Ampool Enterprise was purpose built to optimize the performance of BI and Analytics on your existing and future Big Data Infrastructure.
Many enterprises are struggling to get accelerated value from their Big Data and other information assets to support today’s business demands. Advanced analytics is the path to competitive advantage and organizations with a modern data and analytics architecture can derive insights from information to surpass their competition and achieve market dominance.
To solve that challenge, we built a new In-Memory Solution leveraging best in class open core and patent pending technologies to amp up the speed, agility, flexibility of existing and future big data infrastructure. We Enable organizations to super charge their existing environment seamlessly without replacing or disrupting existing production data lakes or data warehouses.
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.