Workload sizing
Identify bottlenecks using data volume, task duration, concurrency and partition distribution before selecting a processing engine.
Big Data Services
Big Data Services
Process datasets whose size, arrival rate or computational demands exceed the practical limits of the current platform.
Inside the delivery
Prove the workload before distributing the processing
Measure data volume, arrival rate, skew and the transformations that exceed current limits. The flow below shows the main delivery stages and the evidence produced at each step.
- 01
Workload profile
Measure data volume, arrival rate, skew and the transformations that exceed current limits.
Output
Workload sizing and bottleneck assessment
- 02
Partition strategy
Choose storage layout and compute partitions around access patterns and uneven key distribution.
Output
Partitioning and execution design
- 03
Distributed processing
Implement jobs with checkpointing, controlled parallelism and recoverable intermediate outputs.
Output
Distributed job implementation
- 04
Scale and recovery tests
Observe representative runs, worker failures and cost as data size or concurrency changes.
Output
Benchmark evidence and recovery procedure
Controls across the workflow
- Skew monitoring
- Checkpoint policy
- Data lineage
- Compute budgets
Decisions that shape the scope
- When is a distributed platform unnecessary?
- If an indexed database or conventional warehouse meets volume, latency and cost needs, use it. Distributed processing adds coordination, debugging and operational overhead that must be justified by measurement.
- What needs to be available before delivery?
- Representative datasets, growth estimates, current job traces, freshness requirements and acceptable compute costs.
Before you commit
Is this the right engagement?
- What we need from you
- Sample data, growth estimates, workload traces, processing deadlines and current compute and storage costs.
- How you accept the work
- Benchmark a representative workload for throughput, completion time, recovery and cost, including skewed partitions and reprocessing.
- Scope & alternatives
- If an indexed database or conventional warehouse meets volume, latency and cost needs, use it. Distributed processing adds coordination, debugging and operational overhead that must be justified by measurement.
Capabilities
Engineering scope and deliverables
Select the work that addresses your constraint. Responsibilities and acceptance criteria are agreed before delivery.
Distributed processing design
Implement partitioning, file layout and job boundaries that support the required workload and reprocessing behavior.
Scale and recovery evidence
Test worker failures, data skew and replay, then document performance and operating cost under the tested conditions.
Integrations
Selected for your environment
Tools are chosen around your existing systems, access requirements and operating constraints.
Microsoft Azure
AWS
Databricks
Snowflake
dbt
Power BI and Tableau
Frequently asked questions
Questions to resolve before starting
01When is a distributed platform unnecessary?
When is a distributed platform unnecessary?
If an indexed database or conventional warehouse meets volume, latency and cost needs, use it. Distributed processing adds coordination, debugging and operational overhead that must be justified by measurement.
02What should we prepare for the first technical discussion?
What should we prepare for the first technical discussion?
Representative datasets, growth estimates, current job traces, freshness requirements and acceptable compute costs.
03What evidence is available at handover?
What evidence is available at handover?
The agreed delivery includes benchmark evidence and recovery procedure. Observe representative runs, worker failures and cost as data size or concurrency changes.
04How is the engagement estimated?
How is the engagement estimated?
We review the available inputs before estimating: Representative datasets, growth estimates, current job traces, freshness requirements and acceptable compute costs. The proposal identifies dependencies, review milestones and excluded work; the scope determines the schedule.
Start a conversation
Discuss your next technical step
Share your current situation and the constraint you need to resolve. We will use the discovery inputs above to define a practical scope for Big Data Services.