Introduction to Apache Hadoop

The complexity arises from coordinating data flow between different parts of the system, managing limited communication speed, understanding how events happen over time, and handling unexpected breakdowns.

In distributed systems, the ability to deal with component failures is crucial since they are much more likely than in local systems.

Synchronization issues occur when different parts of a distributed system need to access and modify shared data simultaneously.

The limited communication capacity between parts of a distributed system can lead to performance bottlenecks and delays.

It's challenging to handle events that happen across different parts of a distributed system, especially when considering time differences and potential delays in communication.

Modern systems are dealing with massive amounts of data. This data is being generated and accumulated at an unprecedented rate.

Adding more work to the system should not cause it to crash. Instead, performance should gradually decline.

Data in modern systems is measured in terabytes per day and petabytes in total.

Increasing resources like servers or RAM should allow the system to handle proportionally more work

The traditional approach to handling data is no longer sufficient for modern systems.

The ability of a system to handle increasing amounts of workload without significant performance degradation.

The vast amount of data in modern systems creates a bottleneck.

A system with this characteristic can gracefully handle heavier workloads by distributing tasks across multiple components.

We need a new approach to handle the increasingly large amounts of data in modern systems.

A system with this characteristic can handle heavier workloads by increasing the power of individual components.

Sqoop is a tool used to transfer data between Hadoop and relational databases (RDBMS). It facilitates moving information between these systems for analysis and processing.

Sqoop imports data from relational databases into Hadoop, allowing you to analyze large datasets in a distributed manner.

Sqoop exports data from Hadoop into relational databases, making results accessible to other applications or users.

Sqoop uses connectors to interact with different types of relational databases, like MySQL, Oracle, and PostgreSQL.

Sqoop can be used for both incremental and full imports/exports, allowing users to control the amount of data transferred based on their needs.

Each Map task processes a single block of data from HDFS, usually located on the same node where the block is stored.

The Shuffle and Sort phase prepares the data for the Reducer by sorting and grouping intermediate results from all mappers.

The Reducer receives the sorted intermediate data from all mappers and processes it to produce the final output.

MapReduce is a programming model for processing large datasets across a cluster of computers. It simplifies distributed processing by dividing the task into smaller, independent units (Map tasks) that can be executed in parallel.

The Map tasks are designed to handle the initial processing of data, typically involving operations like data parsing, filtering, or transformation. The Reducer then performs final aggregation or summary calculations to produce meaningful results.

In Hadoop, data files are divided into smaller units called blocks. These blocks are then distributed across multiple data nodes in the cluster. This ensures that the data is spread out and readily accessible by different parts of the system.

To ensure data reliability and availability, each block in Hadoop is replicated on multiple data nodes. The default number of replicas is three, meaning that each block exists on three different nodes. This redundancy ensures that even if one node fails, the data remains accessible.

The NameNode plays a crucial role in Hadoop by maintaining metadata about the data files and their corresponding blocks. It keeps track of which nodes store which blocks, helping the system locate the data efficiently.

The NameNode stores information about the files and blocks. It remembers the location of each block and its replicas. This metadata is vital for organizing and managing the distributed data.

Data nodes are responsible for physically storing the blocks of data. These nodes are distributed across the cluster and hold the actual data files that are divided into blocks.

The blocks, which are the units of data storage in Hadoop, are distributed across multiple data nodes. Each data node holds a specific set of blocks. This distributed storage approach allows for parallel processing and efficient data access by different parts of the system.

The Hadoop Distributed File System (HDFS) is designed to handle very large data files. It's a distributed system that breaks files into blocks, replicates them for redundancy, and distributes them across multiple nodes. This ensures scalability, availability, and fault tolerance for massive datasets.

Hadoop is optimized for handling large-scale datasets. The design principles of HDFS, including block storage, replication, and distribution, enable the system to store and process massive amounts of data efficiently and reliably.