Learn Spark, Spark RDD, Spark Streaming, Kafka, SparkR, SparkSQL, MLlib, and GraphX From Industry Experts
As humans, we are immersed in data in our every-day lives. As per IBM, the data doubles every two years on this planet. The value that data holds can only be understood when we can start to identify patterns and trends in the data. Normal computing principles do not work when data becomes huge.
There is massive growth in the big data space, and job opportunities are skyrocketing, making this the perfect time to launch your career in this space.
In this course, you will learn Spark to drive better business decisions and solve real-world problems.
Self-Paced Learning
Instructor-led Trainings
What is Big Data?
Why Now?
Big Data Use Cases
Various Solutions
Spark Ecosystem Walkthrough
Quiz
Understanding the CloudxLab
CloudxLab Hands-On
Spark Hands-on
Quiz and Assessment
Basics of Linux - Quick Hands-On
Understanding Regular Expressions
Quiz and Assessment
Setting up VM (optional)
InputFormat and InputSplit
JSON
XML
AVRO
How to store many small files - SequenceFile?
Parquet
Protocol Buffers
Comparing Compressions
Understanding Row Oriented and Column Oriented Formats - RCFile?
Introduction to Scala?
Accessing Scala using CloudxLab
Getting Started: Interactive, Compilation, SBT
Types, Variables & Values
Functions
Collections
Classes
Parameters
More Features
Quiz and Assessment
What is Apache Spark?
Why Spark?
Using the Spark Shell on CloudxLab
Example 1 - Performing Word Count
Understanding Spark Cluster Modes on YARN
RDDs (Resilient Distributed Datasets)
General RDD Operations: Transformations & Actions
RDD lineage
RDD Persistence Overview
Distributed Persistence
Creating the SparkContext
Building a Spark Application (Scala, Java, Python)
The Spark Application Web UI
Configuring Spark Properties
Running Spark on Cluster
RDD Partitions
Executing Parallel Operations
Stages and Tasks
Project: Churning the logs of NASA Kennedy Space Center WWW server
Common Spark Use Cases
Example 1 - Data Cleaning (Movielens)
Example 2 - Understanding Spark Streaming
Understanding Kafka
Example 3 - Spark Streaming from Kafka
Iterative Algorithms in Spark
Project: Real-time analytics of orders in an e-commerce company
Spark SQL and the SQL Context
Creating DataFrames
Transforming and Querying DataFrames
Saving DataFrames
DataFrames and RDDs
Comparing Spark SQL, Impala, and Hive-on-Spark
GraphX: Graph Processing and Analysis
Understanding Machine Learning
MlLib Example: k-means
SparkR Example
1. Generate movie recommendations using Spark MLlib
2. Derive the importance of various handles at Twitter using Spark GraphX
3. Churn the logs of NASA Kennedy Space Center WWW server using Spark to find out useful business and devops metrics
4. Write end-to-end Spark application starting from writing code on your local machine to deploying to the cluster
5. Build real-time analytics dashboard for an e-commerce company using Apache Spark, Kafka, Spark Streaming, Node.js, Socket.IO and Highcharts
Our Specialization is exhaustive and the certificate rewarded by us is proof that you have taken a big leap in Big Data domain.
The knowledge you have gained from working on projects, videos, quizzes, hands-on assessments and case studies gives you a competitive edge.
Highlight your new skills on your resume, LinkedIn, Facebook and Twitter. Tell your friends and colleagues about it.
In Self-paced learning, you will get,
In Instructor-led training, you will get
1. Basics Of SQL. You should know the basics of SQL and databases. If you know about filters in SQL, you are expected to understand the course.
2. A know-how of the basics of programming. If you understand 'loops' in any programming language, and if you are able to create a directory and see what's inside a file from the command line, you are good to get the concepts of this course even if you have not really touched programming for the last 10 years! In addition, we will be providing video classes on the basics of Python and Scala.
The instructors for this course are industry experts having years of experience in mentoring students across the world.
It will take 2-3 months with 6-8 hours of effort per week.
We understand that you might need course material for a longer duration to make most out of your subscription. You will get lifetime access (Till the company is operational) to the course material so that you can refer to the course material anytime.
In online instructor-led training, Sandeep Giri along with his team of experts will train you with a group of our course learners for 25+ hours over online conferencing software like Zoom. Classes will happen every Saturday and Sunday
We offer mentoring sessions to our learners with industry leaders and professionals so you can get 1 on 1 help with any questions you may have, whether your questions are technical, job-related or anything else.
It is a paid service and exclusively available to learners enrolling for the course. We will provide more information on subscription information for the same after the course is launched.
