Collaboration between big data solution providers driving better cost performance for business users 

The cloud computing revolution remains one of the most influential tech trends in recent years. Where it was once normal for data to be stored across various on-prem solutions like discs, towers, and external hard drives, each year more powerful computing lets us save more of this information remotely, allowing us to access photos and files whenever we need them at the click of a button. 

This shift also accelerated the advancement of sophisticated AI tools by unlocking data silos and creating valuable, free-flowing streams that enable large organizations to make sense of their cloud data and find the insights they need to make operational improvements. 

At the same time, with cloud storage the default in 2024, it’s become a costly problem for businesses and organizations. 

For one, public clouds now host more than half of all enterprise workloads, and some businesses spend upwards of $12 million annually here. However, 30% of this spend doesn’t produce any tangible value for these businesses. This represents huge amount of waste that is creating a growing headache, with 71% of US enterprise organizations regularly experiencing ‘bill shock.’

However, powerful new ways to handle the huge data volumes in the cloud are making a more efficient future feasible. 

Collaborative data solutions 

For large enterprises, the sheer amount of data at their fingertips is seen as the holy grail for AI and analytics, whether it’s shipping efficiencies, unique customer insights, or transforming production processes. Yet making the business more agile and efficient also requires a data infrastructure that’s equally efficient. 

To address the significant amounts of wasted budget that comes with huge enterprise projects, a new archetype of turbocharged data solutions has emerged, setting new benchmarks for speed and efficiency in the process. 

For example, the cloud-based data warehousing platform Snowflake offers range of data solutions. Today, its users have more variety of choice thanks to the launch of a new native connector from SQream, an accelerated data processing platform. 

The connector brings SQream Blue, a data lakehouse solution, to its Snowflake environment. 

The launch of the Snowflake connector from SQream enables users to unlock cost-performance savings by offloading data loads to SQream Blue without exporting data or migrating information from existing Snowflake workflows. 

This move also highlights a growing industry trend that will see a more flexible approach to tech solutions that moves away from ‘vendor lock-in’ to allow organizations to drive higher long-term ROI from their IT initiatives. 

This collaborative approach to data solutions allows enterprises and data specialists to more easily pick the best-in-class options for specific parts of the project or workflows, and is set to have a huge impact on the bottom line, helping to recoup some of the wasted revenue associated with cloud storage. 

Shattering performance benchmarks 

The news of SQream’s native plug-in for Snowflake follows on new results of an independent data analytic benchmark test. 

The nonprofit Transaction Processing Performance Council (TPC) developed the TPCx-BB as a way to objectively compare big data analytics system solutions to drive more informed decisions within the IT market. 

SQream recently completed the TPCx-BB with a specific focus on the performance of its data lakehouse, SQream Blue, which dramatically outperformed Snowflake and its X-Large Virtual Warehouse during the comparison element of the test. 

In this scenario, both tools were tasked with processing 30 TB of data and carrying out a number of tasks that mimicked the typical queries of real-world data analytic projects. 

Here, SQream Blue was found to perform twice as fast at half the cost of the Snowflake X-Large Virtual Warehouse, with some tasks, in particular, coming out five times more efficiently. 

“Cost-performance is the new metric for big data analytics in 2024, and SQream Blue’s proprietary GPU-based technology has shown that processing and analyzing high volume, unstructured datasets can be done at speed while reducing budget,” said Matan Libis, VP Product at SQream. 

“Now, enterprise users can leverage the benefits of Snowflake’s data warehouse platform and complement these with the force of GPU processing thanks to the launch of the native Snowflake plug-in connector,” concluded Libis. 

Seamless integration between data platforms 

Although IT and data specialists can build their own tech stack to combine preferred solutions, connectors like the new offering from SQream provide an immediate way to get ahead and unlock the proven efficiency gains. For one, the connector is available on AWS and GCP Marketplace as a native SaaS solution. 

The pre-built connector also circumvents potential integration challenges. For example, the solution treats Snowflake tables as native objects within SQream Blue, simplifying data access and management and ensuring integration happens seamlessly. 

Meanwhile, users don’t need to export any data from Snowflake, meaning the benefits of SQream’s GPU-accelerators and cost-performance can occur within Snowflake’s environment. 

This enhanced flexibility that allows users to leverage the strengths of multiple platforms will help to offload costly and complex tasks to the right engine to lower cloud bills over time.