Big Data and Hadoop Full Course 2023 | Learn Big Data and Hadoop in 12 hours | Simplilearn

Introduction to the value and generation of big data, highlighting its challenges and storage methods like Hadoop.

Exploration of the basic concepts of big data including the 5 V's: volume, velocity, variety, veracity, and value.

Explanation of how big data is stored and processed using Hadoop, focusing on the Hadoop Distributed File System (HDFS) and MapReduce technique.

Discussing the challenges of processing big data and the need for distributed and parallel processing frameworks like Hadoop.

Overview of the Hadoop framework, its installation process, and key components including HDFS, MapReduce, and YARN.

Guidance on setting up a single-node Cloudera cluster using the Cloudera Quick Start VM for learning and practicing Hadoop concepts.

Explains how SDFS works in a distributed file system, including file division into blocks, default block size, storage across different nodes, and cluster setup.

Discusses setting up a Hadoop cluster, including manual setup in Apache Hadoop, vendor-specific distributions like Cloudera and Hortonworks, and cluster management tools like Cloudera Manager and Ambari.

Explains Hadoop terminologies like demons and roles, differences between Hadoop versions 1 and 2, and specific roles in various distributions like Apache Hadoop, Cloudera, and Hortonworks.

Details the functionality of HDFS, including block storage, replication process, block reports, master-slave architecture, handling of blocks, and fault tolerance.

Demonstrates working with HDFS commands, such as creating directories, copying files, downloading sample data sets, writing data to HDFS, and checking replication status.

Introduces the MapReduce algorithm, explaining the mapping and reducing phases, mapper and reducer classes, parallel processing, large-scale data processing, and storing data on SDFS.

The video explains how Hadoop accepts data in various formats including compressed, parquet, and binary formats. It emphasizes the importance of splitability in compressed data to ensure efficient mapreduce processing in Hadoop.

The mapping phase in Hadoop involves reading and breaking down data into individual elements, typically key-value pairs, based on the input format. It discusses the significance of shuffling and sorting data internally for efficient processing.

This chapter focuses on shuffling and reducing processes in Hadoop where key-value pairs are aggregated and processed further for final output generation. It highlights the benefits of parallel processing in mapreduce.

The video provides an overview of the mapreduce workflow, starting from input data storage in SDFS to mapping, reducing, and generating the final output. It explains the parallel processing approach and how data is handled during the mapreduce process.

Discusses the input and output formats in Hadoop, exploring options like text input format, key-value input format, and sequence file input format. It explains how these formats handle data during processing and output generation.

Discusses the limitations of Hadoop version 1 in terms of scalability and availability, including issues with job tracker failures and resource utilization.

Explains the limitations of Hadoop version 1 and MapReduce, focusing on the lack of support for non-MapReduce applications and real-time processing.

Describes the process of execution in YARN, including how the client submits applications, resource allocation, and container management.

Details the configuration and resource management in YARN, mentioning node managers, resource allocation, container properties, and scheduling.

Provides a guide on how to interact with YARN, covering commands for checking applications, logs, resource managers, and node managers.

Explanation of NoSQL and its usage in slicing and loading data into HDFS while maintaining the database schema.

Setting up the Cloudera Quick Start for the demo showcasing usage of Scoop.

Demonstrating MySQL setup and exploration in Cloudera for data import with Scoop.

Executing commands in MySQL to list databases, show tables, and explore data for importing.

Comparison between running commands via command line and through Hue editor for Scoop operations.

Demonstrating the mapping process in Hadoop during data import using Scoop with MySQL.

Exporting filtered data from Hadoop back to MySQL using Scoop and demonstrating the process.

Overview of Hive, its architecture, services, and data flow within the Hadoop system.

Explanation of Hive data modeling including partitions and buckets for efficient data organization.

Detailing primitive and complex data types in Hive including numerical, string, date-time, and miscellaneous types.

Explanation of key-value pairs and complex data structures in Hive.

Description of Hive operating in local mode and map reduce mode based on the number and size of data nodes.

Contrast between Hive and relational database management systems (RDBMS) in terms of data size, schema enforcement, and data operation model.

Comparison of data size, data operation model, storage structure, and scalability between Hive and RDBMS.

Explanation of the read-once, read-many concept in Hive, used for archiving data and performing data analysis.

Discussion on the data warehousing aspect of Hive, supporting SQL, scalability, and cost-effectiveness.

Overview of features in Hive including HiveQL, table usage, multiple user query support, and data type support.

