You’ve got the right data analytics team in place, have built a scalable data infrastructure, and have data ingestion figured out—now you need to understand data observability.
Data observability used to be nice to have in DataOps, but it is quickly becoming necessary to keep up with data-heavy demands like machine learning, real-time metrics and dashboards, and data science.
In short, data observability helps you understand the health of your data and various states of data throughout your system. It’s a combination of tools and actions that come together to give you end-to-end context on your data processes, data quality, and data sets.
In this quick guide to data observability, we’ll explore exactly what data observability is, how it benefits your business and data teams, and why it matters enough to invest in.
What is data observability?
Data observability is everything you know and control about the health of your data, its performance, and the state of data as it moves through (or sits in) your data ecosystem. It’s an umbrella term that covers a variety of data practices and tools that help you recognize, troubleshoot, and solve data issues in close to real time. It also helps you take action and identify opportunities for growth within your data capabilities, data teams, and data infrastructure.
Data observability includes data monitoring, alerts for unexpected events and anomalies, data tracking, data analysis, and more. It gives you insight into everything about your data—whether your data is at rest in data warehouses, data lakes, and databases or in motion through data pipelines.
The key to understanding data observability is to know that it provides context for your data in relation to the end-to-end data operations workflow. It’s not just a siloed bucket of DataOps information from data monitoring. Of course you need to monitor your data for errors and check your data sets for quality—but data observability goes beyond this.
Isn’t data observability just data monitoring?
No. Data monitoring is just one important aspect out of many that make up data observability. Data monitoring alerts you to a problem so you can identify and fix data quality issues before they cause disruptions or impact your bottom line. It's a critical part of data observability that everyone needs to practice, but it’s only one part of the whole.
Data observability can help you optimize your infrastructure, improve your decision-making process, and gain insights into your customers' behavior. When observability isn’t siloed by departments, business units, or teams, you can even find new business opportunities.
Data observability means going beyond basic data monitoring to make sure your data teams are working together from a dashboard that shows vital metadata that connects to business events. It also helps teams reduce data downtime—one of the biggest issues for DataOps leaders.
Taking an end-to-end approach that contextualizes data across upstream and downstream events gives your data engineers, data scientists, machine learning specialists, and other data analytics team members a chance to improve their processes.
For example, one team might be checking data outputs for business requirements (data monitoring) but the engineers who built the data pipelines might not be able to see how the data transforms along the workflow. It might take time for the business to get clear information back to the data engineers. Ideally they’d have a tool to monitor their data pipelines.
Gaps in visibility can become big problems, and yet they are just one of many kinds of issues that data observability solves for.
Common components of data observability
Data observability is all about context, and each layer of context comes from a combination of different tools, actions, or perspectives. You might be looking at data in motion from an SQL database to your business reporting dashboards, or analyzing the health of your whole static data set. Looking at your data pipelines and dataset are different activities but it’s crucial to unify them for data observability to be effective.
If one team knows everything about your data pipelines and the other knows everything about your whole company data set, but neither understands each other, you’ve got a problematic (and common) siloing effect. Getting your data analytics teams on the same page with a standardized data platform is crucial to bridging this gap.
Following is a short list of the main functions you need to create data observability.
Immediate alerts: Receive notifications whenever an issue arises, without needing to set up additional processes.
Detailed metadata logging: View the wealth of metadata to detect anomalies and changes in duration or resource usage.
Seamless investigations: Browse through data processes and pipelines to easily identify which runs were successful and which ones failed.
Performance analytics: Easily monitor and identify all the trends that you care about, like whether your data runs are getting longer or resource usage is increasing.
Real-time visibility: See the status of all your data pipelines and data processes in real time.
Service Level Agreement (SLA) tracking: Measure your pipeline metadata and data quality against standards for delivery.
It’s important to remember that you can have all of these things in place, but without an organizational adoption of data observability it won’t cohere into a valuable investment. In order to get all the benefits you need more than data monitoring tools—you need to put data into a context that delivers value across the business.
Why is data observability important?
DataOps used to focus on datasets from a few internal data sources that didn’t change much. Now, data analytics teams need to understand data from a growing number of internal and external sources that are transforming from location to location.
This kind of complexity quickly leads to changes in data schemas, unexpected drift in datasets, data transformation, and potential downtime. Implementing data observability helps a business manage these new DataOps risks so that your whole business can rely on the ever-increasing amount of data it requires to run.
