Hadoop is such a popular name in the Big Data domain that today, “Hadoop tutorial” has become one of the most searched terms on the Web. However, if you aren’t aware of Hadoop, it is an open-source Big Data framework designed for storing and processing massive volumes of data in distributed environments across multiple computer clusters by leveraging simple programming models.
It is designed in a way that it can scale up from single servers to hundreds and thousands of machines, each providing local storage and computation.
Read: Future scope of Hadoop.
Doug Cutting and Mike Cafarella developed Hadoop. An interesting fact about Hadoop’s history is that Hadoop was named after Cutting’s kid’s toy elephant. Cutting’s kid had a yellow toy elephant named Hadoop, and that’s the origin story of the Big Data framework!
Before we dive into the Hadoop tutorial, it is essential to get the basics right. By basics, we mean Big Data.
Hadoop Best Practices and Optimization Techniques
Hadoop, the open-source large data processing technology, provides enormous power and scalability. However, in order to achieve maximum performance and effective resource utilization, best practices and optimisation approaches must be followed. Consider the following essential practices:
- Data Modeling: Proper data modeling is crucial for efficient data processing in Hadoop. Utilize techniques like data normalization, denormalization, and data partitioning based on access patterns. Design schemas that align with your specific use cases and optimize data storage and retrieval.
- Cluster Sizing: Carefully plan and size your Hadoop basics cluster based on the expected workload and data volume. Consider factors such as data growth, processing requirements, and future scalability. Monitor cluster performance regularly and adjust resources as needed to avoid underutilization or overloading.
- Data compression: Compressing Hadoop data may drastically decrease storage costs while also improving query performance. Depending on the kind of data and the trade-off between compression ratio and decompression speed, use compression codecs such as Snappy or Gzip.
- Data Partitioning and Bucketing: Partitioning data based on certain columns can enhance query performance by reducing the amount of data scanned. Additionally, bucketing can further optimize data retrieval by dividing data into smaller, more manageable units.
- Optimized MapReduce Jobs: Efficiently write and optimize MapReduce jobs by minimizing unnecessary data shuffling, leveraging combiners and reducers effectively, and utilizing distributed cache for sharing common data. Monitor and tune the number of mappers and reducers based on data size and cluster resources.
By implementing these best practices and optimization techniques, organizations can maximize the efficiency, performance, and ROI of their Hadoop deployments. It’s important to continually assess and adapt these practices as your data and workload evolve over time.
Future Trends in Hadoop and Big Data Analytics
The area of Hadoop basics and big data analytics is continually expanding as technology progresses and data grows tremendously. Here are some future trends to look out for:
- Real-time and Stream Processing: Traditional batch processing is being complemented by real-time and stream processing capabilities. Technologies like Apache Flink and Apache Kafka Streams enable processing data in motion, opening up possibilities for instant insights and faster decision-making.
- Machine Learning Integration: The integration of machine learning with Hadoop is gaining momentum. Data scientists can leverage distributed computing power to train and deploy machine learning models at scale. Tools like Apache Mahout and TensorFlow on Hadoop make it easier to build intelligent applications.
- Data Governance and Security: With increasing concerns about data privacy and regulations, data governance and security have become critical. Solutions like Apache Ranger and Apache Atlas offer capabilities for access control, data classification, and metadata management to ensure compliance and protect sensitive data.
- Data Lakes and DataOps: Data lakes, centralized repositories for structured and unstructured data, are becoming more prevalent. They enable organizations to store and analyze diverse data types efficiently. Furthermore, using DataOps practices that emphasize collaboration, automation, and data quality helps to optimize the data pipeline and boost analytical outputs.
Businesses may gain a competitive advantage in the data-driven era by staying on top of these developments and embracing the expanding environment of Hadoop and big data analytics. Grasp the Hadoop beginner tutorial with this informative blog.
What is Big Data?
Big Data is a term used to refer to large volumes of data, both structured and unstructured (generated daily), that’s beyond the processing capabilities of traditional data processing systems.
According to Gartner’s famous Big Data definition, it refers to the data that has a wide variety, escalates in ever-increasing volumes, and with a high velocity. Big Data can be analyzed for insights that can promote data-driven business decisions. This is where the real value of Big Data lies.
Volume
Every day, a huge amount of data is generated from various sources, including social media, digital devices, IoT, and businesses. This data must be processed to identify and deliver meaningful insights.
Velocity
It denotes the rate at which organizations receive and process data. Every enterprise/organization has a specific time frame for processing data that flows in huge volumes. While some data demands real-time processing capabilities, some can be processed and analyzed as the need arises.
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Variety
Since data is generated from many disparate sources, naturally, it is highly diverse and varied. While the traditional data types were mostly structured and fit well in the relational databases, Big Data comes in semi-structured and unstructured data types (text, audio, and videos, as well. Why The Need For It?
Hadoop Tutorial For Beginners
When talking about Big Data, there were three core challenges:
Storage
The first issue was where to store such colossal amounts of data? Traditional systems won’t suffice as they offer limited storage capacities.
Heterogeneous data
The second issue was that Big Data is highly varied (structured, semi-structured, unstructured). So, the question arises – how to store this data that comes in diverse formats?
