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Average Ratings 0 Ratings
Description
Apache Eagle, referred to simply as Eagle, serves as an open-source analytics tool designed to quickly pinpoint security vulnerabilities and performance challenges within extensive data environments such as Apache Hadoop and Apache Spark. It examines various data activities, YARN applications, JMX metrics, and daemon logs, offering a sophisticated alert system that helps detect security breaches and performance problems while providing valuable insights. Given that big data platforms produce vast quantities of operational logs and metrics in real-time, Eagle was developed to tackle the complex issues associated with securing and optimizing performance for these environments, ensuring that metrics and logs remain accessible and that alerts are triggered promptly, even during high traffic periods. By streaming operational logs and data activities into the Eagle platform—including, but not limited to, audit logs, MapReduce jobs, YARN resource usage, JMX metrics, and diverse daemon logs—it generates alerts, displays historical trends, and correlates alerts with raw data, thus enhancing security and performance monitoring. This comprehensive approach makes it an invaluable resource for organizations managing big data infrastructures.
Description
MLlib, the machine learning library of Apache Spark, is designed to be highly scalable and integrates effortlessly with Spark's various APIs, accommodating programming languages such as Java, Scala, Python, and R. It provides an extensive range of algorithms and utilities, which encompass classification, regression, clustering, collaborative filtering, and the capabilities to build machine learning pipelines. By harnessing Spark's iterative computation features, MLlib achieves performance improvements that can be as much as 100 times faster than conventional MapReduce methods. Furthermore, it is built to function in a variety of environments, whether on Hadoop, Apache Mesos, Kubernetes, standalone clusters, or within cloud infrastructures, while also being able to access multiple data sources, including HDFS, HBase, and local files. This versatility not only enhances its usability but also establishes MLlib as a powerful tool for executing scalable and efficient machine learning operations in the Apache Spark framework. The combination of speed, flexibility, and a rich set of features renders MLlib an essential resource for data scientists and engineers alike.
API Access
Has API
API Access
Has API
Integrations
Amazon EC2
Apache Cassandra
Apache HBase
Apache Hive
Apache Mesos
Apache Spark
Hadoop
Java
Kubernetes
MapReduce
Integrations
Amazon EC2
Apache Cassandra
Apache HBase
Apache Hive
Apache Mesos
Apache Spark
Hadoop
Java
Kubernetes
MapReduce
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
eagle.apache.org
Vendor Details
Company Name
Apache Software Foundation
Founded
1995
Country
United States
Website
spark.apache.org/mllib/
Product Features
IT Alerting
Alert Noise Reduction
Alert Routing
Dynamic Notifications
Enriched Incident Context
Escalation Policies
Incident History Audit
Multi-User Alerting
Multiple Alert Types
On-Call Management
Rich HTML Email Notifications
Product Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization