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What is Katana Graph?

Streamlined distributed computing greatly improves the performance of graph analytics while minimizing the need for significant infrastructure modifications. By integrating a wider array of data for standardization and visualization within the graph framework, the depth of insights can be notably enhanced. The synergy between developments in graph technology and deep learning produces efficiencies that enable rapid insights on the largest graphs available. Katana Graph provides financial services companies with the necessary tools to explore the expansive opportunities presented by graph analytics and AI at scale, facilitating applications that range from real-time fraud detection to detailed customer insights. Utilizing advancements in high-performance parallel computing (HPC), Katana Graph’s smart platform assesses risks and reveals customer insights from vast data sets through swift analytics and AI capabilities that outperform traditional graph technologies. This innovative strategy empowers organizations to remain ahead of emerging trends and make informed, data-driven decisions confidently, ultimately driving business success.

What is Apache Spark?

Apache Spark™ is a powerful analytics platform crafted for large-scale data processing endeavors. It excels in both batch and streaming tasks by employing an advanced Directed Acyclic Graph (DAG) scheduler, a highly effective query optimizer, and a streamlined physical execution engine. With more than 80 high-level operators at its disposal, Spark greatly facilitates the creation of parallel applications. Users can engage with the framework through a variety of shells, including Scala, Python, R, and SQL. Spark also boasts a rich ecosystem of libraries—such as SQL and DataFrames, MLlib for machine learning, GraphX for graph analysis, and Spark Streaming for processing real-time data—which can be effortlessly woven together in a single application. This platform's versatility allows it to operate across different environments, including Hadoop, Apache Mesos, Kubernetes, standalone systems, or cloud platforms. Additionally, it can interface with numerous data sources, granting access to information stored in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and many other systems, thereby offering the flexibility to accommodate a wide range of data processing requirements. Such a comprehensive array of functionalities makes Spark a vital resource for both data engineers and analysts, who rely on it for efficient data management and analysis. The combination of its capabilities ensures that users can tackle complex data challenges with greater ease and speed.

Media

Media

Integrations Supported

5GSoftware
Alibaba Log Service
Baidu Palo
Baidu Sugar
DataNimbus
Jovian
Kedro
Molecula
PySpark
Qlik Staige
RunCode
Saagie
Speedb
Tabular
Timbr.ai
Unravel
Vertex AI
Warp 10
Zepl
matchit

Integrations Supported

5GSoftware
Alibaba Log Service
Baidu Palo
Baidu Sugar
DataNimbus
Jovian
Kedro
Molecula
PySpark
Qlik Staige
RunCode
Saagie
Speedb
Tabular
Timbr.ai
Unravel
Vertex AI
Warp 10
Zepl
matchit

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

Katana Graph

Date Founded

2020

Company Location

United States

Company Website

katanagraph.com

Company Facts

Organization Name

Apache Software Foundation

Date Founded

1999

Company Location

United States

Company Website

spark.apache.org

Categories and Features

Big Data

Collaboration
Data Blends
Data Cleansing
Data Mining
Data Visualization
Data Warehousing
High Volume Processing
No-Code Sandbox
Predictive Analytics
Templates

Data Visualization

Analytics
Content Management
Dashboard Creation
Filtered Views
OLAP
Relational Display
Simulation Models
Visual Discovery

Categories and Features

Big Data

Collaboration
Data Blends
Data Cleansing
Data Mining
Data Visualization
Data Warehousing
High Volume Processing
No-Code Sandbox
Predictive Analytics
Templates

Data Analysis

Data Discovery
Data Visualization
High Volume Processing
Predictive Analytics
Regression Analysis
Sentiment Analysis
Statistical Modeling
Text Analytics

Streaming Analytics

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

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