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Data Observability for Kafka Solution Guide

September 19, 2024
10 Min Read
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Kafka is complex

Kafka is an increasingly critical component of the modern data stack, yet organizationsstruggle to operate Kafka reliably and cost effectively.

Data observability eliminates Kafka complexity

Eliminate blind spots to save time, improve performance & reliability, and lower costs

Scale: Identify and eliminate performance bottlenecks

Optimize: Improve resource efficiency to reduce infrastructure costs

Trust: Deliver reliable & high quality data on time, every time

Comprehensive Visibility:

Eliminate blind spots in data, reliability, performance and resources

  • Data: Data profiling, data quality, schema drift, data drift and anomaly detection. Data reconciliation to/fromKafka and RDBMS, data lakes, data warehouses, applications and more.
  • Platform: Throughput, consumer lag, topic distribution, rebalance events, partition skewness and other metrics for topics, brokers and consumers.
  • Infrastructure: Broker load, disk wait, kernel error detection and more. Support for multi-cluster environments

Rich Analytics:

Gain insights to optimize performance, quality and efficiency

  • Historical analysis & trending: Predict and prevent data and performance issues. Benchmark, track, and optimize reliability &performance.
  • Best practices: Recommendations for data quality rules, data distribution, capacity planning, skewness analysis and more
  • Ad-hoc analysis: Slice-n-dice across metrics and create custom visualizations and dashboards.

Seamless Automation:

Minimize disruption and latency with faster mean time to resolution

  • Notifications & incident management: Highly customisable alerting, pluggableon multi-channel notifications, and incident management
  • Orchestration: Integrate with our SDK and APIs. Data validation windowing and replay is supported.
  • Auto-remediation: Out-of-the-box runbooks to self-heal such as topic rebalancing etc. along with support to integrate custom runbooks.

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