Distributed Processing Pipelines
Spark and similar frameworks for workloads that exceed single-machine limits.
SKAD IT Solutions has merged with Hexagon IT Solutions.
Big Data
Processing at volumes where normal tools stop working.
SKAD IT Solutions builds distributed processing and storage systems for datasets too large or too fast for conventional databases.
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Clear ownership
Decision rights mapped early
Reviewable delivery
Visible work and release cadence
Maintainable handoff
Documentation built into delivery
Organizations SKAD Has Worked With
What We Do
Streaming Data Systems
Real-time ingestion and processing with Kafka and stream processors.
Data Lake Architecture
Storage layouts, partitioning and file formats designed for query cost and speed.
Query Performance Engineering
Partitioning, clustering and format choices that cut query time and spend.
Cost Optimisation
Storage tiering and compute sizing against actual usage patterns.
Migration from Legacy Platforms
Moving off ageing Hadoop or on-premise systems onto current architecture.
Why Teams Choose Us
Cost is a design constraint
At this scale an inefficient query is a line item on the monthly bill.
Right-sized
Not every large dataset needs distributed processing, and we will tell you when it does not.
Operable
Monitoring, alerting and backfill procedures included, because these systems fail in interesting ways.
Tools and Technologies
01
Processing
Spark
Flink
Kafka02
Storage and formats
S3
Delta Lake
Iceberg
Parquet03
Query
Trino
Athena
BigQuery04
Orchestration
Airflow
DagsterFrequently Asked Questions
Roughly the point where a single database instance stops being viable, which for most workloads is somewhere in the terabyte range.
Often both. The lake holds raw and semi-structured data, the warehouse serves analytics.
Usually yes. Partitioning and file format issues account for most avoidable spend.
Batch unless a business decision genuinely requires fresher data.
Yes, and it is one of the more common requests in this area.