Pig is a scripting platform running on Hadoop designed to process and analyze large data sets. It operates on structured, semi-structured, and unstructured data, resembling SQL with some differences. Pig simplifies data analysis and processing compared to mapreduce and Hive.

Pig has a procedural data flow language called Pig Latin for data analysis. The runtime engine executes Pig Latin programs, optimizing and compiling them into mapreduce jobs. Pig's data model includes atoms, tuples, bags, and maps, allowing for nested data types.

Pig works in two execution modes: local mode for small data sets and mapreduce mode for interacting directly with HDFS and executing on a Hadoop cluster. Pig supports interactive, batch, and embedded modes for coding flexibility.

Pig offers ease of programming, requires fewer lines of code, and reduces development time. It handles structured, semi-structured, and unstructured data, supports user-defined functions, and provides various operators like join and filter. A demo showcases basic Pig commands and word count analysis using Pig Latin script.

HBase is a column-oriented database system derived from Google's bigtable, designed for storing and processing semi-structured and sparse data on HDFS. It is horizontally scalable, open source, and supports faster querying in Java.

China Mobile uses HBase to store billions of call detailed records for real-time analysis due to traditional databases' inability to handle the volume of data.

HBase stores genome sequences and disease histories with sparse data to cater to unique genetic and medical details.

HBase is used in e-commerce for storing customer search logs, performing analytics, and targeting advertisements for better business insights.

Differences between HBase and RDBMS include variable schema, handling of structured and semi-structured data, denormalization in HBase, and scalability differences.

HBase features include scalability across nodes, automatic failure support, consistent read and write operations, Java API for client access, block cache, and Bloom filters for query optimization.

HBase uses column-oriented storage with row keys, column families, column qualifiers, and cells to efficiently store and retrieve data.

HBase architecture includes HMaster for monitoring, region servers for data serving, HDFS storage, HLog for log storage, and Zookeeper for cluster coordination.

The HBase write process involves a WAL (Write-Ahead Log), memstore, and HFiles to ensure data durability and consistency.

Basic HBase shell commands include listing tables, creating tables, adding data, scanning tables, and describing table properties for manipulation and data retrieval.

Big data is used in weather forecasting to collect and analyze climate data, wind direction, and other factors to predict accurate weather patterns, aiding in preparedness for natural disasters.

Apache Spark is introduced as an in-demand technology and processing framework in the Big Data world. The history, components, and key features of Apache Spark are discussed.

Apache Spark's inception in 2009 at UC Berkeley, becoming open source in 2010, and its growth to become a top-level Apache project by 2013. The discussion includes the setting of a new world record with Spark.

Apache Spark is defined as an open-source, in-memory computing framework used for data processing in both batch and real-time. The support for multiple programming languages like Scala, Python, Java, and R is highlighted.

A comparison is made between Apache Spark and Hadoop, emphasizing that Spark can process data 100 times faster than MapReduce in Hadoop. The benefits of Spark's in-memory computing for both batch and real-time processing are highlighted.

Key features of Apache Spark, including fast processing, fault tolerance, flexible language support, fault-tolerant RDDs, and comprehensive analytics capabilities, are discussed.

The core components of Apache Spark, including Spark Core, Spark SQL, Spark Streaming, Spark MLlib, and Graphics, are explained with their functionalities and use cases.

The concept of RDDs in Apache Spark, their immutability, fault tolerance, distributed nature, and operations like transformation and action are elaborated, emphasizing lazy evaluation and execution logic.

The usage of Spark SQL for structured data processing, the data frame API for handling structured data efficiently, and the integration of SQL and Hive query languages for data processing are discussed.

An explanation of Spark Streaming, its capability for real-time data processing, breaking data into smaller streams, and processing discretized streams or batches to provide secure and fast processing of live data streams is provided.

A discussion on Apache Spark MLlib for scalable machine learning algorithm development and Apache Spark Graphics for graph-based processing, including graph-based data representation and processing, is covered.

Real-world applications of Apache Spark in various industries like banking, e-commerce, healthcare, entertainment, and video streaming are highlighted, showcasing how Spark is utilized for fraud detection, data analysis, recommendation systems, and more.

The use case of Conviva, a leading video streaming company that leverages Apache Spark for real-time video quality analysis, diagnostics, and anomaly detection to ensure a high-quality streaming experience for users, is discussed.

A step-by-step guide on setting up Apache Spark on Windows, including downloading and configuring Apache Spark, setting environment variables, and launching Spark in local mode via interactive commands, is demonstrated.

