Citedata · Catalog, profiling, query analysis
Know your unused tables
Every table tracked across databases, with stale, least-used and largest tables identified automatically as optimisation targets.
Stale tablesLeast-used tablesLargest tables
DataOps observability & cost optimisation
Your data platform grew into a swamp. That is where the savings are.
Complex ETL and years of platform growth leave thousands of tables, queries and Spark jobs running — many unused, unoptimised or refreshed far more often than anyone needs. Every one of them is an opportunity to optimise.
What you need
To know, in detail, where the cost is going.
The ETL jobs
Which Spark workflows and jobs burn the most compute, and why.
The table storage
Which tables are stale, unused or simply far larger than their value.
The slow queries
Which queries take the longest, cost the most, or run far too often.
— all mapped to the business use cases they serve.
Clarity of spend is what gives you the power to optimise it.
The challenge
Why modern data platform costs creep up.
At scale
What goes wrong
What it costs
1,000s of tables across many databases
Many unused tables; large tables holding little value
High storage and load for unused or low-value data
1,000s of queries from 100s of reports and apps
Unoptimised queries; unnecessarily frequent refreshes
Heavy, avoidable load on the database
100s of Spark ETL jobs loading and processing data
Unoptimised jobs consuming pay-per-use infrastructure
Unnecessary processing and a higher cloud bill
Our approach
Continuous, automated observation — then expert optimisation.
Our accelerators watch the platform every day and surface what to fix. Our Spark experts fix it.
Citedata · Query analysis
Know the queries to optimise
Query logs analysed to find the most costly queries and the most frequent ones, so both can be tuned or run less often.
Most costly queriesExecution frequencyTable access patterns
SparkView · Spark observability
Know the Spark jobs to optimise
Cost per Spark application, mapped to business use case, with job-level insights and optimisation recommendations.
Application insightsJob insightsJob health check
Spark experts
Optimise what is found
Expert data engineers take the costliest jobs and tune them, backed by custom dashboards and automation for ongoing observation.
Memory utilisationData skewDAG & persistCombine jobsRight-size clusters
Step by step
Biggest savings first, then keep finding more.
01 — Tables & queries
Deploy Citedata
Identify table- and query-level optimisations; cut storage and database workload.
02 — Spark jobs
Deploy SparkView
Identify workflow- and job-level optimisations; tune the costliest Spark jobs.
03 — Drill down
Custom automation
Build platform-specific dashboards and automation for continuous, custom optimisation.
Find out where your data platform spend goes. Then cut it.
We deploy our observability accelerators on your platform and show you the biggest optimisation opportunities first.