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Description
An in-memory, column-oriented database combined with a Massively Parallel Processing (MPP) architecture enables the rapid querying of billions of records within mere seconds. The distribution of queries across all nodes in a cluster ensures linear scalability, accommodating a larger number of users and facilitating sophisticated analytics. The integration of MPP, in-memory capabilities, and columnar storage culminates in a database optimized for exceptional data analytics performance. With various deployment options available, including SaaS, cloud, on-premises, and hybrid solutions, data analysis can be performed in any environment. Automatic tuning of queries minimizes maintenance efforts and reduces operational overhead. Additionally, the seamless integration and efficiency of performance provide enhanced capabilities at a significantly lower cost compared to traditional infrastructure. Innovative in-memory query processing has empowered a social networking company to enhance its performance, handling an impressive volume of 10 billion data sets annually. This consolidated data repository, paired with a high-speed engine, accelerates crucial analytics, leading to better patient outcomes and improved financial results for the organization. As a result, businesses can leverage this technology to make quicker data-driven decisions, ultimately driving further success.
Description
An adaptable and efficient key-value storage engine, both persistent and in-memory, is engineered for superior performance and resource optimization. It is crafted to effectively handle data on-disk and in-memory by identifying recurring patterns in serialized bytes, without limiting itself to any particular data model, be it SQL or NoSQL, or storage medium, whether it be Disk or RAM. The core system offers a variety of configurations that can be fine-tuned for specific use cases, while also aiming to incorporate automatic runtime adjustments by gathering and analyzing machine statistics and read-write behaviors. Users can manage data easily by utilizing well-known structures such as Map, Set, Queue, SetMap, and MultiMap, all of which can seamlessly convert to native collections in Java and Scala. Furthermore, it allows for conditional updates and data modifications using any Java, Scala, or native JVM code, eliminating the need for a query language and ensuring flexibility in data handling. This design not only promotes efficiency but also encourages the adoption of custom solutions tailored to unique application needs.
API Access
Has API
API Access
Has API
Integrations
Alteryx
Apache Superset
Astro by Astronomer
Azure Marketplace
CONVAYR
Data Virtuality
DataClarity Unlimited Analytics
DataGrip
DbVisualizer
Emgage
Integrations
Alteryx
Apache Superset
Astro by Astronomer
Azure Marketplace
CONVAYR
Data Virtuality
DataClarity Unlimited Analytics
DataGrip
DbVisualizer
Emgage
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
Exasol
Country
Germany
Website
www.exasol.com
Vendor Details
Company Name
SwayDB
Website
swaydb.io
Product Features
Big Data
Collaboration
Data Blends
Data Cleansing
Data Mining
Data Visualization
Data Warehousing
High Volume Processing
No-Code Sandbox
Predictive Analytics
Templates
