In this PySpark tutorial, you will learn what is PySpark, its features, and how to use RDD and use-cases across industries and more.
The information offered in this PySpark tutorial is all fundamental, clear, and simple enough for beginners eager to learn PySpark and progress their careers in Big Data and Machine Learning to practice.
To understand Pyspark and its use in the big data world, we must first understand Apache Spark. So, let's take a look at Apache Spark. Apache Spark is an open-source cluster computing framework that is used to develop big data applications that can perform fast analytics over large data sets. Spark is written in Scala, but it can also be used from Python using Pyspark. It is very popular and one of the most requested tools in the IT industry because it has in-built tools for SQL, machine learning (ML), and streaming. So now that we understand Apache Spark, it will be easier for us to understand PySpark. So let's dive into it.
What Is PySpark?
Pyspark is a tool developed by Apache Spark Community for integrating Python with Spark. It enables Python users to work with Resilient Distributed Datasets (RDDs). Python's PySpark provides an interface for Apache Spark. It enables you to create Spark applications using Python APIs and gives you access to the PySpark shell, enabling interactive data analysis in a distributed setting. Most of Spark's functionality, including Spark SQL, DataFrame, Streaming, MLlib (Machine Learning), and Spark Core, are supported by PySpark.
Why Is Pyspark Needed?
It's crucial to understand why and when to use Spark with Python if you're going to learn PySpark. Here, we'll review the basic factors to consider while deciding between Python and Scala for Apache Spark programming.
- Data Science Libraries - You don't have to bother about the visuals or data science frameworks with the Python API. The R language's fundamental components can be readily converted to Python.
- Readability of Code - Although internal modifications are simple in the Scala API, the Python API offers superior readability, maintenance, and familiarity with the code.
- Complexity - In contrast to Scala, which produces verbose output and is therefore viewed as a complicated language, the Python API provides an accessible, simple, and comprehensive interface.
- Machine Learning Libraries - Since Python offers several libraries based on machine learning approaches, it is popular for developing machine learning algorithms because it simplifies the process.
- Ease of Learning - Python is simpler to learn and is known for its simple syntax. Compared to Scala, which has a complex syntax and is difficult to learn, it is also extremely productive despite having a basic syntax.
Key Features of PySpark
PySpark focuses on in-memory processing and offers real-time computing on massive amounts of data. The low latency is evident. With the help of Spark Streaming, real-time data processing from various input sources is supported, and the processed data can be stored in various output sinks.
Now let’s look at the second features of PySpark.
Support for Several Languages
Scala, Java, Python, and R are just a few programming languages with which the PySpark framework is compatible. It is the best framework for processing large datasets because of its interoperability. To make its API accessible to different languages, Spark uses an RPC server. All PySpark and SparkR objects are actually JVM object wrappers, as can be seen by looking at the source code. Look at Python DataFrame and R DataFrame.
Now as we learned about several languages supported in Pyspark framework let’s look at another feature.
Consistency of Discs and Caching
The PySpark framework offers powerful caching and reliable disc consistency. When write caching alters the order in which writes are committed, data consistency issues develop because there is a chance that an unexpected shutdown could occur, going against the operating system's expectation that all writes will be committed in a sequential order.
Now we already learned about real-time computing and support for several languages, let’s look at the next feature.
With PySpark, we can process data quickly - roughly 100 times quicker in memory and 10 times faster on the disc. When write caching alters the order in which writes are committed, data consistency issues develop because there is a chance that an unexpected shutdown could occur.
This is the last feature of Pyspark, let’s have a glimpse at it.
Effectiveness With RDD
The dynamic typing of the Python programming language makes working with RDD easier. If you're wondering what RDD is, let me give you a quick explanation of RDD. RDDs are utilized to carry out data operations rapidly and effectively. The in-memory notion makes data retrieval quick, and its ability to be reused makes it effective. So now we have come to the most important feature of Pyspark which is
RDD, If you're wondering what RDD is,
RDD stands for "Resilient Distributed Dataset." It is Apache Spark's primary data structure. RDD in Apache Spark is an immutable group of objects that computes on several cluster nodes.
Using the RDD lineage graph (DAG), the system is resilient or fault-tolerant and can recompute missing or damaged partitions due to node failures. It is distributed because the data is spread among several nodes. The records of the data you work with are represented by datasets. The user can import data sets from external sources using JDBC, whether databases, text files, CSV files, or databases with no particular data format.
