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Intellipaat’s Apache Spark Course lets you master real-time data processing using Spark Streaming, Spark SQL, RDD, machine learning libraries, etc., to clear Cloudera Spark and Hadoop Developer Certification exam. You will learn how to work on real-life projects in this Apache Spark course.
Spark supports two deployment modes. Both of them have been discussed below:
|Client mode||The driver program runs on the same machine where the Spark application is submitted. This mode is typically used for interactive development and debugging, as it does not require any cluster infrastructure.|
|Cluster mode||The driver program runs on a cluster node, and the worker nodes are responsible for executing the tasks. This mode is typically used for production workloads, as it can scale to large datasets and workloads.|
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1.1 Introducing Scala
1.2 Deployment of Scala for Big Data applications and Apache Spark analytics
1.3 Scala REPL, lazy values, and control structures in Scala
1.4 Directed Acyclic Graph (DAG)
1.5 First Spark application using SBT/Eclipse
1.6 Spark Web UI
1.7 Spark in the Hadoop ecosystem.
2.1 The importance of Scala
2.2 The concept of REPL (Read Evaluate Print Loop)
2.3 Deep dive into Scala pattern matching
2.4 Type interface, higher-order function, currying, traits, application space and Scala for data analysis
3.1 Learning about the Scala Interpreter
3.2 Static object timer in Scala and testing string equality in Scala
3.3 Implicit classes in Scala
3.4 The concept of currying in Scala
3.5 Various classes in Scala
4.1 Learning about the Classes concept
4.2 Understanding the constructor overloading
4.3 Various abstract classes
4.4 The hierarchy types in Scala
4.5 The concept of object equality
4.6 The val and var methods in Scala
5.1 Understanding sealed traits, wild, constructor, tuple, variable pattern, and constant pattern
6.1 Understanding traits in Scala
6.2 The advantages of traits
6.3 Linearization of traits
6.4 The Java equivalent
6.5 Avoiding of boilerplate code
7.1 Implementation of traits in Scala and Java
7.2 Handling of multiple traits extending
8.1 Introduction to Scala collections
8.2 Classification of collections
8.3 The difference between iterator and iterable in Scala
8.4 Example of list sequence in Scala
9.1 The two types of collections in Scala
9.2 Mutable and immutable collections
9.3 Understanding lists and arrays in Scala
9.4 The list buffer and array buffer
9.6 Queue in Scala
9.7 Double-ended queue Deque, Stacks, Sets, Maps, and Tuples in Scala
10.1 Introduction to Scala packages and imports
10.2 The selective imports
10.3 The Scala test classes
10.4 Introduction to JUnit test class
10.5 JUnit interface via JUnit 3 suite for Scala test
10.6 Packaging of Scala applications in the directory structure
10.7 Examples of Spark Split and Spark Scala
11.1 Introduction to Spark
11.2 Spark overcomes the drawbacks of working on MapReduce
11.3 Understanding in-memory MapReduce
11.4 Interactive operations on MapReduce
11.5 Spark stack, fine vs. coarse-grained update,, Spark Hadoop YARN, HDFS Revision, and YARN Revision
11.6 The overview of Spark and how it is better than Hadoop
11.7 Deploying Spark without Hadoop
11.8 Spark history server and Cloudera distribution
12.1 Spark installation guide
12.2 Spark configuration
12.3 Memory management
12.4 Executor memory vs. driver memory
12.5 Working with Spark Shell
12.6 The concept of resilient distributed datasets (RDD)
12.7 Learning to do functional programming in Spark
12.8 The architecture of Spark
13.1 Spark RDD
13.2 Creating RDDs
13.3 RDD partitioning
13.4 Operations and transformation in RDD
13.5 Deep dive into Spark RDDs
13.6 The RDD general operations
13.7 Read-only partitioned collection of records
13.8 Using the concept of RDD for faster and efficient data processing
13.9 RDD action for the collect, count, collects map, save-as-text-files, and pair RDD functions
14.1 Understanding the concept of key-value pair in RDDs
14.2 Learning how Spark makes MapReduce operations faster
14.3 Various operations of RDD
14.4 MapReduce interactive operations
14.5 Fine and coarse-grained update
14.6 Spark stack
15.1 Comparing the Spark applications with Spark Shell
15.2 Creating a Spark application using Scala or Java
15.3 Deploying a Spark application
15.4 Scala built application
15.5 Creation of the mutable list, set and set operations, list, tuple, and concatenating list
15.6 Creating an application using SBT
15.7 Deploying an application using Maven
15.8 The web user interface of Spark application
15.9 A real-world example of Spark
15.10 Configuring of Spark
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
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
19.6 Kafka monitoring tools
19.7 Integrating Apache Flume and Apache Kafka
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
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
Practice Essential Tools
Designed By Industry Experts
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Recommend the best movie based on the user's taste. This hands-on Apache Spark project, along with using the MLlib, includes the creation of collaborative filtering, regression, clustering, and dimensionality reduction.
