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Atoti is a real-time analytics platform built around an aggregation engine and a multidimensional data model. It combines an in-memory datastore, direct access to external data, and a built-in semantic layer to deliver fast and consistent analytics across applications and users. The platform supports real-time aggregations on granular data, flexible multidimensional analysis, and non-linear calculations. The semantic layer centralizes dimension, hierarchy, and measure definitions, applying consistent business logic across all analytics consumers and interfaces. The semantic layer is where business logic is centrally defined. It organizes data into consistent elements by grouping data into business categories (dimensions), structuring those categories into levels (hierarchies), and defining standardized calculations (measures). In Atoti, this semantic layer is materialized through the Atoti cube, which 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. Atoti is used to analyze large datasets, perform complex calculations, and support data-driven workflows in fast moving environments. Atoti is relevant for teams that need fast aggregation, flexible multidimensional views, and consistent business logic across all analytics.

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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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What is the Atoti Platform?

The Atoti Platform groups several components that work together. The platform is organized into three main layers: data sources, the Atoti Server, and client applications.

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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:

• Dimensions to organize data
• Measures to calculate results

The engine can work with:

• An in-memory datastore for high-performance analytics
• External databases via DirectQuery

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:

• Atoti UI for an integrated analytical experience
• Custom applications built on public services
• AI agents and workflows
• Business intelligence tools (using Atoti BI Adapters)

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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Atoti Enterprise Risk

Atoti Enterprise Risk combines the core platform components with Atoti Intelligence and Atoti solutions to deliver a unified approach to risk analytics. The platform provides consistent data modeling and aggregation, while the intelligence layer and solutions add advanced analytics capabilities and domain-specific workflows.

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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:

• Atoti Intelligence, enabling AI agents to automate tasks and perform analytics
• Scenario analysis for comparing different situations
• Limits monitoring with alerts when thresholds are breached
• Sign-off workflows for controlled data updates

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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.