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Store and Process Big Data. Excel, MySQL, Spark

👉  Store and Process Big Data. Excel, MySQL, Spark. | Udemy NED

 Store and Process Big Data. Excel, MySQL, Spark.Big Data and Machine Learning. Distributed Data Storage. Quick start guide to MySQL and Spark.

What you'll learn
  • What is big data?
  • What storage options we have today?
  • Public Cloud and Private Cloud.
  • Distribute you data
  • What is MySQL?
  • How Does MySQL Work?
  • Why is MySQL so Popular?
  • MySQL server setup
  • MySQL initial settings
  • Getting Started with MySQL
  • Import Excel data into a MySQL
  • Create a new MySQL table
  • Most Common Queries
  • SELECT, DROP, UPDATE query mysql
  • What is Hadoop?
  • Spark vs MySql
  • Spark. Analytics engine for big data processing
  • Installing Apache Spark
  • updating PATH environment
  • Getting Started with Spark
  • Launching Apache Spark
  • Installing Anaconda On Windows
  • Running the Jupyter Notebook
  • Connecting Jupyter notebook to Spark
  • Connecting Jupyter notebook to Spark
  • How to set up PySpark for your Jupyter Notebook
  • Export Data from Mysql to Spark
  • Importing Spark Dataframes from MySQL on Jupyter notebooks

This course is intended to be an initiation to learn #BigData and #MachineLearning with #Python programming for absolute beginners that have no background in programming.

In this course, we will step by step, using the example of real data, we will go through the main processes related to the topic "Big data and machine learning".
Since the material turned out to be voluminous, I divided the course into five parts.

In this part we will consider the main options for storing big data.

⇉ In practical lesson we will install the MySQL server on computer and learn how to work and edit MySQL databases.
In the fifth lesson we will take one regular exel table and transfer the information from this table to the MySql server.

⇉ Then we will install the spark in order to work with datasets in a distributed manner.Then, to process the distributed data, we export the data from MySQL into spark. And with the help of Jupiter Notebook, we prepare the data for visualization of this data.

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