Apache Spark Consulting & Support for Enterprises

Our Spark consulting covers cluster and job architecture, performance tuning for large-scale ETL, migration from Hadoop MapReduce or legacy ETL tools, Structured Streaming pipeline design, and CI/CD strategy. Every engagement is staffed by a named senior engineer with production Spark experience at petabyte scale.

Yes — performance troubleshooting is one of our most common Spark engagements. We start with the Spark UI's stage and shuffle metrics to find skew, spill, and memory pressure, then tune partition strategy, joins, and executor configuration until the job runs reliably at scale.

Yes. We convert MapReduce jobs to Spark's DataFrame and Dataset APIs, re-platform legacy ETL tool logic to PySpark or Scala, and run parallel validation to confirm output parity before the legacy pipeline is retired.

We work across all three and help you choose based on your existing infrastructure, multi-tenancy requirements, and operational maturity. Most enterprises we work with are migrating from YARN to Kubernetes for better resource isolation and autoscaling.

Yes. We design real-time pipelines with Structured Streaming, including checkpoint strategy, watermark configuration for late data, and backpressure handling — plus the CI/CD and testing setup to keep streaming jobs reliable in production.

We start with the Spark UI's failed stage details and executor logs to isolate whether the failure is a code, data skew, or resource issue, then move to remediation and a documented root cause. Named engineers stay engaged until the pipeline is fully recovered.