Hire a Spark Engineer
Benefits of Hiring a Spark Engineer
Robust Spark Core Engine
An experienced Spark developer uses a variety of Spark features like running SQL-related queries through BI visualizations. They can also process and stream data in real-time.
Fast Data Processing Speed
Spark developers help companies process data much faster. For big data processing, they understand that Spark uses built-in memory computing that makes it much faster than tools like Hadoop.
Easy to Use and Implement
Without Spark expertise, organizations would not be able to use and implement operations. Spark developers understand how to use APIs in order to process huge datasets.
Ensure Compatibility with Tools
Professional and experienced Spark engineers alike understand how to roll out compatible and compliant solutions using different tools. They can run Spark on open source frameworks such as Meos or Hadoop in the cloud with complicated data.
More Business Opportunities
A Spark developer can implement Spark to pave the way for more business opportunities.
FAQs About Hiring Spark Engineers
The role of a Spark engineer primarily entails converting and cleaning valuable data. However, Spark engineers are typically responsible for other common tasks such as:
- Creating applications through Scala, Python, and Java
- Developing Spark tasks for data aggregation and transformation
- Writing Scala style documents with each code
- Designing specific processing pipelines of data
- Conducting unit tests to ensure Spark transformations
- Reviewing code and use-case to ensure that it is up to standards
- Ensuring the technical analysis quality and solves relevant issues
- Collecting user requirements and transferring them into robust technical tasks
- Designing unit testing, coding, and similar SDLC tasks
An experienced Spark engineer will have the knowledge required to develop high-speed and robust data pipelines, achieve optimal performance for streaming and batch data, and leverage their unique skills to facilitate the perfect user experience. Spark developers typically possess:
- Practical knowledge of distributed systems to make partitioning, CAP theorem, consensus, and replication
- Ability to write executable code for Services, Spark components, and Analytics
- In-depth knowledge of standard programming languages such as Python, Scala, and Java
- Extensive knowledge of technologies such as Storm, Kafka, Zookeeper, and Hadoop
The High5 platform matches organizations with the most suitable and talented Spark engineers in the marketplace. High5 initiates a simpler, efficient, and effective approach that makes it easier for companies to find and hire a Spark engineer.
No more traditional hiring costs for small and medium-sized businesses. Instead, SMBs can now opt for the most cost-effective, dedicated, and result-oriented hiring process. It is a perfect way to find Spark engineers that meet your criteria. Plus, you will receive carefully vetted and screened Spark engineers for a smoother and swifter transition. To find the most compatible and talented Spark engineers or other big data developers, reach out to High5.
Guide to Hiring a Spark Engineer
Spark is a high-level, general-purpose programming language that combines several powerful ideas from the world of functional programming into one cohesive framework. As a result, if your organization were to hire a Spark engineer, he or she could write code that scales up for parallel processing, provide a reservoir of libraries for machine learning, data mining, text processing, graph algorithms, and much more.
What is a Spark Engineer?
A Spark engineer is a software engineer that specializes in the Scala programming language, the execution of Apache Spark framework, and other related tools. Typically, these developers specialize in different types of application development such as web applications or data science.
Many Spark developers work for software companies. Others may decide to work for businesses or other types of organizations.
Responsibilities of a Spark Engineer
A few typical day-to-day responsibilities include:
- Assisting in developing analytics code, services, and components in Java, Apache Spark, Kafka, Storm, Redis, and other related technologies like Hadoop and Zookeeper.
- Designing, coding, unit testing, and other SDLC operations.
- Gathering and comprehending requirements, analyzing and translating functional requirements into specific technological tasks, and establishing appropriate effort estimates.
- Working proactively, autonomously, and collaboratively with global teams to solve project requirements and define issues/challenges with sufficient lead time to mitigate project delivery risks.
- Providing technical, analytical knowledge and resolving technical challenges throughout project delivery.
