Hire Apache Spark Developers
Data storage and data processing have become the new currencies of the tech world. An immense amount of data is being generated now and then. Data sets that are being produced need to be processed and analyzed to be of some use. To analyze the data set, companies are using Hadoop as a simple programming tool that provides a flexible, cost-effective, and scalable data solution. The problem with Hadoop is that it can become really slow when dealing with large volumes of data. For speeding the data analyzing process in Hadoop, Apache Spark was introduced in 2009. Many people think that Apache Spark is the advanced version of Hadoop, but this is not the case. Apache Spark is independent of Hadoop. It is just a means of implementing Apache Spark.
The world today is running on tonnes of data generated every day. Data is being generated from social media platforms, healthcare facilities, demographic units, print media, audio and video content, e-commerce, genomics data etc. With such an enormous amount of data, they must get properly processed and used as they are quite meaningful in giving insights. Traditional computing and processing systems were slow and incapable of processing such a huge data volume. To overcome that, large-scale data processing systems such as Apache Spark were introduced to speed up the data analyzing process. Today, there are many data-based companies, and handling and processing the collected data is of utmost importance. The whole process for these companies will get delayed if they do not use a data processing system capable of handling voluminous data. Apache Spark appeared as hope for these companies. The demand for Apache Spark has increased manifold, and companies are looking to hire Apache Spark developers.
Learn more about Apache Spark
Apache Spark is a cluster computing system that has lightning speed. It is used for carrying out speedy computations. The whole system of Apache Spark is based on a map-reduce model, which helps the technology in carrying out various computations. Apache Spark is based on an in-memory concept that helps in increasing the computing efficiency and speed of the application. Spark can carry out stream processing and interactive queries. It also supports the following workloads – streaming and interactive queries, batch applications, and iterative algorithms.
Although the application is developed in Scala, it can support other programming languages such as Java and Python. Apache Spark can also function on a modified Scala interpreter allowing the user in defining variables, RDDs, classes, and functions that can be used parallelly. Using Apache park alongside Hadoop is not compulsory. The technology works well with other data sources– Amazon S3, Cassandra, HBase and Azure Blob Storage. Handling Apache Spark in different languages has pushed companies to hire Apache Spark developers.
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Important features of Apache Spark
- Dynamism: Apache Spark can work on multiple operators, which facilitate building apps parallelly. It also supports the following workloads – streaming and interactive queries, batch applications, and iterative algorithms. The technology works well with other data sources– Amazon S3, Cassandra, HBase and Azure Blob Storage.
- Fault Tolerance: Spark RDDs are designed so that they can handle any type of failures in the cluster system. The fault tolerance of Apache RDDs minimizes the loss of data to zero.
- In-memory Computation: One of the most striking features of Apache Spark is its in-memory computation which allows the data to be cached. Hence, there is no requirement of fetching the data from disks. This way, the time of computing is saved.
- Lazy Evaluation: The RDDs in Spark are lazy. All the transformations that are taking place are not evaluated immediately.
- Speed: Speed is the main feature of Apache Spark, which makes it so popular in the market. The applications running on Hadoop can run 100 times faster in memory and 10 times faster on disk due to Spark.
- Reusability: The Apache Spark code can be reused to join stream and batch processing against run ad hoc queries or historical data streaming.
- Supports Multiple Language: Although the application is developed in Scala, it can support other programming languages such as Java, R and Python. Apache Spark can also function on a modified Scala interpreter allowing the user in defining variables, RDDs, classes, and functions that can be used parallelly.
- Supports Multiple Formats: Apache Spark can support files in multiple formats such as CSV, ORC, JSON, Avro, Parquet etc.
- Cost-efficient: Since Apache Spark is open-source software, it is free in cost.
Roles and responsibilities of Apache Spark developers
Today data-based companies are increasing at a high rate. The demand for Apache Spark has also increased these days significantly. There are innumerable companies in the developing industry that are looking to hire Apache Spark developers. If you have the following skillset and are willing to work in the developing industry, you must get certified and apply to become a professional Apache Spark developer.
- An Apache Spark developer should have in-depth knowledge about Apache Spark and all its nitty-gritty.
- An Apache Spark Developer will have to write and code in Scala, design pipelines for data processing, and carry out data transformation, evaluation, and aggregation.
- An Apache Spark developer should be experienced in Scala programming. He should also be comfortable in the following programming languages: Python, R, and Java or at least more than one of these. He must also know how to carry out SQL data integration.
- An Apache Spark developer should have in-depth knowledge about distributed systems, Apache Spark 2.x, Spark performance optimization and query tuning, Spark RDD, Spark SQL, Spark GraphX, Spark Streaming etc.
- An Apache Spark developer must know how to handle and provide solutions to troubleshoot problems.
- An Apache Spark developer should have the ability to cooperate with other software engineers/developers in a company.
Skuad can help you hire certified and experienced Apache Sparks Developers from all over the world based on your hiring requirements, be it freelance, full-time, or contract.
Salary structure for Apache Spark developers
The salary of an Apache Spark developer varies from one company to another. Usually, small companies and start-ups pay less than bigger companies. According to payscale.com, the average salary of Apache Spark developers is US $112,125. The average salary of an Apache Spark software developer ranges between US $77,000 – US $97,000. Senior Apache Spark Developers can expect an average salary of US $126,000.
Freelancing and Contractual basis work
Some companies hire Apache Spark developers as freelancers on a contractual basis. These companies require Apache Spark developers for particular projects, so they hire them on a contractual basis. If you have the requisite skills, you can sign up on various freelancing sites and choose appealing rates for your service. That way, you can earn by working full-time as a freelancer for multiple projects.
Apache Spark Certification
With so many companies opening up in developing industries and creating huge job opportunities for developers, there is a shortage of qualified and certified Apache Spark developers. Companies want to hire the best skill that is available in the market. There are 50x more chances that a company will hire someone with a certified skill set than someone without. That is why if you have the required skills to become a qualified professional Apache Spark developer, then you should enrol in an Apache Spark certification program today!
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