Processing Speed
The final issue is the processing speed. Since Big Data comes in a large, ever-increasing volume, it was a challenge to speed up the processing time of such vast amounts of heterogeneous data.
To overcome these core challenges, Hadoop was developed. Its two primary components – HDFS and YARN are designed to help tackle the storage and processing issues. While HDFS solves the storage issue by storing the data in a distributed manner, YARN handles the processing part by reducing the processing time drastically.
Hadoop is a unique Big Data framework because:
- It features a flexible file-system that eliminates ETL bottlenecks.
- It can scale economically and deploy on commodity hardware.
- It offers the flexibility to both store and mine any type of data. Plus, it is not constrained by a single schema.
- It excels at processing complex datasets – the scale-out architecture divides workloads across many nodes.
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Core Components Of Hadoop
The Hadoop cluster consists of two primary components – HDFS (Hadoop Distributed File System) and YARN (Yet Another Resource Negotiator).
HDFS
HDFS is responsible for distributed storage. It features a Master-Slave topology, wherein Master is a high-end machine while Slaves are inexpensive computers. In the Hadoop architecture, the Master should be deployed on robust configuration hardware as it constitutes the center of the Hadoop cluster.
HDFS divides Big Data into several blocks, which are then stored in a distributed fashion on the cluster of slave nodes. While the Master is responsible for managing, maintaining, and monitoring the slaves, the Slaves function as the actual worker nodes. For performing tasks on a Hadoop cluster, the user has to connect with the Master node.
HDFS is further divided into two daemons:
NameNode
It runs on the master machine and performs the following functions –
- It maintains, monitors, and manages DataNodes.
- It receives a heartbeat report and block reports from DataNodes.
- It captures the metadata of all the blocks in the cluster, including location, file size, permission, hierarchy, etc.
- It records all the changes made to the metadata like deletion, creation, and renaming of the files in edit logs.
DataNode
It runs on the slave machines and performs the following functions –
- It stores the actual business data.
- It serves the read-write request of the users.
- It creates, deletes, replicates blocks based on the command of the NameNode.
- It sends a heartbeat report to the NameNode after every three seconds.
YARN
As mentioned earlier, YARN takes care of data processing in Hadoop. The central idea behind YARN was to split the task of resource management and job scheduling. It has two components:
Resource Manager
- It runs on the master node.
- It tracks the heartbeats from the Node Manager.
- It has two sub-parts – Scheduler & ApplicationManager. While the Scheduler allocates resources to the running applications, the ApplicationManager acceptS job submissions and negotiates the first container for executing an application.
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Node Manager
- It runs on individual slave machines.
- It manages containers and also monitors the resource utilization of each container.
- It sends heartbeat reports to the Resource Manager.
Hadoop Tutorial: Prerequisites to Learn Hadoop
To begin your Hadoop tutorial and be comfortable with the framework, you must have two essential prerequisites:
Be familiar with basic Linux commands
Since Hadoop is set up over Linux OS (most preferably, Ubuntu), you must be well-versed with the foundation-level Linux commands.
Be familiar with basic Java concepts
When you begin your Hadoop tutorial, you can also simultaneously start learning the basic concepts of Java, including abstractions, encapsulation, inheritance, and polymorphism, to name a few.
Features Of Hadoop
Here are the top features of Hadoop that make it popular
1) Reliable
Hadoop is highly fault-tolerant and dependable. If ever any node goes down, it will not cause the whole cluster to fall apart – another node will replace the failed node. Thus, the Hadoop cluster can continue to function without faltering.
2) Scalable
Hadoop is highly scalable. It can be integrated with cloud platforms that can make the framework much more scalable.
3) Economical
The Hadoop framework can be deployed not only on configuration hardware but also on commodity hardware (cheap machines), as well. This makes Hadoop an economical choice for small to medium-sized firms that are looking to scale.
4) Distributed Storage and Processing
Hadoop divides tasks and files into several sub-tasks and blocks, respectively. These sub-tasks and blocks function independently and are stored in a distributed manner throughout a cluster of machines.
Why Learn Hadoop?
According to a recent research report, The Hadoop Big Data Analytics market is estimated to grow from $6.71 Billion (as of 2016) to $40.69 Billion by 2022 at a CAGR of 43.4%. This only goes to show that in the coming years, the investment in Big Data will be substantial. Naturally, the demand for Big Data frameworks and technologies like Hadoop will also accelerate.
As and when that happens, the need for skilled Hadoop professionals (like Hadoop Developers, Hadoop Architects, Hadoop Administrators, etc.) will increase exponentially.
This is why now is the ideal time to learn Hadoop and acquire Hadoop skills and master Hadoop tools. In light of the significant skills gap in the demand and supply of Big Data talent, it presents a perfect scenario for more and more young aspirants to shift towards this domain.
Due to the talent shortage, companies are willing to pay hefty yearly compensation and salary packages to deserving professionals. So, if you invest your time and effort in acquiring Hadoop skills now, your career graph will definitely be upward sloping in the near future.
In conclusion: Hadoop is a technology of the future. Sure, it might not be an integral part of the curriculum, but it is and will be an integral part of the workings of an organization. So, waste no time in catching this wave; a prosperous and fulfilling career awaits you at the end of the time.
If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore.
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