This is a learning-focused internship opportunity at Web Neural Infotech for students and freshers who want to build a strong foundation in large-scale data processing using Apache Spark. The Apache Spark Intern (Processing) will work alongside experienced engineers to understand distributed computing concepts, data pipelines, and performance optimization techniques. This internship is designed to provide practical exposure to real-world big data scenarios while strengthening analytical and problem-solving skills in a professional environment. Internship Responsibilities Assist in designing and implementing large-scale data processing workflows using Apache Spark Work with structured and unstructured datasets for batch and basic stream processing Support data cleaning, transformation, and aggregation tasks Learn and apply concepts of distributed computing and parallel processing Collaborate with mentors and team members to understand project requirements Document learnings, processes, and implemented solutions Basic Internship Requirements Basic understanding of programming concepts Familiarity with big data fundamentals or interest in large-scale processing Awareness of Apache Spark or willingness to learn it during the internship Logical thinking and problem-solving mindset Ability to learn quickly and work in a team-oriented environment
Internship Benefits Hands-on experience with large-scale data processing projects One-on-one mentorship from experienced professionals at Web Neural Infotech Internship certificate upon successful completion Paid internship / stipend based on performance Industry exposure and skill development aligned with real-world use cases Skills ApacheSpark, BigData, DistributedComputing, DataProcessing, SQL, Python, Scala, Analytics
Web Neural Infotech is a leading AI and Data Science company driving innovation through cutting-edge research and real-world applications. We specialize in Big Data Analytics, Machine Learning, Cloud Computing, and Software Development, delivering in... Read More
