Managing Data in Motion: Considerations in Data Quality 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. With rapidly increasing volumes of real-time data in transit through communication on the internet and within organizational 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 organizations better deliver, manage and leverage data.
One of the key technologies that’s emerged from this exponential real-time data growth is the adoption of event driven architecture (EDA). Organizations 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 helps meet growing demands to immediately react, analyze 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 favorite, with more than a third of Fortune 500 companies and thousands of businesses using it to optimize 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.
This whitepaper explores how a well executed strategy for streaming data mitigates risk, builds trust in data, encourages data utilization and leads to better business insights and decision-making.