Are you a Data Practitioner working in the Big Data space? Do you want to prepare for a Big Data Engineer role? Then upskilling in Hadoop, the go-to technology framework for processing Big Data, is the right pathway for your chosen career goals. Today, Hadoop is the de facto standard platform for Big Data storage and processing, and learning the elements of Hadoop is useful for any wannabe Data Engineer or Data Scientist.
Big Data classes or certification will help you prepare for a career in Big Data and Analytics. The hands-on Hadoop Bootcamp is designed to train you on the Hadoop Distributed File System (HDFC) architecture and supporting tools. At the end of the course, you gain proficiency in data engineering and can look forward to being a part of the Big Data revolution.
There are more reasons why a Hadoop certification is a must-do for a future in Big Data Analytics.
According to a Markets and Markets report, the Hadoop Big Data Analytics Market size is expected to grow at a CAGR of 13 % during the forecast period 2020-2025, to reach USD 23.5 billion by 2025. An increase in the adoption of analytics and remote work culture is driving growth, and every IT Professional wants to master the necessary skill sets to carve a career in the Big Data environment.
A Big Data Hadoop certification includes the major components of Apache Hadoop in the training curve: Hadoop Distributed File System (HDFS), MapReduce, and YARN. This helps you land better positions and lucrative jobs in a Big Data-driven job market.
What is HDFS?
The Hadoop framework handles data processing and storage for Big Data applications using HDFS as the underpinning. The HDFS runs on low-cost commodity hardware where data is parcelled out across highly scalable machines for fault tolerance in the case of failure. As the HDFS forms the key data storage system, it can provide high-throughput access to application data. This helps to manage a voluminous amount of streaming data for high-performance parallel computing.
HDFS is used in environments where very large files of hundreds of megabytes, gigabytes, or more are processed. It is also preferred in Hadoop applications requiring high latency data access.
HDFS enables Big Data processing, the reason why Apache Hadoop is used by enterprises to store and process Big Data in real-time. As the massive datasets cannot be hosted in a central location, the HDFS stores the data across multiple server locations for distributed computing in clusters. This distribution facilitates high-speed computations and makes it easy to scale up when the load increases by adding to the commodity hardware. The ability of the HDFS to facilitate batch processing has further overcome the limitations of traditional DBMS as streaming data can be processed rapidly. With HDFS as the mainstay of Big Data applications running in Hadoop, large-scale data processing and crunching are possible at high speeds.
An overview of the HDFS Architecture
The HDFS is used to scale a single Apache Hadoop cluster to hundreds or thousands of nodes for managing huge amounts of data. It uses the primary-secondary or the client-server architecture to distribute, store and retrieve the data from multiple machines.
As the Big Data storage layer of Hadoop, the HDFS stores data on multiple independent physical servers and allows instant user access for analysis.
HDFS has a block-structured file system where data files are stored in commodity hardware in a large cluster. Each file is stored as a sequence of blocks of size 64MB or other predetermined sizes. Files are divided further into segments as a sequence of one or more “blocks” and distributed across a cluster of one or several machines. The blocks of a file are replicated for fault tolerance, to handle any hardware failure and minimize loss of data and processing time.
The HDFS follows a master-slave architecture model, where the two types of nodes are the NameNode (master) and the DataNodes (slave). The NameNode and DataNodes are pieces of software that run on the commodity machines; where each is assigned functions.
The NameNode acts as the master server to keep track of the storage cluster and execute the file system. It performs tasks like managing the namespace, adjusting access control to the files, and executing the operations. As the nerve center of the HDFS, the NameNode maintains the directory tree of all files in the file system and tracks the location of the data in the cluster. It also records any changes and makes decisions regarding the replication of blocks.
The DataNodes function as the slave nodes. This is where the data resides. They perform operations like block creation, deletion or replication, as well as read-write operations on the file system upon instruction from the NameNode. DataNodes also sends a heartbeat to the NameNode to report the health status of the HDFS data.
Features of HDFS
The features of HDFS offer many benefits to organizations working with Big Data.
Key features are:
- Supports concurrent distributed storage and parallel computing
- Offers storage of terabytes and petabytes of data
- A command-line interface for extended querying
- Access to file system data via streaming.
- Ability to handle shifting workloads
- Scale-out without any downtime
- Supports data integrity by replicating corrupt data.
- Fault-resistance, with auto-distribution of jobs across the working nodes
- Reduces network overload while increasing the throughput
- Has built-in servers of the NameNode and DataNodes
- Health check prompts of the nodes and cluster
- Supports thousands of nodes with minimal intervention
- Provides file permissions and authentication
- Allows normal file systems (FAT, etc.) to be viewed
- Allows rollback to the previous version even after an upgrade.
HDFS is one of the core components of Hadoop, and learning how it works can help you master the Big Data ecosystem and Hadoop architecture.
A Big Data Hadoop certification can kick start your career in Big Data Analytics, and a course that includes HDFS fundamentals can help you gain a stronghold in Hadoop-related job markets.