Hadoop

1. Characteristics and Environment of Hadoop

1.1. Hadoop Overview: Open-source framework for distributed storage and processing of large datasets.

1.2. Core Components:

1.2.1. HDFS (Hadoop Distributed File System): Distributed storage for large datasets with fault tolerance through data replication.

1.2.2. MapReduce: Parallel processing model for data.

1.2.3. YARN: Resource manager for managing and scheduling tasks.

1.2.4. Hadoop Common: Libraries and utilities needed for Hadoop component

1.3. Environment: Suitable for large-scale data processing environments, from data lakes to cloud infrastructure.

2. MapReduce in Hadoop

2.1. MapReduce Model: A programming model for parallel data processing. Map Phase: Data is split into smaller chunks, processed in parallel. Reduce Phase: Results are aggregated from the map tasks.

2.2. Efficiency: Increases processing speed and optimizes resource use by executing tasks in parallel across the cluster.

3. Hadoop Advantages

3.1. Scalability: Horizontally scalable; can process large datasets across distributed clusters.

3.2. Cost-efficiency: Utilizes commodity hardware and open-source software., unstructured).

3.3. Fault Tolerance: Data replication ensures system reliability, even if a node fails.

3.4. High-Throughput: Optimized for high-speed data processing.

3.5. Flexibility: Supports diverse types of data (structured, semi-structured

4. Timeline of the Development of Hadoop

4.1. - 2003: Google introduces the Google File System (GFS).

4.2. 2004: Google publishes the MapReduce programming model.

4.3. 2006: Hadoop is developed by Doug Cutting and Mike Cafarella as an open-source project under Apache.

4.4. 2008: Hadoop becomes a top-level project at Apache Software Foundation.

4.5. Present: Ongoing improvements, extensive adoption in big data solutions.

5. Relation of Big Data Analytics with Machine Learning and Cloud Computing.

5.1. Big Data Analytics: The process of analyzing large datasets to uncover patterns, trends, and associations.

5.1.1. Hadoop provides the infrastructure for storing and processing this data.

5.2. Machine Learning (ML): Uses insights from Big Data to build predictive models and automate decision-making processes.

5.2.1. Hadoop helps process the large datasets needed for training ML models.

5.3. Cloud Computing: Offers on-demand computing resources to handle the storage and computation requirements of big data.

5.3.1. Hadoop integrates with cloud platforms to scale resources as needed for data processing and storage.

6. Google File System and Hadoop

6.1. Google File System (GFS): A distributed file system designed for efficient storage and retrieval of large datasets.

6.2. Hadoop Distributed File System (HDFS): Inspired by GFS, HDFS divides large files into blocks and replicates them across nodes in a cluster to ensure fault tolerance and high availability.