Hadoop Spark Hive Big Data Admin Class Bootcamp Course NYC

Install Hadoop by updating repositories, installing Java, and configuring JAVA_HOME. Download binaries, configure core-site, dfs-site, yarn-site, format the file system, and start dfs on localhost.

Demonstrate a Java-based MapReduce workflow on Hadoop by compiling a mapper and reducer, packaging a jar, and running the job on HDFS with input and output.

Set up a multi-node Hadoop cluster with a master and two workers, configure subnet and hosts, install Java, and enable SSH access.

Coordinate commissioning and decommissioning of data nodes by shutting down, removing corrupted data, updating ip aliases, and adding the node to master and slaves, then run the dfs balancer.

Diagnose multinode environments by verifying node configurations, host aliases, and code consistency. Troubleshoot connectivity with firewall settings and perform reformatting or data folder cleanup when issues persist.

Learn to install MySQL server and MySQL Workbench, configure user access and remote login, and set up Hive with Hadoop, including Hive directories and warehouse permissions.

Practice Hive data imports by defining a schema for structured, delimited data, creating a table, and loading data from local files or the file system, including internal and external tables.

Learn to install sqoop, configure environment variables, and run import and export commands to move data between MySQL and Hadoop, including incremental and timestamp-based imports.

Install and configure MongoDB by removing existing installations, adding repositories, installing MongoDB, and starting the service; then connect with Robomongo, and import JSON or CSV data into the database.

Install HBase within the Hadoop ecosystem by downloading and configuring HBase, Zookeeper, and related components, adjusting Java home, permissions, and startup scripts to run HBase from the bin directory.

install pig 0.15.0 on a local hadoop setup, configure the hadoop user environment, and learn to load comma-delimited structured data with fixed schema, perform joins and group by.

Install Spark and Scala by switching between the prime and Hadoop users, setting Scala path, downloading and unpacking Spark from tar, and configuring a local installation to run Spark programs.

Switch between worksheet mode and Java-like mode to practice Scala, defining objects and running code. Explore arrays, lists, maps, and 2d matrices to learn Scala data structures.

Learn to use PySpark to parallelize workloads with Spark context, build RDDs and in-memory data frames, apply map, flatMap, and lambda transformations, and integrate with Hive.

Install zookeeper and kafka, configure the zookeeper data directory, start the services, create a topic with kafka-topics using replication factor and partitions, then run producers and consumers.

Spark practice teaches processing unstructured text data with Spark shell and RDDs, using flatMap, map, and filter to extract insights and save results to an output folder.

Explore Hadoop streaming, compare it to Java-based and in-memory SPARC performance (eight to ten times slower), then set up Python, mapper, and reducer to count words in Gutenberg texts.

Explore end-to-end big data setup and project work, from commissioning and decommissioning notes, cluster setup, and container debugging to hands-on Hadoop, Spark, Hive, Kafka, and Python workflows.