Struggling to keep up with your organization’s hunger for data? That’s an obstacle that many data teams face when their data stack grows and they don’t have complete control over their complex data pipelines. If that’s a challenge you’re looking to solve, you’re in the right place.
Below, we’ve curated our list of the best data observability tools every data team should know about. Plus, we share some key questions that you should have an answer to when choosing the next tool for your data stack.
8 data observability tools to know in 2023
These eight data observability software solutions offer the full spectrum of features. Choose the tools that will help your data team control workflows and increase data accuracy.
Keboola uses a freemium model with usage-based pricing. Initially, you receive 120 free minutes of computational runtime, followed by a monthly top-up of 60 minutes. Additional minutes can be purchased for $0.14 each.
Keboola stands out for its transparent pricing and budget control features, including automated notifications to monitor spending.
Full data access to all the metadata across your entire data stack via Keboola’s Telemetry Data component. Keboola fully telemeters every data touchpoint - from users to jobs and every automation in between.
Centralizes tools and their observability outputs on a single platform for consistent tracking and full data ecosystem visibility.
Keboola’s ML/AI Services provide data scientists with tools for managing machine learning data flows.
Customizable data quality alerts with both low code (Python, SQL, R, Julia) and no-code options. This includes changing datasets and schemas, changes in dependencies that affect your data models, or any other data quality issues.
AI for error messages & descriptions engine simplifies error messages and speeds up resolution with actionable recommendations.
Plug-and-play design for quick integration of all data ecosystem tools.
No pre-computed data observability metrics. You get full access to the raw metadata and will have to compute your metrics and KPIs from it. Luckily, you can build KPI dashboards in a few clicks using Keboola’s Data Apps.
“The biggest change I noticed with Keboola is that I have more control over the data, which helps me to achieve more, to implement new use cases. And I see my coworkers have more trust in data now.” - Ronnie Persson, CTO at Pincho Nation
"Keboola is an efficient end-to-end ETL tool that provides top-notch observability, extraction, and pipeline orchestration for our data requirements. It has dedicated segments for carrying our replication of incremental data which is hectic if done manually. Also, we can seamlessly integrate multiple data sources as Keboola offers connectors for all our components namely AWS S3, DynamoDB, Redshift, and Apify." - Meghna S., Cloud Engineer
Use Keboola to scale your data workflows without losing control
Datadog is a feature-rich observability platform that covers anything from serverless monitoring to application security management. With 650+ integrations, covering anything from Snowflake data warehouse to Kubernetes nodes, you can use the observability platform to automate infrastructure monitoring, application performance monitoring, and log management.
Datadog has a freemium pricing model. The free tier doesn’t offer the full functionalities of the observability platform, but Datadog also offers a 14-day free trial to test the enterprise packages yourself.
If you want just one of their services (e.g. just log management or just network monitoring), you’re in luck. Customers can pick and choose the services they need and are charged separately for each service (and its usage).
Wide variety of pre-built metrics and gives users the ability to monitor custom metrics (at an additional cost).
Datadog’s Watchdog uses machine learning anomaly detection to automatically alert data engineers of elevated latencies in databases, network issues, abnormal error rates in applications, and others.
With a mixture of service tiers and consumption per service, the pricing can balloon and you can quickly lose control.
The platform is geared towards data engineers, DevSoc professionals, and IT experts. It’s not user-friendly enough to onboard your non-technical domain experts.
Lacks a log unification functionality, making it hard to work with logs from different applications, infrastructure, or data systems.
“Datadog has many features that make it one of the best monitoring tools in the market. I like the ease of using these tools and the enterprise support that comes with it. It supports a lot of integration, making it easy for administrators to monitor any services/machine in the organization.” - Parth G., Lead I Cloud engineer
3. Monte Carlo
Monte Carlo's data observability platform offers a comprehensive solution for managing your entire data infrastructure, providing monitoring and notifications to identify data-related problems within your data warehouses, data lakes, ETL processes, and business intelligence systems.
Monte Carlo doesn't offer transparent pricing. You’ll have to contact sales to get a bespoke quote. In general, the company has a usage-based pricing model that charges based on tables you submit for observability.
Automated detection and alerts for schema changes, volume outliers, and stale data.
Monte Carlo’s automated circuit breaks stop pipelines when data doesn’t meet desired thresholds, limiting the downstream effects of bad data.
Field-level lineage helps you spot data reliability issues in BI reports by tracing data provenance from tables to dashboards.
Users complain Monte Carlo’s automated alert system fires too many false positives, causing alert fatigue.
Monitoring data across different environments (e.g. testing and production) causes a lot of overhead.
A limited number of integrations with the modern data stack and much fewer than its competitors.
