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Big Data Hadoop Course in Houston

5 ( 1,044 ) Ratings

Intellipaat Big Data Course in Houston lets you master Big Data Hadoop and Spark online to get ready for the Cloudera CCA Spark and Hadoop Developer Certification (CCA175), as well as master Hadoop administration with 14 real-time industry-oriented case-study projects. Get the best Hadoop training in Houston from certified mentors.

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Key Highlights

60 Hrs Instructor Led Training
80 Hrs Self-paced Videos
120 Hrs Project & Exercises
Certification and Job Assistance
Flexible Schedule
Lifetime Free Upgrade
24 x 7 Lifetime Support & Access
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Big Data Hadoop Course in HoustonOverview

Intellipaat is one of the leading online training institute popular for providing the most industry-recognized and career-oriented Big Data Hadoop training in Houston, USA. This master’s program equips you with the skills of four domains, i.e., Developer, Admin, Analyst and Testing. This training course will give you a rich hands-on experience in Hadoop and its core and additional components like HDFS, Hive, HBa...

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What will you learn in this Big Data course in Houston?

  1. Core concepts of Hadoop 2.7
  2. Writing applications in YARN
  3. Hadoop components like Spark, MapReduce, Pig, Oozie, ZooKeeper, etc.
  4. Deploying pseudo-node and multi-node clusters
  5. Working on Spark RDDs, MLlib, GraphX, etc.
  6. Managing, monitoring, administering and troubleshooting Hadoop cluster
  7. Working with Avro data formats
  8. Exploring the concepts of Big Data Analytics
  9. Testing Hadoop applications
  • Programming Developers and System Administrators
  • Experienced working professionals and Project Managers
  • Big Data Hadoop Developers eager to learn other verticals like Testing, Analytics and Administration
  • Mainframe Professionals, Architects and Testing Professionals
  • Business Intelligence, Data warehousing and Analytics Professionals
  • Graduates and undergraduates eager to learn the latest Big Data technology

There is no pre-requisite to take up this Big Data training and to master Hadoop. But basics of UNIX, SQL and Java would be beneficial. At Intellipaat, we provide the complimentary Linux and Java courses with our Big Data certification training to brush-up the required skills so that you are good on your Hadoop learning path.

Houston is the second big city in the USA which has the maximum number of tech firms in the entire country. This clearly specifies that the companies are quite ahead in terms of technology. As Big Data Analytics is not just confined to IT firms but is applied in almost every industry, the technologies like Hadoop has a wider scope in this city. This has generated immense job opportunities for beginners and the experienced with the right skill set.

Houston is not just the commercial center in Texas but is also an emerging city in terms of technology. It is considered to be the best city after Silicon Valley for technology innovations. As innovation is always accompanied by extensive research and analytics, the use of Big Data technologies like Hadoop has grown in this city.

  • Global Hadoop market to reach $84.6 billion in two years – Allied Market Research
  • The number of jobs for all the US Data Professionals will increase to 2.7 million per year – IBM
  • A Hadoop Administrator in the US can get a salary of $123,000 – Indeed

With the data emanating from various digital sources each second, the tools that can convert these data into meaningful insights is very much important. Needless to say, Hadoop is one of the most prominent technologies in the domain of Big Data and is widely used by various firms, big and small. Hence, learning it will pave the way to success.

This training provides learners a rich hands-on experience on the four domains of Hadoop through interactive training sessions, lab practices and assignments. Not only this, learners will be carrying out 14 real-time projects based on Hadoop and its components like HDFS, YARN, MapReduce, Spark, Hive, Avro, Weblog analytics, Sqoop, etc. This master’s program helps you score high in CCA175 and CCAH exams.

