Spark

For biotech manufacturing analytics, we built Spark pipelines on Databricks that process over 1 billion sensor readings per day from 60+ IoT machines. The pipeline handles event stream processing, historical pattern analysis, and ML feature engineering at a scale that no single-machine tool can match. The data arrives via AWS Kinesis at 5ms intervals and flows through Spark into Grafana dashboards for real-time monitoring.

Spark is also the standard for ML feature engineering when datasets are large. Building features across billions of rows of user behavior data, sensor readings, or transaction histories requires the distributed compute that Spark provides. The integration with MLflow for experiment tracking means you can build features, train models, and track results in a single pipeline.

Structured Streaming is Spark's answer to near-real-time processing. It processes micro-batches with the same Spark API used for batch work, which means your team does not need to learn a separate streaming framework. For use cases where data freshness measured in minutes (not milliseconds) is sufficient, Structured Streaming avoids the complexity of dedicated streaming platforms like Kafka Streams or Flink.

Spark delivers the most value when the data volume or processing logic genuinely calls for distributed compute: terabyte-scale tables, transformations beyond what SQL can express, and processing across billions of rows. Its distributed systems model (shuffle operations, executor memory tuning, partition strategy) is what makes that scale possible, and we help teams put it to work where that scale is the requirement.

Getting the most from Spark also means tuning the cluster well. On managed platforms like Databricks or EMR, Spark clusters reward deliberate configuration: executor counts, memory allocation, partition tuning. We bring that expertise so your clusters run efficiently from day one and scale cleanly as datasets grow, and we help you match the engine to the workload so Spark is deployed where it earns its place.

Spark's position in AI workflows is strengthening. Feature engineering at scale, training data preparation, and batch inference on large datasets are all natural Spark workloads. As organizations build ML pipelines that need to process the full history of their data (not just recent records), Spark on Databricks with MLflow provides an integrated path from raw data to trained model to production predictions.

For teams building RAG systems or fine-tuning LLMs on proprietary data, Spark handles the data preparation stage (cleaning, chunking, embedding generation at scale) efficiently. The ML pipeline then consumes the prepared data for training or indexing.