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What is Kinetica?

Kinetica is a cloud database designed to effortlessly scale and manage extensive streaming data sets. By leveraging cutting-edge vectorized processors, it significantly accelerates performance for both real-time spatial and temporal tasks, resulting in processing speeds that are orders of magnitude quicker. In a dynamic environment, it enables the monitoring and analysis of countless moving objects, providing valuable insights. The innovative vectorization technique enhances performance for analytics concerning spatial and time series data, even at significant scales. Users can execute queries and ingest data simultaneously, facilitating prompt responses to real-time events. Kinetica’s lockless architecture ensures that data can be ingested in a distributed manner, making it accessible immediately upon arrival. This advanced vectorized processing not only optimizes resource usage but also simplifies data structures for more efficient storage, ultimately reducing the time spent on data engineering. As a result, Kinetica equips users with the ability to perform rapid analytics and create intricate visualizations of dynamic objects across vast datasets. In this way, businesses can respond more agilely to changing conditions and derive deeper insights from their data.

What is Apache Pinot?

Pinot is designed to optimize the handling of OLAP queries with low latency when working with static data. It supports a variety of pluggable indexing techniques, such as Sorted Index, Bitmap Index, and Inverted Index. Although it does not currently facilitate joins, this can be circumvented by employing Trino or PrestoDB for executing queries. The platform offers an SQL-like syntax that enables users to perform selection, aggregation, filtering, grouping, ordering, and distinct queries on the data. It comprises both offline and real-time tables, where real-time tables are specifically implemented to fill gaps in offline data availability. Furthermore, users have the capability to customize the anomaly detection and notification processes, allowing for precise identification of significant anomalies. This adaptability ensures users can uphold robust data integrity while effectively addressing their analytical requirements, ultimately enhancing their overall data management strategy.

Media

Media

Integrations Supported

Amazon S3
Apache Kafka
Astro by Astronomer
Azure Data Lake
Gravity Data
Hadoop
Hue
Kestra
NVIDIA RAPIDS
Onehouse
OpenMetadata
Oracle Cloud Infrastructure Object Storage
Oracle Digital Assistant
Oracle Endeca Commerce
Oracle Machine Learning
Oracle Partner Relationship Management
Oracle Subscription Management
Oracle Textura Pre-Qualification
Oracle Unity Customer Data Platform
StarTree

Integrations Supported

Amazon S3
Apache Kafka
Astro by Astronomer
Azure Data Lake
Gravity Data
Hadoop
Hue
Kestra
NVIDIA RAPIDS
Onehouse
OpenMetadata
Oracle Cloud Infrastructure Object Storage
Oracle Digital Assistant
Oracle Endeca Commerce
Oracle Machine Learning
Oracle Partner Relationship Management
Oracle Subscription Management
Oracle Textura Pre-Qualification
Oracle Unity Customer Data Platform
StarTree

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Kinetica

Date Founded

2009

Company Location

United States

Company Website

www.kinetica.com

Company Facts

Organization Name

Apache Corporation

Date Founded

1954

Company Location

United Statess

Company Website

pinot.apache.org

Categories and Features

Data Warehouse

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

Streaming Analytics

Data Enrichment
Data Wrangling / Data Prep
Multiple Data Source Support
Process Automation
Real-time Analysis / Reporting
Visualization Dashboards

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