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Information is the oil of the 21st century, and analytics is the combustion engine - Peter Sondergaard, Gartner
The global Hadoop Big Data analytics market size is expected to grow from USD 12.8 billion in 2020 to USD 23.5 billion by 2025. - MarketsandMarkets

Career Transition

57% Average Salary Hike

$1,28,000 Highest Salary

12000+ Career Transitions

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Skills Covered

  • Spark
  • Scala
  • Sqoop
  • Pig
  • Apache Flume
  • Hive
  • HCatalog
  • AVRO
  • Scala REPL
  • SBT/Eclipse
  • Apache Kafka
  • Spark Streaming
  • Impala
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Course Fees

Self Paced Training

  • 80 Hrs e-learning videos
  • Lifetime Free Upgrade
  • 24 x 7 Lifetime Support & Access

$264

Online Classroom Preferred

  • Everything in self-paced, plus
  • 60 Hrs of Instructor-led Training
  • 1:1 Doubt Resolution Sessions
  • Attend as many batches for Lifetime
  • Flexible Schedule
07 Aug

SAT - SUN

08:00 PM TO 11:00 PM IST (GMT +5:30)

10 Aug

TUE - FRI

07:00 AM TO 09:00 AM IST (GMT +5:30)

15 Aug

SAT - SUN

08:00 PM TO 11:00 PM IST (GMT +5:30)

21 Aug

SAT - SUN

08:00 PM TO 11:00 PM IST (GMT +5:30)

$ 449 $399 10% OFF Expires in

Corporate Training

  • Customized Learning
  • Enterprise grade learning management system (LMS)
  • 24x7 Support
  • Enterprise grade reporting

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Big Data Hadoop Course Content in Houston

Live Course

Module 01 - Hadoop Installation and Setup

Preview

1.1 The architecture of Hadoop cluster
1.2 What is High Availability and Federation?
1.3 How to setup a production cluster?
1.4 Various shell commands in Hadoop
1.5 Understanding configuration files in Hadoop
1.6 Installing a single node cluster with Cloudera Manager
1.7 Understanding Spark, Scala, Sqoop, Pig, and Flume

Module 02 - Introduction to Big Data Hadoop and Understanding HDFS and MapReduce

Preview

2.1 Introducing Big Data and Hadoop
2.2 What is Big Data and where does Hadoop fit in?
2.3 Two important Hadoop ecosystem components, namely, MapReduce and HDFS
2.4 In-depth Hadoop Distributed File System – Replications, Block Size, Secondary Name node, High Availability and in-depth YARN – resource manager and node manager

Hands-on Exercise:

1. HDFS working mechanism
2. Data replication process
3. How to determine the size of the block?
4. Understanding a data node and name node

3.1 Learning the working mechanism of MapReduce
3.2 Understanding the mapping and reducing stages in MR
3.3 Various terminologies in MR like Input Format, Output Format, Partitioners, Combiners, Shuffle, and Sort

Hands-on Exercise:

1. How to write a WordCount program in MapReduce?
2. How to write a Custom Partitioner?
3. What is a MapReduce Combiner?
4. How to run a job in a local job runner
5. Deploying a unit test
6. What is a map side join and reduce side join?
7. What is a tool runner?
8. How to use counters, dataset joining with map side, and reduce side joins?

4.1 Introducing Hadoop Hive
4.2 Detailed architecture of Hive
4.3 Comparing Hive with Pig and RDBMS
4.4 Working with Hive Query Language
4.5 Creation of a database, table, group by and other clauses
4.6 Various types of Hive tables, HCatalog
4.7 Storing the Hive Results, Hive partitioning, and Buckets

Hands-on Exercise:

1. Database creation in Hive
2. Dropping a database
3. Hive table creation
4. How to change the database?
5. Data loading
6. Dropping and altering table
7. Pulling data by writing Hive queries with filter conditions
8. Table partitioning in Hive
9. What is a group by clause?

