CODAC | Commoditizing Data Analytics in the Cloud

Summary
The goal of this project is to commoditize large-scale data analytics in the cloud, which means (a) to reduce query processing cost to close to what the available hardware is theoretically capable of and (b) to prevent vendor lock-in by making it easy to move data between different clouds and systems. To achieve this, we will design and develop an open and cost-efficient analytical database system for the cloud called CODAC.

The CODAC system consists of three main components: First, an intelligent control component that automatically and transparently selects and manages the cheapest hardware instances for the given workload and makes migration to other (e.g., European) cloud vendors possible. Second, a highly-efficient and scalable query processing engine that is capable of fully exploiting modern cloud hardware. Third, a data lake storage abstraction based on open data formats that enables cheap storage as well as modularity and interoperability across different data systems. The resulting system therefore has the potential for making large-scale data analytics both cheaper and easier, boosting data-driven instruct and science.
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More information & hyperlinks
Web resources: https://cordis.europa.eu/project/id/101041375
Start date: 01-02-2023
End date: 31-01-2028
Total budget - Public funding: 1 498 125,00 Euro - 1 498 125,00 Euro
Cordis data

Original description

The goal of this project is to commoditize large-scale data analytics in the cloud, which means (a) to reduce query processing cost to close to what the available hardware is theoretically capable of and (b) to prevent vendor lock-in by making it easy to move data between different clouds and systems. To achieve this, we will design and develop an open and cost-efficient analytical database system for the cloud called CODAC.

The CODAC system consists of three main components: First, an intelligent control component that automatically and transparently selects and manages the cheapest hardware instances for the given workload and makes migration to other (e.g., European) cloud vendors possible. Second, a highly-efficient and scalable query processing engine that is capable of fully exploiting modern cloud hardware. Third, a data lake storage abstraction based on open data formats that enables cheap storage as well as modularity and interoperability across different data systems. The resulting system therefore has the potential for making large-scale data analytics both cheaper and easier, boosting data-driven instruct and science.

Status

SIGNED

Call topic

ERC-2021-STG

Update Date

09-02-2023
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Horizon Europe
HORIZON.1 Excellent Science
HORIZON.1.1 European Research Council (ERC)
HORIZON.1.1.0 Cross-cutting call topics
ERC-2021-STG ERC STARTING GRANTS
HORIZON.1.1.1 Frontier science
ERC-2021-STG ERC STARTING GRANTS