“The data observability had been great when we POC'd MC and was totally blown over by the features and the ease of setup. Once the alerts were optimized with the support team from MC, we were very happy with the results. However, it's a big investment and the pricing is on the higher end especially when the companies are looking to streamline and limit the money being spent” - Akshat M., Senior Data Engineer
Acceldata is an enterprise data observability platform geared towards operationalizing data reliability and data platform performance. The data observability solution focuses on three branches:
Spend intelligence to identify areas for cost savings.
Data reliability to maximize data quality and eliminate data outages.
Operational intelligence to provide real-time insights that guide decision-making.
Acceldata doesn’t disclose its pricing, nor the pricing tiers. You’ll have to contact them directly to get a quote.
Built-in searchable data catalog that improves data accessibility across your datasets.
Acceldata’s utilization guardrails help you detect issues with spending or long-running pipelines. But they also stop increased usage from ballooning your costs.
The platform predicts future incidents with automated stability tracking.
“Very useful in troubleshooting Spark and Hive issues and also proactively keeping the services/cluster healthy. The depth of insights in the queries points almost precisely where to fix the problem (query tuning, JVM tuning, resource management, etc.). It's excellent for both the Operations and the Developers.” - FNU R., Data Infra Architect
5. New Relic
New Relic is a full-stack observability platform that helps engineers monitor, debug, and deploy software solutions. It stands out with a set of tools geared towards engineers and their observability needs, including APM (Application Performance Monitoring), infra monitoring, and observability tailored to operating systems, browsers, mobile apps, and networks.
New Relic offers pay-as-you-go pricing starting at $0.30 per GB of ingested data with 100GB of free data ingests per month. On the free plan, you get 1 full platform user (admin role) and unlimited basic users (think of it as a viewer role, who can also play with existing configurations).
You’ll have to pay extra for more data ingested, additional full platform users, or add-on features like additional monitor checks, prolonging the data retention window, or accessing vulnerability management.
650+ integrations. Unlike other data observability solutions, many integrations are tailored to monitoring data from open-source solutions (e.g. different integrations for monitoring Ubuntu vs CentOS instances).
The session replay feature allows you to debug broken data flows through every step of the process.
Users can explore their telemetry data using natural language prompts using their chat interface called New Relic Grok.
The data observability solution is tailored to data engineers which makes it less suitable for non-technical users.
“I like mostly the APM (Application performance monitoring) feature, which provides insights into the slow performance of individual transactions with each and every detail.” - Pratishtha K.
Dynatrace uses AIOps to predict and resolve observability issues before they critically affect your business. This cloud-monitoring platform offers all the features you’d expect - application performance monitoring, infrastructure monitoring, log management and analytics, DevSoc, and the like.
Dynatrace’s pricing can be a bit complex. The platform charges for usage (in GB/compute hours/events processes) but the price depends on the functionality you pick (host monitoring, log management, automation workflows, etc.).
Customers can take Dynatrace for a free spin, using the 15-day free trial (no credit card required) to explore its features.
Quickly set up synthetic monitoring of critical workflows using a simple web recorder of browser click paths (no scripting needed).
Monitor user behavior events on mobile, hybrid, or SPAs, without the common downsampling limitations of other analytic solutions.
The platform automatically creates an entity model from your logs (including dependency mapping) using AI technology.
Dynatrace’s OneAgent checks the processes you have running on your host machine and automatically detects which metrics to track - no manual coding is needed.
The complexity can be toohigh, especially for non-technical users.
Costs are hard to gauge from their pricing model and quickly accumulate. Dynatrace is on the more expensive end of data observability tools.
The platform doesn’t offer many features for monitoring databases.
“Dynatrace's one agent is easy to install and you get all the applications on that box instrumented. Dynatrace detects all our services, processes automatically and detects any slowness or unavailability using its AI engine. No need to setup any manual alert” - Ajay P.
Splunk data observability platform offers a range of features across application performance monitoring, infrastructure monitoring, and security monitoring.
Splunk is a bit innovative in its pricing model - it lets you pick the pricing unit you desire:
Workload Pricing charges for the type of workload you use. Can be economical for high data volumes.
Ingest Pricing charges per volume of data ingested. Good for low data volumes with a lot of searches and additional post-load use cases.
Entity Pricing charges by the number of Splunk host machines you use. Good for compute customization.
Activity-based Pricing charges by the number of activities being monitored. Good for organizations with a small set of metrics that are computed over large data volumes.
This customizability comes at the cost of complexity. It is hard to understand which pricing model is the best for your organization.
Can ingest petabytes of data across a hybrid cloud. This makes it a good candidate for fast-scaling enterprises.
Splunk’s Real User Monitoring (RUM) uses AI to correlate issues across distributed systems to identify the root cause of errors affecting your users.
Splunk uses advanced analytics and adaptive thresholding to predict and prevent issues before they happen.
Real-time data monitoring generates disproportionately large logs, increasing usage surcharges.
The software behind Splunk is resource-intensive, and can sometimes limit your scaling.
Expect a steep learning curve to learn how to use this feature-rich platform and set aside time to learn Splunk Search Language if you want to inspect logs.
