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In today’s data-driven world, leveraging reliable data is often central to an organisation's success. But the complexity, scale, and interdependence of modern data systems sometimes can make delivering accurate and reliable data feel like a nightmare task.
That’s where data observability tools come in.
What are data observability tools?
Data observability tools help you understand, diagnose, and manage data health across multiple IT tools throughout the data lifecycle. Think of them as magnifying glasses into your data, allowing your organisation to detect and address data issues promptly and build trust in your valuable data.
This process involves tracking various aspects of data – such as its volume, velocity, variety, and veracity – to ensure data integrity and compliance.
Data observability tools help with this process by providing features such as data monitoring, anomaly detection, data lineage tracking, and alerting. Together, these features help you to keep tabs on your data operations and quickly respond to issues, ultimately leading to better decision-making and more reliable data-driven insights.
Choosing a data observability tool
Choosing the best data observability tool for your organisation requires careful consideration of several factors. Here's a step-by-step guide to help you make your decision:
- Define your data observability goals. What do you hope to achieve with data observability? Are you primarily focused on improving data quality, identifying data pipeline issues, or enhancing data security? Clearly define your goals to narrow down your options and choose a tool that aligns with your priorities.
- Assess your data landscape. Understand the types of data you generate, the sources of that data, and the tools and technologies you use to manage it. This will help you determine the compatibility and integration capabilities of potential data observability tools.
- Evaluate key features. Consider the features and capabilities that are most important to you. Some essential features include data profiling, anomaly detection, root cause analysis, real-time monitoring, and alerting. Prioritize features that address your specific needs and challenges.
- Consider ease of use and integration. Choose a tool that is easy to use and integrate with your existing data infrastructure and tools. This will minimize the disruption to your workflow and ensure a smooth transition to data observability.
- Evaluate pricing and scalability. Data observability tools vary in pricing models and scalability options. Consider your budget and growth plans to choose a tool that fits your financial constraints and can accommodate your future data volume and complexity.
Top data observability tools
There are a variety of data observability tools available today, each offering different features to give you visibility into your data assets. Choosing the best data observability tool for your business is crucial to gaining actionable insights from your data.
In this list, we’re taking a look at ten of the top data observability tools on the market today, exploring their key features, capabilities and price points.
Datafold is a data observability platform that helps you prevent data catastrophes by identifying, prioritising, and investigating data quality issues proactively before they affect production. The platform automates the most error-prone and time-consuming parts of the data engineering workflow, providing a holistic view of data health through data profiling, column-level lineage, and intelligent anomaly detection.
Rather than building yet another app for data practitioners to switch to and from, Datafold insert its data observability features in the existing workflows, for example, in CI/CD for deployment testing and IDEs for testing during development. Meanwhile, the company’s proactive approach to data quality helps data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows.
Next up on our list we have Soda – a data quality and observability platform that helps teams to discover, prioritise, and resolve data issues across the entire data product lifecycle. With Soda, you can embed declarative data quality checks into your data stack and systems, enabling you to test and deliver data that your whole organisation can trust. It does this by providing a unified view of data quality, freshness, schema and lineage across all data sources, preventing downstream issues and improving your pipeline with integrated data quality tests.
Soda provides a comprehensive view of data quality, including column-level statistics, data distribution, and outlier detection, allowing it to monitor data freshness and schema changes in real time. The platform is also built on a number of open-source technologies, including Apache Spark, Apache Presto, and Apache Airflow, making it easy to integrate Soda with existing data infrastructure and tools using Soda’s extensive Python and REST APIs.
One of the leading AI and data analytics companies in the world, Splunk offers real-time visibility and data fidelity across the data ecosystem. From packaged and on-prem applications to cloud-native web applications, the platform delivers end-to-end visibility and correlates issues across your stack, allowing you to predict and detect problems before they impact your customers. It can also collect and analyse data from a wide range of sources, including logs, metrics, and traces. This gives Splunk a holistic view of data health, which is essential for keeping tabs on your data.
With AIOps built in, Splunk makes it easy to detect and investigate changes to your data. It provides AI-assisted, directed troubleshooting that includes business context and offers guidance on where to look when investigating problems. It also automates the process of identifying and resolving many of your data problems, allowing you to scale your data observability efforts and identify and resolve problems quickly and efficiently.
Designed to handle even the most complex data infrastructures, the Dataorios observability platform provides the deep insights and feedback you need to keep your data reliable and accurate. A responsive data lineage solution, the platform allows you to view immediate changes in your data journey for real-time insights, simplified designing and fast maintenance at any stage of your data lifecycle. It also makes it easy to pinpoint and troubleshoot issues before they happen, providing complete monitoring of all data processes to avoid risks and guarantee the delivery of timely, accurate data.
With in-depth metrics into systems, processes and outputs for increased performance and improved data quality, Datorios will help your organisation regain trust in your data environment. The platform makes finding and eliminating data anomalies a breeze by leveraging advanced analytics to predict and mitigate issues with built-in auto-rectification.