This project facilitates learning to analyze the sentiments of the user by a tweet. As a part of the project, the learners will be required to successfully integrate Twitter API and utilize PHP or Python to build a server-side code.
This project has been included to help the learners to combine Spark SQL with ETL applications, perform real-time data analysis, deploy machine learning algorithms, perform batch analysis, build visualizations, and process graphs.
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!
Over 20+ live interactive sessions with an industry expert to gain knowledge and experience on how to build skills that are expected by hiring managers. These will be guided sessions and that will help you stay on track with your up-skilling objective.
Get assistance in creating a world-class resume & LinkedIn Profile from our career services team and learn how to grab the attention of the hiring manager at the profile shortlisting stage
Students will go through several mock interviews conducted by technical experts who will then offer tips and constructive feedback for reference and improvement.
Attend one-on-one sessions with career mentors on how to develop the required skills and attitude to secure a dream job based on a learners’ educational background, experience, and future career aspirations.
Assured Interviews upon submission of projects and assignments. Get interviewed by our 500+ hiring partners.
Exclusive access to our dedicated job portal and apply for jobs. More than 400 hiring partners’ including top start-ups and product companies hiring our learners. Mentored support on job search and relevant jobs for your career growth.
This course is designed for clearing the Cloudera Spark and Hadoop Developer Certification (CCA175) exam. Check our Hadoop Training Course for gaining proficiency in the Hadoop component of the CCA175 exam. The
complete course is created by industry experts for professionals to get top jobs in the best organizations. The training includes real-world projects and case studies that are highly valuable.
On the completion of the training course, you will have quizzes that will help you prepare for the CCA175 certification exam and score top marks.
The Intellipaat certification is awarded on the successful completion of the project work and after its review by experts. The Intellipaat certification is recognized in some of the biggest companies such as Cisco, Cognizant, Mu Sigma, TCS, Genpact, Hexaware, Sony, Ericsson, etc.
I am glad I took this Apache Spark training from Intellipaat. There was extensive interactivity in the sessions throughout the training which made it the best online learning platform according to me.
I firmly believe that Intellipaat is the perfect place to embark on a great professional career in the technology space. Their Spark course was praiseworthy. Amazing experience.
The best thing I liked about the Spark training was the opportunity to work on real life projects that helped me get hands-on learning in one of the fastest Big Data processing engines. Thank you team.
The quality of the Apache Spark online course content is just awesome. I am absolutely happy and equally satisfied to have chosen the right course for my career. Overall, a great set of learning tutorials and videos.
This Spark training program is one of the best in this category. Well-curated curriculum and excellent course material by Intellipaat. The trainers are qualified and I highly recommend it.
This course delivered everything as per my expectations. It offered exactly what I wanted to learn and get hands-on experience in. Great trainers and amazing learning content covered in Spark class by Intellipaat.
I had enrolled in this Spark class and I must say that the course is well planned and structured, that makes it simple to learn. Additionally, the content delivered through Spark training is of high quality for better learning.
Intellipaat provided me with a comprehensive learning platform where I could resolve my doubts and the training was extremely comprehensive. The real world projects gave me industrial experience.
Intellipaat has been extremely helpful in my learning journey and helped me gain skills in all the in-demand tools and technologies in this domain on one single platform. Thank you team.
Lead Recruiter at Accenture, India
The candidates from Intellipaat were very good. They are better than experienced people from the same domain. The learners had hands-on experience. The product managers were very happy with the job-ready recruits.
HR at Genex Corporate Services, India
Even freshers from Intellipaat are technically strong and have hands-on experience. During the interview, they were able to answer everything that was asked. Certification plays a crucial role to move forward any resume.
Intellipaat is a pioneer in Hadoop training in India. It pays to be with a market leader such as Intellipaat to learn Spark and get the best jobs in leading MNCs with competitive salaries. Intellipaat provides the most comprehensive training course that includes real-time projects and assignments, designed by industry experts. The entire course content is fully aligned toward clearing the exam for the Cloudera Spark and Hadoop Developer Certification (CCA175) exam.
Intellipaat offers lifetime access to videos, course material, 24/7 support, and course material upgrades to the latest version at no extra fee. For Hadoop and Spark training, you get the Intellipaat Proprietary Virtual Machine for lifetime and free cloud access for six months for performing training exercises. Hence, it is clearly a one-time investment.
3 technical 1:1 sessions per month will be allowed.
Intellipaat is offering 24/7 query resolution, and you can raise a ticket with the dedicated support team at any time. You can avail of 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 support team. However, 1:1 session support is provided for a period of 6 months from the start date of your course.
Intellipaat provides placement assistance to all learners who have successfully completed the training and moved to the placement pool after clearing the PRT( Placement Readiness Test) More than 500+ top MNC’s and startups hire Intellipaat learners. Our Alumni works with Google, Microsoft, Amazon, Sony, Ericsson, TCS, Mu Sigma, etc.
Apparently, no. Our job assistance 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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