All organizations operate on data, even if it's just looking at sales dashboards. In order to stay competitive, companies need access to more than just their current KPIs and reports—they need insight into everything from their current data to their data lineage so they can learn from past mistakes.
From day to day your data analytics team will benefit the most, but your whole business will get significant value from data observability. Here are some of the main business benefits you’ll experience:
- Reducing data downtime
- Keeping bad data out of warehouses and apps
- Improving business metric accuracy and data visualization
- Increasing data reliability and real-time decision making
- Accelerating product development
In order to be agile and able to iterate quickly on products, data observability is absolutely necessary. Without it data teams cannot be confident in their infrastructure or tools, because errors can't be tracked down easily. But with it, it will be easier to create new features or improvements that your customers love.
Crucial data observability metrics
Data observability gives you insights into the health and performance of data systems like data pipelines, data warehouses, databases, APIs, and connected cloud apps. It can help you identify unexpected changes in data schemas, failures in data processes, and unforeseen events that could lead to data downtime.
Following is a simple list of some metrics you’ll need in order to measure, identify, and fix issues before they cause major problems.
Execution metadata: Collect metadata on pipelines, duration, delays, retries, and times between data runs.
Dataset monitoring: Look at the availability of your dataset (how much is useful), accuracy, and volume of data ingestion and output, and check for schema changes (e.g. new product categories).
Column-level changes: Collect statistics on columns to look at Mean, Max, and Min trends in your columns, You’ll also want to set alerts on changes.
Row-level validation: Validate data at the row level to make sure there aren’t failures.
Usage Stats: Understand which tables or views are accessed the most frequently, where and when they are used, and which parts of the data are truly relevant for other processes.
What are some data observability tools?
The right data tools are a crucial part of making data observability work. Ideally, you’ll have a standardized data platform, a unified data observability platform, and a culture that takes consistent actions across teams to understand your data from an end-to-end perspective.
Here are some tools you can use to achieve data observability:
Bigeye takes care of the data monitoring component of data observability. It helps you build trust in every dataset you have by monitoring the data itself, not just your data pipelines. This gives you the full story about your data and lets you discover data issues before your users do. You can use Bigeye to add monitoring and alerting to your datasets in minutes and it easily integrates with your data stack with extensive APIs.
Monte Carlo delivers end-to-end data observability to prevent data downtime. It uses ML-enabled data anomaly detection and targeted alerting to detect data issues before they turn into lost revenue and wasted time. Monte Carlo gives you data lineage insight in a unified platform so it’s easier to address impact and fix root causes, fast. It integrates with your data tools to make sure your data analytics team knows when data breaks.
Databand is another end-to-end data observability platform built on an open source library that enables you to track data quality information, monitor pipeline health, and automate advanced DataOps processes. It comes with everything you need — from data monitoring and end-to-end lineage insights — to catch bad data before it impacts your business. Databand is unique because it delivers proactive data observability on data in motion, not just data at rest in your warehouse.
Soda gives everyone on your data engineering team the ability to find, analyze, and resolve data issues. It’s another open-source data observability platform that makes it easy to monitor data health and resolve incidents. Soda also lets you “check data as code” by using a common language to check and manage data quality across all data sources, from ingestion to consumption. It makes it easy to align expectations across teams with data quality agreements that make sure data is in the right format in the right places.
Shipyard makes sure your data team never gets caught off guard. With the platform, teams can build workflows (without relying on DevOps) that can be tracked and monitored to enhance end-to-end data observability. Always-on alerts give you insight to avoid unexpected fires and detailed visibility into workflows enables your team to identify problems before they get bigger. Shipyard helps you helps you diagnose issues in real-time so that you can isolate and address the root causes of your data problems to get back up and running in minutes.
How can I get started on data observability?
Chances are you’ve already started working toward data observability. Now you need to make sure you have the right data tools in place, your data analytics team is measuring the right metrics, and that you take an end-to-end approach to data observability.
Shipyard gives you data automation tools and integrations that either work with your existing data stack or modernize your legacy systems. These tools might fill in the missing parts of your data observability puzzle and it’s easy to find out quickly.
Sign up to demo the Shipyard app with our free Developer plan—no credit card required. Start to build data workflows in 10 minutes or less, automate them, and see if Shipyard fits your business.