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Description
Bigtop is a project under the Apache Foundation designed for Infrastructure Engineers and Data Scientists who need a thorough solution for packaging, testing, and configuring leading open source big data technologies. It encompasses a variety of components and projects, such as Hadoop, HBase, and Spark, among others. By packaging Hadoop RPMs and DEBs, Bigtop simplifies the management and maintenance of Hadoop clusters. Additionally, it offers an integrated smoke testing framework, complete with a collection of over 50 test files to ensure reliability. For those looking to deploy Hadoop from scratch, Bigtop provides vagrant recipes, raw images, and in-progress docker recipes. The framework is compatible with numerous Operating Systems, including Debian, Ubuntu, CentOS, Fedora, and openSUSE, among others. Moreover, Bigtop incorporates a comprehensive set of tools and a testing framework that evaluates various aspects, such as packaging, platform, and runtime, which are essential for both new deployments and upgrades of the entire data platform, rather than just isolated components. This makes Bigtop a vital resource for anyone aiming to streamline their big data infrastructure.
Description
Effortlessly load your data into or extract it from Hadoop and data lakes, ensuring it is primed for generating reports, visualizations, or conducting advanced analytics—all within the data lakes environment. This streamlined approach allows you to manage, transform, and access data stored in Hadoop or data lakes through a user-friendly web interface, minimizing the need for extensive training. Designed specifically for big data management on Hadoop and data lakes, this solution is not simply a rehash of existing IT tools. It allows for the grouping of multiple directives to execute either concurrently or sequentially, enhancing workflow efficiency. Additionally, you can schedule and automate these directives via the public API provided. The platform also promotes collaboration and security by enabling the sharing of directives. Furthermore, these directives can be invoked from SAS Data Integration Studio, bridging the gap between technical and non-technical users. It comes equipped with built-in directives for various tasks, including casing, gender and pattern analysis, field extraction, match-merge, and cluster-survive operations. For improved performance, profiling processes are executed in parallel on the Hadoop cluster, allowing for the seamless handling of large datasets. This comprehensive solution transforms the way you interact with data, making it more accessible and manageable than ever.
API Access
Has API
API Access
Has API
Integrations
Hadoop
Apache HBase
Apache Spark
Beats
Impala
Jenkins
Jira
Microsoft 365
Microsoft Power BI
SAS Analytics for IoT
Integrations
Hadoop
Apache HBase
Apache Spark
Beats
Impala
Jenkins
Jira
Microsoft 365
Microsoft Power BI
SAS Analytics for IoT
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
Apache Software Foundation
Founded
1999
Country
United States
Website
bigtop.apache.org
Vendor Details
Company Name
SAS
Founded
1976
Country
United States
Website
www.sas.com/en_us/software/data-loader-for-hadoop.html
Product Features
Software Testing
Automated Testing
Black-Box Testing
Dynamic Testing
Issue Tracking
Manual Testing
Quality Assurance Planning
Reporting / Analytics
Static Testing
Test Case Management
Variable Testing Methods
White-Box Testing
Product Features
Data Preparation
Collaboration Tools
Data Access
Data Blending
Data Cleansing
Data Governance
Data Mashup
Data Modeling
Data Transformation
Machine Learning
Visual User Interface
