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features
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support

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Description

A Kudu cluster comprises tables that resemble those found in traditional relational (SQL) databases. These tables can range from a straightforward binary key and value structure to intricate designs featuring hundreds of strongly-typed attributes. Similar to SQL tables, each Kudu table is defined by a primary key, which consists of one or more columns; this could be a single unique user identifier or a composite key such as a (host, metric, timestamp) combination tailored for time-series data from machines. The primary key allows for quick reading, updating, or deletion of rows. The straightforward data model of Kudu facilitates the migration of legacy applications as well as the development of new ones, eliminating concerns about encoding data into binary formats or navigating through cumbersome JSON databases. Additionally, tables in Kudu are self-describing, enabling the use of standard analysis tools like SQL engines or Spark. With user-friendly APIs, Kudu ensures that developers can easily integrate and manipulate their data. This approach not only streamlines data management but also enhances overall efficiency in data processing tasks.

Description

JaguarDB facilitates the rapid ingestion of time series data while integrating location-based information. It possesses the capability to index data across both spatial and temporal dimensions effectively. Additionally, the system allows for swift back-filling of time series data, enabling the insertion of significant volumes of historical data points. Typically, time series refers to a collection of data points that are arranged in chronological order. However, in JaguarDB, time series encompasses both a sequence of data points and multiple tick tables that hold aggregated data values across designated time intervals. For instance, a time series table in JaguarDB may consist of a primary table that organizes data points in time sequence, along with tick tables that represent various time frames such as 5 minutes, 15 minutes, hourly, daily, weekly, and monthly, which store aggregated data for those intervals. The structure for RETENTION mirrors that of the TICK format but allows for a flexible number of retention periods, defining the duration for which data points in the base table are maintained. This approach ensures that users can efficiently manage and analyze historical data according to their specific needs.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Apache Flink Yes 
Apache NiFi Yes 
Apache Spark Yes 
BigBI Yes 
Cloudera Data Warehouse Yes 
E-MapReduce Yes 
Hadoop Yes 

Integrations

Apache Flink No 
Apache NiFi No 
Apache Spark No 
BigBI No 
Cloudera Data Warehouse No 
E-MapReduce No 
Hadoop No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based No 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
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 No 
Live Rep (24/7) No 
Online Support No 

Types of Training

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

Types of Training

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

Vendor Details

Company Name

The Apache Software Foundation

Founded

1999

Country

United States

Website

kudu.apache.org/overview.html

Vendor Details

Company Name

JaguarDB

Website

www.datajaguar.com

Product Features

NoSQL Database

Auto-sharding No 
Automatic Database Replication No 
Data Model Flexibility No 
Deployment Flexibility No 
Dynamic Schemas No 
Integrated Caching No 
Multi-Model No 
Performance Management No 
Security Management No 

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