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Average Ratings 0 Ratings
Description
Pepperdata autonomous, application-level cost optimization delivers 30-47% greater cost savings for data-intensive workloads such as Apache Spark on Amazon EMR and Amazon EKS with no application changes. Using patented algorithms, Pepperdata Capacity Optimizer autonomously optimizes CPU and memory in real time with no application code changes.
Pepperdata automatically analyzes resource usage in real time, identifying where more work can be done, enabling the scheduler to add tasks to nodes with available resources and spin up new nodes only when existing nodes are fully utilized. The result: CPU and memory are autonomously and continuously optimized, without delay and without the need for recommendations to be applied, and the need for ongoing manual tuning is safely eliminated.
Pepperdata pays for itself, immediately decreasing instance hours/waste, increasing Spark utilization, and freeing developers from manual tuning to focus on innovation.
Description
Zipher is an innovative optimization platform that autonomously enhances the performance and cost-effectiveness of workloads on Databricks by removing the need for manual tuning and resource management, all while making real-time adjustments to clusters. Utilizing advanced proprietary machine learning algorithms, Zipher features a unique Spark-aware scaler that actively learns from and profiles workloads to determine the best resource allocations, optimize configurations for each job execution, and fine-tune various settings such as hardware, Spark configurations, and availability zones, thereby maximizing operational efficiency and minimizing waste. The platform continuously tracks changing workloads to modify configurations, refine scheduling, and distribute shared compute resources effectively to adhere to service level agreements (SLAs), while also offering comprehensive cost insights that dissect expenses related to Databricks and cloud services, enabling teams to pinpoint significant cost influencers. Furthermore, Zipher ensures smooth integration with major cloud providers like AWS, Azure, and Google Cloud, and is compatible with popular orchestration and infrastructure-as-code (IaC) tools, making it a versatile solution for various cloud environments. Its ability to adaptively respond to workload changes sets Zipher apart as a crucial tool for organizations striving to optimize their cloud operations.
API Access
Has API
API Access
Has API
Integrations
AWS Marketplace
Amazon EKS
Amazon EMR
Amazon Web Services (AWS)
Apache Airflow
Apache Spark
Azure Data Factory
Databricks Data Intelligence Platform
Google Cloud Dataproc
Google Cloud Platform
Integrations
AWS Marketplace
Amazon EKS
Amazon EMR
Amazon Web Services (AWS)
Apache Airflow
Apache Spark
Azure Data Factory
Databricks Data Intelligence Platform
Google Cloud Dataproc
Google Cloud Platform
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
Pepperdata, Inc.
Founded
2012
Country
United States
Website
www.pepperdata.com
Vendor Details
Company Name
Zipher
Founded
2023
Country
United States
Website
zipher.cloud/
Product Features
Application Performance Monitoring (APM)
Baseline Manager
Diagnostic Tools
Full Transaction Diagnostics
Performance Control
Resource Management
Root-Cause Diagnosis
Server Performance
Trace Individual Transactions
Cloud Cost Management
Cost Reduction Optimization
Dashboard
Data Import/Export
Data Storage
Data Visualization
Resource Usage Reporting
Roles / Permissions
Spend and Cost Reporting