Gyansetu’s Apache Spark and Scala course will help you understand the Spark Ecosystem & it’s related APIs like Spark SQL, Spark Streaming, Spark MLib, Spark GraphX & Spark Core concepts as well as integration of Spark with tools like Flume and Kafka.

• Understanding Big Data
• Real-world Customer Scenarios for Big Data
• Addressing Limitations of Existing Data Analytics Architecture with Uber Use Case
• Hadoop: Solving the Challenges of Big Data
• Overview of Hadoop
• Core Characteristics of Hadoop
• Exploring Hadoop Ecosystem and HDFS
• Core Components of Hadoop
• Rack Awareness and Block Replication in Hadoop
• Advantages of YARN
• Architecture of Hadoop Cluster
• Different Cluster Modes in Hadoop
• Big Data Analytics: Batch & Real-Time Processing
• Role and Importance of Spark in Big Data Ecosystem
• Spark’s Differentiation from Competitors
• Case Study: Spark Implementation at eBay

• What is Scala? Why Scala for Spark?
• Scala in other Frameworks
• Introduction to Scala REPL
• Basic Scala Operations
• Variable Types in Scala
• Control Structures in Scala
• Foreach loop, Functions and Procedures
• Collections in Scala- Array
• ArrayBuffer, Map, Tuples, Lists, and more

• Functional Programming
• Higher Order Functions
• Anonymous Functions
• Class in Scala
• Getters and Setters
• Custom Getters and Setters
• Properties with only Getters
• Auxiliary Constructor and Primary Constructor
• Singletons
• Extending a Class
• Overriding Methods
• Traits as Interfaces and Layered Traits

• Components and Architecture of Apache Spark
• Deployment Modes of Spark
• Introduction to PySpark Shell
• Submitting PySpark Jobs
• Utilizing Spark Web UI
• Writing PySpark Jobs Using Jupyter Notebook
• Data Ingestion with Sqoop

• Challenges in Existing Computing Methods
• Introduction to Resilient Distributed Datasets (RDDs)
• Operations, Transformations, and Actions on RDDs
• Loading and Saving Data using RDDs
• Key-Value Pair RDDs and Other Pair RDDs
• RDD Lineage and Persistence
• Implementing WordCount Program Using RDD Concepts
• RDD Partitioning and Parallelization Techniques

• Introduction to Spark SQL and its Importance
• Architecture of Spark SQL
• Working with SQL Context and Schema RDDs
• User Defined Functions (UDFs) in Spark SQL
• Data Frames, Datasets, and Interoperability with RDDs
• Loading Data from Different Sources
• Integration of Spark with Hive for Data Warehousing

• Introduction to Machine Learning and its Applications
• Overview of MLlib in Spark
• Supported ML Algorithms and Tools in MLlib
• Supervised Learning (Linear Regression, Logistic Regression, Decision Tree, Random Forest)
• Unsupervised Learning (K-Means Clustering)
• Case Study: Analysis on US Election Data using MLlib
• Exploring Supervised and Unsupervised Learning Algorithms
• Hands-on Examples: Linear Regression, Logistic Regression, Decision Tree, Random Forest, K-Means Clustering

• Introduction to Kafka and its Core Concepts
• Architecture and Components of Kafka
• Use Cases and Configuration of Kafka Cluster
• Introduction to Apache Flume and its Architecture
• Understanding Flume Sources, Sinks, and Channels
• Integration of Flume and Kafka for Data Ingestion

• Challenges in Existing Computing Methods
• Introduction to Spark Streaming and its Features
• Workflow of Spark Streaming
• Implementing Streaming Applications with DStreams
• Windowed Operators for Time-based Processing
• Stateful Operators for State Management
• Overview of Streaming Data Sources
• Kafka and Flume as Streaming Data Sources
• Example: Using Kafka Direct Data Source for Spark Streaming

• Introduction to Graph Processing with Spark GraphX
• Overview of Graph and GraphX Basic APIs
• GraphX Algorithms: PageRank, Personalized PageRank, Triangle Count, Shortest Paths, Connected Components, Strongly Connected Components, Label Propagation

Environment: Hadoop YARN, Spark Core, Spark Streaming, Spark SQL, Scala, Python, Kafka, Hive, Sqoop, Amazon AWS, Elastic Search, Impala, Cassandra, Tableau, Talend, Oozie, Jenkins, Cloudera, Oracle 12c, Linux.

Skills: Java, Scala, Python, SQL, PL/SQL, Pig Latin, HiveQL, Unix, Java Script, Shell Scripting, HDFS, YARN, MapReduce, Hive, Pig, Impala, Sqoop, Flume, Spark, Kafka, Zookeeper, and Oozie, Storm, Spark, Kafka, Yarn and Zookeeper, Spark Streaming, Spark SQL, Spark MLib, Spring RDDs, AWS(EC2&EMR).

Description: The primary objective of this project is to integrate Hadoop (Big Data) with the Relationship Care Application to leverage the raw/processed data that the big data platform owns. It will provide an enriched customer experience by delivering customer insights, profile information and customer journey.

