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, and has grown into the most valuable asset for the majority of successful companies.

 

Digital transformation and big data’s 5 “V’s” are more important than ever.

Volume:

Every day, we generate 328.77 quintillion (328,770,000,000,000,000,000) bytes of data. It is estimated that 90% of the world’s data was generated in the last two years alone.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:

Over 75% of participants say that data will be more important to their organization’s decision-making over the next 12 months.3

Variety:

Organizations are expanding the kinds of data they use and are thinking about how to use a wider set of data (more types of data) across a range of use cases.4

Veracity:

Data must be trustworthy. Poor data quality is costly. 46% of data and analytics professionals trust the data they use for decision making and 70% of those who struggle to trust their data say data quality is their biggest issue.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.

IBM i Data Elements

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

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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.

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:

Business Case

Provide easily configured validations for patterns and conformity, as well as business rules

Insights

Identify real-time and batch issues and generate notifications

Check list

Route and remediate data exceptions to be worked and resolved

Communicate metrics

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.