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Average Ratings 0 Ratings

Total
ease
features
design
support

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Write a Review

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.

Description

Qlik Compose for Data Warehouses offers a contemporary solution that streamlines and enhances the process of establishing and managing data warehouses. This tool not only automates the design of the warehouse but also generates ETL code and implements updates swiftly, all while adhering to established best practices and reliable design frameworks. By utilizing Qlik Compose for Data Warehouses, organizations can significantly cut down on the time, expense, and risk associated with BI initiatives, regardless of whether they are deployed on-premises or in the cloud. On the other hand, Qlik Compose for Data Lakes simplifies the creation of analytics-ready datasets by automating data pipeline processes. By handling data ingestion, schema setup, and ongoing updates, companies can achieve a quicker return on investment from their data lake resources, further enhancing their data strategy. Ultimately, these tools empower organizations to maximize their data potential efficiently.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Advanced Query Tool (AQT)
Amazon S3
BLUE Software
DBHawk
Datametica
DbVisualizer
IRI Voracity
Lyftrondata
Microsoft 365
Nucleon Database Master
Pantomath
RazorSQL
SAS Federation Server
SMART Business Suite
SOLIXCloud
SOLIXCloud CDP
StreamFlux
Style Intelligence
Tenant

Integrations

Advanced Query Tool (AQT)
Amazon S3
BLUE Software
DBHawk
Datametica
DbVisualizer
IRI Voracity
Lyftrondata
Microsoft 365
Nucleon Database Master
Pantomath
RazorSQL
SAS Federation Server
SMART Business Suite
SOLIXCloud
SOLIXCloud CDP
StreamFlux
Style Intelligence
Tenant

Pricing Details

No price information available.
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

IBM

Founded

1911

Country

United States

Website

www.ibm.com/products/netezza

Vendor Details

Company Name

Qlik

Founded

1993

Country

United States

Website

www.qlik.com/us/products/qlik-compose-data-warehouses

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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