Key Concepts & Architecture

Oxla is designed to support analytical query workloads, also known as Online Analytical Processing (OLAP). These workloads are complex queries that analyze a stored dataset, such as joins between numerous extensive databases or aggregations across large tables.

The current build of the Oxla database allows users to import data using .csv and run SQL queries using CLI with various supported clauses, data types, and functions.

Oxla Architecture

Oxla has a uniform architecture that contains the node leader that can distribute workloads equally among replicas and partitions. Data can be processed directly from data warehouses into the OLAP database management system without going through the terminal messaging cluster. Our cutting-edge technology allows users to process data faster with less infrastructure. Users will be able to request data using queries and receive data in real time.

Oxla’s unique architecture uses the Dynamic Oxla cluster in the query processing layer. This cluster can expand and shrink dynamically depending on the analytical demand for data processing. Within the cluster, a node leader coordinates activities across Oxla. It performs key activities such as authentication, query parsing, optimization, etc. Our architecture consists of two layers, query processing, and a database storage layer. We separate the computing layer, a.k.a query processing layer, with the database storage layer to provide greater flexibility and cost savings to our customers for big data and advanced analytics.

Example Use Cases Benefits:

Let’s say a large corporation wants to improve the customer journey by driving insights and running more queries/analyses from their existing customer’s data, such as purchase transactions, social data, etc. They need to process and transform a large amount of data in real-time resulting in the need to improve the computing power and not the storage. In this use case, our decoupled storage and compute design will benefit the corporation since they only need to upgrade the query processing and not include the database storage.  

Example Use Cases Benefits:

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Let’s say a large corporation wants to improve the customer journey by driving insights and running more queries/analyses from their existing customer’s data, such as purchase transactions, social data, etc. They need to process and transform a large amount of data in real-time resulting in the need to improve the computing power and not the storage. In this use case, our decoupled storage and compute design will benefit the corporation since they only need to upgrade the query processing and not include the database storage.  

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