Big Data Engineering Full Course Part 1 | 17 Hours

Introduction to Big Data, explanation of the course content, and the importance of data engineering for beginners and intermediate learners.

Explanation of Hadoop as a solution in the Big Data domain, the difference between Hadoop and Big Data, and common misconceptions about Big Data.

Clarification on the misconception that volume is the only problem in Big Data, discussion on other key problems like quality, velocity, and variety of data.

Explanation of the misunderstanding related to data volume in Big Data, comparison of data volumes in different projects, and addressing common interview questions regarding data volume.

Overview of the various technologies in the Big Data domain, discussion on Hadoop and Apache projects, and the importance of understanding different data layers in Big Data.

Explanation of open source concept, donation of Hadoop project to Apache Software Foundation, and the significance of having projects in Apache for career opportunities.

Customized roadmap for learning Data Engineering, including steps for learning Linux, SQL, programming languages, cloud computing, and data technology stacks.

Guidance on preparing for a career in Data Engineering, covering topics such as project challenges, performance optimization, resume preparation, and job search strategies.

Explaining the components of Hadoop framework and answering frequently asked questions about HDFS (Hadoop Distributed File System) and file systems in general.

Defining file system, examples of distributed file systems like S3, Google file system, and Facebook file system, and explaining the concept of blocks in file systems.

Describing client and server relationship in a distributed environment, differentiating between Master-Slave and Peer-to-Peer cluster types, and highlighting the importance of distributed computing.

Discussing Master-Slave and Peer-to-Peer cluster infrastructures, focusing on the communication and fault tolerance mechanisms in a distributed cluster environment.

Exploring the architectural differences between Hadoop versions, emphasizing the role of Daemon processes, and detailing the cluster and node concepts in a Hadoop environment.

Detailing the configuration of clusters, assigning roles to nodes, selecting hardware configurations, and explaining data distribution on nodes in a Hadoop cluster.

Explaining the read-write architecture in HDFS (Hadoop Distributed File System), describing the process of data storage, replication, and failover mechanisms in a distributed environment.

In Hadoop version 2, a fix for metadata safeguarding was introduced to prevent data loss in case of hard disk crashes.

The client API in Hadoop interacts with the data nodes for read and write operations, ensuring data replication for reliability.

The pipeline process in Hadoop is utilized for write operations, while read operations involve reading only one copy of the data for efficiency.

In case of write request failures, the pipeline process sends acknowledgments and redirects the write request to another node for successful data storage.

Hadoop cluster concepts include multiple nodes such as namenode, job tracker, resource manager, and secondary namenode to ensure high availability and fault tolerance.

HDFS clusters can be set up as single-node pseudo clusters for testing purposes or multi-node clusters for production environments, with each node serving a specific role for data storage and processing.

The Hadoop ecosystem consists of various components like Hive, Pig, Sqoop, Flume, Oozie, and HBase, each serving different data processing and management functions within the framework.

Explained the importance of configurations in Hadoop setup including specifying demons (data node, task tracker), setting up SSH for communication among nodes, and removing the need for entering passwords multiple times.

Detailed the process of configuring SSH for passwordless communication among nodes in a multi-node Hadoop setup, including generating SSH keys, appending public keys, and ensuring seamless communication without password prompts.

Discussed the concept of formatting HDFS, creating Hadoop directory dirs, explaining the implications of errors in the process, and setting up configurations in the slave node.

Demonstrated the process of uploading files to HDFS using command line tools, including creating directories, uploading files, understanding block size, replication, and file management in HDFS.

Explained the significance of space and file count quotas in HDFS, setting quotas, managing disk space allocations, and resolving errors related to quotas for efficient data management in HDFS.

Elaborated on the concept of file compaction in Hive, merging files to reduce file counts, optimize space usage, and enhance performance in Hadoop ecosystem components like Hive.

Provided an introduction to MapReduce, discussing its role in Hadoop, significance in parallel processing, its relation to Spark, and the relevance of understanding MapReduce for job interviews and working with big data technologies.

Explained the execution process of MapReduce jobs, the map and reduce phases, input-output data handling, data locality, and the transformational aspects of mapping and reducing tasks in the MapReduce framework.

Explained the role and decision-making process of reducers in a MapReduce program. Discusses scenarios and options for the number of reducers based on the data processing requirements.

Describes the configuration and selection of mappers and reducers in a MapReduce job. Differentiates and explains the functionality of mapper and reducer classes in the program.

Details the interaction between the job tracker and name node in a MapReduce job. Covers the flow of information retrieval and task initiation based on block distribution.

Explains the functionality of data nodes and task trackers in a MapReduce job. Describes the data transmission and task execution processes between these components.

Describes the execution flow and completion process of reducer tasks in a MapReduce program. Explores the data flow from local file systems to HDFS after reducer completion.

Discusses the procedures for handling task failures in MapReduce jobs. Explains the process of task reassignment and restarting in case of failures.

Explores the concept of parallelism and block distribution in MapReduce jobs. Describes the challenges and considerations for achieving parallelism within nodes.

Explains the importance of input and output formats in MapReduce jobs. Describes the key-value pair requirements for mapper and reducer inputs and outputs.

Explanation of the YARN architecture and how Spark works within it. Details on the application master, assistant manager, resource manager, and the flow of execution in both YARN and Spark.

Steps to set up a project in Eclipse IDE for Hadoop, including creating a project, managing libraries, and resolving dependencies.

Instructions on copying JAR files from Eclipse to a Linux server where Hadoop is running, including creating a lib folder and pasting the jar files.