5.1 Indexing in Hive
5.2 The ap Side Join in Hive
5.3 Working with complex data types
5.4 The Hive user-defined functions
5.5 Introduction to Impala
5.6 Comparing Hive with Impala
5.7 The detailed architecture of Impala

Hands-on Exercise: 

1. How to work with Hive queries?
2. The process of joining the table and writing indexes
3. External table and sequence table deployment
4. Data storage in a different table

6.1 Apache Pig introduction and its various features
6.2 Various data types and schema in Hive
6.3 The available functions in Pig, Hive Bags, Tuples, and Fields

Hands-on Exercise: 

1. Working with Pig in MapReduce and local mode
2. Loading of data
3. Limiting data to 4 rows
4. Storing the data into files and working with Group By, Filter By, Distinct, Cross, Split in Hive

7.1 Apache Sqoop introduction
7.2 Importing and exporting data
7.3 Performance improvement with Sqoop
7.4 Sqoop limitations
7.5 Introduction to Flume and understanding the architecture of Flume
7.6 What is HBase and the CAP theorem?

Hands-on Exercise: 

1. Working with Flume to generate Sequence Number and consume it
2. Using the Flume Agent to consume the Twitter data
3. Using AVRO to create Hive Table
4. AVRO with Pig
5. Creating Table in HBase
6. Deploying Disable, Scan, and Enable Table

8.1 Using Scala for writing Apache Spark applications
8.2 Detailed study of Scala
8.3 The need for Scala
8.4 The concept of object-oriented programming
8.5 Executing the Scala code
8.6 Various classes in Scala like getters, setters, constructors, abstract, extending objects, overriding methods
8.7 The Java and Scala interoperability
8.8 The concept of functional programming and anonymous functions
8.9 Bobsrockets package and comparing the mutable and immutable collections
8.10 Scala REPL, Lazy Values, Control Structures in Scala, Directed Acyclic Graph (DAG), first Spark application using SBT/Eclipse, Spark Web UI, Spark in Hadoop ecosystem.

Hands-on Exercise:

1. Writing Spark application using Scala
2. Understanding the robustness of Scala for Spark real-time analytics operation

9.1 Introduction to Scala packages and imports
9.2 The selective imports
9.3 The Scala test classes
9.4 Introduction to JUnit test class
9.5 JUnit interface via JUnit 3 suite for Scala test
9.6 Packaging of Scala applications in the directory structure
9.7 Examples of Spark Split and Spark Scala

10.1 Introduction to Spark
10.2 Spark overcomes the drawbacks of working on MapReduce
10.3 Understanding in-memory MapReduce
10.4 Interactive operations on MapReduce
10.5 Spark stack, fine vs. coarse-grained update, Spark stack, Spark Hadoop YARN, HDFS Revision, and YARN Revision
10.6 The overview of Spark and how it is better than Hadoop
10.7 Deploying Spark without Hadoop
10.8 Spark history server and Cloudera distribution

11.1 Spark installation guide
11.2 Spark configuration
11.3 Memory management
11.4 Executor memory vs. driver memory
11.5 Working with Spark Shell
11.6 The concept of resilient distributed datasets (RDD)
11.7 Learning to do functional programming in Spark
11.8 The architecture of Spark

12.1 Spark RDD
12.2 Creating RDDs
12.3 RDD partitioning
12.4 Operations and transformation in RDD
12.5 Deep dive into Spark RDDs
12.6 The RDD general operations
12.7 Read-only partitioned collection of records
12.8 Using the concept of RDD for faster and efficient data processing
12.9 RDD action for the collect, count, collects map, save-as-text-files, and pair RDD functions

13.1 Understanding the concept of key-value pair in RDDs
13.2 Learning how Spark makes MapReduce operations faster
13.3 Various operations of RDD
13.4 MapReduce interactive operations
13.5 Fine and coarse-grained update
13.6 Spark stack

