Learn Big Data : Complete Hadoop Ecosystem with Practicals

Learn how to set up a Cloudera Hadoop environment using VirtualBox, including 64-bit OS requirements, downloading the tools, and configuring the workspace for hands-on practice.

Learn hdfs commands and file system navigation in the Hadoop ecosystem, including listing root directory contents with ls, creating directories, and managing files across a cloud environment.

Explore how to use HDFS commands to inspect data size and space usage, navigate the filesystem, and view or manage files with practical demonstrations.

We explore copying data from the local system to the Hadoop cluster using hdfs commands. Verify results by listing files and directories and moving files between local and cluster storage.

Explore practical hdfs commands to copy files between hdfs and the local system, verify operations with directory listings, and manage file transfers across the cluster.

Discover Hive, an open-source component of the Hadoop ecosystem, optimized for analytical workloads with a built-in language, while not suited for online transaction processing.

Explore Hive features and compare Hive with relational database management systems, focusing on data storage, batch processing, open-source tools, and text-based data sources.

Explore the differences between Hive table types, focusing on external tables and their impact on schema and table location within the Hive ecosystem.

Learn how to work with Hive to create and manage a managed Hive table within a Hadoop ecosystem, including starting services, listing databases, creating tables, and querying data.

Explore practical techniques for creating and using Hive external tables within the Hadoop ecosystem, including defining external schemas and querying data on a cluster.

Cassandra is an open source technology designed to manage huge amounts of data in real time. It can handle media in any format and is built for mass-market use.

Explore the Cassandra architecture through a diagram, highlighting a peer-to-peer cluster where every node can read and write data, with replication and gossip-based communication across the data center.

Explore how Cassandra eliminates single point of failure by moving away from master-slave models and enabling peer-to-peer communication in a multi-node cluster across locations.

Explore Cassandra replication factor and data-center aware replication, comparing simple strategy and Babuji strategy to ensure availability across multiple centers in a ring-like network.

Learn how to install Cassandra on Ubuntu, including downloading, extracting, setting the installation path, ensuring Java is installed, and starting the Cassandra service.

Create a Cassandra database and a table using a simple strategy, then use semicolon-terminated commands to set up and verify the structures. Describe tables to confirm the new database setup.

Explore how to create a Cassandra table using a network topology strategy across multiple locations. Apply the strategy to manage the table across interconnected systems.

Explore Cassandra data types in detail, including string, 64-bit long, decimal, text, and 128-bit id. Learn how these types map to tables and indexing in Cassandra.

Learn to create a Cassandra table by defining columns and data types, designate a primary key, and insert data to build a functional schema in the big data ecosystem.

Explore how to work with Cassandra's table and database schema and data, including defining primary keys and organizing columns. See how tables, databases, and data are managed and displayed.

Master updating Cassandra table data by defining a primary key, using update and delete operations with where clauses, and validating changes through read operations.

Learn how to delete table data in Cassandra by using primary key deletes and non-primary column conditions, including targeted row deletions and full-table removal.

Learn how to create a table and insert data from external files into a Cassandra table, including defining columns, loading a dataset, and verifying inserted records.

Learn MongoDB introduction with practicals, focusing on document store concepts, storing data in documents, and using indices to organize data across IDs and formats.

Explore the four to five NoSQL database types, including document stores and key-value stores, and learn how these data models support flexible big data storage.

Learn to install MongoDB on an Ubuntu machine, including adding the repository, updating package lists, installing MongoDB, starting services, and verifying installation.

Learn MongoDB, a cross-platform, open-source document store, and master CRUD operations—create, read, update, and delete—while understanding indexing and scalability across environments.

Explore No-SQL features, its high scalability, and flexible data formats. Understand how these differ from ACID properties and isolation in traditional databases.

Explore how to create a database in MongoDB, store documents in collections, and perform basic insert and read operations.

Create and manage MongoDB collections by performing CRUD operations, inserting documents with dot notation and braces, and exploring collection display and schema basics.

Learn MongoDB update operations with practical CRUD examples, updating document fields, setting new values, and managing collections in a hands-on session.

Master MongoDB delete operations with practical CRUD examples, learning how to remove documents from a collection. Apply these techniques to manage data removal across your MongoDB datasets.

Discover what Redis is and its features, highlighting fast state and real-time capabilities alongside other databases.

Discover a Redis overview and its role in fast data processing within the big data ecosystem, highlighting performance benchmarks and practical implications.

Discover the different Redis data types and how they fit into a practical big data Hadoop ecosystem.

Download the latest stable Redis package from the website, extract it in your downloads, install with sudo make install, start the service, and use info to verify server details.

Start and manage the Redis server from the terminal to access your Hadoop lab environment, verify connections, and learn command line workflows in a cloud setup.

Learn to work with the Redis string data type by setting and getting values, updating keys, and observing how runtime retrieval reflects stored strings.

