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Average Ratings 0 Ratings

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features
design
support

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Write a Review

Description

Numerous customers of Amazon Web Services (AWS) seek a data storage and analytics solution that surpasses the agility and flexibility of conventional data management systems. A data lake has emerged as an innovative and increasingly favored method for storing and analyzing data, as it enables organizations to handle various data types from diverse sources, all within a unified repository that accommodates both structured and unstructured data. The AWS Cloud supplies essential components necessary for customers to create a secure, adaptable, and economical data lake. These components comprise AWS managed services designed to assist in the ingestion, storage, discovery, processing, and analysis of both structured and unstructured data. To aid our customers in constructing their data lakes, AWS provides a comprehensive data lake solution, which serves as an automated reference implementation that establishes a highly available and cost-efficient data lake architecture on the AWS Cloud, complete with an intuitive console for searching and requesting datasets. Furthermore, this solution not only enhances data accessibility but also streamlines the overall data management process for organizations.

Description

Google Cloud Lakehouse is a modern data storage and management solution that combines the capabilities of data warehouses and data lakes into a unified platform. It enables organizations to store, access, and analyze data in open formats like Apache Iceberg, Parquet, and ORC without duplication. By maintaining a single source of truth, the platform eliminates the need for complex data movement and reduces operational overhead. It offers fine-grained security controls, allowing organizations to manage access and governance policies effectively. The Lakehouse runtime catalog provides centralized metadata management and simplifies resource organization. The platform supports scalable analytics and integrates seamlessly with tools like Apache Spark for advanced data processing. It is designed to handle large-scale data workloads while maintaining high performance and reliability. Built-in best practices and guides help users optimize their data architecture. It also supports replication and disaster recovery for enhanced resilience. Overall, Google Cloud Lakehouse provides a flexible and efficient way to unify and analyze enterprise data.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Amazon S3 No 
Apache Iceberg No 
Apache Spark No 
Azure Data Lake No 
Google Cloud BigQuery No 
Google Cloud Storage No 
Presto No 
Rocket Data Replicate & Sync Yes 
VE3 DataWise Yes 

Integrations

Amazon S3 Yes 
Apache Iceberg Yes 
Apache Spark Yes 
Azure Data Lake Yes 
Google Cloud BigQuery Yes 
Google Cloud Storage Yes 
Presto Yes 
Rocket Data Replicate & Sync No 
VE3 DataWise No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

$5 per TB
Free Trial Yes 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) No 
In Person Yes 

Vendor Details

Company Name

Amazon

Founded

1994

Country

United States

Website

aws.amazon.com/solutions/implementations/data-lake-solution/

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

docs.cloud.google.com/lakehouse/docs

Product Features

Data Analysis

Data Discovery No 
Data Visualization No 
High Volume Processing No 
Predictive Analytics No 
Regression Analysis No 
Sentiment Analysis No 
Statistical Modeling No 
Text Analytics No 

Data Management

Customer Data No 
Data Analysis No 
Data Capture No 
Data Integration No 
Data Migration No 
Data Quality Control No 
Data Security No 
Information Governance No 
Master Data Management No 
Match & Merge No 

Product Features

Data Warehouse

Ad hoc Query No 
Analytics No 
Data Integration No 
Data Migration No 
Data Quality Control No 
ETL - Extract / Transfer / Load No 
In-Memory Processing No 
Match & Merge No 

Alternatives

Alternatives

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