Large amounts of data are being generated by organizations in a variety of industries, including IT, e-commerce, banking, and healthcare. There is a great demand for experts who can handle and analyze this data using Hadoop. Gain expertise with the fundamentals in this Big Data Hadoop Tutorial for Beginners. Explore our Big Data Hadoop Course Syllabus to get started.
Big Data Hadoop Introduction
Here are the comprehensive Hadoop tutorial concepts that also cover sample ideas for big data Hadoop projects.
What is Big Data?
Big Data is the term used to describe extraordinarily massive and intricate datasets that are beyond the capabilities of conventional data processing software. The following “5 Vs” describe these datasets:
- Volume: A huge amount of data.
- Velocity: The rate of data processing and generation.
- Variety: The various forms of data, including semi-structured, unstructured, and structured data.
- Veracity: The data’s precision and dependability.
- Value: The ability to glean insightful information.
Big data encompasses not just the volume of data but also the benefits and difficulties brought about by its speed, diversity, and scale. Big Data analysis can result in insightful discoveries, improved decision-making, and new opportunities in a variety of industries.
Big Data Processing Frameworks: An Overview
The inherent difficulties in managing the “5 Vs” of big data, volume, velocity, variety, veracity, and value, make big data processing frameworks necessary. These features are problematic for traditional data processing systems, which makes it hard to effectively store, manage, and analyze such large and complicated datasets.
An explanation of the significance of these frameworks is provided below:
- Handling Volume: Terabytes or petabytes of data are frequently involved with big data.
- Processing Velocity: Data (such as social media feeds and sensor data) can be produced very quickly.
- Handling Variety: Big Data can be semi-structured (XML, JSON), unstructured (text, photos, videos), or structured (databases).
- Ensuring Veracity: Noise and inconsistencies can be found in large datasets.
- Extracting Value: Gaining insightful knowledge for innovation, competitive advantage, and decision-making is the ultimate aim of working with big data.
Big Data processing frameworks offer the tools and infrastructure required to get above the constraints of conventional data processing when handling the complexity, speed, and scale of contemporary data. They make it possible for businesses to effectively store, process, analyze, and eventually extract value from their enormous data resources.
Examples of Big Data processing frameworks include:
- Apache Hadoop: It is a platform that uses the MapReduce programming style to process and store big datasets in a distributed manner.
- Apache Spark: A quick and all-purpose cluster computing solution for processing large amounts of data, Apache Spark supports batch, real-time, graph, and machine learning.
- Apache Flink: A framework for stream processing that offers strong batch and stream processing features.
- Apache Kafka: A distributed streaming platform called Apache Kafka is used to create streaming apps and real-time data pipelines.
- Apache Hive: Built on top of Hadoop, Apache Hive is a data warehouse program that uses a language similar to SQL for data summarization, query, and analysis.
These frameworks offer various features and are frequently combined to handle the many difficulties associated with processing big data.
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Overview of the Hadoop Ecosystem
Built around Apache Hadoop, the Hadoop ecosystem is a group of open-source software tools and frameworks intended to manage the processing, analysis, and storage of massive datasets. It includes a vast range of related projects that offer different features in addition to the fundamental Hadoop components (HDFS, MapReduce, and YARN).
Here is a summary of some of the main elements and groups that make up the Hadoop ecosystem:
Data Processing and Analysis Tools:
Here are the big data processing and analysis tools:
Apache Hive: Built on top of Hadoop, Apache Hive is a data warehousing system that offers a SQL-like interface (HiveQL) for querying and analyzing sizable Hadoop datasets. It converts MapReduce (or other execution engines like Tez or Spark) tasks from HiveQL queries.
Example: You may use SQL-like SELECT, JOIN, and GROUP BY operations to examine data in HDFS after creating tables in Hive that correspond to that data.
Apache Pig: It is a high-level execution framework and data flow language for Hadoop parallel computing. Pig offers Pig Latin, a straightforward scripting language that abstracts the intricacy of MapReduce.
Example: Pig scripts can be used to input data, filter it, combine datasets, group them, and save the outcomes.
Apache Spark: It is a quick and versatile cluster computing platform. It has its own in-memory processing capabilities, which make it substantially faster for a variety of workloads, including batch processing, real-time streaming, machine learning, and graph processing, even though it can work with Hadoop (using HDFS for storage).
Example: Spark can be used to perform machine learning algorithms on big datasets or analyze streaming data from Kafka in real-time.
