Intel®️ Tiber™️ App-Level Optimization blogs: Big Data
Understanding PySpark: Features, Ecosystem, and Optimization
PySpark is a Python library for Apache Spark that allows users to interface with Spark using Python
AWS EMR Tutorial: Configuring and Managing Your First Cluster
Amazon EMR (formerly Amazon Elastic MapReduce) is a managed platform for cluster-based workloads. Learn how to plan, configure, and manage your...
Hadoop vs. Spark: 5 Key Differences and Using Them Together
The Hadoop platform is an open source system that allows storing and processing larger data sets on a cloud base. Apache Spark is an open source...
5 PySpark Optimization Techniques You Should Know
Apache PySpark is the Python API for Apache Spark, an open-source, distributed computing system that is designed for high-speed processing of...
Hadoop: Basics, Running in the Cloud, Alternatives & Best Practices
Hadoop is an open source distributed processing framework that manages data processing and storage for big data applications.
Apache Spark: Architecture, Best Practices, and Alternatives
Apache Spark is an analytics engine that rapidly performs processing tasks on large datasets. It can distribute data processing tasks on...
AWS EMR Clusters and Nodes
Amazon EMR lets you leverage a group of Amazon Elastic Compute Cloud (EC2) instances as a cluster for rapid processing and analysis.
Spark on AWS: Amazon EMR Features & Creating Your First Cluster
Apache Spark is an open source, distributed data processing system for big data applications. It enables fast data analysis using in-memory...
Running Hadoop on AWS: The Basics and 5 Tips for Success
You can run Apache Hadoop on AWS using Amazon EMR, a managed service for processing and analyzing large datasets.