For many companies, Data Science and Machine Learning projects don’t get off the ground due to the lack of a strong data platform. That’s because large amounts of data are collected by web pages, mobile apps, IoT, etc., all live in different places and various formats (audio/video files, images, text, etc).
Without a strong underlying platform and the ability to process all the disparate data, the groundwork needed for these projects is simply not possible.
Getting key insights into this data has become increasingly more significant and valuable, and Data Engineers play a significant role in obtaining this data. In fact, this role is so valuable to organizations that data engineering is positioned to be the fastest-growing tech career—with over 50% year-over-year growth.
Data Engineers are responsible for the consolidation of raw data and the algorithms to process it. They develop robust data processing systems using tools such as Apache Spark, Hadoop, Kafka, Couchbase, etc., and are instrumental in laying the foundation for Data Science and Machine learning.
How is Data Engineering different from Data Science?
Here are some of the key differences between the two roles and how they complement each other:
- Data Engineer - Responsible for designing and maintaining scalable data architecture, tools, and platforms. They extract data from multiple sources and provide a single database ready for query and analysis. They optimize the database for faster queries and ensure data quality by removing corrupted data.
- Data Scientist - Responsible for building models to analyze and probe data. They apply statistical models and machine learning techniques to analyze the data. They spend time mining for patterns in data and remove statistical outliers to obtain clean data.
Data Engineer Roles and Skills in HackerRank
HackerRank now supports the Data Engineer role. By using HackerRank’s Data Engineer assessments, both theoretical and practical knowledge of the associated skills can be assessed. We have the following roles under Data Engineering:
- Data Engineer (JavaSpark)
- Data Engineer (PySpark)
- Data Engineer (ScalaSpark)
Here are the key Data Engineer Skills that can be assessed in HackerRank:
How to Assess Data Engineering Skills
The best way to assess a Data Engineer is using real-world or hands-on projects. These are questions that require a candidate to dive deeper and demonstrate their skill proficiency. By using the hands-on questions in our library, candidates are measured on practical demonstrations and multiple solution paths.
For example, Apache Spark-based questions in the HackerRank library assess the ability to perform in-memory transformations using lambdas, converting RDDs to Data Frames, using broadcast variables and accumulators, writing spark jobs to perform data manipulation tasks, and so on.
Here is an example of Apache Spark hands-on project questions in the HackerRank library:
Similarly, Apache Kafka’s hands-on tasks test the understanding of Apache Kafka architecture, Kafka clusters, Kafka messaging systems, understanding Apache Kafka partitions and brokers, and Kafka producers and consumers, among others. Tasks include Web Analytics, Serialization, Deserialization, CDR, and so on.
Here is an example of Apache Kafka Java hands-on project questions in the HackerRank library:
Multiple Choice Questions [MCQs], in general, assess the conceptual knowledge and understanding of a skill. Hadoop Multiple Choice and hands-on questions, used in the Data Engineering assessments in HackerRank, test knowledge of control flow of a map-side join, MapReduce combiners, and commonly used Hadoop commands, among others.
Here is an example of Hadoop multiple-choice and hands-on project questions in the HackerRank library:
Start Assessing Data Engineering Skills in HackerRank
Data Engineers are essential for the success of Data Science and Machine Learning initiatives. If you would like to see the breadth of our skills for the Data Engineer role or see the list of skills around Data Science, or other in-demand roles, check out the HackerRank Skills Directory.
Darshan Suresh is a product manager at HackerRank. As manager of the content team, Darshan empowers HackerRank customers to make better hiring decisions through insightful technical content. One of the most tenured members of the HackerRank team, he combines his extensive platform knowledge with his expertise in software development to shape impactful hiring experiences.