WHY PLATFORM OBSERVABILITY MATTERS
Your platform has patterns. Read them.
As workloads shift and volumes grow, digna establishes a baseline for each one and tracks cost, runtime and volume against it. Growth becomes a trend you can explain. Everything else surfaces as a deviation.
Platforms rarely become inefficient overnight.
Platforms rarely become inefficient overnight. Databricks, Snowflake and Teradata are built to scale. Complexity comes with it, and it moves quietly.
Growing complexity
New workloads, larger volumes, more complex queries.
Change without failure
Cost drift and runtime instability, with nothing failing and no alert firing.
Shifting resources
Usage patterns evolve, and yesterday's normal stops being a reference.
Beyond point-in-time health
Monitoring tells you the platform is up. Observability tells you how it's changing.
From Metrics to Behavioural Understanding
Raw metrics tell you what happened. Behavioural analysis helps you understand what changed. A single metric value provides limited context. A query took 18 minutes. Is that a problem? It depends on what normally happens. If the same query normally takes 3 minutes, the change is significant. If it normally takes 20 minutes, it may be completely normal. digna Data Anomalies identifies unexpected deviations; digna Data Analytics shows how the underlying metric evolves over time.
Traditional Monitoring
System uptime
Job success or failure
Point-in-time utilization
Threshold alerts
Digna Observability
Behavioral change over time
Gradual cost increases
Runtime variability
Workload drift and hidden inefficiencies
MODULAR CAPABILITIES
Different platform challenges. Combine the capabilities you need.
Cost, performance, stability. Every environment weighs them differently.
Has platform behavior changed unexpectedly?
digna Data Anomalies
Detect deviations in runtime, consumption and workload behavior.
Is this a trend or a one-off?
digna Data Analytics
Track how cost, runtime and volumes evolve over time.
Is usage staying inside its limits?
digna Data Validation
Set consumption thresholds and get alerted when workloads cross them.
Did the platform data arrive?
Data Timeliness
Catch missing, delayed or off-pattern platform data.
Combine these and other digna capabilities to build the observability solution that fits your environment. Monitor what matters. Detect unexpected changes. Understand how your platform is evolving before issues become operational problems.
PLATFORM SIGNALS
What can you observe?
Monitor the signals that matter to your platform.
EXTEND DIGNA
Build and extend with the digna Python SDK.
Integrate observability into your own workflows. Work with digna programmatically across applications, pipelines and operational processes.
RESOURCES
Explore platform observability whitepapers.
Practical insights for modern data platforms. Databricks cost anomaly detection — How cost anomalies emerge, and how digna detects spikes, drift and unstable workloads.