- Reviewing code, testing case reviews, and ensuring that the code created matches the requirements are all part of the process.
Skill Requirements of a Spark Developer
A Spark developer must be a jack of all trades in the technology industry. He or she must have experience with Java and C++, in addition to knowing how to use different development environments like Apache Spark and Eclipse Maven.
Last but not least, they must be skilled at carrying out various tasks associated with the job such as debugging code and managing version control systems like Git.
While this is just a general overview, Spark engineers may have a diverse set of skills depending on their experience level. A few convenient skills that you should look for to find and hire a Spark engineer include:
- Extensive Java experience
- Knowledge of Python and R programming
- Familiarity with the Apache Spark streaming and batch frameworks, Kafka, Storm, and Zookeeper
- Understanding of Redis and Hadoop Equities Analytics and/or electronic execution
- A strong understanding of KDB+/Q
- Experience with the Agile/Scrum methodologies
- Service-oriented architecture (SOA) and data standards such as JSON, Avro, and Protobuf
- Strong communication skills
- The ability to demonstrate ownership and take the initiative
- Prior experience working collaboratively
Typical Use Cases of Spark
From log files to sensor data, data streams are becoming more common. There are various sources of this data. While these data streams may be saved and analyzed later, they must sometimes be evaluated and acted upon quickly.
For example, payment data streams could be evaluated in real-time for fraud detection.
Machine learning becomes increasingly accurate as data volumes grow. A Spark developer can train software to recognize and respond to existing triggers before applying the same solutions to new input. Spark saves data in memory and performs queries quickly to train machine learning algorithms.
Statistical displays showing sales, manufacturing, or stock prices are no longer enough for business analysts and data scientists who want to dig deeper into their data.
This dynamic query procedure relies on systems like Spark in order to react fast.
Data generated by several business systems is seldom clean or consistent enough to be merged for reporting or analysis. With Spark, developers can extract data from several systems, clean it up, then put it into another system for analysis.
As a result, Spark (and Hadoop) are increasingly being used to reduce ETL costs and time.
Why Spark is a Great Choice for Data Analysis
Apache Spark is a multi-role tool in big data analytics. It can handle practically any form of data regardless of structure or size. Listed below are some reasons why Spark is the most powerful big data tool.
Spark may be natively integrated into Hadoop’s HDFS and used as a data processing tool. With YARN, it can run alongside MapReduce jobs on the same cluster.
With the rise of big data, learning Spark has become a worldwide standard, and the best big data developers are Spark developers.
Better Performance than MapReduce
There is a big performance difference between MapReduce and Spark. Spark’s top-level Apache Project status is owing to its lightning-fast speed thanks to In-Memory Processing.
Performance in a Production Environment
Spark’s easy-to-use programming interface supports top programming languages including Scala, Java, and Python. Spark became the leading legend in the Production Environment as a result of its tremendous increase in demand.
Rising Spark Developer Demand
Many top MNCs like Adobe, Yahoo, NASA, and others favor Spark due to its superior capabilities and dependability. In proportion, the need to hire a Spark engineer is rapidly rising.
Why Hire a Spark Engineer Instead of a Hadoop Admin?
It is undeniable that the market for developers in big data occupations outpaces the demand for administrators in this field.
One of the benefits of hiring a Spark developer is that he or she can take over the function of a Hadoop administrator, if necessary. However, an administrator cannot take on the role of a developer unless they have the sufficient programming skills required to do so.
In other words, if you need a programming-savvy employee, then a Spark developer would be the right fit for your project.
Final Words on Hiring a Spark Engineer
Due to the increased usage of data analytics in organizations worldwide, the future is looking bright for Spark developers. As a result, the pool of Spark developers will grow, making it even easier for your business to find and hire a Spark engineer that is perfect for the job.
Once you’re ready to get started, use this article as a guide to navigate the process and know what to expect.
If you’re ready to hire a Spark engineer now, we can help. Join High5 today and start building your dream team.