Ratings and Reviews 0 Ratings

Total 0 out of 5 stars
ease 0 out of 5 stars
features 0 out of 5 stars
design 0 out of 5 stars
support 0 out of 5 stars

This software has no reviews. Be the first to write a review.

Write a Review

Ratings and Reviews 0 Ratings

Total 0 out of 5 stars
ease 0 out of 5 stars
features 0 out of 5 stars
design 0 out of 5 stars
support 0 out of 5 stars

This software has no reviews. Be the first to write a review.

Write a Review

Alternatives to Consider

  • AnalyticsCreator Reviews & Ratings
    4.6 out of 5 stars 46 Ratings
    Company Website for AnalyticsCreator
  • Google Cloud BigQuery Reviews & Ratings
    4.5 out of 5 stars 2,027 Ratings
    Company Website for Google Cloud BigQuery
  • Denodo Reviews & Ratings
    4.6 out of 5 stars 388 Ratings
    Company Website for Denodo
  • KrakenD Reviews & Ratings
    4.7 out of 5 stars 76 Ratings
    Company Website for KrakenD
  • IONOS Cloud Object Storage Reviews & Ratings
    4.6 out of 5 stars 45,199 Ratings
    Company Website for IONOS Cloud Object Storage
  • Google Cloud Platform Reviews & Ratings
    4.5 out of 5 stars 61,066 Ratings
    Company Website for Google Cloud Platform
  • Filerev Reviews & Ratings
    5.0 out of 5 stars 3 Ratings
    Company Website for Filerev
  • Filevine Reviews & Ratings
    4.5 out of 5 stars 591 Ratings
    Company Website for Filevine
  • PeerGFS Reviews & Ratings
    4.1 out of 5 stars 28 Ratings
    Company Website for PeerGFS
  • SCIKIQ Reviews & Ratings
    4.6 out of 5 stars 14 Ratings
    Company Website for SCIKIQ

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 S3
Apache Spark
Google Cloud BigQuery

Integrations Supported

APERIO DataWise
Apache DataFusion
Arroyo
Astera Dataprep
Blotout
CSViewer
Data Sentinel
Flyte
IBM Db2 Event Store
Indexima Data Hub
MLJAR Studio
OrcaSheets
Querri
SAS Studio
SSIS Integration Toolkit
Streamkap
Tenzir

API Availability

Has API

API Availability

Pricing Information

$5 per TB
Free Trial Offered?

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

Windows
Mac
Linux

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Webinars
On-Site Training

Training Options

Documentation Hub
Webinars

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 Lake

Not specified

Data Warehouse

Not specified

Categories and Features

Columnar Databases

Not specified

Popular Alternatives

Popular Alternatives

Apache Iceberg Reviews & Ratings

Apache Iceberg

Apache Software Foundation
Archon Data Store Reviews & Ratings

Archon Data Store

Platform 3 Solutions
5.0 out of 5 stars