Monitoring and observability are crucial in developing trustworthy, reliable data pipelines that effectively fuel dashboards, reports, and analytics. Despite its significance, traditional monitoring methods fail to appropriately address the nuances of modern data. Purely reactive alerts, paired with fragmented oversight, miss the observability mark for many enterprises.
Examining the next evolution of data observability, experts from Astronomer joined DBTA’s webinar, Best Practices for Data Observability and Reliable Apache Airflow Pipelines, armed with valuable insights and strategies for adapting data observability to the needs of modern data and business.
At its core, data’s growing strategic importance emphasizes the significance of high-functioning data teams. This rings even more true with the rise of GenAI, as the proliferation of new models reasserts the necessity of high-quality data to power these advanced systems, explained Naveen Sukumar, head of PMM and DevRel, Astronomer.
Yet several challenges impede data’s success: From siloed, fragmented environments to limited skills availability, non-differentiated toil and expense, and limited oversight on data quality and governance, data teams have no short of obstacles that inherently slows time-to-market, increases cost and risk, and inhibits AI/MLOps innovation.
DataOps, according to Sukumar, is the key toward overcoming present data challenges. Unifying data orchestration and observability, DataOps enables:
- Increased development agility and collaboration
- Improved speed, scale, and predictability of data delivery
- Enhanced data quality, trust, and governance with cost transparency
The value of DataOps is clear; as Gartner predicts, “By 2026, a data engineering team guided by DataOps practices and tools will be 10 times more productive than teams that do not use DataOps.”
Focusing on the first part of DataOps, data orchestration enables the movement of data from one system to another in an automated, often scheduled fashion. This ultimately helps unify complex data estates, enable cross-team collaboration, simplify tech stacks, and drive governance through visibility.
“Orchestration is how we’re making this fragmented data stack work for you by unifying your estate and making everything integrated and cohesive,” said Sukumar.
The second half of DataOps, data observability, has evolved significantly, according to Sukumar. Data observability—or the ability to understand the health of your data and its state across your data ecosystem, at any point in time—is crucial in driving data quality, trust, and reliability. Its evolution expands beyond simply monitoring—which captures the “known unknowns”—and captures the “unknown unknowns,” as Sukumar put it. It has become the deep introspection of the data pipeline to fuel proactive resolution, which, when paired with data orchestration, is a critical component of supporting data-driven applications and decision making.
Examining some data observability best practices, Sukumar pointed to the following as ways to transition from reactive firefighting to proactive management:
- Establish clear ownership: Clear ownership ensures accountability and high impact.
- Implementing proactive alerting with context: Proactive alerting prevents SLA breaches and minimizes impact.
- Documenting ownership in a centralized location: Centralized documentation reduces confusion but lacks urgency.
- Defining meaningful SLAs aligned with business needs: Business-aligned SLAs enhance relevance.
Sukumar further added that establishing comprehensive visibility with a system of continuous improvement is a formidable approach to data observability. This includes monitoring the entire data supply chain; organization-wide visibility through dashboards; incorporating observability into development workflows; monitoring resource consumption and execution patterns; and more.
Astronomer, the company helping shape and streamline open source Apache Airflow, delivers value-added solutions that take Airflow to the next level. Astro Observe, Astronomer’s latest product, delivers a single pane of glass to govern and optimize the data product and pipeline lifecycle with full lineage, alerting, and proactive recommendations, Sukumar explained. It offers capabilities such as:
- Pipeline-level visibility
- Data product SLAs
- Asset catalog for easy discoverability
- Proactive optimizations of pipelines
- Data health dashboard
- Timeline view of task-level performance
Jake Roach, field data engineer, Astronomer, then led webinar viewers through a demo of the Astro Observe solution.
This is only a snippet of the full Best Practices for Data Observability and Reliable Apache Airflow Pipelines webinar. For the full webinar, featuring more detailed explanations, live polls, a Q&A, and more, you can view an archived version of the webinar here.