Quick reference guide to key components in the Hadoop ecosystem based on the udemy course The Ultimate Hands-On Hadoop - Tame your Big Data!. We'll start by describing the components of the Hadoop and link to different guides for each component.
Hadoop Core is the foundational framework of the Apache Hadoop ecosystem, providing essential components for distributed data processing and storage.
- Storage system for big data across a cluster.
- Provides reliable, scalable, and distributed data storage.
- Replicates data for fault tolerance.
- Optimized for batch processing workloads.
- Manages resources in a Hadoop cluster.
- Handles allocation of nodes and computing capacity.
- Efficiently utilizes resources for applications.
- Programming model for distributed data processing in Hadoop.
- Divides tasks into map and reduce phases.
- Enables parallel execution across the cluster.
Additional components that complement the Hadoop ecosystem can be found here.
- High-level language for data analysis and transformation on Hadoop.
- Allows complex queries and data processing.
- Utilizes SQL-like syntax for ease of use.
- Supports data processing pipelines and custom processing logic.
- Data warehouse infrastructure for querying structured data stored in Hadoop.
- Provides a SQL-like interface.
- Manages metadata for easy data exploration.
- Supports schema evolution and query optimization.
- Management and monitoring tool for Hadoop clusters.
- Offers comprehensive view and control of components and services.
- Facilitates cluster administration and monitoring.
- Resource management platform for Hadoop clusters.
- Efficiently allocates resources, such as nodes and computing capacity.
- Works alongside YARN or serves as an alternative resource negotiator.
- Fast and powerful data processing engine for Hadoop.
- Supports in-memory processing, real-time streaming, and machine learning.
- Enables interactive queries and analysis.
- Provides a rich set of libraries and APIs for various data processing tasks.
- Framework for optimizing data processing in Hadoop.
- Utilizes Directed Acyclic Graphs (DAGs) for efficient execution of complex queries.
- Often used in conjunction with Hive.
- Distributed, column-oriented NoSQL database for Hadoop.
- Enables low-latency, random access to large volumes of structured and semi-structured data.
- Real-time stream processing system for Hadoop.
- Handles continuous streams of data.
- Enables real-time analytics and decision-making.
- Supports fault-tolerance and complex stream processing topologies.
- Workflow scheduler for managing complex Hadoop jobs.
- Allows defining and executing interconnected tasks.
- Facilitates job coordination, scheduling, and monitoring.
- Coordination service for distributed systems in Hadoop.
- Maintains shared configuration, synchronization, and naming services.
- Facilitates coordination and management of cluster components.
- Provides distributed coordination and high availability features.
Tools for ingesting data into the Hadoop ecosystem:
- Tool for transferring data between Hadoop and relational databases.
- Facilitates importing data into Hadoop or exporting data from Hadoop to external databases.
- Distributed data collection and aggregation system for Hadoop.
- Enables reliable and scalable ingestion of streaming data into Hadoop.
- Distributed streaming platform for collecting, storing, and processing real-time data streams.
- Publishes data from various sources to Hadoop.
- Widely used open-source relational database management system.
- Can be integrated with Hadoop for external data storage and processing.
- Highly scalable and fault-tolerant distributed NoSQL database.
- Suitable for storing large volumes of structured and unstructured data.
- Document-oriented NoSQL database with flexible schema design.
- Provides high performance and scalability for storing and querying data.
Engines for querying and analyzing data within the Hadoop ecosystem:
- Distributed SQL query engine for analyzing data in various formats, including NoSQL databases.
- Supports querying structured and semi-structured data.
- Web-based interface for interacting with Hadoop components, such as Hive and HBase.
- Provides a user-friendly environment for querying and data exploration.
- SQL query engine for HBase.
- Enables executing SQL queries directly on HBase, facilitating interaction with HBase data.
- Distributed SQL query engine for interactive analytics on large datasets.
- Provides high performance.
- Supports querying data from multiple sources.
- Web-based notebook interface for data exploration and visualization.
- Supports multiple query engines.
- Provides an interactive environment for data analysis.
This section covers the basic installation of the HDP Sandbox on a local machine. The HDP Sandbox is a pre-configured virtual machine that can be used to run Hadoop on a single node. Further installation & configuration steps for different components will be covered in their respective sections.
- VirtualBox: Download VirtualBox
- Hortonworks Sandbox VirtualBox Image: Download Hortonworks Sandbox
- Dataset: MovieLens 100K Dataset
- Ambari: localhost:8080 (username: maria_dev, password: maria_dev)
- SSH:
ssh maria_dev@127.0.0.1 -p 2222 - Admin Access to Ambari:
sudo su- Run
ambari-admin-password-resetto set the admin password.