If you're new to Big Data, you've probably heard of frameworks such as Spark. The technologies can be in Python (PySpark) or Scala.
How do you choose a programming language? Various factors are considered to answer this question. Let us figure out the answer to this question by understanding the differences between the two.
Now we have a better understanding of Pyspark and its features. So let's understand the need for Pyspark
Difference Between Scala and PySpark
What Are the Benefits of Using Pyspark?
- Easy to understand and use - Data consistency problems arise when write caching changes the order in which writes are committed since there is a potential that an unexpected shutdown could happen.
- Swift Processing - Using PySpark will probably result in high data processing speeds of roughly 10x on the disc and 100x in memory. This would be possible by lowering the number of read-write disc operations.
- In-Memory Computation - You can accelerate processing speed by using in-memory processing. And the best part is that the data is cached, so you don't have to retrieve it from the disc each time.
- Libraries - Python has a far better selection of libraries than Scala does. The majority of R's data science-related components have been converted to Python due to the abundance of libraries available. Well, in the case of Scala, this does not occur.
- Simple to write - We can state that writing parallelized code for simple tasks is relatively simple.
A DataFrame in PySpark is a distributed grouping of rows with named columns. In simpler terms, it is equivalent to an excel sheet with column headers or a table in a relational database. The spreadsheet is on a single machine, whereas dataframe is partitioned across servers in data centers. It also has some features in common with RDD, such as being immutable because we can only build a DataFrame or RDD once without being able to edit it. Furthermore, RDD and DataFrame are both distributed in nature.
Large collections of organized or semi-structured data can be processed using Data Frames. Petabytes of data can be handled using DataFrame in Apache Spark. Data Frame supports a variety of data formats and sources. For example, Python, R, Scala, and Java all have API support.
After learning different topics about Pyspark in this session, we have reached our final topic: Pyspark’s use in industries. Let's take a look at it.
Use- case of Pyspark in Industries
Apache Spark is gaining popularity and becoming more widely used by its users. From startups to multinationals, Apache Spark is being used to create, develop, and innovate big data systems. Here are various spark use examples for particular industries that show how to create and execute quick large data apps.
- E-commerce Industry - Let’s understand this with an example. Like Shopify wanted to analyze the kind of goods its clients were selling to find suitable retailers with whom it may collaborate to grow its business. Its data warehousing infrastructure could not resolve this issue since it frequently timed out when processing data mining queries on millions of documents. Shopify successfully developed a list of stores for collaboration after processing 67 million entries using Apache Spark in a matter of minutes.
- Healthcare - Spark is used in genomic sequencing. Before Spark, organizing all the chemical compounds with genes took several weeks. With Spark, it only takes a few hours now.
MyFitnessPal, the biggest health and fitness community, uses Spark to clean user-entered data to identify high-quality food products. The food calorie information of roughly 80 million individuals has been scanned by MyFitnessPal using Spark.
- Media and Entertainment - With the help of Apache Spark, Pinterest can identify patterns in valuable user engagement data to quickly respond to emerging trends by understanding users' online behavior thoroughly. To offer online suggestions to its users, Netflix leverages Spark for real-time stream analysis.
- Software and Information Service - The Spark developers, have created Databricks. It is a platform that has been tailored for the cloud to run Spark and ML apps on AWS and Azure, as well as a thorough training course. To grow the project and advance it, they are working on Spark. Financial services provider FINRA assists in gaining real-time data insights from billions of data occurrences. It can test things on actual market data using Apache Spark.
- Travel Industry - Apache Spark usage in the travel sector is growing quickly. It facilitates error-free consumers’ travel planning by accelerating customized recommendations. By comparing numerous websites, they may also utilize it to advise tourists on where to book hotels. Spark is being used by TripAdvisor, a popular travel website that assists consumers in creating the ideal trip, to speed up its tailored client suggestions.
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Pyspark helps develop machine learning pipelines, perform exploratory data analysis at scale, and develop ETLs for a data platform. So, we have learned about PySpark, its key features and its uses in industries. PySpark is useful to learn if you're already familiar with Python and its tools, such as Pandas, to build more scalable analyses and pipelines. Now the question is, what are the best Big Data Technology courses you can take to boost your career? Enroll in Simplilearn's Big Data Engineer Master's Course that will help you to kickstart your career as a Big data engineer.
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