Google Expands BigQuery Ecosystem via Partnerships

More partners means more tools for customers to use to develop data-driven applications based on the analytics service, Google says.

Google's BigQuery

Google's BigQuery cloud-hosted service lets enterprises run batch and real-time data analytics applications against really large data sets. Recently, the company announced several enhancements to the technology, including support for faster data streaming, new security controls and expanded availability of the service in Europe.

Continuing in the same vein, Google this week announced new partner support for BigQuery to give enterprises more tools and support for developing data-driven applications based on the analytics service.

For organizations that have data both on premises and in the cloud, Google has new integrations with SnapLogic, Talend and xPlenty. The partnerships with these vendors of Extract, Transform, Load (ETL) tools will give enterprises more options for preparing their data for analytics in BigQuery, Google Strategic Partner Development Manager Tanya Shastri said Monday.

"We've heard your request to make it easier to load data into BigQuery and we've increased the choices you have here," Shastri said.

Similar partnerships with Bime, Looker, Tableau and QlikTech now provide enterprises with a way to build visually interactive front ends for accessing and analyzing large data sets in BigQuery, Shastri said. Also available are integrations with open-source tools like Fluentd for ingesting data from multiple sources, re:dash for data collaboration, and a Hadoop connector that lets Hadoop and Spark applications access BigQuery.

In addition, as part of the growing BigQuery ecosystem, Google has partnerships with big data analytics consultancy Archipelago and service partners like Bimotics, which provides a self-service business intelligence capability based on BigQuery, Shastri said.

The latest announcement follows one that Google made in April in which the company announced the availability of several new features in BigQuery. One of them boosts the ability for BigQuery customers to stream data into the service for real-time analytics applications. To allow organizations to stream data at a faster rate, Google changed the default inset-rate limit for BigQuery from 10,000 data rows per second to 100,000 rows per second per table. It also increased row-size limit from 20KB to 1MB for the same reason.

As part of its April announcement, Google also added new row-level permissions and data expiration controls to bolster security around BigQuery. The added features are designed to enable secure access to shared resources, such as data that might be needed by both finance and HR, Google had noted at that time. Support for row-level permissions eliminates the need for developers to build different views for users who need shared access to the same data, the company said.

In April, Google also announced that it would be making encryption available for data at rest in BigQuery.

In January, Google entered into an agreement with VMware that enables VMware's vCloud Air customers to consume BigQuery services for big data analytics purposes.

Google's efforts to build out the BigQuery ecosystem through strategic partnerships appear designed to bolster enterprise confidence in the company's growing cloud technologies. Soon after Google's partnership announcement with VMware, Gartner analyst Kyle Hilgendorf noted how the company's biggest challenge with its cloud technologies is building trust and relevance among enterprise customers.

"From the Google standpoint, few would question Google's technical abilities and innovations," Hilgendorf blogged at that time. Google compares very favorably with other cloud vendors in terms of core technical capabilities. Where the company has struggled is in areas like support, service levels, management, pricing and billing.

Jaikumar Vijayan

Jaikumar Vijayan

Vijayan is an award-winning independent journalist and tech content creation specialist covering data security and privacy, business intelligence, big data and data analytics.