New Google BigQuery benchmark helps enterprises assess emerging options for Business Intelligence on Big Data

AtScale releases industry’s first Business Intelligence Benchmark for Google BigQuery

San Mateo, California, April 6, 2017 — AtScale, the first company to provide enterprises with a fast and secure self-service Business Intelligence platform for Big Data, released today the results of a Business Intelligence benchmark for Google BigQuery. The benchmark results can be viewed at

The study, which evaluated Google BigQuery performance for key Business Intelligence (BI) workloads, reveals key insights that enterprise IT leaders should consider when modernizing their business intelligence infrastructure.

Big Data’s “New Normal”: Heterogeneous & Hybrid

Enterprise CIOs are increasingly having to accommodate the reality of a complex Big Data world: the average employee expects to connect to any enterprise data with consistent speed, security and scale from any BI tool. However, enterprise IT stores data in multiple data stores, each of which has different deployment models and delivery speeds: Hadoop on-premises, Hadoop in the Cloud, RDBMSs on-premises, and databases in the Cloud.

As enterprise buyers weigh which technologies to deploy, it is essential to consider the ramifications their choices will have for their users. For instance, the benchmark results show that Google BigQuery’s serverless architecture enables IT departments to quickly launch a Big Data service without the need to manage scalability. However, these new serverless architectures also require customers to consider the types of queries that will be run on this new platform. Queries with high-complexity and numerous table joins tend to perform differently on BigQuery than on traditional Hadoop systems. Additionally, different big data platforms handle filtered query results using varied approaches, resulting in differences in performance profiles across these solutions.

“The AtScale benchmark provides enterprise leaders with useful comparisons they need in order to make BI work on Big Data. As the data world grows more complex and diverse, these benchmark stats help enterprises understand leading Big Data query options and make better decisions critical to supporting BI infrastructure,” says Doug Henschen, Vice President and Principal Analyst at Constellation Research.

This benchmark aims to shed a light on the performance of Google BigQuery in comparison with other more traditional SQL-on-Hadoop engines. In performing the benchmark, AtScale focused on the success criteria that satisfy enterprise Business Intelligence requirements, namely:

  • Performs on Big Data: The database engine must be able to consistently analyze billions or trillions of rows of data without generating errors and with response times on the order of 10s or 100s of seconds.
  • Fast on Small Data: The engine needs to deliver interactive performance on known query patterns and return results in no greater than several seconds on small data sets (on the order of thousands or millions of rows).
  • Stable for Many Users: Enterprise BI user bases consist of hundreds or thousands of data workers and as a result, the underlying query engine must perform reliably under highly concurrent analysis workloads.

Some of the benchmark findings include:

  • Data loading: The process of moving data to the Google cloud and loading it into BigQuery was simple and well-documented.
  • Management: The BigQuery management console, query tools, and documentation make the product easy to use and support rapid on-boarding.
  • Out-of-the-box performance: The BigQuery engine performs quite well “out-of-the-box”, requiring minimal query tuning and no system configuration.
  • Impressive concurrency: BigQuery serverless model means concurrent query performance on small data sets shows no query degradation, even at query volumes above 25 concurrent BI users.

“These results of this benchmark indicate a rapid evolution in the Big Data market,” says Matt Baird, CTO and co-founder at AtScale. “Such a pace can be daunting for enterprises as they are already dealing with a fair amount of complexity: should they use Hadoop? Should they use BigQuery? What’s the difference between on-premise Hadoop, in-cloud Hadoop and a serverless model like Google’s?”

These new developments are indeed an indication that the Big Data market has matured to its tipping point and that platform vendors like Google have become a viable solution to add to an enterprise’s arsenal mix.

This benchmark also offers insights for how enterprises can deploy Business Intelligence on any data, without compromising experience and speed. As enterprise CIOs’ data investment spans across heterogeneous and hybrid data environments, they will have to set a bar for performance across all systems. They will also need to look for a solution that simplifies the complex and brings order to their Big Data chaos.

“That’s why we started AtScale,” Baird concludes. The benchmark results can be viewed at

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About AtScale

AtScale makes BI work on Big Data. With AtScale, business users get interactive and multi-dimensional analysis capabilities, directly on Big Data, at maximum speed, using the tools they already know, own and love – from Microsoft Excel to Tableau Software to QlikView. Built by Big Data veterans from Yahoo!, Google and Oracle, AtScale is already enabling the BI on Big Data revolution at major corporations across healthcare, telecommunications, retail and online industries. To see how AtScale can help your company, go to