Audience

Organizations seeking a hybrid data warehousing solution to simplify deployment and management through container technology

About dashDB Local

As the newest edition to the IBM dashDB family, dashDB Local rounds out IBM's hybrid data warehouse strategy, providing organizations the most flexible architecture needed to lower the cost model of analytics in the dynamic world of big data and the cloud. How is this possible? Through a common analytics engine, with different deployment options across private and public clouds, analytics workloads can be moved and optimized with ease. dashDB Local is now an option when you prefer deployment on a hosted private cloud or on-premises private cloud through a software-defined infrastructure. From an IT standpoint, dashDB Local simplifies deployment and management through container technology, with elastic scaling and easy maintenance. From a user standpoint, dashDB Local provides the speed needed to quickly cycle through the process of data acquisition, applies the right analytics to meet a specific use case, and operationalizes the insights.

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Integrations

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Ratings/Reviews

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
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Company Information

IBM
Founded: 1911
United States
www-01.ibm.com/common/ssi/cgi-bin/ssialias

Videos and Screen Captures

dashDB Local Screenshot 1
dashDB Local Screenshot 1
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Product Details

Platforms Supported
Cloud
On-Premises
Training
Documentation
Live Online
Webinars
In Person
Support
Phone Support
24/7 Live Support
Online

dashDB Local Frequently Asked Questions

Q: What kinds of users and organization types does dashDB Local work with?
Q: What languages does dashDB Local support in their product?
Q: What kind of support options does dashDB Local offer?
Q: What other applications or services does dashDB Local integrate with?
Q: What type of training does dashDB Local provide?

dashDB Local Product Features

Data Warehouse

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