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Atoti cubes operate on top of a database. The database is where the fact-level data resides. Facts represent granular records such as financial trades, currency rates, price vectors, and other transactional data. These facts are available for querying in Atoti.

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What are the supported methods for handling fact-level data in Atoti?

Fact-level data can be stored in two ways:
  • Internal datastore: Atoti’s in-memory datastore is optimized for fast, multidimensional analytics. It stores data directly in memory, enabling low-latency query responses.
  • External databases: External systems store data persistently. Atoti connects to these sources and queries them dynamically using DirectQuery.

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Cost considerations

Using an external database via DirectQuery reduces memory usage and infrastructure costs, especially for large datasets. Performance depends on the external system’s responsiveness and availability. A hybrid approach is possible: keep recent, frequently accessed data in memory and store historical or “cold” data in an external database.

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What is the difference between the internal datastore and DirectQuery?

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Why is the in-memory datastore fast?

Atoti’s in-memory datastore is built for high-performance analytics, using internal storage and compression techniques to minimize memory usage and keep queries fast. This core architecture is designed for speed, making it ideal for use cases such as intraday risk monitoring, limit management, interactive what-if analysis, and operational workflows.

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How DirectQuery works with external data warehouses

DirectQuery enables Atoti to perform analytics on data stored in external warehouses without loading it into memory.
  • Benefits: Reduces in-memory storage requirements and lowers infrastructure costs for large datasets.
  • How it works: Atoti cubes use DirectQuery connectors to translate cube requests into external database queries (mostly SQL). By default, no data is stored in memory. However, aggregate providers can be configured to cache frequently accessed results if needed.
This approach leverages the scalability of external storage while minimizing memory usage on the Atoti server.

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How to use the Datastore and DirectQuery together

Atoti supports combining its in-memory datastore with DirectQuery connectors to external databases. The tables in the in-memory datastore are not joined with tables accessed through DirectQuery. But the data in the in-memory datastore can be used alongside the data accessed through DirectQuery in the same cube. This hybrid approach allows you to optimize performance without increasing infrastructure costs. Frequently accessed or frequently updated data is stored in memory while less critical data is in the external systems.

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Why use a hybrid approach?

  • Faster measure calculations: Lookup data stored in memory can be accessed instantly during measure calculations, avoiding round-trips to the external database.
  • Reduced memory usage: Fact tables and their related tables remain in the external database, keeping the memory footprint small.

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How does it work?

  • In-memory datastore: Stores lookup tables as isolated stores. These are accessed during measure calculations.
  • DirectQuery: Handles the fact table and tables joined to it. Queries are executed on the external database.
Find out more about configuring the datastore in Atoti: