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What is Google Cloud Lakehouse?

Google Cloud Lakehouse is an advanced data platform that unifies data warehouses and data lakes into a single, integrated storage and analytics solution. It enables organizations to work with open data formats such as Apache Iceberg, Parquet, and ORC, ensuring flexibility and interoperability across systems. By allowing access to a single copy of data, it eliminates the need for duplication and complex data pipelines. The platform includes a centralized runtime catalog for managing metadata, resources, and access controls efficiently. It provides fine-grained security through IAM roles and table-level permissions, ensuring strong governance and compliance. Google Cloud Lakehouse supports scalable data processing and integrates with tools like Apache Spark for advanced analytics and machine learning workflows. It is designed to handle large volumes of data while maintaining performance and reliability. The platform includes features for replication and disaster recovery, helping ensure data availability and resilience. Comprehensive documentation, guides, and training resources make it easier for teams to get started and optimize their workflows. It also simplifies the management of Iceberg tables and other data structures. The system supports modern data architectures, enabling seamless integration with other Google Cloud services. By unifying storage and analytics, it reduces operational complexity and improves efficiency. Overall, Google Cloud Lakehouse empowers organizations to manage, analyze, and scale their data more effectively in a single platform.

What is Apache Parquet?

Parquet was created to offer the advantages of efficient and compressed columnar data formats across all initiatives within the Hadoop ecosystem. It takes into account complex nested data structures and utilizes the record shredding and assembly method described in the Dremel paper, which we consider to be a superior approach compared to just flattening nested namespaces. This format is specifically designed for maximum compression and encoding efficiency, with numerous projects demonstrating the substantial performance gains that can result from the effective use of these strategies. Parquet allows users to specify compression methods at the individual column level and is built to accommodate new encoding technologies as they arise and become accessible. Additionally, Parquet is crafted for widespread applicability, welcoming a broad spectrum of data processing frameworks within the Hadoop ecosystem without showing bias toward any particular one. By fostering interoperability and versatility, Parquet seeks to enable all users to fully harness its capabilities, enhancing their data processing tasks in various contexts. Ultimately, this commitment to inclusivity ensures that Parquet remains a valuable asset for a multitude of data-centric applications.

Media

Media

Integrations Supported

Amazon Data Firehose
Amazon S3
Arroyo
Astera Dataprep
Autymate
Data Sentinel
Flyte
Google Cloud Storage
Gravity Data
Hadoop
IBM Db2 Event Store
Mage Sensitive Data Discovery
Meltano
PI.EXCHANGE
Querri
QuerySurge
StarfishETL
Tenzir
Visplore
e6data

Integrations Supported

Amazon Data Firehose
Amazon S3
Arroyo
Astera Dataprep
Autymate
Data Sentinel
Flyte
Google Cloud Storage
Gravity Data
Hadoop
IBM Db2 Event Store
Mage Sensitive Data Discovery
Meltano
PI.EXCHANGE
Querri
QuerySurge
StarfishETL
Tenzir
Visplore
e6data

API Availability

Has API

API Availability

Has API

Pricing Information

$5 per TB
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Google

Date Founded

1998

Company Location

United States

Company Website

docs.cloud.google.com/lakehouse/docs

Company Facts

Organization Name

The Apache Software Foundation

Date Founded

1999

Company Location

United States

Company Website

parquet.apache.org

Categories and Features

Data Warehouse

Ad hoc Query
Analytics
Data Integration
Data Migration
Data Quality Control
ETL - Extract / Transfer / Load
In-Memory Processing
Match & Merge

Categories and Features

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