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 you get back
- 1. Diagnosis What works, what is blocked, and why.
- 2. Recommendation Audit, advisory, sprint, or pause.
- 3. Scope Next action, boundaries, and timing.
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 reduce waste in long-running jobs |
| 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 platforms for teams that need distributed processing, lakehouse discipline, and job behavior they can debug under production load.
Related articles
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