Avoid errors and improve data quality in Apache Kafka

Product Analytics

Apache Kafka is a distributed streaming platform used to build real-time data pipelines and streaming applications. However, Kafka’s complexity and the scale at which it operates can lead to challenges around data integrity, consistency, and monitoring. Trackingplan provides automated monitoring of Kafka event streams, ensuring that all data flows accurately and efficiently through your Kafka pipelines.

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COMMON PROBLEMS

Most common mistakes when using Apache Kafka

Data Loss During Kafka Stream Failures

Challenge: Kafka stream failures or downtime can lead to the loss of data if not properly handled.

Impact: Missing data disrupts downstream processing, analytics, and business decision-making.

Inconsistent Data Across Partitions

Challenge: Inconsistent data due to improper partitioning or load balancing.

Impact: Causes data discrepancies and makes it harder to aggregate data accurately for analysis.

Untracked Kafka Consumer Errors

Challenge: Missing tracking or misconfigured error logging for Kafka consumers.

Impact: Issues in consuming data go unnoticed, leading to delays in real-time data processing and analytics.

Slow Event Processing Due to Overloaded Brokers

Challenge: Brokers become overloaded with too many messages, leading to slow processing and delays.

Impact: Slower data processing times compromise the real-time analytics benefits Kafka is known for.

Unreliable Data Formatting in Kafka Streams

Challenge: Improper data formatting or incompatible schemas in Kafka streams.

Impact: Results in errors when consuming or processing data, leading to incorrect analytics and application behavior.

Prevent Data Loss During Stream Failures

Trackingplan monitors your Kafka streams for failures and notifies you in real time, allowing you to take immediate action to prevent data loss.

Ensure Consistent Data Across Partitions

By monitoring partition assignments and data flows, Trackingplan ensures consistency across Kafka partitions, preventing discrepancies and ensuring accurate data aggregation.

Real-Time Kafka Consumer Error Monitoring

Trackingplan tracks the health of your Kafka consumers, detecting errors in real-time and ensuring that issues are addressed before they affect downstream systems.

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Optimize Kafka Broker Load Balancing

Trackingplan helps identify overloading issues with brokers, alerting you to potential slowdowns or delays in event processing, allowing for immediate action.

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HOW TRACKINGPLAN HELPS

How Trackingplan solves your problems with Apache Kafka

Trackingplan ensures the smooth and accurate operation of your Kafka streams, monitoring for issues that could affect data quality and system performance.

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Support

Frequently asked questions about Apache Kafka

How do I prevent data loss during a Kafka stream failure?

Any disruption in Kafka can cascade across your data stack. Trackingplan detects stream failures and provides immediate alerts so you can respond before data is lost.

What should I do if I have issues with Kafka consumers?

Trackingplan monitors consumer behavior in real time and alerts you to errors, lags, or misconfigurations that might compromise data delivery.

How do I avoid issues with data formatting in Kafka streams?

Incorrect schemas or formats can break downstream integrations. Trackingplan checks for formatting consistency and schema compatibility before data is consumed.

Still have questions?

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Happy tracking, happy clients

Because life’s too short for tedious data work

Pablo Urueña

Digital Analytics Manager

Trackingplan is the eye that sees everything, delivering value even in areas outside our responsibilities."

Bert Reila

Digital Analytics Manager

"Using Trackingplan for our team translates to saving significant resources—both time and money."

Álvaro Salmerón

Digital Analytics Consultant

“Before Trackingplan, we spent around five to six hours per project every month just debugging."

Our results in numbers

Achieve more by getting rid of manual processes and validations

From weeks to hours

Reduction of measurement error resolution time

90%

Hours saved per month per FTE

30h

Reduction in data errors in reports

80%

Improvement in campaign performance

15%

Efficiency increase in marketing automation

25%

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