Apache Spark Engineering
Distributed data processing 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 next
- 1. Context We review the situation and constraints.
- 2. Fit We recommend an appropriate next step.
- 3. Scope If relevant, we discuss scope.
Large-Scale Data Processing Infrastructure
We architect and optimize Apache Spark platforms for batch ETL, streaming ingestion, ML feature stores, and lakehouse pipelines where distributed processing is justified.
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 idempotent write patterns and recovery controls |
| 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 waste while preserving workload expectations |
Engineering Standards
| Standard | Why It Matters |
|---|---|
| Medallion architecture | Bronze, silver, and gold layers keep raw, cleaned, and serving data responsibilities separate |
| Structured Streaming controls | Watermarks and stateful aggregation design keep late-arriving data explicit |
| Memory and shuffle tuning | Executor sizing, spill behavior, and shuffle plans are inspected before jobs become expensive to operate |
| Lineage tracking | Unity Catalog and metadata tagging make ownership, source, and downstream use easier to review |
| Spark job CI/CD | Parameterized jobs, Databricks Asset Bundles, and integration tests reduce release risk |
| Job monitoring | Spark UI metrics and observability exports expose failures before downstream users find them |
When to Use This
| If Your Situation Is | Then We Recommend |
|---|---|
| Large batch ETL, complex transformations, ML feature engineering | Apache Spark / Databricks: this page |
| Low-latency streaming with stateful processing | Apache Flink: streaming-first processing, 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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Schema evolution in Kafka-to-AI pipelines: compatibility modes, the changes that break models silently, and the validation patterns that catch them before production.
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Discuss your Apache Spark Engineering path
Tell us about your system, the decision ahead, and the constraints. We will review the context and recommend the next step.
Direct contact with a principal engineer.