Apache Spark at Shivacha
Distributed processing for large-scale data engineering and ML.
Overview
Spark processes large datasets in parallel for batch and streaming analytics and ML feature engineering. We use it for heavy data transformation workloads.
Why we use it
- Distributed processing
- Batch and streaming
- ML libraries
- Scales to large data
How we use it
Apache Spark in our engineering work
Feature engineering
ML datasets.
Data transformation
Large ETL jobs.
Historical processing
Blockchain backfills.
Services
Services that use Apache Spark
AI Data Solutions
Data engineering for AI: pipelines, warehouses, feature stores, vector indexes and governance that make AI systems accurate.
Learn moreMachine Learning Development
Custom machine learning models for prediction, scoring, forecasting and recommendation, deployed and monitored with MLOps.
Learn moreNLP Development
Natural language processing for classification, entity extraction, sentiment, search and multilingual text understanding.
Learn moreComputer Vision Development
Computer vision systems for inspection, detection, recognition and visual analytics — from cloud APIs to edge devices.
Learn morePairs well with
What we combine with Apache Spark
Pipelines, streaming, warehousing and real-time analytics.
Build with Apache Spark.
Tell us about your project, or the engineers you need, and we will propose an approach.