Setting up and checking the file in Spark directory, starting Spark shell, working with transformations and actions, using Spark shell interactively on Windows machine, quitting Spark shell, and working with Pi Spark.

Setting up IDE for Spark applications, using Eclipse, adding Scala plugin, configuring build path for Spark, writing and compiling code, packaging application as JAR, running applications from IDE, and using Maven or SBT for packaging.

Downloading and configuring Spark, setting up Spark standalone cluster, updating the bash file and configuration files, starting the master and worker processes, checking Spark UI, and starting history server.

Introduction to MapReduce, history of its introduction by Google, solving data analysis challenges, key features of MapReduce, and analogy of MapReduce with a vote counting process.

Explaining the MapReduce operation steps through a word count example, including input, splitting, mapping, shuffling, and reducing phases in detail.

In the partition phase, information is sent to the master node after completion. Each mapper determines which reducer will receive each output based on a key. The number of partitions equals the number of reducers, and input data is fetched from all map tasks for the reduced tasks bucket in the shuffle phase.

In the shuffle phase, all map outputs undergo a merge sort, followed by the application of a user-defined reduce function in the reduce phase. The key-value pairs are exchanged and sorted by keys before being stored in HDFS based on the specified output file format.

In a distributed two-node environment, map execution assigns mappers to input splits based on the input format. The map function is applied to each record, generating intermediate outputs stored temporarily. Records are then assigned to reducers by a partitioner.

The essential steps in each MapReduce phase are highlighted, starting with the user-defined map function applied to input records, followed by a user-defined reduce function called for distinct keys in the map output. Intermediate values associated with keys are then processed in the reduce function.

A MapReduce job is a program that runs multiple map and reduce functions in parallel. The job is divided into tasks by the application master, and the node manager executes map and reduce tasks by launching resource containers. Map tasks run within containers on data nodes.

Explains how to use job ID to access the YARN UI, view map and reduce tasks, Node and logs, reduce task counters, and other information.

Demonstrates using a telecom giant's call data records to find phone numbers making more than 60 minutes of STD calls using MapReduce programming model.

Provides a sample code using Eclipse for map and reduce tasks to analyze data and find phone numbers making long STD calls.

Discusses HDFS, challenges of traditional systems, features of HDFS like cost-effective storage, high speed, and reliability.

Explains how HDFS stores files in blocks, replicates them across nodes, and resolves disk IO issues with larger block sizes.

Details the architecture of HDFS including the name node, metadata, block splitting, and data nodes.

Describes YARN resource manager functionality, client communication, resource allocation, and container launch process.

Discusses how to monitor the progress of YARN applications, view information such as current state, running jobs, finished jobs, and additional details on a web interface.

Recap of the demo on calculating word count and monitoring YARN progress. Overview of Apache Spark's history from its inception at UC Berkeley to becoming a top-level Apache project.

Defines Apache Spark as an open-source, in-memory computing framework used for data processing on cluster computers. Discusses its support for multiple programming languages and its popularity in the Big Data industry.

Contrasts Apache Spark with Hadoop, highlighting Spark's faster processing speed and support for both batch and real-time processing. Explains the differences in programming languages and paradigms.

Explores Apache Spark's features, including fast processing, resilient distributed datasets (RDDs), support for multiple languages, fault tolerance, and its applications in processing, analyzing, and transforming data at scale.

Explains Resilient Distributed Datasets (RDDs) in Spark, how they are created, distributed across nodes, and used in processing data. Details the concept of transformations and actions in RDD operations.

Details the components of Spark, including Spark Core for parallel and distributed data processing, Spark SQL for structured data processing, Spark Streaming for real-time data processing, MLlib for machine learning, and Graphics for graph processing.

Descriptive analytics summarizes past data into a form that can be understood by humans. It helps analyze past data like revenue over the years to understand performance using various conclusions.

Diagnostic analytics focuses on why a particular problem occurred by looking into the root cause. It uses techniques like data mining to prevent the same problem from happening again in the future.

Predictive analytics makes predictions about the future using current and historical data. It helps in predicting trends, behavior, and potential fraudulent activities, like in the case of PayPal.

Prescriptive analytics prescribes solutions to current problems by combining insights from descriptive and predictive analytics. It helps organizations make data-driven decisions and optimize processes.

Various tools like Hadoop, MongoDB, Talendi, Kafka, Cassandra, Spark, and Storm are used in big data analytics to store, process, and analyze large datasets efficiently.

