Spark is one of the omnipresent platforms for data processing. It has taken over the traditional MapReduce framework. Spark is constantly proven to outperform MapReduce by several orders of magnitude in numerous benchmarks and performance studies. The Spark library is quickly becoming essential work in artificial intelligence. Getting a Hadoop certification and Spark certification will excel your skillset. You can register for CCA-175 exams for Spark developer certification and Hadoop Developer Certification.
Spark can handle multiple volumes of data at once, distributed across a cluster of thousands of collaborating physical or virtual servers. It has a large set of developer and bee libraries and supports languages like Java, Python, R, and Scala. Its flexibility makes it well for a series of uses. Spark is often used with data stores distributed as HPOP HDFS HDFS and S3 Amazon Data fabric, With the famous NoSQL databases such as HPE Ezmeral data fabric, Apache Hbase, Apache Cassandra, and MongoDB and with distributed messaging archives such as HPE Ezmeral Data Fabric and Apache Kafka.
Why is Spark so powerful?
Spark is a general-purpose distributed data processing engine. The data processing engine contains libraries for SQL, machine learning, graph computation, and stream processing. These libraries can be used together in an application. Spark supports various programming languages such as Java, Python, Scala, and R.
- A wide range of technology vendors are supporting Spark and they are recognizing the opportunity to extend big data products in areas where Spark delivers real value, such as interactive queries and machine learning. There are many reasons to choose Spark as it is simple, speed, and support.
- Spark can easily be accessed via a set of rich APIs and It is designed in such a way that it can operate both in memory and on disk. These APIs are well-documented and well structured. It is straightforward for data scientists and application developers and it helps them put their work quickly.
- It is designed in such a way, which can operate both in memory and on disk. Spark can perform better when it supports interactive queries of data stored in memory. In this type of situation, the spark can affirm that it can be 100 times faster than the patch of Hadoop. Its lightning-fast performance and its capability brought Spark its place amongst the top-level projects.
- It can integrate with Hadoop’s HDFS and it can work as an excellent Data Processing tool. It will run on the same cluster along the side of MapReduce jobs. It meets global standards and it is an impeccable raise in the world of Big Data Analytics.
- It is a simple and faster programming interface. It can support top-notch programming languages like Scala, Java, and python. It is one of the leading legends in the production environment with a massive surge in its demand.
- It is one of the most demanding platforms and it has capabilities and reliability, which is preferred by many top MNCs companies like Adobe, Yahoo, NASA, IBM, and many more. Therefore the demand for Spark developers is rising rapidly.
Roadmap to becoming a Spark Developer
To become an expert in Spark as a developer you need to get certified and get expertise in the industry. You should be certified by the real-time industry. There are various online courses and programs available on the internet. Once you are done with the training, you should start up with real-time own projects to understand the working terminology. Spark’s major building blocks are the datasets and data frames. It is integrated with high-performance programming languages like Python, Scala, and Java.
Once you get a better grip on the major building blocks of Spark. you can concentrate on
- Spark Streaming.
After all these procedures, you can go ahead for certifications, CCA-175 Spark and Hadoop Certification Examination. You need to register for the CCA-175 examination and your excel will showcase your Spark and Hadoop Developer certification.
Skill Set required for Spark Developer
- Understanding every component of the Hadoop ecosystems such as HBase, Pig, Hive, Sqoop, Flume, Oozie, and many more.
- Essentials of Java
- Linux commands and basic knowledge.
- Strong analytical and problem-solving skills.
Responsibilities of Spark Developers
- Data loading using ETL tools from data platforms into the Hadoop platform.
- Understanding the data mapping and managing multiple files.
- Data cleaning through streaming API based on the business requirements.
- Hadoop Job Flows and Scheduling.
- working and handling Hive and HBase for schema operations.
- Deploying HBase clusters
- Working on pig and hive scripts and applying different HDFS formats.
Average Salary Packages for Spark Developers
In India, the average salary package for Spark developers starts from 6LPA and goes upto 10 LPA for beginners. For experienced Spark developers, the salary trend starts at 25 LPA and goes up to 40 LPA. With the experience, salary will increase automatically. If you gain the Hadoop and Spark developer certification it will add more value to your career, job profile with the knowledge. The adds value for your future scope in your career with the amazing salary package.
Job trends for Spark Developers in India
Spark Developers is one of the high-demand job profiles in India and the globe. This is one of the most sought skills for Big Data companies. Top companies are rolling out job offers to these professionals. These professionals will get an opportunity to work in different industries like retail, Software, media, entertainment, Consulting, healthcare, Networking, and many more. Companies like Amazon, Alibaba, eBay, Yahoo, and many more are adopting Spark as their primary big data processing framework. Hence opportunities for Spark developers are increasing rapidly all over the globe. The demand for these Spark developers is increasing exponentially every year. These companies offer good salary packages. If you search the Spark developers-related jobs in top job search engines like Naukar, LinkedIn, you can find more than 50,000 job openings for them(as a Spark developer, or related job roles). So you have multiple choices of choosing a company or related job roles to choose from.
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