Explore practical Redis string data types and time-to-live concepts by setting and expiring keys, retrieving values before and after expiration, and observing time-based behavior in real time.

Explore practical handling of the Redis string data type, including setting and retrieving values, working with arrays, and extracting specific values from a sequence.

Explore the Redis list data type with practicals on creating lists, pushing values via lpush and rpush, and retrieving ranges with lrange, including zero-based indexing and order options.

Explore Redis list data type and practical operations, including left and right pushes, indexing, and retrieving list elements, with live examples and continued exploration of list objects.

Learn how to work with Redis list data type by accessing elements by index, retrieving values, and using range queries to explore and manipulate list data.

explore HBase as a core component of the Hadoop ecosystem, a scalable non-relational database that serves as a data source manager for large-scale big data.

Explore why hbase fits big data within the hadoop ecosystem and how to view data available in collections with column-oriented concepts.

Explore the HBase architecture in detail with a diagram, and examine how the master coordinates with ZooKeeper and other components in the Hadoop ecosystem.

Explore how HBase uses tables with column families and columns, organizing data by time stamps and rules, and prepare for practical setup of HBase features.

Learn the most important HBase commands and how to pull data from tables. Apply these concepts within the Hadoop ecosystem.

Learn practical steps to start HBase services and open the terminal in a Cloudera environment, initialize the HBase master, and operate the shell for hands-on configuration.

Explore practical HBase commands using the HBase shell: launch the shell, list and inspect tables, query data with scan and get, and describe table structures.

Learn to integrate HBase with Hive by creating and mapping tables, loading external data, and querying across HBase and Hive to manage employee data.

Examine ACID vs CAP theorem, focusing on consistency, isolation, and durability, and how partitioning affects transaction behavior in distributed systems.

Explore spark, an open-source processing engine for fast cluster computing, and its components, including MLlib for machine learning, batch processing, and streaming, with Java and Scala support.

Explore Spark architecture with a detailed diagram, highlighting the roles of the cluster manager, driver and executors, how applications run across multiple machines, and data caching and job execution flows.

Explore the concept of resilient distributed datasets (rdds) in the hadoop ecosystem, highlighting their immutability, lazy evaluation, fault tolerance, and partitioned in-memory processing for scalable parallel data tasks.

Learn basic Scala commands by using the Scala shell to execute simple expressions, define values, and perform arithmetic in the Scala language.

Load data from a file in Spark and prepare it for analysis. Explore reading source data line by line to process and transform it.

Explore how to work with list objects in Spark using Scala, with practical guidance on declaring, accessing, and manipulating lists to build data processing workflows.

Explore how to create and apply functions in Spark for big data processing, with practical demonstrations within the Hadoop ecosystem.

Explore variable implementation in spark with scala, focusing on reassignment, memory management, and practical examples to build understanding of spark basics.

Explore variable implementation in Spark and strengthen your foundation with practical insights from the Hadoop ecosystem course, focusing on building resilient data processing workflows.

Learn to create your first Spark program using Eclipse, leveraging Scala development in the Hadoop ecosystem and practical steps for setting up and running Spark applications.

Discover the most important spark keywords and how they relate to cluster management, Yarn resource managers, and spot cluster management for efficient big data processing.

Learn to create and read a list in Eclipse using Scala, manage dependencies, and work with list collections to count six values and display results in the console.

Read data using Spark context by loading a text file, applying map and collect transformations, and building a Scala-based Spark application to process and print flight data.

Learn how to read JSON data in Spark by loading JSON files, parsing simple and complex structures, and handling data formats for scalable big data workflows.

Explore reading parquet format data in Spark, and work within the Hadoop ecosystem to load, display, and inspect data results.

Learn how to read data from Hive in Spark, connect to Hive, and load Hive tables into Spark for processing.

Apply Hive filters and conditions in Spark to query large datasets within the Hadoop ecosystem, using practical, hands-on exercises that reinforce real-world data processing.

Discover what big data is and why we use it. Explore how Pig provides a high-level language as an abstraction to process data in the Hadoop ecosystem, initiated by Yahoo.

Explore different Pig execution modes within the Hadoop ecosystem and see how map executions and file system interactions shape Pig scripts. Leverage user defined functions to customize processing.

Explore why Pig is essential in the Hadoop ecosystem for big data tasks, offering easy scripting, map and sort operations, and a testing-friendly environment that complements Java.

Explore the different features of Pig within the Hadoop ecosystem, including set operators, data handling, extensibility, and practical usage for big data workflows.

Explore pig data relations in the big data context through an employee data example, showing how records link via fields and related data.

Execute a pig script from a file in local mode and manage job execution within the Hadoop ecosystem, using practical steps demonstrated.

learn how to execute a pig script using a file in hdfs mode, move data between local and hdfs locations, and manage file staging in a practical Hadoop cluster.