Big data finds applications in sectors like e-commerce, education, healthcare, media, banking, and government. It helps in predicting trends, personalizing recommendations, analyzing customer behavior, and improving services across various industries.

Data scientists require skills like analytical thinking, data wrangling, statistical thinking, and visualization to derive meaningful insights from data. They use tools like Python and libraries to build data models and predict outcomes efficiently.

Hadoop is used for storing and processing big data in a distributed manner, while MapReduce is a framework within Hadoop for processing data. The mapper and reducer functions play key roles in the mapreduce process.

Apache Spark is a faster alternative to Hadoop MapReduce, offering resilience and faster data processing through RDDs. It provides integrated tools for data analysis, streaming, machine learning, and graph processing.

Discusses popularly known vendor-specific distributions in the market such as Cloudera, Hortonworks, MapR, Microsoft, IBM's Infosphere, and Amazon Web Services.

Provides information on where to find more about Hadoop distributions, suggesting to look into Google and check the Hadoop different distributions Wiki page.

Explains the importance of Hadoop configuration files like environment.sh, core-site.xml, hdfs-site.xml, mapred-site.xml, and yarn-site.xml in every Hadoop distribution.

Describes the three modes in which Hadoop can run: Standalone mode, Pseudo-distributed mode, and Fully distributed mode for production setups.

Compares regular file systems with HDFS, highlighting the fault tolerance, data distribution, and scalability aspects of HDFS.

Explains the fault tolerance mechanism of SDFS through data replication on multiple data nodes and maintaining copies of data blocks across nodes.

Details the architecture of SDFS, including the roles of namenode, data nodes, metadata storage in RAM and disk, and the process of data replication.

Differentiates between Federation and High Availability features in Hadoop, focusing on horizontal scalability and fault tolerance aspects.

Calculates the number of input splits created in SDFS for a 350 MB input file, explaining how the file is split into blocks and distributed across nodes.

Discusses the concept of rack awareness in Hadoop, emphasizing the placement of nodes across racks for fault tolerance and data redundancy.

Explains the process of restarting NameNode and other demons in Hadoop using scripts, detailing the differences between Apache Hadoop and vendor-specific distributions like Cloudera and Hortonworks.

Introduces the command for checking the status of blocks and file system health in Hadoop using the fsck utility, which provides information on block status and replication.

Discusses the impact of storing too many small files in a Hadoop cluster on NameNode RAM usage and the importance of following data quota systems.

Guides on how to copy data from a local system to SDFS using commands like put and copy, with options for overwriting existing files.

Explains the use of commands like DFS admin refresh nodes and RM admin refresh nodes in Hadoop for refreshing node information during commissioning or decommissioning activities.

Details the process of changing the replication factor of files after they are written in SDFS using commands like set rep, allowing replication modifications even after data is stored.

Explores the concepts of under and over replicated blocks in a cluster, discussing scenarios where block replication may fall short or exceed requirements.

Describes the roles of Record Reader, Combiner, Partitioner, and Mapper in the MapReduce processing paradigm, highlighting their functions and significance.

Explores speculative execution in Hadoop, explaining how it helps in load balancing and task completion in case of slow nodes or tasks.

Differentiates between Identity Mapper and Chain Mapper in MapReduce, showcasing the default and customized mapping functionality in Hadoop programs.

Lists the essential configuration parameters needed in MapReduce programs, including input and output locations, job configurations, and job formats.

Explains the important configuration parameters to consider for a MapReduce program such as packaging classes in a JAR file, using map and reduce functions, and running the code on a cluster.

Contrasts map side join and reduce side join in MapReduce, highlighting how join operations are performed at the mapping phase and by the reducer, respectively.

Describes the role of the output committer class in a MapReduce job, including tasks such as setting up job initialization, cleaning up after completion, and managing job resources.

Explains the concept of spilling in MapReduce, which involves copying data from memory buffer to disk when the buffer usage reaches a certain threshold.

Discusses how the number of map tasks and reduce tasks can be customized by setting properties in config files or providing them via command line when running a MapReduce job.

Explains the implications of a node failure running a map task in MapReduce, leading to re-execution of the task on another node and the role of the application master in such scenarios.

Explores the ability to write MapReduce output in various formats supported by Hadoop, including text output format, sequence file output format, and binary output formats.

Introduces YARN (Yet Another Resource Negotiator) in Hadoop version 2, focusing on its benefits, such as scalability, availability, and support for running diverse workloads on a cluster.

Explains how resource allocation works in YARN, detailing the role of the resource manager, scheduler, and application manager, along with container management and dynamic resource allocation.