What is Spark
When used alone or in conjunction with other distributed computing tools, Apache Spark is a data processing framework that can quickly conduct operations on very large data sets and distribute operations across several machines. These two characteristics are essential to the fields of big data and machine learning, which call for the mobilization of enormous computer power to process vast data warehouses. With an intuitive API that abstracts away most of the tedious labor of distributed computing and big data processing, Spark also relieves developers of some of the programming responsibilities associated with these activities.
Features of Spark
Processing is quick. The speed of Apache Spark is the key component that has led the big data industry to favor it over competing technologies. Big data requires faster processing since it has greater volume, diversity, velocity, and veracity. Spark is almost ten to one hundred times quicker than Hadoop because it has a Resilient Distributed Dataset (RDD) that speeds up reading and writing operations.
- Flexibility – The development of applications in Java, Scala, R, or Python is possible because of Apache Spark’s multilingual support.
- In-memory computing – Spark keeps the data in the RAM of the servers, enabling quick access and speeding up analytics.
- Real-time processing – Spark can handle streaming data that is processed in real-time. Spark can process real-time data, which enables it to generate immediate results, in contrast to MapReduce, which only processes stored data.
- Better analytics – Spark contains a lot more than only the Map and Reduce methods, in contrast to MapReduce. Apache Spark has a comprehensive library of SQL queries, machine learning techniques, advanced analytics, etc. With the aid of Spark, analytics may be carried out more effectively thanks to all these features.
Benefits of Spark
- Speed – Processing speed is important when it comes to big data. Due to its speed, Apache Spark is incredibly well-liked by data scientists. For processing massive amounts of data, Spark is 100 times faster than Hadoop. Hadoop stores data in local memory, whereas Apache Spark uses an in-memory (RAM) computing environment. Spark is capable of processing clustered data with more than 8000 nodes and many petabytes at once.
- Simple to use – Easy-to-use APIs are provided by Apache Spark for working with big datasets. Building parallel apps is simple thanks to its more than 80 high-level operators.
- Advanced Analytics – Spark offers more than just MAP and Reduce. Additionally, it supports graph methods, streaming data, SQL queries, and machine learning (ML).
- Dynamic in Nature – You can create parallel apps quickly using Apache Spark. Over 80 high-level operators are available from Spark.
- Multilingual – Python, Java, Scala, and other programming languages are supported by Apache Spark.
- Apache Spark is powerful – Due to its capacity to process data in memory with low latency, Apache Spark is capable of handling a variety of analytics difficulties. It offers well-built libraries for machine learning and graph analytics methods.
- Increased access to Big data – Big data is becoming more accessible because of Apache Spark, which is also According to a recent poll by IBM, the company said it would train more than 1 million data scientists and engineers in Apache Spark.
- Demand for Spark Developers – Apache Spark is advantageous to both you and your company. Due to the high demand for Spark developers, businesses will offer alluring benefits and flexible work hours just to secure their services. The average pay for a Data Engineer with Apache Spark abilities, according to PayScale, is $100,362. People who are interested in a profession in big data can learn Apache Spark. You may fill the skills gap for employment involving data in several ways, but the ideal option is to enroll in formal training that will provide you with practical work experience and allow you to learn through hands-on projects.
- Open-source community – The fact that Apache Spark is supported by a sizable Open-source community is its strongest feature.
Spark certification list
- Scala with Spark Essential Training
- Scala with Spark Intermediate Training
- Scala with Spark Advanced Training
Spark certification path
There is no official certification for Spark but still, you can get certified by DevOpsSchool. DevOpsSchool is one of the best training institutes in India. Even it deals outside of India as well. It is IT industry-recognized. DevOpsSchool comes with many benefits just to help you achieve your career goal. They provide instructor-led online training to train you from their best IT trainers who have 15+ years of experience. Even their course contents are also designed as per IT standards. Get connected with DevOpsSchool to know more and get certified.
Spark certification cost
- Scala with Spark Essential Training – Rs 4,999/-
- Scala with Spark Intermediate Training – Rs 13,999/-
- Scala with Spark Advanced Training – Rs 19,999/-
Spark certified professional salary
Spark Developer salary ranges from Rs 4.5 Lakhs to Rs 15.5 Lakhs in India.