At the end, of course, you will work on a real-time project. You will receive a problem statement along with a data-set to work on CloudxLab. Once you are done with the project (it will be reviewed by an expert), you will be awarded a certificate which you can share on LinkedIn.
Enrollment into self-paced course entails 90 days of free access to CloudxLab. Enrollment into instructor-led course entails 90 days of free access to Cloudxlab, depending on date of enrollment.
Yes. Java is generally required for understanding MapReduce. MapReduce is a programming paradigm for writing your logic in the form of Mapper and reducer functions. We provide a self-paced course on Java for free. As soon as you signup, it would be available in your account section.
Course requires a good internet (1 Mbps or more) and a browser to watch videos and do hands-on the lab. We've configured all the tools in the lab so that you can focus on learning and practicing in a real-world cluster.
For self-paced course, we provide 100/% fees refund if the request is raised within 7 days from enrollment date. Thereafter, no refund is provided.
For instructor-led course, we provide 100% refund if not more than 1 live session has been conducted -- and we provide 50% refund if 2-4 live sessions have been conducted. If 5 or more live sessions have been conducted, then no refund will be provided.
Yes, you can renew your subscription anytime. Please choose your desired plan for the lab and make payment to renew your subscription.
Yes, you can upgrade from self-paced course to instructor-led course by paying the differential amount. Please contact us at reachus@cloudxlab.com for the same
Have more questions? Please contact us at reachus@cloudxlab.com
Must have for practicing and perfecting hadoop. To setup in PC you need to have a very high end configuration and setup will be pseudo node setup.. For better understanding I recomend CloudxLab
They are great. They take care of all the Big Data technologies (Hadoop, Spark, Hive, etc.) so you do not have to worry about installing and running them correclty on your pc. Plus, they have a fantastic customer support. Even when I have had problems debugging my own programs, they have answered me with the correct solution in a few hours, and all of this for a more than reasonable price. I personally recommend it to everyone :)
I have been using CloudxLab for last 3 months for learning Hadoop and Spark, and I can vouch for it.
It’s a platform where you can learn from the tutorial videos and then practice in the lab they provide on cloud. The study materials are well-planned and I would be lying if I say its not great.
The video lectures explains the technical stuffs in very simple ways which makes it easier to grasp the concepts. Also, the customer service is great.
So, thumbs up for the team associated with CloudxLab.
To conclude my views, I would just say that, if you are willing to learn Big Data related stuff, I strongly recommend CloudxLab.
I think I can give some points on this . Am using cloudxlab for more than an year… my intention is for continuous learning.
For Students and technology change professionals :
In General Big data hadoop, (a) you can learn on your personal PC, but for that the minimum configuration of 12 GB Ram with good processing speed, still when you execute jobs it will take more time for processing jobs as it will be acting as single node.(b) If you try to install each and every components, it will take hell a lot of admin work , and some thing happens , you have to invest lot of time for debugging.
The main advantage of using cloudxlab,
a) Get 6 node production cluster with all installed components, just getting user and password, you can start working on it.
b) You have almost all the access.
c) Good amount of components installed.
d) You can play around with each of them with 5gb of test data.
e) So far I didnt experience any down time.
f) You can Practice in your college lab, on free time.
g) Good email support on technical perspective.
h) They have couple of test data, I use my own.
i) vi and nano editor supported.
j) Some of the components which I remember are HDFS,MapReduce2, YARN, Tez, ZooKeeper,Falcon,Storm, Kafka,Spark,Jupyter Notebook, Hive,HBase, Pig, Sqoop, Oozie, Flume,Accumulo,Ambari.
I have been using CloudxLab for sometime and based on my usage experience I can say that they have done a fabulous job.
The first problem anyone faces while learning Big Data technologies is running the VMs on his/her laptop. VMs require a good amount of dedicated RAM and so most of the times we end up spending in hardware upgrade. But even after an upgrade the requirement of a cluster is never met. The examples we try alaways runs on a single node setup.
To try this on a production like cluster setup we have something like AWS, but there is a good amount of cost involved in that. Also, they keep the credit card details with them which I feel not everyone feels safe to share.
And this is where I feel CloudxLab seems to be a better bet.Their pricing is very much competitive compared to the other offerings and also it doesn’t require any specific hardware requirement. Any desktop/laptop with any configuration which has connectivity to net is good for getting started.
No need to do any setups.Their clusters are fully loaded with all the latest Big Data packages.You can access them from anywhere.
The only thing you need to concentrate is on your learning :)
Hope this helps to anyone who is looking for an option beyond VMs.