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Python Spark Certification Training Using Pyspark

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Python Spark Certification Training Using Pyspark

PMP stands Project Management Professional (PMP). PMP Certification, offered by the Project Management Institute(PMI), is an industry-recognized credential for project managers. PMP certification demonstrates the project manager's experience, knowledge, skills, and competencies required to lead and direct projects.The PMP® certification greatly expands your skills value and potential job opportunities. Recognized globally, a PMP® can help you land lucrative roles in IT, manufacturing, finance, healthcare, and other exciting industries. Certified PMP®'s also report better project performance and steep pay raises.

Why Should You Choose PMP Certification?

  • Use Python and Spark together to analyze Big Data.
  • Work on Consulting Projects that mimic real world situations.
  • Learn how to use the new Spark 2.0 DataFrame Syntax.
  • Learn how to use Spark's Gradient Boosted Trees.
  • Use Spark's MLlib to create Powerful Machine Learning Models.
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Python Spark Certification Using Pyspark - Instructor Led Training

15 th  October
Sat&Sun (4 Weeks) Weekends Batches
Timings: 07:00 AM - 11:30AM(IST)
Sold Out
25 th  October
Sat&Sun (4 Weeks) Weekends Batches
Timings: 07:00 AM - 11:30AM(IST)
Filling Fast
1 st  November
Sat&Sun (4 Weeks) Weekends Batches
Timings: 07:00 AM - 11:30AM(IST)
Pending

Curriculum

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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)
  • 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
  • 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
  • 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

About Python Spark Certification Using Pyspark

  • Overview of Big Data Hadoop including HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator)
  • Comprehensive knowledge of various tools that falls in Spark Ecosystem like Spark SQL, Spark MlLib, Sqoop, Kafka, Flume and Spark Streaming
  • The capability to ingest data in HDFS using Sqoop Flume, and analyze those large datasets stored in the HDFS
  • The power of handling real time data feeds through a publish-subscribe messaging system like Kafka
  • The exposure to many real-life industry-based projects which will be executed using Edureka’s CloudLab
  • Projects which are diverse in nature covering banking, telecommunication, social media, and govenment domains
  • Rigorous involvement of a SME throughout the Spark Training to learn industry standards and best practices
  • Master the concepts of HDFS
  • Understand Hadoop 2.x Architecture
  • Learn data loading techniques using Sqoop
  • Understand Spark and its Ecosystem
  • Implement Spark operations on Spark Shell
  • Understand the role of Spark RDD
  • Work with RDD in Spark
  • Implement Spark applications on YARN (Hadoop)
  • Implement machine learning algorithms like clustering using Spark MLlib API
  • Understand Spark SQL and it’s architecture
  • Understand messaging system like Kafka and its components
  • Integrate Kafka with real time streaming systems like Flume
  • Use Kafka to produce and consume messages from various sources including real time streaming sources like Twitter
  • Learn Spark Streaming
  • Use Spark Streaming for stream processing of live data
  • Solve multiple real-life industry-based use-cases which will be executed using Edureka’s CloudLab
  • Developers and Architects
  • BI /ETL/DW Professionals
  • Senior IT Professionals
  • Mainframe Professionals
  • Freshers
  • Big Data Architects, Engineers and Developers
  • Data Scientists and Analytics Professionals

As you know, nowadays, many organizations are showing interest in Big Data and are adopting Spark as a part of solution strategy, the demand of jobs in Big Data and Spark is rising rapidly. So, it is high time to pursue your career in the field of Big Data & Analytics with our PySpark Certification Training Course.

Frequently Asked Question's

There are no such prerequisites for PySpark Training Course. However, prior knowledge of Python Programming and SQL will be helpful but is not at all mandatory.

Your system must fulfill the following requirements:-

  • 64-bit Operating System
  • 8GB RAM

Please send us an email to info@transgemini.com, and we will answer any queries you may have!