Apache Spark with Scala Training Objectives
- The Apache Spark with Scala Certification course provides participants with a comprehensive understanding of the Apache Spark with Scala platform. Students will gain a thorough understanding of the fundamentals of distributed computing, along with the necessary tools and techniques for developing and deploying applications in the Apache Spark with Scala environment.
- Apache Spark with Scala uses the Scala programming language as well as Java and Python. It is used to develop data-intensive applications that can be used in distributed computing environments. Scala provides a strong type system, type inference, and other features like pattern matching, which make it an excellent choice for Apache Spark applications.
- Apache Spark with Scala is not particularly complex, although it does require a basic understanding of programming concepts and familiarity with the Scala syntax. It is a great tool for building powerful data-intensive applications, but it is not necessarily a beginner-level language.
- The perfect Apache Spark with Scala training participant is someone who is already familiar with object-oriented programming concepts and has some experience working with distributed systems. Additionally, they should have a good understanding of the Scala language and be comfortable working with the Spark API.
- The Apache Spark with Scala program covers a wide range of topics, including distributed systems, data analysis principles, Scala programming, and the Spark API. Additionally, participants will be tested on their understanding of these topics through practical exercises, quizzes, and exams.
- Apache Spark with Scala is most helpful in situations where large data sets need to be processed and analyzed in a distributed environment. In particular, it is well suited for tasks such as machine learning, graph processing, streaming data analysis, and interactive data exploration.
- Businesses use Apache Spark with Scala to efficiently process and analyze large data sets in a distributed environment. Apache Spark offers low-latency processing, high-level APIs for common data processing tasks, and support for multiple languages, including Scala.
- Yes, Apache Spark with Scala is regarded as a technical skill. It requires a solid understanding of object-oriented programming concepts, data analysis principles, distributed computing techniques, and the Scala language.
- Yes, coding is required for Apache Spark with Scala. In particular, participants should be proficient in the Scala programming language, as well as have a good understanding of the Spark API. Without a solid understanding of these elements, it will be difficult to work with Apache Spark.
- Yes, individuals are often interested in taking a course in Apache Spark with Scala. This is because Apache Spark is widely used in data science and software engineering, and having a solid understanding of the technology can open up new job opportunities and increase earning potential.
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Top Companies Placement
A professional application developer is an irreproachable source code creator of the software. Technoscientifically, application developers involves in the end-to-end software development life cycle. They create, test, deploy, and help to upgrade software as per the requirement of clients. They are often rewarded with substantial pay raises as shown below.
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Annual SalaryHiring Companies
Skills You Will Gain
- Big Data, HDFS
- YARN, Spark
- MapReduce
- PIG, HIVE
- Mahout, Spark MLLib
- Solar, Lucene
- Zookeeper
Online Classroom Batches Preferred
No Interest Financing start at ₹ 5000 / month
Corporate Training
- Customized Learning
- Enterprise Grade Learning Management System (LMS)
- 24x7 Support
- Enterprise Grade Reporting
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Apache Spark with Scala Course Curriculam
Trainers Profile
Trainers are certified professionals with 11+ years of experience in their respective domains as well as they are currently working with Top MNCs. As all Trainers from Apache Spark with Scala Certification Course are respective domain working professionals so they are having many live projects, trainers will use these projects during training sessions.
Pre-requisites
Following is sufficient for basics of Apache Spark with Scala technologies :
Syllabus of Apache Spark with Scala Online Course Download syllabus
- What is Big Data?
- Big Data Customer Scenarios
- What is Hadoop?
- Hadoop’s Key Characteristics
- Hadoop Ecosystem and HDFS
- Hadoop Core Components
- Rack Awareness and Block Replication
- YARN and its Advantage
- LHadoop Cluster and its Architecture
- Hadoop: Different Cluster Modes
- Why Spark is needed?
- What is Spark?
- How Spark differs from other frameworks?
- 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
- Variables in Scala
- Methods, classes, and objects in Scala
- Packages and package objects
- Traits and trait linearization
- Java Interoperability
- Introduction to functional programming
- Functional Scala for the data scientists
- Types and hierarchies
- Performance characteristics
- Java interoperability
- Using Scala implicits
- Apache Spark installation
- Spark Applications
- The back bone of Spark – RDD
- Loading Data
- What is Lambda?
- Using the Spark shell
- Actions and Transformations
- Associative Property
- What is RDD, Its Operations, Transformations & Actions
- Data Loading and Saving Through RDDs
- Key-Value Pair RDDs
- RDD Lineage
- RDD Persistence
- WordCount Program Using RDD Concepts
- RDD Partitioning & How It Helps Achieve Parallelization
- Passing Functions to Spark
- Need for Spark SQL
- What is Spark SQL?
- Spark SQL Architecture
- SQL Context in Spark SQL
- User Defined Functions
- Data Frames & Datasets
- Interoperating with RDDs
- JSON and Parquet File Formats
- Spark – Hive Integration
- Need for stream analytics
- Real-time data processing using Spark streaming
- Fault tolerance and check-pointing
- Stateful stream processing
- DStream and window operations
- Spark Stream execution flow
- Connection to various source systems
- Performance optimizations in Spark
- A brief introduction to graph theory
- GraphX
- VertexRDD and EdgeRDD
- Graph operators
- Pregel API
- PageRank
- Why Machine Learning?
- What is Machine Learning?
- Where Machine Learning is Used?
- Different Types of Machine Learning Techniques
- Introduction to MLlib
- Features of MLlib and MLlib Tools
- Various ML algorithms supported by MLlib
- Optimization Techniques
- Machine Learning MLlib
- K- Means Clustering
- Linear Regression
- Logistic Regression
- Decision Tree
- Random Forest
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Exam & Certification
- Participate and Complete One batch of Apache Spark with Scala Training Course
- Successful completion and evaluation of any one of the given projects
- Complete 85% of the Apache Spark with Scala course
- Successful completion and evaluation of any one of the given projects
- Oracle Certified Associate (OCA)
- Oracle Certified Professional (OCP)
- Oracle Certified Expert (OCE)
- Oracle Certified Master (OCM)
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