Scala Training Objectives
- Scala ecosystem is constantly growing. The number of great libraries and frameworks is amazing. Scala also supports new technologies like Data Science or Blockchain with its amazing tools. Yet if you have your one favourite Java library you can easily adapt it to Scala's environment because Scala is running on JVM.
- Scala is, and most likely will still be, the most widely used typed functional language. The only thing that could threaten it's position would be OCaml (because of the ReasonML effort: backend/frontend synergy, backed by Facebook, ...), but the current trend doesn't suggest it will happen any time soon.
- Performance. Scala is frequently over 10 times faster than Python. Scala uses Java Virtual Machine (JVM) during runtime which gives is some speed over Python in most cases. ... In case of Python, Spark libraries are called which require a lot of code processing and hence slower performance.
- LinkedIn. Twitter. Foursquare. Netflix. Tumblr. The Guardian. precog. Sony.
- Despite popular opinions on the Internet, Scala is not a difficult language to try. It's mainly because of its seamless compatibility with Java and the kind of dual nature (Functional Programming vs Object-Oriented Programming). You can get your hands dirty just by starting to write Java-like code in Scala.
- Scala is an ideal choice of language for those working with high-volume data sets. Multi-paradigmatic: Scala programmers can have the best of both worlds. Both object-oriented and functional programming paradigms available to them. Scala is compiled to Java bytecode and runs on a JVM.
- Complexity of Code: Despite helping programmers to accomplish tasks with less code, Scala has a more complex structure than Java. ... So many beginners find it easier to learn Java than Scala. Functional Programming: Java was introduced in 1995. So it was designed like other conventional programming languages.
- Software developers
- Web designers
- Programming enthusiasts
- Engineering graduates
- Students who all want to become Scala developers
- A lot of things, ranging from machine learning to web apps. As a high-level general purpose language, Scala boasts an extensive range of possible applications. Scala allows developers to make good use of standard JVM features and Java libraries..
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Top Companies Placement
The Professionally certified Scala Developers should create Scala data transformation and aggregation in writing code in Scala Produce unit tests for Spark transformations. Their methods to write Scala doc-style documentation in that all code is Strong Expertise in functional Programing area of Scala and rewarded with substantial pay raises shown below.
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Annual SalaryHiring Companies
Top Skills You Will Gain
- Stairway to Scala Applied
- Functional Programming in Scala
- Apache Spark with Scala
- Scala + Spark Core,SQL and Streaming
- Scala for Big Data, Rock the JVM! Scala
- Learn Scala from Scratch
- Learn Spark from a Big Data
- Scalable programming
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
Scala Course Curriculam
Trainers Profile
Trainers are certified professionals with 12+ years of experience in their respective domains as well as they are currently working with Top MNCs. As all Trainers from Scala Course are respective domain working professionals so they are having many live projects, trainers will use these projects during training sessions.
Pre-requisites
If you are beginner, there is NO PRIOR knowledge of any technology or learning is needed to learn Scala. Anyone (tech or non.tech) can learn Scala.
Syllabus of Scala Online Course Download syllabus
- High Availability
- Scaling
- Advantages and Challenges
- What is Big data
- Big Data opportunities,Challenges
- CharLearnoVitaristics of Big data
- Scala Distributed File System
- Comparing Scala & SQL
- Industries using Scala
- Data Locality
- Scala Architecture
- Map Reduce & HDFS
- Using the Scala single node image (Clone)
- HDFS Design & Concepts
- Blocks, Name nodes and Data nodes
- HDFS High-Availability and HDFS Federation
- Scala DFS The Command-Line Interface
- Basic File System Operations
- Anatomy of File Read,File Write
- Block Placement Policy and Modes
- More detailed explanation about Configuration files
- Metadata, FS image, Edit log, Secondary Name Node and Safe Mode
- How to add New Data Node dynamically,decommission a Data Node dynamically (Without stopping cluster)
- FSCK Utility. (Block report)
- How to override default configuration at system level and Programming level
- HDFS Federation
- ZOOKEEPER Leader Election Algorithm
- Exercise and small use case on HDFS
- Map Reduce Functional Programming Basics
- Map and Reduce Basics
- How Map Reduce Works
- Anatomy of a Map Reduce Job Run
- Legacy Architecture ->Job Submission, Job Initialization, Task Assignment, Task Execution, Progress and Status Updates
- Job Completion, Failures
- Shuffling and Sorting
- Splits, Record reader, Partition, Types of partitions & Combiner
- Optimization Techniques -> Speculative Execution, JVM Reuse and No. Slots
- Types of Schedulers and Counters
- Comparisons between Old and New API at code and Architecture Level
- Getting the data from RDBMS into HDFS using Custom data types
