MapReduce is a powerful model for processing massive amounts of data in parallel through distributed systems like Apache Hadoop. This technology, popularized by Google, enables automatic parallelization and fault tolerance, allowing for efficient data processing at scale. Learn about the motivation

2 views • 33 slides

HDFS (Hadoop Distributed File System) is designed for handling large data sets across commodity hardware. It emphasizes throughput over latency and is well-suited for batch processing applications. The architecture includes components like NameNode (master) and DataNode (participants), focusing on s

0 views • 15 slides

Distributed systems encompass a range of technologies aimed at improving storage efficiency and reliability. This includes RAID (Redundant Array of Inexpensive Disks) strategies such as RAID levels, Lustre Linux Cluster for high-performance clusters, MogileFS for fast content delivery, and HDFS (Had

0 views • 23 slides

Learn about SQL Server Polybase, a data virtualization feature that allows distributed query processing and data virtualization across various sources such as HDFS, Cosmos DB, and more. Discover how to use Polybase to build a data hub within SQL Server, enabling efficient query performance and analy

0 views • 20 slides

Big data security is a crucial aspect in today's computing landscape, especially with the increasing reliance on cloud computing and distributed frameworks like Hadoop. This overview covers key topics such as data classification, Hadoop security mechanisms, and challenges in securing the Hadoop Dist

0 views • 61 slides

Explore the advanced features of Hadoop Distributed File System (HDFS) including Highly Available NameNode setup, HA NameNode Failover, ZooKeeper lock management, HDFS Federation benefits, and Federated NameNodes scalability beyond heap size. Learn about ensuring fault tolerance, performance, and sc

0 views • 37 slides