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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