Read MoreVarada enables data architects to seamlessly accelerate and optimize workloads, using dynamic analysis and adaptive indexing, resulting in optimal control over performance and cost.
Varada’s data platform runs in your cloud environment, enabling any SQL consumer to easily query any data source.
Varada’s dynamic and adaptive indexing technology ensures queries will run at interactive response time, especially highly selective queries. Varada automatically analyzes and detects which datasets to accelerate, and then applies the optimal index, while giving data architects full control in prioritizing workloads and define budgets and performance requirements.
To ensure performance, many enterprises compromise on accessing all their available data and settle for isolated data silos that have been prepared and modeled to enable speedy queries. Varada instantaneously transforms your available data into operational data so you can say goodbye to lengthy data preparation projects and leverage x10 more data . Varada enables queries to run on any column at its source granularity, unlocking the true value of data.
Varada’s machine-learning optimization tools continuously track cluster performance and data usage. Varada monitors queries on a workload-basis to see which tables are used the most, how queries are running and where bottlenecks form, and automatically adapts the operational dataset. Varada also leverages advanced cost-based optimization to ensure the best possible resource utilization.
Varada is deployed as a private managed service, running in your Virtual Private Cloud (VPC), so that data is not duplicated and does not need to leave your account. Varada seamlessly connects to all your existing data sources and serves any SQL data consumers out-of-the-box.
AWS Athena, is an easy and native implementation of Presto, serves ad-hoc queries very well. But when it comes to optimizing for price and performance at scale, data architects run into significant challenges. This new whitepaper explores the alternatives to standardize on a data virtualization architecture and serve a very wide range of use cases on a single data platform and without the need to move data elsewhere.
