Apache Spark, more commonly known as Spark, has been adopted by enterprises across a wide range of industries. Get tuned here to learn about the best Spark Tutorials.
Internet powerhouses such as Netflix, Yahoo, and eBay have already deployed Spark. These are collectively processing multiple petabytes of data on clusters of over 8,000 nodes.
Spark tutorial is becoming increasingly popular among developers across the world. It has largely contributed to the largest open source community in big data, with over 1000 contributors from 250+ organizations.
In this apache spark tutorial blog, we will discuss in detail the importance of Spark tutorials. We’ll learn more about the best apache-spark tutorials to choose from, and what you can learn from them.
Apache Spark is a fast, in-memory data processing engine with elegant and expressive development APIs. This allows data workers to efficiently execute streaming, machine learning or SQL workloads. The requirement is fast iterative access to datasets.
By taking a Spark tutorial you can learn how Spark runs on Apache Hadoop YARN. Further, you can create applications derive insights, and lessen data science workloads within a single, shared dataset in Hadoop.
What is Apache Spark?
Apache Spark, widely known as a lightning-fast cluster computing designed for fast computation was built on top of Hadoop MapReduce. It extends the MapReduce model to efficiently use more types of computations which includes Interactive Queries and Stream Processing.
Spark was developed as one of Hadoop’s subproject in 2009 at UC Berkeley’s AMPLab by MateiZaharia. It was Open Sourced in the following year under a BSD license. Later Spark was donated to Apache software foundation. Today Apache Spark stands as of the top-level Apache projects since February 2014.
Spark 2.0 is already available for use as a preview and a full release will happen soon. This will ensure a real step forward with the unification of datasets and data frames.
The Hadoop YARN-based architecture that provides a foundation that enables Spark and other applications to share a common cluster and dataset. In the backend, it must ensure consistent levels of service and response. Spark is now one of many data access engines that work with YARN in HDP.
Why is Apache Spark Tutorial so important?
So, you are all set to take up a Spark Tutorial. If you have prior exposure to Scala programming, database concepts, and any of the Linux operating system flavors. This is the best career decision for you. Even if you have a programming or a mathematics background, you can enroll for an Apache Spark tutorial as well.
What do you learn in a typical Apache Spark Tutorial?
Most Adobe Spark Tutorials gives you an overview of Spark in Big Data. Thereafter, you will be formally introduced to Apache Spark Programming. The fundamental of Spark components are of primal importance in Apache Spark Tutorial. In addition, you will also learn about Spark’s core abstraction and Spark RDD. Almost all Apache Spark Tutorials cover spark features, Spark limitations, and Spark Use cases.
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While choosing a Spark Tutorial, make sure the course includes the following:
Spark Tutorial: Apache Spark Components
Spark Core which is of pivotal importance in any Spark tutorial provides an execution platform for all the Spark applications. In addition, Spark offers a generalized platform for a wide array of applications, a major advantage of using Spark.
Spark SQL enables users to run SQL/HQL queries. It allows us to process structured as well as semi-structured data. Also, Spark SQL offers to run unmodified queries up to 100 times faster on existing deployments.
One of the best things about Spark is that it enables a powerful interactive and data analytics application. What more, the live streams are converted into micro-batches that are executed on top of spark core.
Machine Learning library high-quality algorithms. Spark MLlib, one of the Apache Spark Tutorial components is highly recommended by data scientists. Moreover, Spark MLlib’s remarkable capacity of in-memory data processing optimizes the performance of the iterative algorithm.
Spark GraphX, another important spark tutorial component is a graph computation engine built on top of Apache Spark. It enables to process graph data at scale.
SparkR is required to use Apache Spark from R. In more terms, we may say that it is an R package to ensure a light-weight frontend. Also, it is useful for analyzing large datasets and allows running jobs interactively on them from the R shell. The principal idea behind SparkR was to explore different techniques to integrate the usability of R with the scalability of Spark.
To know more about all the components of Spark in detail, you may look for online resources on Apache Spark Ecosystem.
Differences between Spark Tutorial and Adobe Spark Tutorial
Sometimes people confuse between Adobe Spark Tutorial and Apache Spark Tutorial. But let me tell you that the two products, though may sound similar (namesake, you may say) are as different from each other as chalk and cheese.
Apache Spark Tutorial as you know is an open-source big data processing framework built around speed, ease of use, and sophisticated analytics.
It is fast gaining acceptance for its comprehensive, unified framework that manages big data processing requirements with diverse data sets. Moreover, with Apache Spark, you can write applications in Java, Scala, or Python. With a set of over 80 high-level operators, it can be used it interactively to query data within the shell.
Enrolling for Apache Spark tutorial will also show you it is better than other big data and MapReduce technologies like Hadoop and Storm.
