When data volume, velocity, or variety exceeds what traditional tools can handle, big data engineering takes over. We design distributed processing architectures on Apache Spark, Databricks, and cloud-native platforms that process billions of events reliably — and cost-efficiently.
Discuss Your ProjectApache Spark workloads that process billions of rows in minutes, not hours.
Elastic compute that scales to match your peak load — and scales back down.
Databricks spot cluster strategies and optimized Spark configs that cut compute costs by up to 60%.
Volume, velocity, and variety assessment to size the architecture correctly.
Data lake zones, processing layers, and cluster topology design.
Spark job development, Delta Lake tables, and streaming pipelines.
Performance profiling, cost governance, and SLA monitoring.
Architectural BIM, scan-to-BIM, 3D visualisation, and automation — all under one roof.
Common questions about our Big Data service.
When your data volume exceeds what a single database can query within acceptable time — typically hundreds of millions to billions of rows — or when you need sub-minute latency on high-velocity streaming data. Below that threshold, a well-indexed cloud warehouse is usually sufficient and much cheaper.
Databricks for teams that want managed infrastructure, Delta Lake ACID transactions, and collaborative notebooks with minimal ops overhead. EMR for cost-optimised batch jobs where your team has Spark operational experience and wants fine-grained cost control.
Delta Lake adds ACID transactions, schema enforcement, time-travel queries, and MERGE operations to a standard data lake on S3 or GCS. It turns an unreliable data swamp into a reliable, version-controlled data asset.
We profile jobs with the Spark UI to identify shuffles, skewed partitions, and serialisation bottlenecks. Common fixes include broadcast joins for small tables, repartitioning strategies, caching of reused DataFrames, and upgrading to adaptive query execution.
We use spot instance policies for non-critical jobs, job cluster termination on completion, autoscaling configured to realistic bounds, and Databricks Unity Catalog cost attribution. Most unoptimised environments can be reduced by 40–60%.
Yes — we run structured migrations from Hadoop/HDFS to S3 or GCS with Spark job re-platforming. We validate output equivalence before decommissioning any on-premise infrastructure.
Our team will scope your requirements and come back with a clear proposal within 48 hours.