14.1 Comparing the Spark applications with Spark Shell
14.2 Creating a Spark application using Scala or Java
14.3 Deploying a Spark application
14.4 Scala built application
14.5 Creation of the mutable list, set and set operations, list, tuple, and concatenating list
14.6 Creating an application using SBT
14.7 Deploying an application using Maven
14.8 The web user interface of Spark application
14.9 A real-world example of Spark
14.10 Configuring of Spark

15.1 Working towards the solution of the Hadoop project solution
15.2 Its problem statements and the possible solution outcomes
15.3 Preparing for the Cloudera certifications
15.4 Points to focus on scoring the highest marks
15.5 Tips for cracking Hadoop interview questions

Hands-on Exercise:

1. The project of a real-world high value Big Data Hadoop application
2. Getting the right solution based on the criteria set by the Intellipaat team

16.1 Learning about Spark parallel processing
16.2 Deploying on a cluster
16.3 Introduction to Spark partitions
16.4 File-based partitioning of RDDs
16.5 Understanding of HDFS and data locality
16.6 Mastering the technique of parallel operations
16.7 Comparing repartition and coalesce
16.8 RDD actions

17.1 The execution flow in Spark
17.2 Understanding the RDD persistence overview
17.3 Spark execution flow, and Spark terminology
17.4 Distribution shared memory vs. RDD
17.5 RDD limitations
17.6 Spark shell arguments
17.7 Distributed persistence
17.8 RDD lineage
17.9 Key-value pair for sorting implicit conversions like CountByKey, ReduceByKey, SortByKey, and AggregateByKey

18.1 Introduction to Machine Learning
18.2 Types of Machine Learning
18.3 Introduction to MLlib
18.4 Various ML algorithms supported by MLlib
18.5 Linear regression, logistic regression, decision tree, random forest, and K-means clustering techniques

Hands-on Exercise: 

1. Building a Recommendation Engine

19.1 Why Kafka and what is Kafka?
19.2 Kafka architecture
19.3 Kafka workflow
19.4 Configuring Kafka cluster
19.5 Operations
19.6 Kafka monitoring tools
19.7 Integrating Apache Flume and Apache Kafka

Hands-on Exercise: 

1. Configuring Single Node Single Broker Cluster
2. Configuring Single Node Multi Broker Cluster
3. Producing and consuming messages
4. Integrating Apache Flume and Apache Kafka

20.1 Introduction to Spark Streaming
20.2 Features of Spark Streaming
20.3 Spark Streaming workflow
20.4 Initializing StreamingContext, discretized Streams (DStreams), input DStreams and Receivers
20.5 Transformations on DStreams, output operations on DStreams, windowed operators and why it is useful
20.6 Important windowed operators and stateful operators

Hands-on Exercise: 

1. Twitter Sentiment analysis
2. Streaming using Netcat server
3. Kafka–Spark streaming
4. Spark–Flume streaming

21.1 Introduction to various variables in Spark like shared variables and broadcast variables
21.2 Learning about accumulators
21.3 The common performance issues
21.4 Troubleshooting the performance problems

22.1 Learning about Spark SQL
22.2 The context of SQL in Spark for providing structured data processing
22.3 JSON support in Spark SQL
22.4 Working with XML data
22.5 Parquet files
22.6 Creating Hive context
22.7 Writing data frame to Hive
22.8 Reading JDBC files
22.9 Understanding the data frames in Spark
22.10 Creating Data Frames
22.11 Manual inferring of schema
22.12 Working with CSV files
22.13 Reading JDBC tables
22.14 Data frame to JDBC
22.15 User-defined functions in Spark SQL
22.16 Shared variables and accumulators
22.17 Learning to query and transform data in data frames
22.18 Data frame provides the benefit of both Spark RDD and Spark SQL
22.19 Deploying Hive on Spark as the execution engine

23.1 Learning about the scheduling and partitioning in Spark
23.2 Hash partition
23.3 Range partition
23.4 Scheduling within and around applications
23.5 Static partitioning, dynamic sharing, and fair scheduling
23.6 Map partition with index, the Zip, and GroupByKey
23.7 Spark master high availability, standby masters with ZooKeeper, single-node recovery with the local file system and high order functions

