Intel Granulate blogs: Big Data
Apache Spark Architecture Simplified
Apache Spark’s architecture handles large-scale data processing. It uses a central coordinator known as the Spark Driver and distributed...
PySpark vs. Spark: 7 Key Differences and How to Choose
Apache Spark is a unified engine for large-scale data processing. PySpark is its Python API, allowing Python programmers to leverage Spark's...
Hadoop Cluster: Architecture, Pros/Cons, and a Quick Tutorial
A Hadoop cluster is a group of machines used for storing and analyzing unstructured data in a distributed environment.
Ultimate Guide to AWS EMR: Use Cases, How It Works, Pricing and More
AWS EMR processes data across a Hadoop cluster of virtual servers on Amazon Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2)
Spark Security: Top Vulnerabilities and 6 Ways to Secure Your Spark
Apache Spark security includes methodologies, tools, and best practices to protect Spark applications and data from unauthorized access, theft,...
Kubernetes vs. YARN for Resource Management: How to Choose
Explore what Kubernetes and YARN do, how they differ and how to choose the best solution to get the most out of your containerized environment.
Cloudera vs. Databricks: 6 Key Differences and How to Choose
Cloudera offers a platform for data analytics and machine learning, while Databricks provides a cloud-based data analytics platform, using...
Optimizing AI: Real-time Stream Processing
The latest in Intel Granulate's series on optimizing AI and the applications that support them, learn about optimization for real-time stream...
Cloudera vs Databricks vs Snowflake: Choosing the Right Data Management Platform for Your Needs
Dive into the world of three major players in data management: Cloudera, Databricks, and Snowflake, and discover which one is right for your...