Apache Flink: Another robust open-source stream processing framework with batch processing capabilities is Apache Flink. It has a stellar performance record and provides at-least-once and exactly-once processing guarantees.
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Introduction to Hadoop
An open-source platform called Apache Hadoop is used to handle and store big datasets in a distributed manner. Hadoop is extremely scalable and fault-tolerant since it clusters several commodity hardware units to process data in parallel rather than depending on a single sophisticated computer.
Here are the Hadoop’s main features:
- Distributed Processing: Hadoop divides processing jobs and big datasets into manageable chunks that can be carried out concurrently by several cluster nodes.
- Scalability: As your data expands, you can quickly expand a Hadoop cluster by adding more commodity hardware.
- Fault Tolerance: Hadoop is built to withstand malfunctioning hardware. Because data is replicated over several nodes, processing can continue even in the event of a node failure.
- Cost-effective: Compared to conventional high-end systems, Hadoop offers a more economical large data processing and storage solution by leveraging commodity technology.
- Flexibility: Hadoop doesn’t need to define a schema up front in order to store and process many kinds of data, including unstructured, semi-structured, and structured data.
Core Hadoop Components:
Hadoop Distributed File System (HDFS): The distributed file system that offers dependable and scalable data storage across a cluster of commodity hardware is called Hadoop Distributed File System (HDFS).
Example: HDFS will divide a 1GB file with a block size of 128MB into several 128MB blocks (as well as a smaller leftover block) and store these blocks on various cluster DataNodes. Usually, each block is duplicated on several nodes (e.g., three times by default).
MapReduce: It is an execution framework and programming approach for processing huge datasets in parallel. The “Map” phase, which processes incoming data in parallel, and the “Reduce” phase, which collects and summarizes the results, are the two stages into which it divides processing.
Example: Counting the instances of every word in a sizable collection of papers. For every word, the “Map” function could produce (word, 1), and the “Reduce” function could add up the counts for every distinct word.
YARN (Yet Another Resource Negotiator): Hadoop’s resource management layer. It schedules jobs and controls the cluster’s resources. It isolates the components of application processing (such as MapReduce) from resource management.
Example: YARN is in charge of assigning containers—resources like memory and CPU—to the cluster’s nodes so that the map and reduce tasks may be completed when you submit a MapReduce job.
How Hadoop Works:
Here is how Hadoop works:
- Data Ingestion: HDFS receives large datasets.
- Data Distribution: HDFS creates replicas and divides the data into blocks, which are then dispersed among the cluster’s nodes.
- Parallel Processing: YARN organizes jobs and distributes resources across the nodes that house the data when a processing job (such as a MapReduce program) is submitted. The data is processed concurrently by the “Map” tasks.
- Shuffling and Sorting: The “Map” tasks’ intermediate output is sorted and shuffled.
- Aggregation: To create the final result, the “Reduce” jobs process the sorted data.
- Result Storage: HDFS or another data storage system may be used to store the processed results.
Hadoop offers the fundamental layer for distributed, robust processing and storing of enormous volumes of data. It is now the foundation of many big data systems, and a thriving tool ecosystem has developed around it to offer further features.
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Hadoop Distributed File System (HDFS)
The main data storage system that Hadoop apps employ is HDFS. It is intended to process and store very big files on a collection of commodity hardware. HDFS offers high-throughput access to application data and is incredibly fault-tolerant.
Here are the key aspects of HDFS:
Architecture: Master-Slave
The master-slave architecture used by HDFS is made up of:
NameNode (Master):
The file system namespace is managed by the NameNode (Master). It keeps track of every file and directory in the HDFS tree’s metadata, or information about data. File names, directories, permissions, and the location of each block of a file are all included in this.
- It doesn’t keep the real data.
- It is aware of the DataNodes that contain all of the blocks for a specific file.
- It controls file access for clients.
- It carries out file system functions, such as renaming, closing, and opening files and directories.
- It maintains the metadata in RAM for quick access.
- It keeps two local disk persistent files:
- FsImage: A snapshot of the metadata on the file system.
- EditLog: Since the last FsImage, any recent modifications to the file system metadata are recorded in the EditLog.
DataNodes (Slaves):
- Data blocks should be used to store the actual data.
- There is usually one DataNode for each cluster node.
- As requested by the client, execute read-write actions on the file systems.
- Create, remove, and replicate blocks in accordance with the NameNode’s commands.
- To verify their status and the list of blocks they are keeping, send the NameNode a block report and a heartbeat on a regular basis.
