Google Cloud Unleashes Data Management Services

Alphabet’s Google Cloud unveiled a raft of announcements at its Google Cloud Summit last week aimed at the so-called Google Data Cloud. The vendor’s goal is to simplify big data for a range of enterprise purposes, from transaction processing through to business intelligence (BI) and analytics, all informed by artificial intelligence (AI).

Leading the charge was the preview of BigLake, described by Google as “a storage engine” that integrates data lakes on Google Cloud Storage with the data warehousing functions of Google’s BigQuery service. BigLake also can be used to integrate BigQuery with data lakes on AWS and Microsoft Azure, says Google Cloud. In short, BigLake is meant to be a unifying force that lets BigQuery access data across lakes and warehouses in multicloud environments.

In a blog, Gerrit Kazmaier, Google Cloud’s VP and GM of Database, Data Analytics, and Looker, described BigLake as follows:

“Managing data across disparate lakes and warehouses creates silos and increases risk and cost, especially when data needs to be moved. BigLake allows companies to unify their data warehouses and lakes to analyze data without worrying about the underlying storage format or system, which eliminates the need to duplicate or move data from a source and reduces cost and inefficiencies.”

BigLake offers tools to let users define “find-grained” security policies. It is integrated with BigQuery Omni, which offers multicloud support for BigQuery. It will feature an application programming interface (API) for use in Google Cloud as well as with a range of file formats and open-source tools.

More Google Cloud Integrations and Simplifications

Along with BigLake, Google Cloud announced tools to improve the reach and performance of BigQuery analytics. Vertex AI, Google Cloud’s machine learning (ML) development service, is enhanced with the addition of Vertex AI Workbench, a service that lets developers create ML models using input from Google Cloud services BigQuery, Serverless Spark, and Dataproc. According to Kazmaier, Vertex AI Workbench enables teams to “build, train and deploy ML models 5X faster than traditional notebooks.”

Google Cloud also aims to simplify data reporting with enhancements to Looker, its BI platform service that runs in GCP as well as AWS and Azure. Looker now is connected to Google Sheets, and Google’s Data Studio, Google’s free data presentation service, can access Looker data models. The result, said Kazmaier, is a “unified BI experience.”

Changing channels, Google Cloud also has improved its Spanner SQL database service, which also is federated with BigQuery. A new feature called change streams is a real-time change tracking function that speeds up data processing. Google Cloud says this addition will further improve Spanner’s processing speed, which the vendor claims is now over 2 billion requests per second at peak with up to 99.999% availability.

Google Cloud: A Data “Free for All”

With these and other announcements made at this week’s conference, Google Cloud is following a general trend among cloud hyperscalers toward improved data engineering. But Google Cloud seems eager to differentiate itself from rivals AWS and Azure with a hyper-focus on data management and its openness to support multicloud environments.

Indeed, Google Cloud made a show of openness by announcing a Data Cloud Alliance today that includes Databricks, MongoDB, Redis, Accenture, and others with a stated “commitment to accelerating adoption across industries through common industry data models, open standards, processes, and end-to-end integrated products and solutions.”

Google Cloud’s Future Depends on Data

Google Cloud’s efforts to distinguish itself through data management aren’t going unnoticed. There are signs that Google Cloud has become more strategic for parent Alphabet. (Nasdaq: GOOGL). On the company’s earnings call in February, Alphabet CEO Sundar Pichai noted: “For the full year 2021, compared with the full year 2020, we saw over 80% growth in total deal volume for Google Cloud Platform and over 65% growth in the number of deals over $1 billion.”

These numbers help justify Alphabet/Google LLC’s hefty investment in GCP, including buying cybersecurity firm Mandiant for $5.4 billion to add more security to the platform. So far, the strategy seems to be working; Operating loss for Google Cloud in the final quarter of 2021 was $890 million, down 28% from a year ago. That compares to a 4% reduction in operating loss for the fourth quarter of 2020 compared to 2019. Perhaps the new data management innovations could narrow those losses even more.