DQ for streaming data

“Big data” continues to grow at an astonishing rate—over 2.5 quintillion bytes of data are created every single day. As we continue to create—and store—massive amounts of data, advancing technologies have not only impacted the scale of data but fundamentally changed the way we do business.

Considerations in Data Quality for Streaming Data

With rapidly increasing volumes of real-time data in transit through communication on the internet and within organisational networks, systems, and processes, by 2025 more than 25% of all data created will be real-time, according to IDC’s Data Age 2025.

This convergence of heightened expectations and real-time data has further accelerated the speed of business, prompting the development of new solutions that help organisations better deliver, manage and leverage data.

Enter the event-drive Architecture (EDA)

One of the key technologies that have emerged from this exponential real-time data growth is the adoption of event-driven architecture (EDA).

Organisations are increasingly turning to software design architecture that models data as a stream of individual records, messages, or actions, known as “events,” to send data between systems. EDA uses messaging system software that can quickly communicate changes to data as they occur and enable real-time API updates.

EDA allows businesses to not only address the need for real-time data but also meets growing demands to immediately react, analyse and act on critical data. Among the varied messaging options on the market, the open-source, distributed streaming platform Apache Kafka has quickly emerged as the favourite, with more than a third of Fortune 500 companies and thousands of businesses using it to optimise their streaming data strategy.

Kafka pros and cons

The benefits of Kafka for streaming data are clear. It delivers high-throughput, low-latency streaming, flexible data retention, redundancy, and scalability. Kafka can quickly send trillions of messages from source systems or applications (producers) daily to any number of consumers who “subscribe” to specific topics, ingesting all topic-related data from any producer. Kafka maintains multiple copies of this data for a defined period, to help provide a fault-tolerant solution and guard against data loss.

Yet, to fully leverage Kafa companies must address data integrity challenges including reliability, quality and connectivity.

This Precisely whitepaper explores how a well-executed strategy for streaming data mitigates risk, builds trust in data, encourages data utilisation and leads to better business insights and decision-making.

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