Best Observability Tools for Apache Spark

Compare the Top Observability Tools that integrate with Apache Spark as of July 2025

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This a list of Observability tools that integrate with Apache Spark. Use the filters on the left to add additional filters for products that have integrations with Apache Spark. View the products that work with Apache Spark in the table below.

What are Observability Tools for Apache Spark?

Observability tools are software platforms that help monitor, measure, and gain insights into the performance and health of systems, applications, and infrastructure. These tools provide a comprehensive view of the system by collecting and analyzing data from various sources, including logs, metrics, traces, and events. Observability tools are essential for identifying and diagnosing issues, improving system reliability, and optimizing performance. They enable real-time monitoring, anomaly detection, root cause analysis, and alerting, which allows teams to respond proactively to potential problems. By offering detailed insights into system behavior, observability tools are critical for DevOps, cloud-native environments, and microservices architectures. Compare and read user reviews of the best Observability tools for Apache Spark currently available using the table below. This list is updated regularly.

1

Sematext Cloud

Sematext Group

Sematext Cloud is an innovative, unified platform with all-in-one solution for infrastructure monitoring, application performance monitoring, log management, real user monitoring, and synthetic monitoring to provide unified, real-time observability of your entire technology stack. It's used by organizations of all sizes and across a wide range of industries, with the goal of driving collaboration between engineering and business teams, reducing the time of root-cause analysis, understanding user behaviour and tracking key business metrics. The main capabilities range from log monitoring to APM, server monitoring, database monitoring, network monitoring, uptime monitoring, website monitoring or container monitoring Find complete details on our website. Or better: start a free demo, no email address required.

62 Ratings

Starting Price: $0

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2

Sifflet

Sifflet

Automatically cover thousands of tables with ML-based anomaly detection and 50+ custom metrics. Comprehensive data and metadata monitoring. Exhaustive mapping of all dependencies between assets, from ingestion to BI. Enhanced productivity and collaboration between data engineers and data consumers. Sifflet seamlessly integrates into your data sources and preferred tools and can run on AWS, Google Cloud Platform, and Microsoft Azure. Keep an eye on the health of your data and alert the team when quality criteria aren’t met. Set up in a few clicks the fundamental coverage of all your tables. Configure the frequency of runs, their criticality, and even customized notifications at the same time. Leverage ML-based rules to detect any anomaly in your data. No need for an initial configuration. A unique model for each rule learns from historical data and from user feedback. Complement the automated rules with a library of 50+ templates that can be applied to any asset.

1 Rating

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3

IBM Databand

IBM

Monitor your data health and pipeline performance. Gain unified visibility for pipelines running on cloud-native tools like Apache Airflow, Apache Spark, Snowflake, BigQuery, and Kubernetes. An observability platform purpose built for Data Engineers. Data engineering is only getting more challenging as demands from business stakeholders grow. Databand can help you catch up. More pipelines, more complexity. Data engineers are working with more complex infrastructure than ever and pushing higher speeds of release. It’s harder to understand why a process has failed, why it’s running late, and how changes affect the quality of data outputs. Data consumers are frustrated with inconsistent results, model performance, and delays in data delivery. Not knowing exactly what data is being delivered, or precisely where failures are coming from, leads to persistent lack of trust. Pipeline logs, errors, and data quality metrics are captured and stored in independent, isolated systems.

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