Apache Spark is a general purpose and lightning fast cluster computing platform. In other words, it is an open source, wide range data processing engine. Spark can perform batch processing and stream processing. Batch processing is processing of the previously collected job in a single batch. Spark stream processing deals with processing of live data streams and thus helps it integrate with all Big Data tools. Spark has its own cluster management system. Apache Spark offers high-level APIs to users, such as Scala. Scala is comparatively new to the programming scene but has become popular very quickly. It is a high-level programming language which is a combination of object-oriented programming and functional programming. In other words it is a pure object-oriented programming language that offers features of functional languages.
Who will benefit from learning Apache Spark & Scala Training:
Apache Spark is widely used by data engineers, data scientists and application developers. As mentioned below, it is open and accelerates community innovation. It is 100 times faster than MapReduce and it supports agile data science to iterate rapidly. It is all about large-scale data processing. So, an Apache Spark certification is a reflection of the holder’s expertise and efficiency and will certainly take the holder’s career a long way.
If you want more reasons to take up this course, then here are 10 reasons which may convince you:
- It provides Libraries:Apache Spark helps application developers through its support of widely used analytics application languages such as Scala. These eliminate programming complexity by providing libraries such as MLib and simplifying development operations.
- It’s a powerful open source engine:There is no powerful engine in the industry that can process the data both in real-time and batch mode. Also, the requirement that one engine should respond in sub-second and perform in-memory processing is satisfied by Apache-Spark, the powerful open source engine that offers real-time stream processing.
- It enables pipelined machine-learning workflows:Spark helps data engineers by providing the ability to abstract data access complexity. It also enables near real-time solutions at web scale, such as pipelined machine-learning workflows.
- It simplifies Development operations:Spark helps application developers through its support of widely used analytics application languages such as Scala. It helps eliminate programming complexity by providing libraries such as MLib, and it can simplify development operations (DevOps). Spark also makes embedding advanced analytics into applications easy.
- It can receive data input from sensors- General application:General classes of applications are moving to Spark, including compute-intensive applications and applications that require input from data streams such as sensors or social data. Compute intensive applications can benefit from in-memory processing, and applications requiring streaming data tend to be intelligent and provide advanced analytics that can engage end users such as healthcare providers or equipment operators.
- Scala uses JVM:Scala uses Java Virtual Machine during runtime which gives some speed over Python. Moreover, Scala is native for Hadoop as it’s based on JVM. Hadoop is important because Spark was made on top of Hadoop’s filesystem HDFS.
- Scala has better Scalability, so it is used for Apache Spark:Apache Spark is written in Scala because of its scalability on JVM. It is the most prominently used programming language, by big data developers for working on Spark projects. The performance achieved using Scala is better than many other traditional data analysis tools like R or Python.
- Spark uses Micro-batching:Spark uses Micro-batching for real-time streaming. Apache Spark works with the system to distribute data across the cluster and process the data in parallel.
- Scala is cost-effective:Using Scala application are less costly to maintain and easier to evolve because Scala is a functional and object-oriented programming language that makes light bend reactive and helps developers write code that’s more concise than other options.
- Scala makes it easy to write native Hadoop applications:In the case of Python, when Spark libraries are called, they require a lot of code processing and hence this results in slower performance. In this scenario, Scala works well for limited cores. Moreover, Scala is native for Hadoop as it is based on JVM. That’s why it’s very easy to write native Hadoop applications in Scala.
Above is the 10 reasons why you should undergo Apache Spark training to enhance your skills and be recognized in your workplace. This would create additional opportunities for your career growth. You can enrol for the course Apache Spark & Scala training course to enrich your knowledge in this field.