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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 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

Amazon EMR
Google Cloud Managed Service for Apache Spark

Integrations Supported

Amazon EMR
Google Cloud Managed Service for Apache Spark
Amazon SageMaker Data Wrangler
Apache HBase
Apache Kudu
Azure HDInsight
Botify.cloud
Coginiti
Great Expectations
IBM Cloud SQL Query
IBM Intelligent Operations Center for Emergency Mgmt
IBM watsonx.data
Kubernetes
Kylo
Oracle AI Data Platform (AIDP)
RunCode
Sync
Zepl

API Availability

API Availability

Pricing Information

Pricing not provided
Free Trial Offered?

Pricing Information

Pricing not provided
Free Version

Supported Platforms

SaaS
Windows
Mac

Supported Platforms

SaaS

Customer Service / Support

Standard Support

Customer Service / Support

Not specified

Training Options

Documentation Hub
Webinars
On-Site Training

Training Options

Documentation Hub

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

1999

Company Location

United States

Company Website

spark.apache.org

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

Big Data

Not specified

Data Analysis

Not specified

Data Modeling

Not specified

Query Engines

Not specified

Streaming Analytics

Data Enrichment
Data Wrangling / Data Prep
Multiple Data Source Support
Process Automation

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