Data processing at the scale your business demands
As your data grows, your ability to extract value from it must grow with it. Apache Spark provides a single engine for SQL analytics, machine learning, graph processing and stream computation, handling datasets from gigabytes to petabytes with the same programming model. Node deploys Spark as the analytical powerhouse of your automation platform.
What Spark does and why it matters
Apache Spark is a distributed computing engine designed for large-scale data processing. Originally developed at UC Berkeley's AMPLab, it replaced earlier batch processing frameworks by introducing in-memory computation that runs workloads up to 100 times faster than disk-based alternatives.
What makes Spark distinctive is its unified approach. Rather than requiring separate tools for batch processing, interactive queries, machine learning and streaming, Spark provides consistent APIs across all these workloads. Your data engineers write Spark SQL for analytics, your data scientists use MLlib for model training, and your streaming pipelines use Structured Streaming, all operating on the same cluster with the same data.
Spark is the most actively developed open source project in big data, with over 1,800 contributors. It powers analytical workloads at Apple, Netflix, NASA, CERN, Barclays and thousands of other organisations.
How we deploy Spark for business automation
We deploy Spark as the analytical and machine learning engine within your automation stack. Airflow orchestrates Spark jobs on a schedule or in response to events. Kafka feeds real-time data streams into Spark Structured Streaming for continuous processing. The outputs flow to Superset for dashboarding or back to Kafka for downstream consumption.
For AI and machine learning workloads, Spark handles the computationally intensive data preparation and feature engineering that models require. Training data that would take hours to process on a single machine completes in minutes distributed across a Spark cluster. Once models are trained, Spark can serve batch predictions at scale or feed features to real-time inference endpoints.
Key capabilities we implement
Spark SQL and DataFrames - run SQL queries and structured data operations across distributed datasets. Connect to any data source through JDBC, Parquet, Delta Lake, CSV, JSON or custom connectors. Query performance is optimised automatically through the Catalyst query planner.
MLlib for machine learning - build and deploy ML pipelines with classification, regression, clustering, collaborative filtering and dimensionality reduction algorithms. MLlib handles feature extraction, transformation and selection, with model persistence for production deployment.
Structured Streaming - process continuous data streams with exactly-once guarantees using the same DataFrame API as batch processing. Build streaming ETL pipelines, real-time dashboards and event-triggered automation without learning a separate framework.
Graph processing with GraphX - analyse relationship data, social networks, supply chain dependencies and knowledge graphs at scale. Compute PageRank, connected components, shortest paths and custom graph algorithms across billions of edges.
Delta Lake integration - bring ACID transactions, schema enforcement and time travel to your data lake. Delta Lake adds reliability guarantees to Spark workloads, making your data pipelines more robust and your data warehouse queries more consistent.
Spark in your automation stack
Spark provides the computational muscle that other tools in the stack rely on. Airflow schedules and monitors Spark jobs. Kafka streams data into Spark for processing. NiFi routes data to and from Spark clusters. Superset queries Spark-processed datasets for visualisation. Together, they form an analytics platform that handles everything from daily reports to petabyte-scale machine learning.
Trusted in production worldwide - Apache Spark processes data at a scale few other engines can match. Apple uses it for Siri data processing, Netflix powers its recommendation engine with it, and Uber runs large-scale machine learning workloads through Spark clusters. NASA analyses satellite imagery with it and CERN processes particle physics data from the Large Hadron Collider. Node deploys and operates Spark with the same reliability these organisations require.
Talk to us about data processing and analytics.
Drop us a line, and our team will discuss how Spark can power your analytical and ML workloads.