Google Cloud To Remove All Limits Of Data With BigLake

Google Cloud To Remove All Limits Of Data With BigLake

Google announced the creation of the Data Cloud Alliance initiative to help global enterprises experience digital transformation.

The tech giant previewed its BigLake data lake storage engine and announced plans to remove data limitations through data lake and storage integration.

According to Google “Managing data across multiple lakes and warehouses creates silos and increases risk and cost, especially when they need to move the data. 

BigLake, a new cross-platform data storage engine 

With BigLake, Google hopes to consolidate data lakes and warehouses so businesses can analyze their data without worrying about the underlying format or storage system. It eliminates the need to duplicate or move source data, reducing costs and inefficiencies.

Google Cloud customers can obtain access control through BigLake through APIs, including open file formats such as Google Cloud and Parquet, and open-source processing engines such as Apache Spark. The company says this capability extends a decade of innovation with BigQuery to data lake on Google Cloud Storage, enabling a flexible and cost-effective architecture for open lakes.

Sudhir Hasbe, Product Manager for data analytics at Google Cloud, said BigLake will be central to the company’s overall strategy from a storage perspective.

“We are going to make sure all of our tools and capabilities work seamlessly with BigLake,” he said. “Similarly, all of our analytics engines, whether it’s BigQuery, whether it’s our Spark engine, or whether it’s our data flow engine, all of these engines will seamlessly integrate out of the box with BigLake.”

Google Data Cloud Alliance

The company also announced the creation of the Data Cloud Alliance, a new initiative designed to enable global enterprises to access the data more seamlessly they need for digital transformation.

The alliance includes Google Cloud, Accenture, Confluent, Databricks, Dataiku, Deloitte, Elastic, Fivetran, MongoDB, Neo4j, Redis, and Starburst. These companies say they are committed to making data accessible and portable across different systems, platforms, and business environments.

Google Cloud says the proliferation of data means companies must increasingly work toward common digital data standards and open data to use it effectively for digital transformation.

The Data Cloud Alliance is committed to accelerating the adoption of data analytics, artificial intelligence, and machine learning best practices across industries. Tech giant Vertex unveiled AI Workbench, which brings data and ML systems together in a single interface, enabling teams to share a common set of tools for data analysis, computing, and machine learning. It is designed to directly integrate all AI and data products, including BigQuery, Serverless Spark, and Dataproc.

Alliance members work together to reduce customer concerns and complexity through data management, privacy, data loss prevention, and global compliance. It provides infrastructure, APIs, and integration support to ensure data portability and availability across platforms and products in multiple environments.

Security Model

These guidelines apply to the following organizational roles:

Data lake administrators (IAM) to manage Identity and Access Management policies, for Cloud Storage buckets and objects.

Data warehouse administrators to create, delete, and update BigLake tables. To do so they requires the following IAM roles:

  • BigQuery Administrator or BigQuery Data Owner
  • Data Analysts have a major role, they can read data and run queries. 
  • The data navigation administrator grants read access to connections managed by the data warehouse admins. Data warehouse administrators, in turn, define BigLake tables, configure appropriate access controls, and share tables with data analysts.

Google Cloud consulting services company can help organizations to take advantage of this Google Cloud Platform (GCP). Additionally, each alliance member will collaborate on new data models, processes, and integrations with common industry platforms to increase data portability and reduce the complexity of global data management and compliance.


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