Transformation

Apache Spark

Distributed processing engine for large-scale data transformation and ML

https://spark.apache.org

What It Is

Apache Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Python (PySpark), Scala, Java, and R, along with an optimized engine that supports both batch and streaming workloads. Spark’s ability to handle ETL, SQL analytics, machine learning, and graph processing on a single platform has made it the de facto standard for big data processing.

How We Use It

We use Spark (primarily PySpark) for transformation workloads that exceed what a single-node database or a warehouse-native SQL engine can handle efficiently. This includes multi-terabyte batch processing, complex data lake transformations, feature engineering for ML pipelines, and scenarios where data needs to be processed across heterogeneous sources before landing in a warehouse. Spark is also central to our work with Databricks.

Our Expertise

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PySpark Development

We build production-grade PySpark applications: DataFrame API, Spark SQL, UDFs, and broadcast variables for optimized JOINs.
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Performance Tuning

We optimize Spark jobs through partition management, shuffle reduction, join strategy selection, and memory tuning.
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Data Lake Transformations

We design Spark-based ETL pipelines for S3, GCS, or ADLS: schema evolution, file format optimization (Parquet, Delta, Iceberg), and compaction.
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Structured Streaming

We build streaming pipelines for near-real-time processing: event-time windowing, watermarking, and exactly-once semantics.
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ML Integration

We use Spark's MLlib for distributed feature engineering and integrate with MLflow for experiment tracking.