Environment: Hadoop YARN, Spark Core, Spark Streaming, Spark SQL, Scala, Kafka, Hive

Tools & Techniques used: Hadoop+HBase+Spark+Flink+Beam+ML stack, Docker & KUBERNETES, Kafka, MongoDB, AVRO, Parquet

Description:You will be creating a Batch/Streaming/ML/WebApp stack to locally test the jobs or submit to Yarn resource manager. Docker will be used to build the environment and Docker-compose will provision it with required components.

Job Opportunities Guaranteed

Get a 100% Guaranteed Interview Opportunities Post Completion of the training.

Access to Job Application & Alumni Network

Get chance to connect with Hiring partners from top startups and product-based companies.

Mock Interview Session

Get One-On-One Mock Interview Session with our Experts. They will provide continuous feedback and improvement plan until you get a job in industry.

Live Interactive Sessions

Live interactive sessions with industry experts to gain knowledge on the skills expected by companies. Solve practice sheets on interview questions to help crack interviews.

Career Oriented Sessions

Personalized career focused sessions to guide on current interview trends, personality development, soft skill and HR related questions.

Resume & Naukri Profile Building

Get help in creating resume & Naukri Profile from our placement team and learn how to grab attention of HR’s for shortlisting your profile.

There is no such prerequisite to join this course, but knowing Scala and SQL is an additional benefit.

Though there are many online courses available online but we at Gyansetu understand that teaching any course is not difficult but to make someone job-ready is the most important task. This is the reason we have our course curriculum designed and delivered by industry experts along with capstone industry ready projects which will drive your learning through real-time IT industry scenarios and help in clearing interviews.

Total duration of the Apache Spark and Scala Certification course is 160 hours (80 Hours of live Instructor-Led training and 80 hours of self-paced learning).

We have seen getting a relevant interview call is not a big challenge in your case. Our placement team consistently works on industry collaboration and associations which help our students to find their dream job right after the completion of training. We help you prepare your CV by adding relevant projects and skills once 80% of the course is completed. Our placement team will update your profile on Job Portals, this increases relevant interview calls by 5x.

Interview selection depends on your knowledge and learning. As per the past trend, the initial 5 interviews are a learning experience of :-

• What type of technical questions are asked in interviews
• What are their expectations?
• How should you prepare?

Our faculty team will constantly support you during interviews. Usually, students get job after appearing in 6-7 interviews.

We have seen getting a technical interview call is a challenge at times. Most of the time you receive sales job calls/ backend job calls/ BPO job calls. No Worries!!

Our Placement team will prepare your CV in such a way that you will have a good number of technical interview calls. We will provide you with interview preparation sessions and make you job ready. Our placement team consistently works on industry collaboration and associations which help our students to find their dream job right after the completion of training. Our placement team will update your profile on Job Portals, this increases relevant interview call by 3x.

Interview selection depends on your knowledge and learning. As per the past trend, initial 8 interviews are a learning experience of –

• What type of technical questions are asked in interviews
• What are their expectations?
• How should you prepare?

Our faculty team will constantly support you during interviews. Usually, students get a job after appearing in 6-7 interviews.

We have seen getting a technical interview call is hardly possible. Gyansetu provides internship opportunities to the non-working students, so they have some industry exposure before they appear in interviews. Internship experience adds a lot of value to your CV and our placement team will prepare your CV in such a way that you will have a good number of interview calls. We will provide you with interview preparation sessions and make you job ready. Our placement team consistently works on industry collaboration and associations which help our students to find their dream job right after the completion of training and we will update your profile on Job Portals, this increases relevant interview call by 3x.

Interview selection depends on your knowledge and learning. As per the past trend, initial 8 interviews are a learning experience of :-

• What type of technical questions are asked in interviews
• What are their expectations?
• How should you prepare?

Our faculty team will constantly support you during interviews. Usually, students get job after appearing in 6-7 interviews.

Yes, a 1:1 faculty discussion and demo session will be provided before admission. We understand the importance of trust between you and the trainer. We will be happy if you resolve all your queries before you start classes with us.

We understand the importance of every session. Session’s recording will be shared with you and in case of any query, faculty will give you extra time to answer your queries.

Yes, we understand that self-learning is most crucial and for the same we provide students with PPTs, PDFs, class recordings, lab sessions, etc., so that a student can get a good handle of these topics.

We provide an option to retake the course within 3 months from the completion of your course, so that you get more time to learn the concepts and do the best in your interviews.

We believe in the concept that having less students is the best way to pay attention to each student individually and for the same our batch size varies between 5-10 people.

Yes, we have batches available on weekends. We understand many students are in jobs and it’s difficult to take time for training on weekdays. Batch timings need to be checked with our counsellors on +91-9999201478.

Yes, we have batches available on weekdays but in limited time slots. Since most of our trainers are working, the batches are available in morning hours or in the evening hours. You need to contact our counsellors to know more about this on +91-9999201478.

You don’t need to pay anyone for software installation, our faculties will provide you with all the required software’s and will assist you in the complete installation process.

Our faculties will help you in resolving your queries during and after the course.