Known as BigLake, Google presents the product as a recording engine designed to provide a single, integrated server and the power to record multiple files, with multi-cloud , data stores and data ponds – no matter the style.
The open source platform is designed to integrate Google’s critical data storage resources with other sources, whether in the form of competition or in the customer’s own reputation.
Google Cloud data analytics product manager Sudhir Hasbe said BigLake is “critical” in connecting with different worlds.
BigLake eliminates the need to “copy data, move data across your object stores, such as Google Cloud Storage, S3, or Azure into a multi-cloud environment,” reports that users can access all their data from one place.
BigLake supports all open file types, including Parquet, as well as basic welding engines such as Apache Spark or Beam, as well as many table types, including Iceberg and Delta.
The engine enhances the ability of Google’s 11 -year -old BigQuery to store data in Google Cloud Storage.
BigQuery is a Google Cloud-managed, serverless, multicloud data warehouse that allows customers to perform real-time analyzes of large amounts of data: it works on over 110 terabytes of customer data. in seconds, on average.
‘BigLake expands BigQuery’s quality and columnarity lineup to platforms in data storage stores such as Amazon S3, Azure Data Lake Storage Gen2, and Google Cloud Storage. BigLake separates table access from footprint data by permitting it, “said Google.
BigLake will be at the heart of Google Cloud’s data platform development, Hasbe said, with the cloud provider making sure to integrate with all of its tools.
“When you think about unlimited data, that’s when we end the separation between cloud stores and data pools,” said Gerrit Kazmaier, vice president of Google Cloud and general manager for repository, data analytics and Looker.
“Google is doing this in an independent way.”
Also this week, Google announced the creation of the Data Cloud Alliance, which is partnered with other founders to improve data portability. Members will provide systems, APIs and integration support to facilitate the movement of data, and access to multiple platforms and products, across multiple communities.
Available in Google, Confluent, Databricks, Dataiku, Deloitte, Elastic, Fivetran, MongoDB, Neo4j, Redis, and Starburst.
At the same time, the company launched the Vertex AI Workbench, a software platform designed to make it easier for the ‘average user’ to search their data using powerful AI, machine learning and numbers. Workbench is built to work with BigQuery, Serverless Spark and Dataproc, as well as a variety of AI and data solutions.
Google also provides a tool for people to modify machine learning models through the Vertex AI Model Registry.
The Registery Model is currently under review, and is intended as a database for finding, using, and managing machine learning models, including those hosted on BigQuery ML.
Google is releasing “unlimited” data with Big Cloud and a new company
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