Infographic
Delivering Trusted Data in a Real-Time World Using Apache Kafka
Check out our infographic that addresses why Apache 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.
From its roots as a byproduct of business, data has grown into the most valuable asset for the majority of successful companies.
We’ve entered into an age of digital transformation and big data’s 5 “V’s” are more important than ever.
Volume:
Every day, we generate 2.5 quintillion (2,500,000,000,000,000,000) bytes of data. More data crosses the internet every second than was stored in the entire internet just 20 years ago.1
Velocity:
The speed of business and consumer demand are increasing, and IDC predicts that by 2025, nearly a third of all data will be generated in real-time.2
Value:
Data is valuable when it can be turned into insights. Businesses leveraging big data report an 8–10% increase in profits and a 10% decrease in overall costs.3
Variety:
E-commerce, IoT and social media are exponentially increasing the variety of data, as there are now more than 2.8 billion online shoppers, 3.5 billion social media users and more than 10 billion IoT devices worldwide.4
Veracity:
Data must be trustworthy. Poor data quality is costly, as organizations report that 40% of business objectives fail due to inaccurate data.5
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.
It’s like hand-delivering a case of water versus pouring it directly from the tap.
With a case of water, or a batch file, you simply need to get it from point A to point B, intact and undamaged. With water from a faucet, or Kafka, data is no longer delivered in bulk, its being streamed instantaneously to consumers. You must maintain data integrity all along the data pipeline from point A (producer) to potentially many 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 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
To learn more about how Precisely Data360 for Kafka enables end-to-end data quality for streaming, download our data sheet.
Sources
1 https://learn.g2.com/big-data-statistics
2 https://www.zdnet.com/article/by-2025-nearly-30-percent-of-data-generated-will-be-real-time-idc-says/
3 https://learn.g2.com/big-data-statistics
4 https://thenextweb.com/contributors/2019/01/30/digital-trends-2019-every-single-stat-you-need-to-know-about-the-internet/
5 https://blog.zoominfo.com/b2b-database-infographic/