Apache Spark Engineering
Distributed data processing at petabyte scale. We build Spark clusters for batch ETL, streaming ingestion, ML feature engineering, and lakehouse architecture on Delta Lake — with query optimization, memory tuning, and cost-controlled Databricks deployments.
What happens after you submit specs
1. Context
We inspect the system, constraints, and where delivery or architecture risk is most likely to surface.
2. Recommendation
You get a direct recommendation: audit, advisory track, scoped build, or a clear signal that the work is not ready yet.
3. Next Step
If there is a fit, we define the shortest path to a useful engagement and a production-ready outcome.
Large-Scale Data Processing Infrastructure
We architect and optimize Apache Spark clusters that process terabytes of raw data into production-grade datasets — from batch ETL and streaming ingestion to ML feature stores and lakehouse pipelines.
What We Build
| Capability | What We Deliver |
|---|---|
| Batch and streaming ETL | PySpark pipelines for structured and semi-structured data ingestion from S3, HDFS, Kafka, and JDBC sources with exactly-once write guarantees |
| Lakehouse architecture | Delta Lake tables with ACID transactions, time travel, schema enforcement, and Z-ORDER optimization for analytical workloads |
| ML feature engineering | Spark ML and Spark SQL pipelines that compute features at scale, feed feature stores, and integrate with MLflow experiment tracking |
| Query performance tuning | partition pruning, broadcast joins, AQE configuration, and shuffle optimization that cut job runtimes by 40-70% |
| Cost-controlled Databricks | cluster policies, spot instance strategies, and job scheduling that reduce compute spend without sacrificing SLAs |
Engineering Standards
- Delta Lake medallion architecture (bronze/silver/gold) with schema evolution and data quality checks
- Structured Streaming with watermarks for late-arriving data and stateful aggregations
- Memory and shuffle tuning: executor sizing, off-heap configuration, spill thresholds
- Data lineage tracking through Unity Catalog and custom metadata tagging
- CI/CD for Spark jobs: parameterized notebooks, Databricks Asset Bundles, automated integration tests
- Monitoring: Spark UI metrics, Ganglia, and custom Prometheus exporters for job health
When to Use This
| If Your Situation Is | Then We Recommend |
|---|---|
| Batch ETL at terabyte+ scale, complex transformations, ML feature engineering | Apache Spark / Databricks — this page |
| Sub-second latency streaming with stateful processing | Apache Flink — true streaming, not micro-batch |
| Event streaming, message queues, real-time ingestion | Apache Kafka — transport layer, not processing |
| Cloud data warehouse for BI and analytics | Snowflake — SQL analytics, not Spark jobs |
| Lightweight ETL without distributed compute overhead | Python + dbt — Spark is over-engineering |
Depth of Practice
We maintain published articles on PySpark internals, Delta Lake patterns, Spark performance tuning, and Databricks operations on the ActiveWizards blog. Our engineers operate Spark clusters processing multi-terabyte workloads across financial analytics, healthcare data platforms, and e-commerce recommendation systems.
Related articles
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80% of production ML failures trace to features, not models. Here's which feature types break first and how to detect and prevent drift before it reaches users.
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A technical guide to selecting NoSQL databases for production AI: MongoDB, Cassandra, Neo4j, Redis, and when PostgreSQL extensions replace a dedicated store.
MLOpsML Pipeline Orchestration: Airflow, Kubeflow, and Temporal Compared for Production Model Training
A direct comparison of Airflow, Kubeflow Pipelines, and Temporal for ML training pipelines — covering GPU scheduling, retry semantics, and operational fit.
Discuss your Apache Spark Engineering path
Submit system context, constraints, and delivery pressure. A Principal Engineer reviews every submission and recommends the right next step.
1. Context
We review the system, constraints, and where risk is most likely to surface.
2. Recommendation
You get a direct recommendation: audit, advisory, sprint, or pause.
3. Next Step
If there is a fit, we define the shortest useful engagement.
No SDRs. A Principal Engineer reviews every submission.