Infographic
Delivering Trusted Data in a Real-Time World Using Kafka
Check out our infographic that addresses why Kafka has become a powerful tool for managing real-time data, and identifies the biggest data quality challenges that drain value from your streaming data.
Data is constantly changing and evolving, and has grown into the most valuable asset for the majority of successful companies.
In today’s fast-paced digital landscape, the speed and volume of data have skyrocketed, necessitating real-time data processing to stay competitive. However, the velocity of streaming data introduces significant risks to data quality.
This video explores the critical importance of maintaining high-quality data in motion.
Digital transformation and big data’s 5 “V’s” are more important than ever.
Volume:
Velocity:
Value:
Variety:
Veracity:
The means of sending data from point A to point B has evolved over time, from manually delivering tapes to sending data in real-time on distributed streaming platforms like Apache Kafka.
The means of sending data from point A to point B has evolved over time, from manually delivering tapes to sending data in real-time on distributed streaming platforms like Apache Kafka.
Size, speed and diversity of data continues to grow, so does the need to deliver quality data—and insights—in real-time.
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.
Resource Managing Data in Motion: Considerations in Data Quality for Streaming Data
It’s like hand-delivering a case of water versus pouring it directly from the tap.
With a case of water, you simply need to get it from point A to point B, intact and undamaged. This is similar to moving a batch file. Streaming data is similar to water from a faucet. Streaming data is being streamed continuously to consumers. You must maintain data integrity all along the data pipeline from point A (producer) to different points (consumers) who have subscribed to a specific topic.
Kafka is invaluable for use cases, such as enabling real-time data processing for...
e-Commerce transactions
Managing user activity for streaming services
Handling complex rideshare data
To build trust and make better business decisions organizations who rely on Kafka need to ensure end-to-end data quality throughout the journey across the data pipeline.
To build trust and make better business decisions organizations who 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 should:
Provide easily configured validations for patterns and conformity, as well as business rules
Identify real-time and batch issues and generate notifications
Route and remediate data exceptions to be worked and resolved
Communicate metrics through visuals and dashboards
To learn more about how Precisely data quality for Kafka enables end-to-end data quality for streaming, download our data sheet.
Sources
1 https://www.g2.com/articles/big-data-statistics
2 https://www.zdnet.com/article/by-2025-nearly-30-percent-of-data-generated-will-be-real-time-idc-says/
3 451 Research’s Data-Driven Decision-Making: Trends Challenges and Solutions
4 Gartner; “Use Multistructured Analytics for Complex Business Decisions”; David Pidsley; November 2022
5 2023 Data Integrity Trends and Insights report