As data becomes an increasingly important asset for organizations, ensuring its quality and reliability is critical. Data observability is a practice that helps organizations maintain visibility into their data pipelines and quickly identify and resolve any issues that arise.
Here are the top takeaways for data observability:
- Data observability is the ability to see what's happening inside your organization's data, predict how it will change over time, and make better decisions using that information.
- There are several data observability platforms available, each with its own unique features and strengths.
- Some of the most notable data observability platforms include Integrate.io, Monte Carlo, Bigeye, Acceldata, Databand, and Datafold.
- Features that make these platforms stand out include real-time alerts, machine learning algorithms to detect anomalies, automated reporting, intuitive interfaces, and powerful API integration capabilities.
- By implementing a data observability framework and using one of these platforms, organizations can gain valuable insights into their data, make better decisions, and reduce risks.
In this article, we will explore the top 7 data observability tools that are poised to be popular choices in 2024.
Table of Contents
- The Top 7 Data Observability Tools
- What You Should Know Before Choosing a Data Observability Tool
- Discover How Integrate.io Makes Data Observability Simple
As data becomes more central to our lives, it becomes more important to understand what it means and how it works. Data observability is the ability to see what's happening inside your organization. It's about knowing what your data looks like, where it comes from, how it’s collected—and how you can take advantage of that information to make better decisions.
Data observability is about more than just understanding what your data looks like right now. It's about predicting how your data will change over time and how other factors might impact its growth or decline. Data observability tools can help you make sense of your data to make better decisions and take fewer risks.
However, tools alone can not do much if you don’t follow a data observability framework, so make sure your organization is prepared.
In this article, we'll look at seven observability platforms that will help you get better insights into your data. As you look forward into 2024, you can use these insights so you can determine which data observability platform is right for you.
The Top 7 Data Observability Tools
Integrate.io's Data Observability platform provides comprehensive visibility into the health of your systems and data. With its intuitive email alert system, your team will be instantly notified when a metric goes out of range, allowing you to quickly act and resolve the issue. This is particularly beneficial for DataOps teams, as they can quickly detect any upstream data issues and notify the responsible team to investigate and fix the problem.
You can get started with Integrate.io's Data Observability platform in no time. It's the only tool on the market that offers 3 alerts for free, making it the most cost-effective way to get started with data observability.
What makes Integrate.io stand out?
Integrate.io offers powerful data analytics and transfer capabilities to help you quickly and efficiently move, query, and analyze data from any source.
Some of its features include:
- Cost-effective for startups - Set up to 3 alerts completely free when you sign up;
- Multiple alert types - Set up to 9 different types of alerts, including Nulls, Cardinality, Median, Variance, Skewness and Freshness;
- Real-time alerts - Receive notifications in real-time when any data issues arise so you can manage and resolve them with ease;
- Analytics and reporting - Identify trends and recurring issues in your data sets to resolve possible problems before they arise.
Monte Carlo works by leveraging machine learning to analyze data sets. The machine learning algorithm can detect anomalies, which means it can predict problems before they occur.
What makes Monte Carlo stand out?
Monte Carlos algorithm learns from previous examples of what went wrong and then uses that information to predict when it will happen again in future data sets. Here are some other features that make Monte Carlo a great data observation platform:
- Real-time monitoring - allows businesses to track their data in real-time and identify potential issues as they arise;
- Advanced analytics capabilities - uncover hidden trends and patterns in a company’s data, and help make more informed decisions;
- Data visualization - intuitively explore and understand data.
Bigeye’s powerful analytics and data visualization capabilities provide teams with the insights they need to make data-driven decisions. It helps measure, improve, and communicate data quality quickly and clearly.
What makes Bigeye stand out?
Bigeye’s easy-to-use interface allows you to configure data and ensure accuracy and consistency, while its advanced features can help you spot potential data issues before they become costly problems:
- Automated reporting - easily share data insights with different departments or stakeholders;
- Versatile dashboard - allows multiple people to track and monitor data quality metrics in real-time;
- Sophisticated algorithms - Bigeye helps you identify potential data issues early on;
- Intuitive interface - enables data teams to explore their data in depth, uncovering deeper insights that may have been previously overlooked;
- Powerful API integration capabilities - easily connect data from multiple sources.
Acceldata's data observability cloud is a revolutionary data observation platform that allows businesses to quickly monitor, analyze, and manage their data. With the platform, data teams can gain real-time insights and promptly identify and address any issues.
What makes Acceldata stand out?
Acceldata’s intuitive user interface makes it easy to identify and monitor data trends, while its fully-automated reliability checks help organizations uncover erroneous data on thousands of tables.
