Real Time Spark Project for Beginners: Hadoop, Spark, Docker

Install and configure Docker Toolbox on Windows 64-bit, launch the Docker Engine, and run containers to isolate software versions. Learn how to access container IP addresses.

Create a single node kafka cluster on docker by pulling and running a zookeeper image, launching containers, and exposing the master node IP for broker communication.

Create a single node Apache Hadoop and Spark cluster on Docker by configuring images, starting ZooKeeper and Postgres, and launching the Spark and Hadoop master services.

Set up and configure IntelliJ IDEA Community Edition on Windows to build a Spark project using Scala, sbt, and plugins, covering dependencies and local execution.

Install and configure PyCharm Community Edition as your development environment, set up the Python interpreter, and create a Spark project to run sample data processing with Apache Spark.

Set up the Django web framework by installing Python 3.8.5 on Windows, create a virtual environment, install Django 3.1.1, verify the Django admin, and prepare for project development.

This lecture demonstrates a Python-based event simulator that generates real-time data center status messages using a producer–consumer messaging system, encoding country, capital city, timestamp, and severity into JSON every second.

Build a real-time streaming data pipeline with Scala and Spark Structured Streaming by consuming a Kafka topic, performing processing and aggregation, and loading results into PostgreSQL.

Learn to build a real-time data pipeline with PySpark and Spark Structured Streaming by consuming from sources, applying transformations and aggregations, and storing results in Postgres.

Set up a PostgreSQL database inside a Docker container for a real-time streaming project, log in, create a user and database, and review tables before building a visualization dashboard.

Build a real-time streaming dashboard using Django and Flexmonster to visualize live data with charts and dashboards, including data pipelines, migrations, and city-level visualizations.

Watch a real-time Spark project demo that builds a streaming data pipeline from a simulator to a preprocessing stage and Postgres, with a dockerized dashboard for live insights.

Launch a real-time streaming data pipeline on a docker-based spark cluster, log into the master node, copy files, start services, publish data with a simulator, and monitor the dashboard.

Explore Docker containerization, including the Docker engine, Docker images, and containers that package an application with libraries and dependencies, stored in a repository.

Install Docker on Ubuntu 18.04 by configuring the official repository, updating packages, installing the Docker engine, and verifying with a running container from Docker Hub.

Explore essential Docker commands for managing engines, images, and containers, including installing and verifying Docker, pulling and running images, listing and stopping containers, and checking resource usage.

Create your first docker image and container by writing a dockerfile, pulling a base Ubuntu image, building with docker build, tagging the image, and running the container.

Create and run a MySQL Docker container, pull the image, configure port mapping and environment variables, log in, create a database and table, and insert records.

Pull the official Cassandra Docker image and run it as a container. Connect to the cluster to create a keyspace and a table, then insert and query data.

Learn to run MongoDB in a docker container by pulling the MongoDB image, mounting local data, launching a container, and creating a database and collection with sample documents.

Learn to set up docker compose and run a multi-container application by configuring services, images, and ports for a web app and database.

Create and manage Docker volumes to persist data across containers, map them to local folders, share data between containers, inspect volumes, and remove unused ones.