Details on configuring the Java build path in Eclipse by adding jar files to the project's build path to resolve dependencies.

Demonstration of running a MapReduce job on a Linux server with Hadoop, including uploading input files to HDFS, executing the job, and checking the output in HDFS.

Explains the concept of input split in MapReduce and how it divides data into logical blocks for processing across nodes, ensuring efficient processing and data retrieval.

Description of speculative execution in MapReduce, where duplicate tasks are launched to handle slow or stuck tasks without killing the original tasks, ensuring job completion.

Overview of Hive as a query language for Hadoop, its architecture, and how it abstracts MapReduce to allow SQL-like queries for data processing and transformation.

Explanation of Hive metastore, where table metadata is stored in an RDBMS separate from data storage, detailing the use of embedded and remote metastores in Hive installations.

Explains the installation process of Hive in MySQL using commands like 'sudo apt iPhone get install' and configuring the MySQL connector jar for meta store storage.

Discusses running Hive queries on map reduce or Spark rth engine instead of MySQL or Oracle to avoid any relationship between Hive and MySQL.

Describes the process of creating a metadata store in MySQL for Hive, including downloading the MySQL connector jar, renaming it to 'Hive site dot XML' and initializing the schema in MySQL.

Demonstrates creating a meta store database in MySQL and initializing the schema using commands in Hive and MySQL for storing metadata tables.

Explains loading data into Hive tables from the local file system and HDFS, including using commands like 'load data local in path' and 'insert into' for loading data into Hive tables.

Highlights the process of running a Spark program to load data directly into Hive tables, emphasizing the importance of using 'insert into' commands for bulk data loads.

Explores the concept of partition in Hive, comparing it to logical partitions on a laptop's hard drive and the significance of partitioning data for improved performance.

Introduces bucketing in Hive tables, detailing the use of buckets for optimizing performance through data distribution and explaining the concept of hash partitioning for bucket selection.

Explains the concept of using modulus to determine bucket positions in HDFS based on remainder values. Illustrates how to assign records to buckets based on the remainder obtained from division.

Discusses the importance of custom partitioning and the decision-making process behind choosing buckets. Emphasizes the significance of understanding bucket use cases for optimal data organization.

Explores the use case of bucketing in partitioning strategies. Provides insights into how bucketing aids in organizing and querying data efficiently, especially in scenarios where unique values pose challenges for partitioning.

Walks through the process of creating bucketed tables in Hive and explains the significance of bucket counts. Demonstrates the creation of buckets and how data is distributed across them.

Contrasts the efficiency of ORC file format with text file format in Hive, highlighting the advantages of ORC in terms of compression rates and performance optimization. Illustrates the impact of storage format on data size and query execution time.

Introduces Hive acid tables in Hive for online analytical processing, detailing the behavior of acid tables in terms of insert, update, and delete operations. Discusses the importance of setting up and implementing acid tables for enhanced data management.

Explains the concept of user-defined functions (UDFs) in Hive, showcasing how to create and use custom functions to perform specific data transformations. Provides a step-by-step guide on designing and executing UDFs in Hive with Java programming.

Demonstrates the process of implementing custom UDFs in Hive using Java programming. Shows how to write UDFs for data manipulation and concatenation operations, emphasizing the flexibility and functionality of custom functions in data processing workflows.

Understanding the basics of Spark and its prerequisites such as Hadoop knowledge, programming languages (Java, Scala, Python), and core concepts of big data frameworks.

Exploring the components of Spark including Spark SQL for structured APIs, Spark Streaming for real-time data processing, and Spark MLlib for machine learning activities.

Distinguishing between batch processing and stream processing in data processing, with examples and use cases explained.

Discussing how Spark integrates with Hadoop, the components involved, and the differences between Hadoop and Spark processing methods.

Exploring how Spark can connect with various big data technologies and ETL tools outside the Hadoop ecosystem for seamless integration and data processing.

Guidance on installing and setting up Spark, including prerequisites like Java, understanding deployment modes (Standalone, YARN), and configuring environmental variables.

Introduction to the three main Spark APIs (RDD, DataFrames, and DataSets), their characteristics, and their role in distributed and fault-tolerant data processing.

Explaining the concepts of transformations and actions in Spark, and how they shape the coding and execution process in Spark programs.

Understanding the concept of lazy evaluation in Spark, ensuring actions are called to trigger Spark processing efficiently.

Explaining the testing and development process in Spark using shells (Scala shell, Python shell) for quick code testing and debugging.

The video discusses starting the Spark service and explains the processes involved in initiating the Spark cluster including starting the master and worker processes, using the JPS command, and understanding the different Java processes involved in Spark.

Details about running programs in IDE, building jar files, and running in cluster mode are explained along with accessing the Spark Master UI using localhost and port number 8080. The significance of RPC and web port numbers for monitoring processes is also highlighted.

An overview of the Spark shell, its UI, and the distinction between spark and python shells is provided. The concept of Spark Context and its role in initializing and executing programs in Spark is discussed.

In-depth details on parallel processing technology, mapping, reducing, and the key concepts of grouping tasks in Spark. A comparison with Hadoop's map-reduce approach is made, emphasizing the differences in terminology and execution.

The execution flow of Spark jobs is explained, starting from submitting a job, creating drivers and executors, resource allocation, data distribution, task execution, and the role of the cluster manager in managing resources efficiently.

Details on fault tolerance in Spark, including scenarios of executor failures, actions taken by the Spark Master in case of failures, data replication strategies, and handling failures in a Spark Standalone architecture. The importance of avoiding single points of failure is emphasized.