While Starburst’s query engine already leads the industry in both performance and cost-efficiency, combining Varada’s proprietary and patented indexing technology sets a new benchmark in data lake analytics, empowering organizations to more quickly and efficiently derive greater insights from their data.
In addition to its technology, Varada engineering and product leadership will be joining the Starburst team. The integration of the technology is expected to roll out to select customers in the next 30 days, with general availability by fall 2022. Companies using Trino can try the Varada Community Edition connector for free.
Current economic dynamics are forcing IT leaders to find cost efficiencies wherever possible. The move to the cloud and the pay-as-you-go consumption models give IT managers more flexibility to scale expenses upward or reduce them downward.
However, when running an application in the cloud, you’re not only running the application, but also the underpinning data, network resources, infrastructure resources, storage, and security that are part of the application’s total workload. With the acquisition and integration of Varada’s technology, customers using Starburst for their analytical workloads can reduce cloud compute costs by 40%+ and query response times by up to 7x.
Varada was one of the analytics query accelerators recognized in the 2022 Gartner Market Guide for Analytics Query Accelerators. Gartner states that “data and analytics leaders should use these offerings to accelerate time to value of their data lake initiatives.”
Varada’s core technology is a proprietary and patented caching and indexing solution which helps customers:
- Speed query performance with autonomous indexing. Varada splits the data to be processed into blocks and then automatically chooses the most effective index for each block based on the data content and structure. This ensures data is available for fast analysis, reducing query response times up to 7x.
- Adapt to business requirements with elastic resource management. Varada’s solution includes a smart cache which caches frequently accessed data to speed performance, but also provides data teams with the tools to adjust settings to meet performance & budget requirements. With the integration, Starburst Enterprise and Varada can elastically scale together to optimize for cost and performance.
- Reduce operational costs with workload-level monitoring. Varada’s workload-level monitoring detects hot data and bottlenecks, alerting data engineering teams to areas for improvement. With the solution overall reducing the need to move & model data, customers see up to 40% cloud compute cost reduction on AWS.
“This acquisition is about helping customers take their data lake analytics to the next level, helping them move faster with critical decision-making while reducing data management costs,” said Justin Borgman, Starburst Co-Founder and CEO. “With the addition of Varada’s indexing technology, we can help data teams better serve the business, providing the right data right now. This powerful combination couldn’t come at a better time when an uncertain economy is forcing companies to re-evaluate their budgets, as business demands only increase.”
“We are pleased to join Starburst and bring our innovative technology to a global audience,” said Roman Vainbrand, Varada Co-Founder & VP R&D. “We believe the combined Starburst & Varada solution will deliver the best performance and cost benefits in the analytics query market, enabling organizations to accelerate the time-to-insight, while optimizing infrastructure operations and investments.”
“At Varada, we had to place a bet on which data lake query engine would win the data lake analytics race,” Said Eran Vanounou, Varada CEO. “Trino stood out immediately for its flexibility, vibrant community, and success as the query engine of choice for the largest internet-based businesses like Netflix, Lyft, and LinkedIn. Not only was it the perfect fit for Varada’s smart indexing technology, but now Starburst and Varada can join forces and deliver a new standard for speed and cost savings for data lake analytics.”