Question 1

Who provides the certification and how long is it valid for?

Accepted Answer

Upon successful completion of the PySpark certification training in Hyderabad, Simplilearn will provide you with an industry-recognized course completion certificate which has lifelong validity.

Question 2

How do I become a PySpark developer?

Accepted Answer

This PySpark course gives you an overview of Apache Spark and how to integrate it with Python using the PySpark interface. The training will show you how to build and implement data-intensive applications after you know about machine learning, leveraging Spark RDD, Spark SQL, Spark MLlib, Spark Streaming, HDFS, Flume, Spark GraphX, and Kafka. It helps you gain the skills required to become a PySpark developer.

Question 3

What do I need to do to unlock my Simplilearn certificate?

Accepted Answer

To obtain the PySpark course certification, you must complete the online self-learning training.

Question 4

What is PySpark?

Accepted Answer

Apache Spark is an open-source real-time cluster processing framework which is used in streaming analytics systems. Python is an open-source programming language that has a plethora of libraries that support diverse applications. PySpark is an integration of Python and Spark used for Big Data analytics. The Python API for Spark enables programmers to harness the simplicity of Python and the power of Apache Spark.

Question 5

How does a beginner learn PySpark?

Accepted Answer

PySpark is Python's library to use Spark which handles the complexities of multiprocessing. Simplilearn’s PySpark training course will help you learn everything from scratch and gives you an overview of the Spark stack and lets you know how to leverage the functionality of Python as you deploy it in the Spark ecosystem.

Question 6

What is RDD in PySpark?

Accepted Answer

RDD is an abbreviation for Resilient Distributed Dataset, the primary building block of Apache Spark. RDD is a fundamental data structure of Apache Spark, which is a constant distributed collection of objects. Each dataset in an RDD is divided into logical partitions that may be computed on different nodes of the cluster.

Question 7

Is PySpark a programming language?

Accepted Answer

PySpark is not a programming language. It is a Python API for Apache Spark deployments that Python developers can leverage to create in-memory processing applications.

Question 8

PySpark vs Scala

Accepted Answer

Python and Scala both are the languages used to analyze data using Spark. PySpark is a Python API for Spark used to leverage the simplicity of Python and the power of Apache Spark. Scala is ahead of Python in terms of performance, ease of use, parallelism, and type-safety. On the other hand, Python is more user friendly, has easy syntax, and standard libraries.

Question 9

Who are the instructors and how are they selected?

Accepted Answer

All of our highly qualified PySpark trainers are Big Data industry experts with years of relevant industry experience working with front-end development technology. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty.

Question 10

How do I enroll in this PySpark certification training?

Accepted Answer

You can enroll in this PySpark certification training on our website and make an online payment using any of the following options:


	Visa Credit or Debit Card
	MasterCard
	American Express
	Diner&rsquo;s Club
	PayPal


Once payment is received, you will automatically receive a payment receipt and access information via email.

PySpark Certification Training in Hyderabad

World’s #1 Online Bootcamp

Pyspark Course Overview

Skills Covered

Training Options

Self-Paced Learning

Pyspark Course Curriculum

Eligibility

Pre-requisites

Course Content

PySpark Training

Lesson 1 A Brief Primer on PySpark

1.1 A Brief Primer on PySpark

1.2 Brief Introduction to Spark

1.3 Apache Spark Stack

1.4 Spark Execution Process

1.05 Newest Capabilities of PySpark

1.6 Cloning GitHub Repository

Lesson 2 Resilient Distributed Datasets

2.1 Resilient Distributed Datasets

2.2 Creating RDDs

2.3 Schema of an RDD

2.4 Understanding Lazy Execution

2.5 Introducing Transformations – .map(…)

2.6 Introducing Transformations – .filter(…)

2.7 Introducing Transformations – .flatMap(…)

2.8 Introducing Transformations – .distinct(…)

2.9 Introducing Transformations – .sample(…)

2.10 Introducing Transformations – .join(…)

2.11 Introducing Transformations – .repartition(…)

Lesson 3 Resilient Distributed Datasets and Actions

3.1 Resilient Distributed Datasets and Actions

3.2 Introducing Actions – .collect(…)

3.3 Introducing Actions – .reduce(…) and .reduceByKey(…)

3.4 Introducing Actions – .count()

3.5 Introducing Actions – .foreach(…)

3.6 Introducing Actions – .aggregate(…) and .aggregateByKey(…)

3.7 Introducing Actions – .coalesce(…)

3.8 Introducing Actions – .combineByKey(…)

3.9 Introducing Actions – .histogram(…)

3.10 Introducing Actions – .sortBy(…)

3.11 Introducing Actions – Saving Data

3.12 Introducing Actions – Descriptive Statistics

Lesson 4 DataFrames and Transformations

4.1 DataFrames and Transformations

4.2 Creating DataFrames

4.3 Specifying Schema of a DataFrame

4.4 Interacting with DataFrames

4.5 The .agg(…) Transformation

4.6 The .sql(…) Transformation

4.7 Creating Temporary Tables

4.8 Joining Two DataFrames

4.9 Performing Statistical Transformations

4.10 The .distinct(…) Transformation

Lesson 5 Data Processing with Spark DataFrames

5.1 Data Processing with Spark DataFrames

5.2 Filtering Data

5.3 Aggregating Data

5.4 Selecting Data

5.5 Transforming Data

5.6 Presenting Data

5.7 Sorting DataFrames

5.8 Saving DataFrames

5.9 Pitfalls of UDFs

5.10 Repartitioning Data

Python for Data Science

Lesson 01: Introduction

Python for Data Science

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Pyspark Exam & Certification