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What is Pepperdata?

Pepperdata's autonomous, application-level cost optimization achieves significant savings of 30-47% for data-heavy tasks like Apache Spark running on Amazon EMR and Amazon EKS, all without requiring any modifications to the application. By utilizing proprietary algorithms, the Pepperdata Capacity Optimizer effectively and autonomously fine-tunes CPU and memory resources in real time, again with no need for changes to application code. The system continuously analyzes resource utilization in real time, pinpointing areas for increased workload, which allows the scheduler to efficiently allocate tasks to nodes that have available resources and initiate new nodes only when current ones reach full capacity. This results in a seamless and ongoing optimization of CPU and memory usage, eliminating delays and the necessity for manual recommendations while also removing the constant need for manual tuning. Moreover, Pepperdata provides a rapid return on investment by immediately lowering wasted instance hours, enhancing Spark utilization, and allowing developers to shift their focus from manual tuning tasks to driving innovation. Overall, this solution not only improves operational efficiency but also streamlines the development process, leading to better resource management and productivity.

What is MLlib?

MLlib, the machine learning component of Apache Spark, is crafted for exceptional scalability and seamlessly integrates with Spark's diverse APIs, supporting programming languages such as Java, Scala, Python, and R. It boasts a comprehensive array of algorithms and utilities that cover various tasks including classification, regression, clustering, collaborative filtering, and the construction of machine learning pipelines. By leveraging Spark's iterative computation capabilities, MLlib can deliver performance enhancements that surpass traditional MapReduce techniques by up to 100 times. Additionally, it is designed to operate across multiple environments, whether on Hadoop, Apache Mesos, Kubernetes, standalone clusters, or within cloud settings, while also providing access to various data sources like HDFS, HBase, and local files. This adaptability not only boosts its practical application but also positions MLlib as a formidable tool for conducting scalable and efficient machine learning tasks within the Apache Spark ecosystem. The combination of its speed, versatility, and extensive feature set makes MLlib an indispensable asset for data scientists and engineers striving for excellence in their projects. With its robust capabilities, MLlib continues to evolve, reinforcing its significance in the rapidly advancing field of machine learning.

Media

Media

Integrations Supported

Apache Spark
AWS Marketplace
Amazon EKS
Amazon EMR
Google Cloud Managed Service for Apache Spark

Integrations Supported

Apache Spark
Amazon EC2
Apache Cassandra
Apache HBase
Apache Hive
Apache Mesos
Hadoop
Java
Kubernetes
MapReduce
Python
R
Scala

API Availability

API Availability

Has API

Pricing Information

Pricing not provided
Free Trial Offered?

Pricing Information

Pricing not provided

Supported Platforms

SaaS
Windows
Mac

Supported Platforms

SaaS

Customer Service / Support

Standard Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub
Webinars
On-Site Training

Training Options

Documentation Hub
Webinars
On-Site Training

Company Facts

Organization Name

Pepperdata, Inc.

Date Founded

2012

Company Location

United States

Company Website

www.pepperdata.com

Company Facts

Organization Name

Apache Software Foundation

Date Founded

1995

Company Location

United States

Company Website

spark.apache.org/mllib/

Categories and Features

Application Performance Monitoring (APM)

Baseline Manager
Diagnostic Tools
Full Transaction Diagnostics
Performance Control
Resource Management
Root-Cause Diagnosis
Server Performance
Trace Individual Transactions

Auto Scaling

Not specified

Cloud Cost Management

Cost Reduction Optimization
Dashboard
Data Visualization
Resource Usage Reporting
Roles / Permissions
Spend and Cost Reporting

Categories and Features

Machine Learning

Not specified

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