In the world of data and analytics, data observability is an important but often overlooked process.
Much of what goes on in data science today isn't observable because we built most data pipelines to move data, not monitor it. In the same vein, you can measure data but not track it.
Much of what goes on in data science today isn't observable...
Since we know what goes into data pipelines and what comes out, why is it important to know what happens in-between?
Before we answer that question, let's first define it.
Data observability describes the ability to measure the internal state of data systems based only on their outputs. In the case of distributed systems like service meshes and microservices, these outputs are essentially telemetry data (or logs, metrics, and traces).
Data observability tools help developers better understand multi-layered architectures. They allow them to quickly figure out what's broken, what's slow, and what demands improvement.
In a production system, observability also makes it easy to navigate from effect to cause.
Data observability is important because it lets you know why something is happening and how you can fix it.
Data observability has its roots in control theory and helps you understand the whole system and how it fits together. You can then use this information to figure out what you should care about and when.
On the other hand, data monitoring demands that you already know what you care about before you even know that you should care about it (if that makes sense).
For example, if you have a dependency deep in the stack creating problems with your service, observability will highlight this information; monitoring will not.
In data pipelines, observability matters because pipelines are now highly complicated with many independent and concurrent systems. Complexities can create dangerous dependencies, and you need an opportunity to avoid them. That's where data observability tools come into play.
At Panoply, we know that it takes an enormous effort to trawl through research data and identify the perfect tool for you. That's why we did the legwork and formulated this list of top 6 data observability tools.
While delivering the power of observability, Monte Carlo helps data engineers ensure reliability and avoid potentially costly data downtime.
Monte Carlo comes with several features like data catalogs, automated alerting, and observability on several criteria out of the box. There's no manual setup to get your initial results, and business data doesn't leave your network (just metadata).
However, users report issues with the UI, especially when doing things in bulk.
TL;DR: Monte Carlo is perfect for data engineers and analytics teams who want to avoid costly downtimes. However, some users report issues with the platform's UI. As the platform is barely 6 months old, it should improve with time.
Monte Carlo pricing: available upon request.
This AI-powered platform helps you discover where the data pipelines broke before any bad data manages to squeeze through. As such, Databand essentially provides data engineering teams with the utilities they require to ensure smooth operations.
Purpose-built for data engineers, Databand helps teams gain unified visibility into their data flows. This approach helps ensure that pipelines complete successfully while keeping a close tab on resource consumption and costs.
What's more, Databand plugs into cloud-native tools like Apache Airflow, Snowflake, and other machine learning tools in the modern data stack.
TL;DR: Databand helps data engineers understand why a process has failed or why it's running late with unified visibility. However, it's best suited for data engineers working on machine learning projects.
Databand pricing: starts at $500 per month, and a free trial is available.
Data observability tools like Acceldata Pulse help data engineering teams gain comprehensive, cross-sectional visibility into complex and often interconnected data systems.
It's also the go-to observability tool in the finance and payment industry.
What's cool about Acceldata Pulse is that it's great at synthesizing signals across multiple layers and workloads on a single pane of glass. This approach helps numerous teams work together to ensure reliability by predicting, identifying, and fixing data issues.
However, users report problems when customizing metrics and bringing in data from external sources.
TL;DR: Acceldata Pulse does more than performance monitoring. It helps data teams observe and ensure data reliability at scale. However, it's probably best to look elsewhere if you're working with several external monitoring tools.
Acceldata Pulse pricing: available upon request.
Oberve.ai features like speech analytics and quality management are truly game-changing innovations for the industry.
Unlike other data observation tools on this list, Observe.ai comes with automatic speech recognition, agent assistance, and natural language processing. It's not about ensuring data reliability; it's about boosting agent performance and customer support experiences.
However, users report that it's more expensive than other tools and that it falls short when it comes to reporting. For example, value charts don't display any comparisons.
TL;DR: Observe.ai is perfect for contact centers, BPOs, or any support services vertical. Unlike other data observability platforms on this list, it helps teams identify issues in delivering enhanced customer service experiences.
Observe.ai pricing: available upon request.
You can engage in data QA with data profiling and make table comparisons across databases or within a database.
With Datafold, you'll also be able to create smart alerts from any SQL query with a single click using its automated metrics monitoring module.
Data teams have the capability monitor ETL code changes with data transfers and integrate them with their CI/CD to instantly review the code.
TL;DR: Datafold is good at observing and monitoring data quality. As it's still relatively new, you might run into some issues when using this platform.
Datafold pricing: is customized to your specific needs, and a free version is available.
You can quickly check your data immediately and create rules to test and validate data; whenever a test fails, you can react programmatically.
For example, you can stop data processes immediately and quarantine data.
You can also use the Soda SQL command-line tool to scan data and view the Soda SQL results. However, as this technology is essentially brand new, don't expect extensive community support.
TL:DR: Soda is a data observability platform geared toward advanced data users. As it's a brand new platform, you might run into bugs in the system and deal with a lack of community support.
Soda pricing: available upon request, and a free trial is available.
Data observability is a reasonably new niche, but we have a growing number of data observability tools to choose from.
Although we can't compare them based on price (just yet), we can look at how they work and the industries they cater to.
At Panoply, we love all things data. That's why we consistently provide robust ETL pipelines and data warehousing solutions with unparalleled support.
Although we built Panoply for data scientists and analysts, business users can also reap the benefits.
To get a feel for what we do, get started for free or request a personalized demo.