Franz Kafka’s short story, The Metamorphosis, describes a salesperson who wakes one morning to find himself transformed into a cockroach.
His namesake, Apache Kafka , is a popular open-source distributed event streaming platform used for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications.
Streaming data allows us to send more data to more places, faster than ever before.
But the risks are also higher than ever!
Just because data moves faster, doesn’t mean the data quality is better.
Like the salesman in Kafka’s story, our data is at risk of arriving as a cockroach.
How do we maintain data integrity within our Kafka streams?
To build trust and make better business decisions organizations that rely on Kafka need to ensure end-to-end data quality throughout the journey across the data pipeline.
They need a solution that conﬁrms data quality at the source, within the pipeline and at the target systems for both streaming and non-streaming data.
Data quality checks for Kafka should:
- Provide real-time and batch validations for patterns and conformity
- Identify thresholds and generate notiﬁcations
- Route and remediate data exceptions to be worked and resolved
- Communicate metrics through visuals and dashboards
By 2025, nearly 30% of data generated will be realtime
The growth of real-time data will be driven in part by consumer demand for access to highly personalised, data-driven products and services wherever they are, and on any device.
Another driver is the increase in IoT / machine-driven data – with manufacturing and healthcare businesses, in particular, driving this demand.
Trusted analytics is dependent on both data quality, as discussed above, and on the movement of data through the Kafka stream and into other points of integration. Data Lineage for Apache Kafka must be a consideration for any reporting stack where Kafka forms part of the data integration stack.
Larger organisations must also grapple with the challenge of delivering data from legacy mainframe and IBM i systems to the cloud using Apache Kafka
Mere connectivity if not enough.
Speed, scale, accuracy and reliability matter as well.
Download this free whitepaper from our partner, Precisely, to understand how to put together a well-defined strategy for streaming data to mitigate risks, build trust in data, and improve data utilisation and insights, or contact us for more information on our technology solutions to deliver trusted and reliable data with Kafka.