Here I present a curriculum as to the current state of my Cloudera courses.

My Hadoop courses are based on Vagrant so that you can practice and destroy your virtual environment before applying the installation onto real servers/VMs.

.

For those with little or no knowledge of the Hadoop eco system Udemy course : Big Data Intro for IT Administrators, Devs and Consultants

.

I would first practice with Vagrant so that you can carve out a virtual environment on your local desktop. You don’t want to corrupt your physical servers if you do not understand the steps or make a mistake. Udemy course : Real World Vagrant For Distributed Computing

.

I would then, on the virtual servers, deploy Cloudera Manager plus agents. Agents are the guys that will sit on all the slave nodes ready to deploy your Hadoop services Udemy course : Real World Vagrant – Automate a Cloudera Manager Build

.

Then deploy the Hadoop services across your cluster (via the installed Cloudera Manager in the previous step). We look at the logic regarding the placement of master and slave services. Udemy course : Real World Hadoop – Deploying Hadoop with Cloudera Manager

.

If you want to play around with HDFS commands (Hands on distributed file manipulation). Udemy course : Real World Hadoop – Hands on Enterprise Distributed Storage.

.

You can also automate the deployment of the Hadoop services via Python (using the Cloudera Manager Python API). But this is an advanced step and thus I would make sure that you understand how to manually deploy the Hadoop services first. Udemy course : Real World Hadoop – Automating Hadoop install with Python!

.

There is also the upgrade step. Once you have a running cluster, how do you upgrade to a newer hadoop cluster (Both for Cloudera Manager and the Hadoop Services). Udemy course : Real World Hadoop – Upgrade Cloudera and Hadoop hands on

Note : This course is built on top of the “Real World Spark 2 – Jupyter Python Spark Core – Toyin Akin” course

Jupyter Notebook is a system similar to Mathematica that allows you to create “executable documents”. Notebooks integrate formatted text (Markdown), executable code (Python), mathematical formulas (LaTeX), and graphics and visualizations (matplotlib) into a single document that captures the flow of an exploration and can be exported as a formatted report or an executable script.,

The Jupyter Notebook is a web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, machine learning and much more.

Big data integration

Leverage big data tools, such as Apache Spark, from Python

The Jupyter Notebook is based on a set of open standards for interactive computing. Think HTML and CSS for interactive computing on the web. These open standards can be leveraged by third party developers to build customized applications with embedded interactive computing.

Spark Monitoring and Instrumentation

While creating RDDs, performing transformations and executing actions, you will be working heavily within the monitoring view of the Web UI.

Every SparkContext launches a web UI, by default on port 4040, that displays useful information about the application. This includes:

A list of scheduler stages and tasks A summary of RDD sizes and memory usage Environmental information. Information about the running executors

Why Apache Spark …

Apache Spark run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk. Apache Spark has an advanced DAG execution engine that supports cyclic data flow and in-memory computing. Apache Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python and R shells. Apache Spark can combine SQL, streaming, and complex analytics.

Apache Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application.

Real World Quant Analyst/Developer – Bond Pricing

Note : This course is built on top of the “Real World Spark 2 – Jupyter Python Spark Core – Toyin Akin” course

Jupyter Notebook is a system similar to Mathematica that allows you to create “executable documents”. Notebooks integrate formatted text (Markdown), executable code (Python), mathematical formulas (LaTeX), and graphics and visualizations (matplotlib) into a single document that captures the flow of an exploration and can be exported as a formatted report or an executable script.,

The Jupyter Notebook is a web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, machine learning and much more.

Big data integration

Leverage big data tools, such as Apache Spark, from Python

The Jupyter Notebook is based on a set of open standards for interactive computing. Think HTML and CSS for interactive computing on the web. These open standards can be leveraged by third party developers to build customized applications with embedded interactive computing.

Spark Monitoring and Instrumentation

While creating RDDs, performing transformations and executing actions, you will be working heavily within the monitoring view of the Web UI.

Every SparkContext launches a web UI, by default on port 4040, that displays useful information about the application. This includes:

A list of scheduler stages and tasks A summary of RDD sizes and memory usage Environmental information. Information about the running executors

Why Apache Spark …

Apache Spark run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk. Apache Spark has an advanced DAG execution engine that supports cyclic data flow and in-memory computing. Apache Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python and R shells. Apache Spark can combine SQL, streaming, and complex analytics.

Apache Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application.