Atoti - 2026 Pricing, Features, Reviews & Alternatives

Atoti delivers:

• Interactive analysis of large datasets
• High dimensionality
• Mixed workload
• Universal semantic layer
• Fast start-up
• Cloud agnostic
• What-if analysis
• Alerts and limits monitoring
• Future-proof
• Data visualization
Interactive analysis of large datasets
Atoti's speed and multidimensional modeling capabilities enable free exploration of complex metrics over large datasets, from top-of-the-house level to single records. Group metrics using any combination of hierarchies, filter out elements, create your own dashboards on the fly – all independently.

• Don’t just watch numbers – understand and explain them.

• Don’t just detect a risk figure issue – track the issue down to the single record that caused it.

• Freely explore the metrics and add new metrics intraday.

High dimensionality
A key challenge of traditional OLAP systems is quickly aggregating data across different dimensions. Unlike most OLAP engines that only support a small number of analysis dimensions, Atoti supports “many-dimensional” queries via:
• A multidimensional bitmap index that filters billions of records and supports hundreds of dimensions.
• A column store indexer that enables the processing of multidimensional queries at unparalleled speeds for data that is updated on the fly.
• An efficient data compression mechanism using a dictionary and Java primitive types, which, when combined, enable efficient data storage and access in memory.
• Multi-core architecture designed from the ground up, with parallel algorithms running on multiple threads that leverage the computing power of multi-CPU machines.
• Polymorphic distribution that enables developing simple independent applications for heterogeneous data, which are federated on the fly.

Mixed workload
Typically, users are forced to select either an OLAP or OLTP (Online Transactional Processing) system, depending on the workload. Atoti offers a mixed workload solution that supports both a modern, multidimensional OLAP aggregation engine and an OLTP capable of easily handling write-backs, updates and adjustments. The same database engine performs data updates and complex analytical queries so that analytical work is always done on up-to-date data reflecting the current state of the business. Atoti’s mixed workload is enabled via:

• Multi-version concurrency control that supports multiple queries to the database (both big and small) as well as write-backs simultaneously.
• An in-memory database engine offering ultra-fast data access and the ability to insert/update large amounts of data while concurrently performing queries and analytics.
• Multi-threaded algorithms that maximize the usage of all computer cores and process transactions and queries simultaneously to deliver the best possible throughput and response time.
• An updateable column store that preserves the underlying facts, so they are easily updateable and quickly accessible for multidimensional queries.
• A continuous real-time push engine that ensures the data used for analytical queries is always the most recent and propagated in real time to end users.

Universal semantic layer
Atoti integrates with most data sources and risk engines, commercial or in-house.
Many financial institutions have migrated to open architectures that decouple analytics, allow federated data from several systems and blend in external data. Atoti supports this open architecture as the semantic layer turning data into actionable business metrics.

Atoti provides users with a global picture that consolidates data from position-keeping systems, market data providers, and metrics generated by risk engines such as prices, sensitivities and simulations. Data can be read from open format files such as CSV, CRIF, and Parquet, fetched from relational databases (including Snowflake, BigQuery, and RedShift), datalakes or streamed from messaging queues such as Kafka.

Atoti integrates with any in-house or commercial risk engine.