Data Storage: Blocks
Files are divided into block-sized portions by HDFS. Usually, 128MB is the default block size (adjustable).
- Every block is a separate storage unit that can be dispersed over several DataNodes.
- Keeping really massive files that are larger than what one system can store is made easier using this block-based method.
- Data processing in parallel, so multiple nodes can process distinct blocks at the same time.
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Fault Tolerance: Replication Factor
HDFS replicates every data block over several DataNodes to guarantee data availability and dependability.
The number of copies of each block is determined by the replication factor. Three is the replication factor by default.
Replication management is the responsibility of the NameNode. In order to preserve the intended replication factor, the NameNode detects when a DataNode fails (no heartbeat) and starts replicating the blocks that were on the failed node to other DataNodes that are accessible.
Replication Placement Strategy: HDFS’s replication placement strategy aims to strike a compromise between performance and dependability. For a replication factor of three, a typical approach is:
- One replica on the local rack’s DataNode.
- One replica on a separate rack’s DataNode.
- One duplicate on the same distant rack on a separate DataNode.
Rack Awareness: HDFS knows which nodes are in the same rack and the cluster’s network topology. By distributing copies across racks, this aids in improving performance and fault tolerance through the optimization of read operations and data replication.
Data Access
The HDFS client is how clients communicate with HDFS.
The client first communicates with the NameNode to obtain the metadata (block locations) before reading a file. To obtain the data, the client then makes direct communication with the DataNodes where the blocks are kept.
To create a file and obtain the DataNodes to write to, the client must additionally communicate with the NameNode. The replication procedure then begins once the client writes data blocks to the designated DataNodes.
Read and Write Operations in HDFS
Let’s examine the Hadoop Distributed File System’s (HDFS) read and write functions.
Read Operation in HDFS
The following actions usually take place when a client wishes to read a file from HDFS:
- Client Request: By getting in touch with the NameNode, the client starts a read request for a particular file.
- Metadata Retrieval: To determine the locations of the blocks that make up the requested file, the NameNode looks through its metadata. After that, a list of the DataNodes, along with their addresses, where each file block is kept is returned.
- Direct Data Retrieval: The client then gets in touch with the DataNodes that store the blocks directly to retrieve the data. According to network topology, the client will usually attempt to connect to the nearest DataNode.
- Data Transfer: The requested data blocks are streamed back to the client by it.
- Assembly: To reconstruct the entire file, the HDFS client puts the blocks together in the right sequence.
- Completion: The read process is finished after every block has been received.
Key aspects of the read operation:
- The NameNode does not participate in the actual data transfer; it merely provides the metadata (block locations). This lessens the NameNode’s workload.
- High-throughput data access is made possible by clients communicating directly with DataNodes to retrieve data.
- The client can obtain a necessary block from another DataNode that contains a replica if the DataNode holding the block is not available.
Write Operation in HDFS
The procedure is more complicated when a client want to write a file to HDFS:
Client Request: The client contacts the NameNode to start a write request.
Namespace Check: The NameNode determines whether the client has the required rights to create a new file and whether an existing file with the specified name already exists. The NameNode logs the intent to create the file in its metadata if the checks are successful. At this point, no data blocks are allocated by the NameNode.
Block Allocation: The HDFS client divides the data into blocks as soon as the client begins writing data. Based on the replication factor and placement policy, the client requests that the NameNode select a group of DataNodes to house the copies of the initial block.
Pipeline Creation: The NameNode gives the client the addresses of the selected DataNodes. After that, a pipeline of these DataNodes is created by the HDFS client. The pipeline might seem as follows for a replication factor of 3: Client → DN1 → DN2 → DN3.
Data Streaming: The client sends the first data block to the pipeline’s first DataNode (DN1). The block is stored by DN1 and sent to the pipeline’s subsequent DataNode (DN2). The block is stored by DN2 before being forwarded to DN3, and so on. Effective data replication is made possible by this pipeline technique.
Acknowledgement: The last DataNode (DN3 in our example) sends an acknowledgement back to the preceding one (DN2) once all the DataNodes in the pipeline have stored the block. DN2 then sends an acknowledgement back to DN1, and DN1 ultimately sends an acknowledgement back to the client.
Subsequent Blocks: The client repeats steps 3-6 for later blocks of the file. For every new block, the NameNode may select a different collection of DataNodes.
Close Operation: The client closes the file after writing all of the data. This lets the NameNode know that the file is finished. After that, the NameNode commits the metadata modifications, enabling the file to be read.