- Distributed Cache and Scala Streaming (Python, Ruby and R)
- YARN
- Sequential Files and Map Files
- Enabling Compression Codec’s
- Map side Join with distributed Cache
- Types of I/O Formats: Multiple outputs, NLINEinputformat
- Handling small files using CombineFileInputFormat
- Hands on “Word Count” in Map Reduce in standalone and Pseudo distribution Mode
- Sorting files using Scala Configuration API discussion
- Emulating “grep” for searching inside a file in Scala
- DBInput Format
- Job Dependency API discussion
- Input Format API discussion,Split API discussion
- Custom Data type creation in Scala
- ACID in RDBMS and BASE in NoSQL
- CAP Theorem and Types of Consistency
- Types of NoSQL Databases in detail
- Columnar Databases in Detail (HBASE and CASSANDRA)
- TTL, Bloom Filters and Compensation
- HBase Installation, Concepts
- HBase Data Model and Comparison between RDBMS and NOSQL
- Master & Region Servers
- HBase Operations (DDL and DML) through Shell and Programming and HBase Architecture
- Catalog Tables
- Block Cache and sharding
- SPLITS
- DATA Modeling (Sequential, Salted, Promoted and Random Keys)
- Java API’s and Rest Interface
- Client Side Buffering and Process 1 million records using Client side Buffering
- HBase Counters
- Enabling Replication and HBase RAW Scans
- HBase Filters
- Bulk Loading and Co processors (Endpoints and Observers with programs)
- Real world use case consisting of HDFS,MR and HBASE
- Hive Installation, Introduction and Architecture
- Hive Services, Hive Shell, Hive Server and Hive Web Interface (HWI)
- Meta store, Hive QL
- OLTP vs. OLAP
- Working with Tables
- Primitive data types and complex data types
- Working with Partitions
- User Defined Functions
- Hive Bucketed Tables and Sampling
- External partitioned tables, Map the data to the partition in the table, Writing the output of one query to another table, Multiple inserts
- Dynamic Partition
- Differences between ORDER BY, DISTRIBUTE BY and SORT BY
- Bucketing and Sorted Bucketing with Dynamic partition
- RC File
- INDEXES and VIEWS
- MAPSIDE JOINS
- Compression on hive tables and Migrating Hive tables
- Dynamic substation of Hive and Different ways of running Hive
- How to enable Update in HIVE
- Log Analysis on Hive
- Access HBASE tables using Hive
- Hands on Exercises
- Pig Installation
- Execution Types
- Grunt Shell
- Pig Latin
- Data Processing
- Schema on read
- Primitive data types and complex data types
- Tuple schema, BAG Schema and MAP Schema
- Loading and Storing
- Filtering, Grouping and Joining
- Debugging commands (Illustrate and Explain)
- Validations,Type casting in PIG
- Working with Functions
- User Defined Functions
- Types of JOINS in pig and Replicated Join in detail
- SPLITS and Multiquery execution
- Error Handling, FLATTEN and ORDER BY
- Parameter Substitution
- Nested For Each
- User Defined Functions, Dynamic Invokers and Macros
- How to access HBASE using PIG, Load and Write JSON DATA using PIG
- Piggy Bank
- Hands on Exercises
- Sqoop Installation
- Import Data.(Full table, Only Subset, Target Directory, protecting Password, file format other than CSV, Compressing, Control Parallelism, All tables Import)
- Incremental Import(Import only New data, Last Imported data, storing Password in Metastore, Sharing Metastore between Sqoop Clients)
- Free Form Query Import
- Export data to RDBMS,HIVE and HBASE
- Hands on Exercises
- Flume Installation
- Introduction to Flume
- Flume Agents: Sources, Channels and Sinks
- Log User information using Java program in to HDFS using LOG4J and Avro Source, Tail Source
- Log User information using Java program in to HBASE using LOG4J and Avro Source, Tail Source
- Flume Commands
- Use case of Flume: Flume the data from twitter in to HDFS and HBASE. Do some analysis using HIVE and PIG
- HUE.(Hortonworks and Cloudera)
- Workflow (Action, Start, Action, End, Kill, Join and Fork), Schedulers, Coordinators and Bundles.,to show how to schedule Sqoop Job, Hive, MR and PIG
- Real world Use case which will find the top websites used by users of certain ages and will be scheduled to run for every one hour
- Zoo Keeper
- HBASE Integration with HIVE and PIG
- Phoenix
- Proof of concept (POC)
- Spark Overview
- Linking with Spark, Initializing Spark
- Using the Shell
- Resilient Distributed Datasets (RDDs)
- Parallelized Collections
- External Datasets
- RDD Operations
- Basics, Passing Functions to Spark
- Working with Key-Value Pairs
- Transformations
- Actions
- RDD Persistence
- Which Storage Level to Choose?
- Removing Data
- Shared Variables
- Broadcast Variables
- Accumulators
- Deploying to a Cluster
- Unit Testing
- Migrating from pre-1.0 Versions of Spark
- Where to Go from Here
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Exam & Certification
- Participate and Complete One batch of Scala Training Course
- Successful completion and evaluation of any one of the given projects
- Complete 85% of the Scala Certification 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)
- Learn About the Certification Paths.
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- Solve Sample Tests that would help you to Increase the Speed needed for attempting the exam and also helps for Agile Thinking.

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Scala Course FAQ's
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