Ampool’s robust in-memory platform shortens the ingest-prepare-analyze process from several hours to a matter of seconds.
Unlike traditional disk-based database technologies that are able to accommodate large data volumes but lag in real-time data analysis, Ampool creates an adaptive memory pool of chip-based memory (both Volatile and Non-Volatile) across the entire cluster as an active data tier for concurrent, high-speed data access.
Integrate Ampool across your data and application environment to analyze transactional and historical data in real-time and enable streaming analytics, batch processing, and machine learning.
Apache Spark, Scala, Data Torrent RTS
Apache Hive, Cask Data Application Platform
Apache Spark ML Application Platform, R Data Science Environment
Java, REST API,
Get answers in seconds, not hours. Ampool’s powerful in-memory performance helps achieve low latency for rapid response to queries on transactional or historical data.
Connect applications and databases to ingest and analyze data in an instant
Powerful in-memory computing enables enterprises to perform complex processes and analysis on live data
Optimized memory based storage empowers enterprises to generate real-time insights from live and historical data
Ampool’s powerful in-memory storage and computing enable optimal performance, availability, and multi-tenancy that are relevant in multiple scenarios.
Analyze application data in real-time by eliminating costly ETL, building and updating advanced predictive models, and serving those models from a single in-memory data store.
Use spare clustered memory for hot data analysis without altering data pipelines, using any combinations of the same Hadoop and Apache Spark-based tools.
Save system resources and reduce CapEx and OpEx by performing Streaming, Batch, BI, Reporting, and Ad-Hoc analytics on the same data without creating unnecessary copies.
Ampool supports integrations at all levels, from low-level language bindings, to interfaces with compute and storage frameworks, and full-stack data platforms.