One Pager Cheat Sheet

• Data Engineers build and maintain data infrastructure and applications, managing ingestion, organization, processing, storage, and warehousing of data from various sources according to hardware architecture and storage capacity.
• Being a Data Engineer requires knowledge in software engineering, data warehousing, data modeling, data integration and big data technologies.
• Acquiring proficiency in programming languages such as Python, SQL, Java, Scala, R, Ruby, and Perl and understanding data structures, algorithms, and practical problem-solving are key to succeeding in data engineering.
• The map() function can be used to transform a vector of strings into integers, by mapping each string to a specific corresponding integer value.
• Mastering SQL is an essential part of being a successful Data Engineer, and proficiency in it indicates proficiency in Big Data Frameworks such as KafkaSQL, SparkSQL and Python libraries.
• The COUNT() function in combination with the GROUP BY clause can be used to identify and get an exact count of duplicate records in a column.
• Datamodelingis an integral part of the system design process that involves creating a data model following particular data patterns and use cases.
• Data models are the foundations of a data system and can be classified into three main types: Conceptual, Logical, and Physical.
• Get familiar with the fundamental processes and tools used in data engineering by exploring real-life examples, trying to develop a small test version of an online store, and learning the basics of database architecture and design.
• The correct approach to validate a data migration process from one database to another would involve running tests to check for data type, record count and other discrepancies, whereas Digital Preservation requires a different set of processes to protect digital information over time.
• Emphasizing the importance of soft skills, it is essential for a Data Engineer to have excellent communication, problem-solving and team working skills in order to stand out in the job market.
• Explaining new technology to unfamiliar coworkers requires excellent communication skills and the ability to illustrate concepts in a way that is easy to understand.
• Preparing for a data engineering interview may require solving practical coding problems, such as constructing a SQL query to reveal the unique number of occurrences of one class within a single column, as well as knowing certain terms and libraries such as Numpy, Pandas, TensorFlow, and Hadoop with its two main components HDFS and MapReduce.