer behavior.

Access to up-to-date data on a global scale is critical today. In our recent project for a top global pharmaceutical company, we implemented real-time integration of customer data between the global MDM solution and the rest of the organization. By implementing real-time processing from distributed data sources around the world through multi-channel system integration, current data is exposed and easily accessible. Global systems no longer need to worry about the location of data; it has been transparently and efficiently consolidated into a single nervous system.We improved global data quality through the use of declarative and flexible rules. Event-triggered services process data on the fly and deliver new functionality to the ecosystem.The flexibility of the abstraction layer has reduced the time and cost of implementing new changes, and we have dramatically reduced licensing and infrastructure costs. The implementation of observability and data reconciliation mechanisms has reduced the risk of providing inaccurate and outdated data. The main technologies used were Apache Kafka and Snowflake, with a microservices approach deployed in the AWS cloud.

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