Following topics will be available only in self-paced mode:

24.1 Create a 4-node Hadoop cluster setup
24.2 Running the MapReduce Jobs on the Hadoop cluster
24.3 Successfully running the MapReduce code
24.4 Working with the Cloudera Manager setup

Hands-on Exercise:

1. The method to build a multi-node Hadoop cluster using an Amazon EC2 instance
2. Working with the Cloudera Manager

25.1 Overview of Hadoop configuration
25.2 The importance of Hadoop configuration file
25.3 The various parameters and values of configuration
25.4 The HDFS parameters and MapReduce parameters
25.5 Setting up the Hadoop environment
25.6 The Include and Exclude configuration files
25.7 The administration and maintenance of name node, data node directory structures, and files
25.8 What is a File system image?
25.9 Understanding Edit log

Hands-on Exercise:

1. The process of performance tuning in MapReduce

26.1 Introduction to the checkpoint procedure, name node failure
26.2 How to ensure the recovery procedure, Safe Mode, Metadata and Data backup, various potential problems and solutions, what to look for and how to add and remove nodes

Hands-on Exercise:

1. How to go about ensuring the MapReduce File System Recovery for different scenarios
2. JMX monitoring of the Hadoop cluster
3. How to use the logs and stack traces for monitoring and troubleshooting
4. Using the Job Scheduler for scheduling jobs in the same cluster
5. Getting the MapReduce job submission flow
6. FIFO schedule
7. Getting to know the Fair Scheduler and its configuration

27.1 How ETL tools work in Big Data industry?
27.2 Introduction to ETL and data warehousing
27.3 Working with prominent use cases of Big Data in ETL industry
27.4 End-to-end ETL PoC showing Big Data integration with ETL tool

Hands-on Exercise:

1. Connecting to HDFS from ETL tool
2. Moving data from Local system to HDFS
3. Moving data from DBMS to HDFS,
4. Working with Hive with ETL Tool
5. Creating MapReduce job in ETL tool

28.1 Importance of testing
28.2 Unit testing, Integration testing, Performance testing, Diagnostics, Nightly QA test, Benchmark and end-to-end tests, Functional testing, Release certification testing, Security testing, Scalability testing, Commissioning and Decommissioning of data nodes testing, Reliability testing, and Release testing

29.1 Understanding the Requirement
29.2 Preparation of the Testing Estimation
29.3 Test Cases, Test Data, Test Bed Creation, Test Execution, Defect Reporting, Defect Retest, Daily Status report delivery, Test completion, ETL testing at every stage (HDFS, Hive and HBase) while loading the input (logs, files, records, etc.) using Sqoop/Flume which includes but not limited to data verification, Reconciliation, User Authorization and Authentication testing (Groups, Users, Privileges, etc.), reporting defects to the development team or manager and driving them to closure
29.4 Consolidating all the defects and create defect reports
29.5 Validating new feature and issues in Core Hadoop

30.1 Report defects to the development team or manager and driving them to closure
30.2 Consolidate all the defects and create defect reports
30.3 Responsible for creating a testing framework called MRUnit for testing of MapReduce programs

31.1 Automation testing using the OOZIE
31.2 Data validation using the query surge tool

32.1 Test plan for HDFS upgrade
32.2 Test automation and result

33.1 Test, install and configure

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Big Data Hadoop Course Projects

Peer Learning

Via Intellipaat PeerChat, you can interact with your peers across all classes and batches and even our alumni. Collaborate on projects, share job referrals & interview experiences, compete with the best, make new friends – the possibilities are endless and our community has something for everyone!

Big Data Hadoop Certification in Houston

This training course is designed to help you clear the Cloudera Spark and Hadoop Developer Certification (CCA175) exams. The entire training course content is in line with these certification programs and helps you clear these certification exams with ease and get the best jobs in the top MNCs.

