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Atoti is built for teams that need fast aggregation, flexible multidimensional analysis, and consistent business logic across all analytics consumers. The semantic layer is materialized through the Atoti cube, which centralizes business logic and defines:
  • Dimensions to organize data by business categories
  • Hierarchies to define how those categories break down into levels
  • Measures to define calculations and aggregations
This ensures all users and systems apply the same logic when analyzing data.

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Why use Atoti?

Atoti provides a platform for fast and flexible analytics at scale. Key benefits include:
  • Real-time aggregation on granular data.
  • Analysis of large datasets.
  • Flexible slicing across any dimension.
  • Support for non-linear and context aware calculations.
  • Blended data from in-memory and from external warehouses to optimize costs and maintain speed.
  • Integrated workflows for scenario analysis, limits, and sign off.
  • Interfaces for UI, BI tools, Python, Java, and AI agents.

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How is Atoti structured?

Atoti groups several components that work together. It is organized into three main layers: data sources, the Atoti Server, and client applications. Atoti Server diagram.png

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Data sources

The platform ingests data from multiple sources:
  • External files (Parquet, CSV, Avro, JSON, etc.)
  • Databases accessed through JDBC
  • Streaming systems such as Kafka or RabbitMQ
  • External data warehouses such as Databricks, Snowflake, or ClickHouse
Data can either be loaded into memory or queried directly from external systems.

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Atoti Server

At the core of the platform is the Atoti Server. It contains the aggregation engine and manages data access and calculations.

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Aggregation engine

The aggregation engine processes queries and computes results. At the center of the engine is the Atoti cube, which defines: The engine can work with: This allows fast aggregation while keeping large datasets in external systems when needed. The aggregation engine scales across cores and nodes. It can use many CPU cores on one machine and can also run across multiple machines.

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Content server

The content server stores the metadata required for an Atoti Server to run. This includes dashboards, measures, KPIs, and user settings. It can run embedded within Atoti Server or as a separate service.

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Clients and interfaces

The results of the aggregation engine are accessed through client applications. These include: These clients query the aggregation engine and present results to end users or downstream systems.

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Administration and extensibility

Atoti is an open and extensible platform. It provides:
  • Server management through Java.
  • An Atoti Java SDK and an Atoti Python SDK.
  • Deployment options for on-premise and cloud environments.
  • Integration points for BI tools and custom applications.

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What is Atoti Enterprise Risk?

Atoti Enterprise Risk is a complete risk analytics solution that combines the Atoti Engine, Atoti Intelligence, and Atoti solutions into a unified offering. The engine delivers consistent data modeling and aggregation, while the intelligence and solutions layers add advanced analytics capabilities and domain-specific workflows. atoti-solutions.png

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Atoti Intelligence

The intelligence layer builds on the output of the query engine and provides reusable capabilities for analytical workflows. It includes:

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Atoti solutions

Atoti solutions are packaged, configurable applications that use the platform’s components and reference models. They include prebuilt semantic models, measures, calculations, and workflows. Available solutions include:
  • Atoti for Front Office
  • Atoti for Market Risk
  • Atoti for FRTB
  • Atoti for xVA
  • Atoti for Counterparty Credit Risk
  • Atoti for Liquidity Risk
  • Atoti for Collateral and Margin Optimization
Solutions can be used as is or extended with custom data models, calculations, and workflows.

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Which use cases does Atoti support?

Atoti is frequently used for applications that require real time analytics or complex multidimensional calculations. Common use cases include, and are not limited to:
  • Real-time risk and P&L monitoring.
  • Counterparty credit risk
  • Liquidity risk
  • Enterprise risk consolidation
  • Scenario analysis and stress testing
  • Regulatory capital calculation and simulation
  • Collateral and margin optimization
  • Portfolio and exposure analysis
  • Operational workflows that require fast validation and adjustments