CertAchiever

Python Spark Certification Training using PySpark

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Instructor-led live online classes

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Python Spark Certification Training using PySpark

$399

Python Spark Certification Training using PySpark

CertAchiever Python Spark Training is created to help you master skills that are required to become a successful Spark developer using Python. This Spark online course is live, instructor-led & helps you master key PySpark concepts, with hands-on demonstrations. This PySpark course is fully immersive where you can learn and interact with the instructor and your peers.

Training Features

Instructor-led Sessions

Both Weekdays and Weekend batches are available.

Real-Life Case Studies

During the sessions you will be working on the case studies provided by the trainer.

Lifetime Access

You will get the life time access of training material including videos & study guides..

Assignments

Each class will be followed by practical assignments to practice the concepts covered in class

24 x 7 Expert Support

We have a lifetime 24x7 online support team to resolve all your technical queries,through a ticket based tracking system..

Certification

Towards the end of the course, you will be awarded with Training competition certification based on you performance.

Course Curriculum

Learning Objectives

In this module, you will understand Big Data, the limitations of the existing solutions for Big Data problem, how Hadoop solves the Big Data problem, Hadoop ecosystem components, Hadoop Architecture, HDFS, Rack Awareness, and Replication. You will learn about the Hadoop Cluster Architecture, important configuration files in a Hadoop Cluster. You will also get an introduction to Spark, why it is used and understanding of the difference between batch processing and real-time processing.

Topics Covered

What is Big Data?, What is Big Data?, Big Data Customer Scenarios, Limitations and Solutions of Existing Data Analytics Architecture with Uber Use Case, How Hadoop Solves the Big Data Problem?, What is Hadoop?, Hadoop’s Key Characteristics, Hadoop Ecosystem and HDFS, Hadoop Core Components, Rack Awareness and Block Replication, YARN and its Advantage, Hadoop Cluster and its Architecture, Hadoop: Different Cluster Modes, Big Data Analytics with Batch & Real-Time Processing, Why Spark is Needed?, What is Spark?, How Spark Differs from its Competitors?, Spark at eBay, Spark’s Place in Hadoop Ecosystem

Learning Objectives

In this module, you will learn basics of Python programming and learn different types of sequence structures, related operations and their usage. You will also learn diverse ways of opening, reading, and writing to files.

Topics Covered

Overview of Python, Overview of Python, Different Applications where Python is Used, Values, Types, Variables, Operands and Expressions, Conditional Statements, Loops, Command Line Arguments, Writing to the Screen, Python files I/O Functions, Numbers, Strings and related operations, Tuples and related operations, Lists and related operations, Dictionaries and related operations, Sets and related operations

Hands On

Creating “Hello World” code, Creating “Hello World” code, Demonstrating Conditional Statements, Demonstrating Loops, Tuple - properties, related operations, compared with list, List - properties, related operations, Dictionary - properties, related operations, Set - properties, related operations

Learning Objectives

In this Module, you will learn how to create generic python scripts, how to address errors/exceptions in code and finally how to extract/filter content using regex.

Topics Covered

Functions, Functions, Function Parameters, Global Variables, Variable Scope and Returning Values, Lambda Functions, Object-Oriented Concepts, Standard Libraries, Modules Used in Python, The Import Statements, Module Search Path, Package Installation Ways

Hands On

Functions - Syntax, Arguments, Keyword Arguments, Return Values, Functions - Syntax, Arguments, Keyword Arguments, Return Values, Lambda - Features, Syntax, Options, Compared with the Functions, Sorting - Sequences, Dictionaries, Limitations of Sorting, Errors and Exceptions - Types of Issues, Remediation, Packages and Module - Modules, Import Options, sys Path

Learning Objectives

In this module, you will understand Apache Spark in depth and you will be learning about various Spark components, you will be creating and running various spark applications. At the end, you will learn how to perform data ingestion using Sqoop.

Topics Covered

Spark Components & its Architecture, Spark Components & its Architecture, Spark Deployment Modes, Introduction to PySpark Shell, Submitting PySpark Job, Spark Web UI, Writing your first PySpark Job Using Jupyter Notebook, Data Ingestion using Sqoop

Hands On

Building and Running Spark Application, Building and Running Spark Application, Spark Application Web UI, Understanding different Spark Properties

Learning Objectives

In this module, you will learn about Spark - RDDs and other RDD related manipulations for implementing business logics (Transformations, Actions, and Functions performed on RDD).

Topics Covered

Challenges in Existing Computing Methods, Challenges in Existing Computing Methods, Probable Solution & How RDD Solves the Problem, What is RDD, It’s Operations, Transformations & Actions, Data Loading and Saving Through RDDs, Key-Value Pair RDDs, Other Pair RDDs, Two Pair RDDs, RDD Lineage, RDD Persistence, WordCount Program Using RDD Concepts, RDD Partitioning & How it Helps Achieve Parallelization, Passing Functions to Spark

Hands On

Loading data in RDDs, Loading data in RDDs, Saving data through RDDs, RDD Transformations, RDD Actions and Functions, RDD Partitions, WordCount through RDDs

Learning Objectives

In this module, you will learn about SparkSQL which is used to process structured data with SQL queries. You will learn about data-frames and datasets in Spark SQL along with different kind of SQL operations performed on the data-frames. You will also learn about the Spark and Hive integration.

