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Varada 3.0 Includes Elastic Scaling Capabilities, Extending TCO And Performance Advantages

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Varada has introduced version 3.0 of its data analytics platform, delivering a cost-effective alternative to offerings like Snowflake, Redshift, Athena, Preso, Trino and BigQuery for at-scale big data analytics users who rely on the power of indexing to extract insights from massive, unstructured data sets.

According to the company, the new version delivers an increase in cost performance and cluster elasticity as compared to the previous version.

In addition, version 3.0 eliminates the need to keep high-performance and expensive SSD NVMe (Solid-State Drive Nonvolatile Memory Express) compute instances idling when the cluster is not in use.

Data teams are often evaluated on how quickly they can react to spikes in demand. The separation of compute and storage in version 3.0 lets them elastically scale clusters out and in as query traffic fluctuates, avoiding the waste of overprovisioning and idle resources.

Version 3.0 offers rapid and elastic scaling capabilities that let users add and remove nodes and clusters rapidly depending on current workload needs, further improving TCO for large-scale users.

Version 3.0 of the Varada platform includes three layers. The first is the hot data and index layer, in which SSD NVMe attached nodes (in the customer’s Virtual Private Cloud) are used to process queries and store hot data and cache for optimal performance. The second is the warm index and data layer, where an object storage bucket on the customer’s data lake is used to store all indexes for scaling purposes.

The third layer is the customer data layer (“cold”), which remains the single source of truth.

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