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Average Ratings 0 Ratings

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ease
features
design
support

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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.

Description

Fully compatible with Netezza, this solution offers a streamlined command-line upgrade option. It can be deployed on-premises, in the cloud, or through a hybrid model. The IBM® Netezza® Performance Server for IBM Cloud Pak® for Data serves as a sophisticated platform for data warehousing and analytics, catering to both on-premises and cloud environments. With significant improvements in in-database analytics functions, this next-generation Netezza empowers users to engage in data science and machine learning with datasets that can reach petabyte levels. It includes features for detecting failures and ensuring rapid recovery, making it robust for enterprise use. Users can upgrade existing systems using a single command-line interface. The platform allows for querying multiple systems as a cohesive unit. You can select the nearest data center or availability zone, specify the desired compute units and storage capacity, and initiate the setup seamlessly. Furthermore, the IBM® Netezza® Performance Server is accessible on IBM Cloud®, Amazon Web Services (AWS), and Microsoft Azure, and it can also be implemented on a private cloud, all powered by the capabilities of IBM Cloud Pak for Data System. This flexibility enables organizations to tailor the deployment to their specific needs and infrastructure.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Amazon S3
Arcion
Coginiti
DBHawk
Datametica
Digna
Google Cloud Storage
IBM watsonx.data
IRI Voracity
Impetus
Lyftrondata
Microsoft 365
Microsoft Azure
Nucleon Database Master
Presto
RazorSQL
SOLIXCloud CDP
Semarchy xDI
Style Intelligence

Integrations

Amazon S3
Arcion
Coginiti
DBHawk
Datametica
Digna
Google Cloud Storage
IBM watsonx.data
IRI Voracity
Impetus
Lyftrondata
Microsoft 365
Microsoft Azure
Nucleon Database Master
Presto
RazorSQL
SOLIXCloud CDP
Semarchy xDI
Style Intelligence

Pricing Details

$5 per TB
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

docs.cloud.google.com/lakehouse/docs

Vendor Details

Company Name

IBM

Founded

1911

Country

United States

Website

www.ibm.com/products/netezza

Product Features

Data Warehouse

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

Product Features

Data Warehouse

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

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