The Big Data world is quite familiar with Apache Spark- a powerful open-source processing engine which is mainly built around speed, incredible analytics, and ease of use. As one of the world’s most popular big data processing frameworks, it has gained huge popularity and rapid growth in the last few years.
If you’re new to the field or looking for support to help you get started with Apache Spark, this is a comprehensive guide with top 7 tips that enables you to write simple codes in Apache Spark.
1. Use Scala for Apache Spark.
If you are working with Apache Spark then you would definitely know that it has 4 different APIs support for different languages including Scala, Python, Java, and R.
Although each of these programming languages has its own benefits, Scala is the most preferred as well as more advantageous as compared to other languages for writing codes in Apache Spark. Among the reasons which make Scala completely taking over the big data world include-
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- Scala is much faster than Python and R because it is compiled language
- Scala is a functional language
- Scala leads to more productive outcomes than working with Java
Investing in Spark and Scala training can be instrumental in helping you start with top-level Apache projects in the data processing.
2. Learn the Spark ecosystem.
To get started with Apache Spark in a systematic manner, it is imperative to learn everything about Spark ecosystem. Spark with scala course can help you with all the essential elements of apache spark including-
Spark SQL + Dataframes: A module that provides a structured data processing interface via SQL-a language used for communicating with a database.
Spark core: Offers the base functionality for the components like scheduling and monitoring of jobs in a cluster, as well as handling faults.
ML: Machine Learning (ML) module provides various state of the art algorithms to allow you to learn from data and make models with it, to be able to make accurate predictions.
Streaming: The module can be used in instances where real-time performance is required.
GraphX: This module can handle graph-structured data at scale.
3. Handle large sets of data with Apache Spark.
Since Spark is fully optimized for both speed and computational efficiency by storing most of the data in memory instead of disk, it can be used to handle huge sets of data. Apache Spark, due to its speed & efficiency, can underperform Hadoop MapReduce especially when the size of the data becomes so big that insufficient RAM becomes an issue.
4. Learn Apache Spark to get more access to Big Data.
Apache Spark helps developers to explore big data making it easier for companies to solve many big data related complex problems. Such is the popularity that it has become an increasingly growing platform for data scientists.
5. Get hands-on experience of Apache Spark Installation.
The installation of Apache Spark is not a single step process and there are various steps to be followed. It is important to note here that Java and Scala are the language prerequisites to install Spark. Below are the 7 steps required in Apache Spark installation process-
- Verification if Java is installed
- Confirmation of Scala installation
- Download Scala
- Install Scala
- Download Spark
- Install Spark
- Verification of spark installation
6. Apache Spark Use Cases.
Once the installation is done, it’s time to learn the uses of Apache spark. Businesses mainly adopt Apache Spark due to below reasons:
- High performance
- Real-time data streaming
- Ease of use
- Advanced analytics
- Ease of deployment
7. Industries Where You Can Use Apache Spark.
Top Apache Spark use cases in different industries include –
- Healthcare Industry
Apache Spark is used in various healthcare applications and helps in the enhancement of overall healthcare quality.
- Security Industry
Apache Spark stack plays a crucial role in the security industry with uses such as detection and authentication purposes in systems.
- Finance Industry
Apache Spark helps the finance industry to get the insights for risk management, customer satisfaction, and targeted advertisement.
Get Started with Learning Spark.
With the explosion of Big Data in the recent times and an equally rapid increase in the speed of computational power, robust tools such as Apache Spark and other Big Data Analytics engines will soon be an indispensable part of Data Science industry. If you’re interested in diving deeper into this incredible technology, get yourself certified in Apache Spark with Scala and enjoy unlimited opportunities offered by the domain.
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