With Adobe Spark you can create social graphics, web pages, and video stories anywhere for free. Moreover, Spark with its premium features is offered as part of Adobe Creative Cloud plan. It is also available as a stand-alone subscription, that is to say, you can customize the product according to your brand to create something unique.
You may take up an Adobe Spark Tutorial for a rewarding career in graphic designing. You will learn how to create stylish, effective social media posts. An Adobe Spark Tutorial also teaches you to use imagery that resonates with your target audience and works with your layout. It helps to answer your queries; how to use your brand’s colors in a way that creates an emotional connection; how to create branded templates that can save you time and money.
Spark Tutorial: Spark Tutorial Python
Python, one of the major programming languages, comes with a number of modules for interacting with the operating system, searching text with regular expressions, and accessing the Internet. The best thing about Python is that it is a dynamic, interpreted language that makes it unnecessary to declare variables or deal with memory management bugs, unlike what you do with C.
Python, combined with Apache Spark, makes it easier to process Big Data either in real-time or with scripts. The MapR distribution gives you everything you need to process Big Data in your favorite language.
Interested in a career in Python? Both beginners, as well as experienced programmers, can take up a Spark Tutorial Python, which will teach them how to use Python API bindings and gain knowledge on performing basic data analysis operations.
Know more about Spark Tutorial Python in the following video:
Best Places to Learn Apache Spark Tutorial?
If you are looking for a career in Python programming or data science, you must sign up for an Apache Spark Tutorial. Even though it is primarily meant for Big Data analytics professionals, software developers, IT administrators, Data Scientists, graduates willing to make a career in big data analytics domain are also welcome.
Intellipat, Eduflair, tutorials point, Udemy, and Edureka are some of the big names for Apache Spark Tutorial. All of them have certified courses for beginners and advanced learners.
You will learn to work Apache Spark’s primary abstraction, resilient distributed datasets (RDDs) to process and analyze large data sets, develop Apache Spark 2.0 applications using RDD transformations and actions and Spark SQL, how to scale up Spark applications on a Hadoop YARN cluster through Amazon’s Elastic MapReduce service and also to analyze structured and semi-structured data using Datasets and data frames.
The Future of Apache Spark
Developers across the world are leveraging the Spark framework in Scala, Java, and Python. Apart from offering flexibility to run applications in your favorite languages, Apache Spark also empowers you to build faster and better apps.
Big shots like Amazon, Yahoo, Alibaba, eBay, Hitachi, Shopify, have invested in talent around Spark. Apache Spark offers an in-memory data processing component that attends to real-time and batch actions. In addition, it provides a flavor for the cloud business with or without Hadoop. Hence, there are some top-notch companies which are using Spark.
Several organizations run Spark on clusters with thousands of nodes and there is a huge opportunity in your career to become a Spark certified professional. Now that you are reading this Spark Tutorial blog, I suppose you have a clear understanding of the importance of Apache Spark tutorial. Learn about the best places to enroll for Apache Spark tutorial.
I suggest you also look up for online resources and videos on Spark Streaming. In addition, go through Spark Interview Questions, Spark MLlib, for being better prepared for a career in Apache Spark.
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Careers in Apache Spark
For grabbing lucrative career opportunities in Python, you should have a thorough knowledge of the various scientific and numerical modules and write tight algorithms. Skill sets like concurrent algorithms, SIMD vectorization, and multicore programming are some of the other requirements. These will give your Python career the much-needed boost.
Salaries for experienced Python programmers may go up as high as 8 lakhs-12 lakhs per annum. A data scientist with a strong command over Python tools and libraries is better equipped to deal with data.
According to a report published by the Hindu Business Line, the Indian IT industry is estimated to add around 1.80 lakh to 2 lakh new jobs in 2018. Data analytics, machine, and AI will largely contribute to this. Data Analytics knowledge is great for exploring career opportunities in Python.
You can take your data analytics and predictive modeling skills to the next level with Python tools and libraries. This can be done by completing a certified course in Data Analytics for Python programming. As a beginner, you need to learn the basics of the Python programming environment, including fundamental python programming techniques.
Learn about more career opportunities
You should also know about data manipulation and cleaning techniques using the popular Python Pandas data science library. As a data analyst you would be expected to take tabular data, analyze data, process and manipulate it, and finally, run basic inferential statistical analyses.
You can take your knowledge of Apache Spark to the next level with Python tools. In addition, get help through libraries by completing a certified course in Data Analytics for Python programming. As a beginner, you need to learn the basics of the Python programming environment, including fundamental python programming techniques.
Career opportunities in Python have multiple merits. Tremendous growth, learning, and lucrative salary are some of the well-known perks. You can be part of the changing technological landscape and influence it in your own way.
Python career also offers diversity in terms of career choices. One can start off as a developer or programmer and later switch to the role of a data scientist.
With a substantial amount of experience and Data Science online Course Certification, one can also become a certified trainer in Python. But the bottom line remains the same. Perform to prosper in python.