Topics Covered

Need for Spark SQL, Need for Spark SQL, What is Spark SQL, Spark SQL Architecture, SQL Context in Spark SQL, Schema RDDs, User Defined Functions, Data Frames & Datasets, Interoperating with RDDs, JSON and Parquet File Formats, Loading Data through Different Sources, Spark-Hive Integration

Hands On

Spark SQL – Creating data frames, Spark SQL – Creating data frames, Loading and transforming data through different sources, Stock Market Analysis, Spark-Hive Integration

Learning Objectives

In this module, you will learn about why machine learning is needed, different Machine Learning techniques/algorithms and their implementation using Spark MLlib.

Topics Covered

Why Machine Learning, Why Machine Learning, What is Machine Learning, Where Machine Learning is used, Face Detection: USE CASE, Different Types of Machine Learning Techniques, Introduction to MLlib, Features of MLlib and MLlib Tools, Various ML algorithms supported by MLlib

Learning Objectives

In this module, you will be implementing various algorithms supported by MLlib such as Linear Regression, Decision Tree, Random Forest and many more.

Topics Covered

Supervised Learning: Linear Regression, Logistic Regression, Decision Tree, Random Forest, Supervised Learning: Linear Regression, Logistic Regression, Decision Tree, Random Forest, Unsupervised Learning: K-Means Clustering & How It Works with MLlib, Analysis of US Election Data using MLlib (K-Means)

Hands On

K- Means Clustering, K- Means Clustering, Linear Regression, Logistic Regression, Decision Tree, Random Forest

Learning Objectives

In this module, you will understand Kafka and Kafka Architecture. Afterward, you will go through the details of Kafka Cluster and you will also learn how to configure different types of Kafka Cluster. After that you will see how messages are produced and consumed using Kafka API’s in Java. You will also get an introduction to Apache Flume, its basic architecture and how it is integrated with Apache Kafka for event processing. You will learn how to ingest streaming data using flume.

Topics Covered

Need for Kafka, Need for Kafka, What is Kafka, Core Concepts of Kafka, Kafka Architecture, Where is Kafka Used, Understanding the Components of Kafka Cluster, Configuring Kafka Cluster, Kafka Producer and Consumer Java API, Need of Apache Flume, What is Apache Flume, Basic Flume Architecture, Flume Sources, Flume Sinks, Flume Channels, Flume Configuration, Integrating Apache Flume and Apache Kafka

Hands On

Configuring Single Node Single Broker Cluster, Configuring Single Node Single Broker Cluster, Configuring Single Node Multi-Broker Cluster, Producing and consuming messages through Kafka Java API, Flume Commands, Setting up Flume Agent, Streaming Twitter Data into HDFS

Learning Objectives

In this module, you will work on Spark streaming which is used to build scalable fault-tolerant streaming applications. You will learn about DStreams and various Transformations performed on the streaming data. You will get to know about commonly used streaming operators such as Sliding Window Operators and Stateful Operators.

Topics Covered

Drawbacks in Existing Computing Methods, Drawbacks in Existing Computing Methods, Why Streaming is Necessary, What is Spark Streaming, Spark Streaming Features, Spark Streaming Workflow, How Uber Uses Streaming Data, Streaming Context & DStreams, Transformations on DStreams, Describe Windowed Operators and Why it is Useful, Important Windowed Operators, Slice, Window and ReduceByWindow Operators, Stateful Operators

Hands On

WordCount Program using Spark Streaming, WordCount Program using Spark Streaming

Learning Objectives

In this module, you will learn about the different streaming data sources such as Kafka and flume. At the end of the module, you will be able to create a spark streaming application.

Topics Covered

Apache Spark Streaming: Data Sources, Apache Spark Streaming: Data Sources, Streaming Data Source Overview, Apache Flume and Apache Kafka Data Sources, Example: Using a Kafka Direct Data Source

Hands On

Various Spark Streaming Data Sources, Various Spark Streaming Data Sources

Learning Objectives

FinanceStatement: A leading financial bank is trying to broaden the financial inclusion for the unbanked population by providing a positive and safe borrowing experience. In order to make sure this underserved population has a positive loan experience, it makes use of a variety of alternative data--including telco and transactional information--to predict their clients' repayment abilities. The bank has asked you to develop a solution to ensure that clients capable of repayment are not rejected and that loans are given with a principal, maturity, and repayment calendar that will empower their clients to be successful. Project 2- Domain: Media and Entertainment Statement: Analyze and deduce the best performing movies based on the customer feedback and review. Use two different API's (Spark RDD and Spark DataFrame) on datasets to find the best ranking movies.

Topics Covered

Learning Objectives

In this module, you will be learning the key concepts of Spark GraphX programming concepts and operations along with different GraphX algorithms and their implementations.

Topics Covered

Introduction to Spark GraphX, Introduction to Spark GraphX, Information about a Graph, GraphX Basic APIs and Operations, Spark GraphX Algorithm - PageRank, Personalized PageRank, Triangle Count, Shortest Paths, Connected Components, Strongly Connected Components, Label Propagation

Hands On

The Traveling Salesman problem, The Traveling Salesman problem, Minimum Spanning Trees

FAQ's

You will never miss a lecture at CertAchiever! You can choose either of the two options:

View the recorded session of the class available in your LMS.

·You can attend the missed session, in any other live batch."

We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in the class.

You can give us a CALL at +918882136965 (IN) OR email at admin@certachiever.com

Certification

Once your training will complete, you will be awarded with training completion certification from the Trainer.