Why data engineers need a single pane of glass for data observability

Data engineers manage data from multiple sources and throughout pipelines. But what happens when a data deficiency occurs? Data observability provides engineers with the information and recommendations needed to fix data issues, without them having to comb through huge piles of data. Read on about what is observability and the best way to implement it.

If you’re interested in learning more, you can listen to the podcast this blog post is based on below or here.

In today’s world, we have the capacity and ability to track almost any piece of data. But attempting to find relevant information in such huge volumes of data is not always so easy to do. Data observability is the techniques and methodologies that bring the right and relevant level of data information to data engineers at the right time, so they can understand problems and solve them.

Data observability provides data engineers with metrics and recommendations that help them understand how the system is operating. Through observability, data engineers can better set up systems and pipelines, observe the data as it flows through the pipeline, and investigate how it affects data that is already in their warehouse. In other words, data observability makes it easier for engineers to access their data and act upon any issues that occur.

With data observability, data engineers can answer questions like:

• Are my pipelines running with the correct data?
• What happens to the data as it flows through the pipelines?
• What does my data look like once it’s in the warehouse, data lake, or lakehouse?

Achieving observability is never easy, but ingesting data from multiple sources makes it even harder. Enterprises often work with hundreds of sources, and even nimble startups rely on a considerable number of data sources for their products. Yet, today’s data engineering teams aren’t equipped with tools and resources to manage all that complexity.

As a result, engineers are finding it difficult to ensure the reliability and quality of the data that is coming in and flowing through the pipelines. Schema changes, missing data, null records, failed pipelines, and more – all impact how the business can use data. If engineers can’t identify and fix data deficiencies before they make a business impact, the business can’t rely on it.

The data ecosystem is fairly new and it is constantly changing. New open source and commercial solutions emerge all the time. As a result, the modern data stack is made up of multiple point solutions for data engineers. These include tools for ETLs, operational analytics, data warehouses, dbt, extraction and loading tools, and more. This fragmentation makes it hard for organizations to manage and monitor their data pipeline.

A recent customer in the Cryptocurrency Industry said this:

“We spend a lot of time fixing operational issues due to fragmentation of our data stack.“

Tracking data quality, lineage, and schema changes becomes a nightmare.”

The one solution missing from this stack is a single overarching operating system for orchestrating, integrating and monitoring the stack, i.e – a single tool for data observability. A single pane of glass for observability could enable engineers to look at various sources of data in a single place and see what has changed. They could identify changed schemas or faulty columns. Then, they could build automated checks to ensure errors wouldn’t recur.

For engineers, this is a huge time saver. For organizations, this means they can use their data for making decisions.

As we see the data ecosystem flow from fragmentation to consolidation, here are a few features a data observability system should provide data engineers with:

• Visualization – enabling data engineers to see data reads, writes and lineage throughout the pipeline and the impact  of new data on warehouse data.
• Supporting all data sources – showing data from all sources, and showing it as early as ingestion
• Supporting all environments – observing all environments, pre-prod and prod, existing and new
• Alerts – notifying data engineers about any anomalies, missed data deliveries, irregular volumes, pipeline failures or schema changes and providing recommendations for fixing issues
• Continuous testing – running through data source, tables and pipelines multiple times a day, and even in real-time if your business case requires it (like in healthcare or gaming)

Databand provides unified visibility for data engineers across all data sources. Learn more here.