Key aspects of the write operation:
- Involved in the original request, the NameNode selects DataNodes for storage and completes the process.
- A pipeline-style stream of data is sent to the DataNodes for effective replication.
- The successful replication of the data across the necessary number of DataNodes is guaranteed by acknowledgments.
- The actual data transport is not handled by the NameNode.
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HDFS Commands for File Management
These are a few of the frequently used HDFS file management commands. These commands are usually run with the prefixes hadoop fs or hdfs dfs.
Basic File and Directory Operations:
- ls <path>: Lists the files and directories in the specified HDFS path.
- Example: hdfs dfs -ls /user/alice
- ls -R <path>: Recursively lists all files and directories under the specified HDFS path.
- Example: hdfs dfs -ls -R /data
- mkdir <path>: Creates a new directory in HDFS at the given path.
- Example: hdfs dfs -mkdir /user/alice/new_folder
- Use -p to create parent directories if they don’t exist: hdfs dfs -mkdir -p /user/alice/nested/folder
- rm <path>: Removes a file or an empty directory from HDFS.
- Example: hdfs dfs -rm /user/alice/old_file.txt
- rm -r <path>: Recursively removes a directory and its contents from HDFS. Use with caution!
- Example: hdfs dfs -rm -r /user/alice/old_folder.
- put <localsrc> <dst>: Copies files or directories from the local file system to HDFS.
- Example: hdfs dfs -put local_file.txt /user/alice/
- You can also use copyFromLocal: hdfs dfs -copyFromLocal local_file.txt /user/alice/
- get <src> <localdst>: Copies files or directories from HDFS to the local file system.
- Example: hdfs dfs -get /user/alice/hdfs_file.txt local_destination/
- You can also use copyToLocal: hdfs dfs -copyToLocal /user/alice/hdfs_file.txt local_destination/
- cat <path>: Displays the content of a file in HDFS on the console.
- Example: hdfs dfs -cat /user/alice/data.txt
- cp <src> <dst>: Copies files or directories from one location to another within HDFS.
- Example: hdfs dfs -cp /user/alice/file1.txt /user/bob/
- mv <src> <dst>: Moves files or directories from one location to another within HDFS.
- Example: hdfs dfs -mv /user/alice/temp.txt /user/bob/final.txt
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Advanced File Management:
- du <path>: Shows the disk space usage of the files and directories within the given HDFS path.
- Example: hdfs dfs -du /user/alice
- dus <path>: Shows the total size of the files and directories within the given HDFS path.
- Example: hdfs dfs -dus /user/alice
- chmod <mode> <path>: Changes the permissions of a file or directory in HDFS.
- Example: hdfs dfs -chmod 755 /user/alice/script.sh
- chown <owner>[:<group>] <path>: Changes the owner and/or group of a file or directory in HDFS.
- Example: hdfs dfs -chown bob:developers /user/alice/data.txt
- setrep -w <replication> <path>: Changes the replication factor of a file or directory in HDFS. The -w option waits for the replication to complete.
- Example: hdfs dfs -setrep -w 5 /user/alice/important_file.dat
- test -e <path>: Checks if a file or directory exists.
- Example: hdfs dfs -test -e /user/alice/check_file.txt; echo $? (will output 0 if exists, 1 if not)
- touchz <path>: Creates an empty file of zero length in HDFS.
- Example: hdfs dfs -touchz /user/alice/empty_file.txt
- getmerge <src-dir> <localdst>: Merges all the files in the source HDFS directory into a single local file.
- Example: hdfs dfs -getmerge /user/alice/output /local/merged_output.txt
The -help option can be used to obtain additional information about a particular command:
hdfs dfs -help <command>
Example: hdfs dfs -help put
Key Advantages of HDFS:
Here are the primary advantages of HDFS:
- Fault Tolerance: Even in the event that some nodes fail, data replication guarantees high availability.
- Scalability: Able to scale to thousands of nodes and manage petabytes of data.
- High Throughput: Offers a large aggregate data bandwidth and is optimized for batch processing.
- Economical: Capable of operating on standard hardware.
- Data Locality: Aims to reduce network traffic by bringing computing closer to the data.
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Conclusion
Although this big data Hadoop tutorial for beginners offers a basic overview, the Hadoop environment is extensive and always changing. These fundamental elements are built upon by tools like Hive, Pig, Spark, and HBase, which provide even more potent methods for working with and analyzing big data. Get the best Big Data Hadoop Training in Chennai for comprehensive learning and practices.
