Event pipelines
Collect and process high-volume events with replay and deduplication.
Big Data Consulting & Development
Plan and build distributed data processing for large event streams, historical datasets and analytics workloads that exceed a single-system workflow.
OrdersEventsExports
Partition A
Partition B
Late arrivals
Curated analytics tables
Schema checksDeduplicationLineage
Tools & technologies
- Spark
- Kafka
- Object storage
- SQL warehouse
Workloads that need distributed processing.
Data platforms
Organize lake or warehouse storage around ownership, access and query patterns.
Analytics modernization
Replace slow batch jobs with partitioned processing and measurable data freshness.
Pipelines, contracts and operating evidence.
- Data-platform architecture
- Ingestion and transformation jobs
- Quality and reconciliation checks
- Capacity and operating-cost model
- Data-platform architecture
- Ingestion and transformation jobs
- Quality and reconciliation checks
- Capacity and operating-cost model
Reconcile late order events
Capability Example
Order events arrive out of sequence, with some duplicates from retries.
Illustrative data. Nothing is sent to an external system.
Example workspace1 / 3
Incoming events
1order_id: 104
2event_time: 09:10
3received_at: 09:18
Keep immutable source events with event time and ingestion time.
Partition job
1partition: business_date
2deduplicate: event_id
3late events: separate window
Deduplicate by event ID and recompute affected partitions.
Published aggregate
1daily_orders: reconciled
2late adjustment: recorded
3source partition: traceable
Compare aggregate totals with the transactional source.
A refreshed daily order table with traceable corrections and a recorded freshness timestamp.
Follow the record through the next step
Volume is only one sizing decision.
Volume and velocity
Size the design using actual event rates, retention and query demand.
Ownership
Assign data owners and access rules before combining operational sources.
Questions before we start.
Do we need distributed processing?
Not automatically. Consulting starts by measuring the bottleneck; a database or scheduled job may be sufficient.
Can you migrate an existing Hadoop or warehouse workload?
Yes. Inventory jobs, dependencies and reconciliation rules, then move bounded workloads with a rollback path.
Bring your starting point.
Bring source volumes, current processing times and the reports or decisions waiting on that data.