“Splunk is an amazing tool where we can monitor and get logs of every activity done in the system. The best thing about Splunk is its visualization and reporting ability. We can create customized dashboards for monitoring. Overall it's an amazing tool.” - Tarang N., Operations Executive - Trainee
8. Instana (IBM)
IBM Instana offers real-time data observability for application, infrastructure, mobile, and web monitoring.
Instana offers many pricing tiers, starting at $75 per host/per month for its managed cloud service (self-hosting will cost you more). However, the standard license requires a minimum of 10 hosts.
Instana offers a 14-day free trial to try out the platform before committing.
User traces are monitored at no additional costs.
Instana automatically discovers and maps all services, observability metrics, and dependencies, to save you time instead of configuring observability entities yourself.
Low latency between errors happening and alerts being sent out.
Initial setup, configuration, and integration are complex and require experienced data engineering and DevOps skills.
It’s a read-only data observability tool. Instana can be used to find issues, but then the user needs to log in to their other stack to solve it.
Resource-hungry. Expect to consume a lot of CPU power.
Doesn’t integrate well with other data monitoring tools like Grafana or the ELK stack.
“IBM Instana is a cool application for monitoring my complex applications. The insights are good to decide for our future planning of applications and resources for our cloud platforms. The monitoring by IBM Instana is real-time so that we can see our bottlenecks and perform immediate actions if something is not in line.” - Sanfiya P., Senior Research Executive
Key questions to answer when choosing the right data observability tool
Use these questions to evaluate potential data observability solutions:
What data sources and technologies does the tool support? You need to ensure the tool is compatible with your existing data infrastructure, databases, platforms, and data formats. Check its integrations against your owned data assets.
How user-friendly is the tool's interface? Evaluate the tool's usability and how easily your team can navigate and make the most of its features. Pay special attention to the target user: Is it a skilled data engineer or a non-technical domain expert?
What monitoring and alerting capabilities does it offer? Look for features such as real-time monitoring, customizable alerts, and automated anomaly detection to ensure issues are promptly identified and addressed.
Can it provide end-to-end visibility? The tool should offer a comprehensive view of your data pipeline, from source to destination, to pinpoint bottlenecks or performance issues.
What types of data does it monitor (logs, metrics, traces, etc.)? Ensure the tool can monitor different types of data sources and provide a holistic view of data health. Compare the tool monitoring ability with your data governance policies.
Is it scalable to accommodate future data growth? Consider your organization's growth and ensure the tool can handle increasing data volumes and pipeline complexity. Pay special attention to its resource consumption - does running the data observability tool impact the performance of your data operations?
What level of support and community resources are available? Consider the availability of support, documentation, and a user community to help troubleshoot issues and maximize the tool's potential.
What is the total cost of ownership (TCO)? Evaluate the tool's pricing model, including licensing, maintenance, and operational costs, to ensure it fits within your budget.
Is the tool future-proof? Investigate the tool's development roadmap and long-term viability to ensure it can adapt to evolving data observability needs.
Take control of data observability with Keboola
Keboola helps you run data operations and observe all your data pipelines with no added overhead.
With 250+ integrations, an intuitive user experience for all users, automated alerts, real-time end-to-end monitoring, AI features to speed up data tasks, and a vibrant community, Keboola is the go-to choice for data teams that need to scale their data operations without losing control.
A data observability tool monitors the internal state of your data system (data downtime, errors, speed, data quality issues, etc.), detects issues, and alerts you. Advanced data observability tools take it one step further with self-healing issue corrective measures or features that help you prevent issues from happening.
What are the pillars of data observability?
Data observability is built upon three pillars:
Metrics provide quantitative data on data pipeline performance, including throughput, latency, and error rates, enabling real-time monitoring and issue detection.
Logs act as the chronological records of events and activities within the data pipeline and offer detailed insights for debugging, auditing, and compliance.
Traces enable end-to-end visibility by tracking data flow across interconnected services, helping to identify bottlenecks and performance issues.
What are the benefits of data observability tools?
Data observability tools unlock 5 advantages for modern enterprises:
Enhanced data quality: Data observability tools help maintain and improve data quality by identifying and rectifying anomalies, errors, and inconsistencies in real-time, ensuring data reliability and data quality.
Faster issue resolution: These tools enable prompt detection of issues and bottlenecks within data pipelines, reducing downtime and accelerating issue resolution, which is crucial for maintaining business continuity.
Performance optimization: Data observability tools provide insights into the performance of data pipelines, allowing organizations to optimize their processes and make data-driven decisions more efficiently.
Reduced operational costs: By detecting and resolving issues early, these tools can reduce operational costs associated with data pipeline maintenance, minimizing the need for manual intervention.
Improved data transparency: Organizations gain deeper visibility into their data pipelines, fostering greater transparency and trust in data-related processes and decision-making, ultimately enhancing overall data management. Transparency is also a driving force behind regulatory compliance and data governance.
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