Data teams can eliminate complexity and streamline their data operations using Acceldata’s powerful features:
- Drag-and-drop interface - Analyze data pipelines across multiple layers and platforms with drag-and-drop or coding capabilities;
- Fully-automated reliability checks - Quickly identify any missing, delayed, or incorrect data;
- Reusable SQL and UDFs - Segment the data to analyze reliability across dimensions in five programming languages;
Databand is the perfect platform for businesses looking to ensure the accuracy of their data. The platform's data observability tools can detect any data issues quickly and easily, eliminating surprises and ensuring that everything runs smoothly.
What makes Databand stand out?
Databand can save you resources as it identifies bad data before it has a chance to impact your business. It manages to be highly proactive with its set of features:
- Cross-stack visibility - gain an overview of all your data tasks from start to finish;
- Alert system - prioritize data incidents efficiently by knowing which alert is causing the most corruption;
- Standardize DataOps - end-to-end data lineage to ensure data accuracy and reliability.
Datafold is a data observation platform that detects and fixes data quality issues before they become a problem. You can integrate Datafold with other data management tools, which makes it easy to move data between systems and ensure consistent data quality across your data ecosystem.
What makes Datafold stand out?
The platform provides detailed analysis of data quality issues, allowing you to quickly pinpoint the source of a problem and fix it quickly through an array of features:
- Column-level lineage - See the impact of any code change on downstream datasets and BI dashboards;
- 1-click regression testing - Automate regression testing with integration into the CI process through GitHub and GitLab;
- Custom alerts - Turns SQL queries into smart alerts so you can stay on top of any problem that may occur.
Soda is a data observation platform that helps organizations monitor, check, and align data expectations.
What makes Soda stand out?
The platform allows you to identify and troubleshoot data issues in real-time without having to wait for data engineers to investigate and fix them manually. Here are some of the essential features:
- Data As Code - enables teams to check and manage data quality across all data sources in plain English;
- Anomaly detection - automatically monitor and manage the health of your data;
- Incident resolution - helps you break down data silos and resolve data issues quickly and efficiently.
What You Should Know Before Choosing a Data Observability Tool
Data observability is critical for any data-driven business. It's crucial for companies to have the right tools to monitor their data and ensure it is being used efficiently.
Here are some of the most frequently asked questions about data observability tools so that you can make an informed decision about which one is best suited for your business:
What are common signs that you need a data observability platform?
You need a data observability platform when your business has reached a point where you have more data than you can manage.
Here are some signs that you should probably invest in a data observability platform:
- You're no longer able to keep up with your data, and it's starting to affect your bottom line;
- You need more insight into how your business is doing, and you want to be able to take action based on what you learn;
- You have multiple tools for storing and analyzing your data, and it's hard to keep track of what's happening with all of them;
- Your team needs help to retrieve or interpret the data they need from their systems and communicate actionable insights to stakeholders.
What is the difference between data observability and data monitoring?
Data observability refers to the ability to access and understand the internal state of a system. This includes being able to view and analyze data that is generated within the system, as well as the ability to trace the flow of data through the system. Data observability is important for understanding how a system is functioning and for identifying and troubleshooting issues.
Data monitoring, on the other hand, refers to the ongoing process of collecting and analyzing data in order to understand the performance of a system and identify any potential problems. Data monitoring involves setting up monitoring tools and systems to continuously collect data from various sources within the system, and using that data to create reports or alerts that help identify issues or anomalies.
What other tools are important for data-driven decision-making?
There are plenty of tools that can help you make data-driven decisions.
You'll want to start with ETL tools, which extract data from a source and load it into your database or data warehouse.
If you're looking to cleanse your data – meaning, you want to make sure that extracted data is clean and up to date – you'll also want to consider using data cleansing tools.
Moreover, data pipeline tools help you move your data between systems and databases, while data warehousing tools are a critical part of data-driven decision-making. They provide the necessary framework for collecting, analyzing, and storing data and allow you to have access to this information whenever you need it.
Finally, if your company needs secure storage for sensitive information and enhanced cloud security, you should also invest in data security tools.
Discover How Integrate.io Makes Data Observability Simple
Integrate.io allows you to keep track of all the data you need to make better business decisions, no matter where it's stored. You can connect to any data source—even if it's not in a database—and pull that data into one place so you can do your analysis and make critical decisions faster.
You'll be able to identify problems with your data, check for accuracy, format it correctly, and make sure it complies with your company's data governance guidelines.
Schedule an introductory call with one of our experts, or contact us for a free demo to see how we can improve your data ecosystem.