As a data warehousing solution, Integrate.io has a host of pre-built observability features to help your team get a complete picture of your data. The platform’s no-code/low-code data connectors, for instance, eliminate the need for manual pipeline building and observability, helping you take control of the data that exists in your enterprise.
Unlike manual data observability, Integrate.io’s data warehousing integration platform do all the hard work, removing the need for data engineers and data analysts. You can solve data reliability issues, detect data-related problems, and protect data from external dangers like data breaches all in a single platform. And when anomalies do crop up, Integrate.io send alerts w analysis, helping you improve data management across your organisation.
Combining data observability with intelligence and automation throughout the modern data stack, Unravel Data has helped companies gain visibility into their data for over ten years. The platform’s AI-powered tools and features help automate critical tasks such as ensuring data pipelines and AI models run reliably, the data platform costs less and scales efficiently, and data applications generate correct results. Data engineering, product, business, and finance teams finally have a single platform to deliver the AI goals of their companies.
With Unravel, you can extract and correlate data in seconds. It has telemetry designed for the modern data stack that captures millions of granular details from every system and contextualises them for the task at hand in an intuitive, unified view. It also provides proactive alerts and policy-based autonomous corrective actions that put the breaks on cost overruns and nip performance issues in the bud before they become problems.
Founded in 2019, Bigeye has quickly emerged as one of the leading observability tools on the market today thanks to its intuitive and user-friendly interface and host of automated monitoring, data profiling and anomaly detection tools. With Bigeye, you get instant pipeline health monitoring across your data warehouse and deep machine-learning-powered quality monitoring on the data that matters most. The platform provides instant-on metadata monitoring to ensure data is accurate and ready for business, using column-level data profiling machine learning, and 70+ pre-built metrics to provide deeper monitoring recommendations for your most important data assets.
When things go wrong in your data pipeline, Bigeye takes the burden of writing and maintaining manual tests away from data teams so that they proactively resolve data fires rather than sounding the alarm. Its anomaly detection monitors the health of your data pipelines and the quality of the data in them so you are the first to know about problems when they happen, preventing any impact on your users.
There’s a reason G2, GigaOM and Ventana rank Monte Carlo’s data observability platform as the #1 solution on the market. The platform is a powerful, end-to-end solution that reduces data downtime by ensuring your data is reliable at every stage of the data pipeline. It stands out for its incredible ML-powered algorithm, which can automatically detect data issues, assess their impact, and notify those who can take action so you can respond quickly. It also learns from previous examples of what went wrong and uses that information to predict when it will happen again in future data sets, allowing you to always stay one step ahead of your data challenges and solve issues before they happen.
With Monte Carlo, you gain complete control over your data. The platform generates insights to help you understand what data matters most to your business, where you can cut costs, and how data quality has improved over time. It also provides automated insights to help your data team make better decisions when changing fields, tables, schema, and more. Getting started is easy too. The platform connects to your existing data stack in minutes, monitoring and alerting you to freshness, volume, and schema changes out of the box.
When it comes to data quality monitoring and data observability, Anonmalo has you covered. The company’s powerful suite of observability tools means that your data teams and stakeholders can detect, alter and resolve issues rapidly so that everyone can feel confident in the data driving your business. With Anomalo, your team can generate a no-code check that calculates a metric, plots it over time, generates a time series model, sends intuitive alerts to tools like Slack, and returns a root cause analysis – all within just six clicks. The platform automatically detects data issues as soon as they appear so that you don’t have to, ensuring that no one else is impacted.
Anomaly is the only data quality provider that provides both foundational observability (automated checks for data freshness, volume, and schema changes) and deep data quality monitoring (automated checks for data consistency and correctness). It's incredibly powerful unsupervised ML-powered intelligent alerting automatically also readjusts time series models, using secondary checks to out false positives. Anomalo also automatically generates a root cause analysis that saves users time determining why an anomaly is occurring. Its triage feature orchestrates a resolution workflow and can seamlessly integrate with remediation processes like ticketing systems.
While it may be one of the newer data observability tools on this list, don’t let Sifflet’s youth in the market fool you. Founded in 2021, the platform has quickly become one the best data observability platforms available today, acting as an all-seeing layer to the Data Stack to make sure data is reliable from ingestion to consumption. Whether the data is in transit or at rest, the Sifflet platform can detect data quality anomalies, assess business impact, identify the root cause, and instantly alert data teams of these issues as soon as it find them. This is thanks to its over 50 quality checks, extensive column-level lineage, and over 30 connectors across the Data Stack, which together make sure no anomaly can slip through the cracks.
Data discovery is also made easy through Sifflet’s extensive data catalogue and powerful metadata search engine. The platform automatically covers thousands of tables with ML-based anomaly detection and over 50 custom metrics so you can monitor your data assets, metadata and infrastructure all in one place. It also provides centralised documentation for all data assets through an exhaustive mapping of all dependencies between assets – ingestion to BI – to allow your data analysts, data scientists, and data consumers to navigate through your data easily and better understand it.
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