Big Data Engineering Services

You need distributed processing when single-node databases can no longer load, transform or query your data within the time you need, typically with high event volumes, long histories or real-time requirements. Many organizations do not, and a well-designed cloud warehouse is simpler and cheaper. We assess your volumes, growth and latency needs honestly and recommend the simplest architecture that meets them.

Choose based on how quickly a decision must be made. Most reporting is well served by hourly or daily batch processing, which is simpler and cheaper to run. Streaming is worth the extra complexity when value depends on reacting within seconds or minutes, such as fraud detection, live operations or personalization. Many platforms combine both, and we design them so the same logic can serve batch and streaming where possible.

Yes. We inventory HDFS data, Hive metadata, workflows and custom code, then decide per workload whether to rehost, refactor to Spark on a managed service or replace it. Data moves in phases, and both environments run in parallel while we reconcile outputs. Decommissioning happens only after results match and downstream consumers have been repointed.

We start by measuring which jobs consume the most compute, then address the usual causes: data skew, small files, unnecessary shuffles, full reprocessing of history, oversized or idle clusters and inefficient formats. Fixes include partition and file compaction, incremental processing, autoscaling, spot or preemptible capacity and job-level cost attribution. We measure every change so savings are verified, not assumed.

We design for failure. Pipelines use durable logs such as Kafka, checkpointing, idempotent writes or transactional sinks, and dead-letter queues for malformed messages. Schema registries prevent incompatible changes, and replay procedures allow reprocessing from a known offset. Monitoring tracks consumer lag and end-to-end completeness so problems surface quickly.

We apply network isolation, encryption at rest and in transit, role-based access to tables and topics, secrets management and audit logging. Sensitive fields are classified and masked or tokenized according to policy. Infrastructure is defined as code and reviewed, which keeps environments consistent and auditable across development, testing and production.