As part of this Big Data Course in Houston, you will be working on real-time projects and assignments that have immense implications in the real-world industry scenarios, thus helping you fast-track your career effortlessly.

At the end of this Big Data Hadoop training in Houston, there will be quizzes that perfectly reflect the type of questions asked in the respective certification exams and help you score better.

Intellipaat Course Completion Certificate will be awarded upon the completion of the project work (after expert review) and upon scoring at least 60% marks in the quiz. Intellipaat certification is well recognized in top 80+ MNCs like Ericsson, Cisco, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered, TCS, Genpact, Hexaware, etc.

Big Data Hadoop Training Reviews in Houston

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FAQ’s on Big Data Hadoop Training

Why Should I Learn Big Data Hadoop Training in Houston from Intellipaat?

It is a known fact that the demand for Hadoop professionals far outstrips the supply. So, if you want to learn and make a career in Hadoop, then you need to enroll for Intellipaat Hadoop course online which is the most recognized name in Hadoop training and certification. Intellipaat Hadoop training includes all major components of Big Data and Hadoop like Apache Spark, MapReduce, HBase, HDFS, Pig, Sqoop, Flume, Oozie and more. The entire Intellipaat Big Data training in Houston has been created by industry professionals. You will get 24/7 lifetime support, high-quality course material and videos and free upgrade to latest version of course material. Thus, it is clearly a one-time investment for a lifetime of benefits.

Intellipaat has a plethora of courses that will help you become a Data Analyst. The comprehensive Data science certification training, Big Data, Python, Machine Learning, Data Scientist Masters program and others will help you to process, inspect, cleanse, transform, and create model data to gain useful information.

At Intellipaat, you can enroll in either the instructor-led online training or self-paced training. Apart from this, Intellipaat also offers corporate training for organizations to upskill their workforce. All trainers at Intellipaat have 12+ years of relevant industry experience, and they have been actively working as consultants in the same domain, which has made them subject matter experts. Go through the sample videos to check the quality of our trainers.

Intellipaat is offering the 24/7 query resolution, and you can raise a ticket with the dedicated support team at anytime. You can avail of the email support for all your queries. If your query does not get resolved through email, we can also arrange one-on-one sessions with our trainers.

You would be glad to know that you can contact Intellipaat support even after the completion of the training. We also do not put a limit on the number of tickets you can raise for query resolution and doubt clearance.

Intellipaat is offering you the most updated, relevant, and high-value real-world projects as part of the training program. This way, you can implement the learning that you have acquired in real-world industry setup. All training comes with multiple projects that thoroughly test your skills, learning, and practical knowledge, making you completely industry-ready.

You will work on highly exciting projects in the domains of high technology, ecommerce, marketing, sales, networking, banking, insurance, etc. After completing the projects successfully, your skills will be equal to 6 months of rigorous industry experience.

Intellipaat actively provides placement assistance to all learners who have successfully completed the training. For this, we are exclusively tied-up with over 80 top MNCs from around the world. This way, you can be placed in outstanding organizations such as Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, and Cisco, among other equally great enterprises. We also help you with the job interview and résumé preparation as well.

You can definitely make the switch from self-paced training to online instructor-led training by simply paying the extra amount. You can join the very next batch, which will be duly notified to you.

Once you complete Intellipaat’s training program, working on real-world projects, quizzes, and assignments and scoring at least 60 percent marks in the qualifying exam, you will be awarded Intellipaat’s course completion certificate. This certificate is very well recognized in Intellipaat-affiliated organizations, including over 80 top MNCs from around the world and some of the Fortune 500companies.

Apparently, no. Our job assistance program is aimed at helping you land in your dream job. It offers a potential opportunity for you to explore various competitive openings in the corporate world and find a well-paid job, matching your profile. The final decision on hiring will always be based on your performance in the interview and the requirements of the recruiter.

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