Remote Spark developer jobs

Due to high speed, ease of use, and complex analytics, Spark has grown tremendously in recent years, becoming the most effective data processing and AI analytical engine in enterprises today. Spark has a high cost because it requires a lot of RAM to run in memory.

Spark combines data and AI by facilitating large-scale data preparation from a variety of sources. It also has a uniform set of APIs for data engineering and data science workloads and seamlessly integrates with popular libraries like TensorFlow, PyTorch, R, and SciKit-Learn.

The popularity of Spark has increased recently as more companies are relying on the data to develop their business strategies. Therefore, Spark development is undeniably a stable and well-paying career option for you.

Big data is the way of the future, and Spark provides a broad set of tools for handling enormous amounts of data in real-time. Spark is a future technology because of its lighting, speed, fault tolerance, and efficient in-memory processing.

Take a look at some pointers that demonstrate why companies prefer Spark.

• SQL queries, streaming data, machine learning (ML), and graph processing are all supported by this unified engine.
• For lesser workloads, in-memory processing, disc data storage, and other techniques, it is 100 times quicker than Hadoop.
• APIs are available for manipulating and transforming semi-structured data that are simple to use.

Web development has advanced to a level that no one could have envisioned 20 years ago. Spark is one of the most popular open-source unified analytics engines these days, and you'll have plenty of career options in the Spark development field.

The primary duties of a Spark developer include providing ready-to-use data to feature developers and business analysts by analyzing massive amounts of raw data from diverse systems using Spark. This encompasses both ad-hoc requests and data pipelines incorporated in our production environment.

The main responsibilities of a remote Spark developer jobs include:

• Write Spark components, analytics, and services executable code.
• Learn essential programming languages such as Java, Python, and Scala
• Should know Apache Kafka, Storm, Hadoop, and Zookeeper, as well as other relevant technologies.
• Be prepared to handle system analysis, which covers design, coding, unit testing, and other SDLC tasks.
• Take user needs and transform them into strong technical tasks, and provide cost estimates.
• Validate the accuracy of technical analysis and expertise in problem-solving.
• Evaluate the code and use-case to confirm that it complies with the specifications.

There is a fine line between becoming a certified Spark developer and being an actual Spark developer capable of performing in a real-time application.

Here are some recommendations to help you find remote Spark development jobs.

• To be an expert, you must follow the right path with expert-level assistance from certified real-time industry professionals in the industry.
• You can also enroll in any of the training and certification programs.
• Once the certification process has commenced, you should begin working on your projects to understand Spark better.
• RDDs (Resilient Distributed Datasets) and Dataframes are Spark's main building components. You need to have an understanding of these.
• Spark may also be integrated with several high-performance programming languages, including Python, Scala, and Java. PySpark RDDs are the best example of Python and Apache Spark working together.
• After you've mastered the fundamentals of Spark, you can go on to understand the Major Components of Apache Spark, which are listed below:
  - SparkML-Lib
  - Spark GraphX
  - SparkR
  - Spark Streaming

Once you've completed the necessary training and certification, it's time to create a Spark developer resume and practice what you have learned as much as possible.

Let's take a look at the skills and tactics that a successful Spark developer will require.

The first step toward landing remote Spark developer jobs is to learn the core skills. Now, let's have a closer look at it.

1. Big data framing and analysis

Big data analytics uses advanced analytic techniques to extensive, diverse data sets, which can contain structured, semi-structured, and unstructured data and data from many sources and sizes ranging from terabytes to zettabytes. This is an essential skill to get hired for remote Spark developer jobs.

2. Python

Python is an interpreted high-level, general-purpose programming language. Its design philosophy emphasizes code readability by using a lot of indentation. Python’s object-oriented approach is developed to help programmers write clear, logical code for both small and large-scale projects.

3. Scala

Scala is an acronym that stands for Scalable Language. It's a programming language with multiple paradigms. The Scala programming language combines functional and object-oriented programming techniques. It's a statically typed programming language. Its source code is converted to bytecode and run by the Java virtual machine (JVM).

4. Java

Java is an object-oriented programming language with a few implementation dependencies. Java programming language is guaranteed to be a write-once, run-anywhere language. A Java program is compiled into bytecode during compilation. This bytecode format is platform-independent, meaning it may be run on any machine, and it also provides security. Java programs can be run on any machine having Java Runtime Environment installed.

5. Spark SQL

Spark SQL is a structured data processing Spark module. It offers DataFrames as a programming abstraction and may also serve as a distributed SQL query engine. It also has a strong connection to the rest of the Spark ecosystem (e.g., integrating SQL query processing with machine learning). You should develop a grip on the skill to land remote Spark developer jobs.

6. Spark Streaming

Spark Streaming is a Spark API extension that allows data engineers and scientists to analyze real-time data from various sources, including (but not limited to) Kafka, Flume, and Amazon Kinesis. Data can be pushed to file systems, databases, and live dashboards after it has been analyzed.

7. MLlib

MLlib is a scalable machine learning library built on top of Spark that includes classification, regression, clustering, collaborative filtering, dimensionality reduction, and underlying optimization primitives, as well as other standard learning methods and utilities.

8. Elastic MapReduce

Amazon Elastic MapReduce (EMR) is a web service that provides a managed framework for easily, cost-effectively, and securely running data processing frameworks, including Apache Hadoop, Apache Spark, and Presto. It's used for various purposes, including data analysis, online indexing, data warehousing, financial analysis, and scientific simulation. You need to master this to get hired for the best Spark developer jobs.

9. Spark DataFrames and Datasets

Datasets are an extension of data frames in Spark. Essentially, it earns two types of API characteristics: strongly typed and untyped. Unlike data frames, datasets are by default a collection of strongly typed JVM objects. It also makes use of Spark's Catalyst optimizer.

10. GraphX library

GraphX combines ETL, exploratory analysis, and iterative graph computation within just one system. The Pregel API allows you to see the same data as graphs and collections, rapidly transform and combine graphs with RDDs, and implement custom iterative graph algorithms.

Spark development is one of the most versatile careers since it allows you to work from any place with an internet connection and a computer. You can work from home or at your favorite workstation if your job allows it! That is precisely what Spark developer jobs can provide.

Working remotely has a lot of advantages. And, the competition has also gone up recently. Therefore, to land successful remote Spark developer jobs, you need to stay on top of your technical skills and establish a productive work routine.

Turing offers the best Spark developer jobs that suit your career trajectories as a Spark developer. Grow your development career by working on challenging technical and business problems using the latest technologies. Join a network of the world's best developers & get full-time, long-term remote Spark developer jobs with better compensation and career growth.

At Turing, every Spark developer is allowed to set their compensation. However, Turing will recommend a salary at which we know we can find a secure and long-term opportunity to grow your Spark developer career. Our recommendations are based on analyzing the prevailing market conditions and the demand from our customers.