Top Data Engineering Skills You Should Acquire
One of the primary responsibilities of management is to arrive at effective decision-making in the least possible time. Effective decisions made on time can do wonders for companies and result in significant savings. However, if the table is turned, companies can suffer considerable losses in monetary terms and brand value due to one wrong decision. The effectiveness of decisions highly relies on the data it is based upon. The absence of correct data or wrong interpretation of data can result in incorrect or delayed decisions.
Data generation takes place at an unprecedented rate and scale to help in decision-making or resolve critical issues. Vast amounts of data are posing several challenges for companies across industries. Some of the key challenges include collections, storage, and handling of large volume, variety, the velocity of data, summarizing the finding, and making meaningful inferences from data. Another challenge is handling both volume and variety of data together since most of the data collected nowadays belong to unstructured categories from various sources and in multiple formats.
Companies rely on the latest tools, techniques, and best practices to counter the challenges along with skilled resources. Most companies are adopting big data technologies for processing raw data and are increasing spending to develop infrastructures and employees skills. As a result, roles such as Big data analytics and data engineer courses have evolved in recent years.
The present article will try to focus on the data engineering role and major skills required.
Who is a Data Engineer?
A data engineer primarily works on developing infrastructure and data pipelines for data transformation in a format suitable for other experts to process further. Data engineers need to ensure data availability at all times to other team members. In other words, data engineers work to prepare, clean, manipulate data obtained from multiple sources using moderate to complex scripts.
Data Engineering Skill Set
A data engineer is a dynamic and challenging role that requires ample skills and knowledge of numerous tools. Major skills required for data engineers to secure a successful career are as follows.
Sound programming knowledge is one of the primary skills for a data engineer role. Expert level is recommended in programming as one will be required to code ETL processes and build data pipelines. Scripting allows data engineers to communicate with numerous machines. Many popular languages are available such as Python, Scala, Java, and R. Among which Python is the easiest to learn and has the richest library. Python is extensively used for data preprocessing in conjunction with Spark, machine learning, web scraping, and a default language for airflow.
Storing structured or unstructured data is one of the core requirements in data engineering. Database knowledge is vital for aspiring individuals to be successful data engineers. One needs to learn how to store and retrieve data from databases. SQL is the major scripting language used to manage relational database systems (RDBMS) for handling structured data. RDBMS contains data in the form of tables of rows and columns. SQL is a must-learn language for data experts of all kinds. However, it is not enough to handle unstructured data.
NoSQL databases are designed to handle such data, which are non-tabular and random. NoSQL databases are of different types depending on their data model. Some common forms are key-value data stores, document stores, wide column stores, and graph stores. Some advanced NoSQL database systems work on multiple node models and store/retrieve vast amounts of data. Data engineers have to select the appropriate database based on use-case. Few popular and extensive databases are MongoDb, Cassandra and, ApacheHBase, etc.
Big Data FrameWork:
Experience of Big data framework and understanding tools like Apache Hadoop, Apache Spark, is compulsory. Apache Hadoop and Apache Spark are open-source big data processing frameworks with few fundamental differences. Hadoop utilizes MapReduce to process raw data, whereas Spark utilizes resilient distributed datasets (RDDs). Apache Spark addresses various Apache Hadoop’s shortcomings and very fast. It uses in-memory processing and has libraries built on top of it for big data analytics.
Automation plays a critical role in improving functional efficiency. Experience in one of the automation tools such as Azkaban, Luigi, and Airflow is essential. Automation tools help set up data pipelines, trigger processes at pre-defined times, and run processes in the desired order.
Artificial intelligence and machine learning are future technologies that data scientists mostly use. For data engineers, basic knowledge should be acquired. It comes in handy for data pipeline development as data engineers work closely with Data scientists.
Data Warehousing Solutions:
Data warehouses are used to store volumes of data for query and analysis. Experience in Amazon Redshift is preferred, which is available at AWS cloud computing platform. Redshift is an extensively used data warehouse system wherein you can query vast amounts of structured and semi-structured data.
An API is an interface used by software applications to access data. API enables two applications to communicate with each other and accomplish various tasks. Data engineers are required to create APIs to allow data scientists or analysts to query the data. A typical example for APIs is web applications which enable the front-end user to access back-end data.
In addition to the above skills, data engineers need to develop the following soft skills.
Good communication is paramount for data engineers. Data engineers are in constant contact with various data experts such as data scientists, machine learning engineers, data analysts, developers, or subject matter experts. It depends on the strength of the team and the type of organization. In some organizations, data engineers might need to work on other data experts’ responsibilities and may need to interface with customers for requirement gathering.
Data engineers mostly work in teams of diverse expertise wherein everyone depends on each other. To run a project seamlessly, collaboration within a group is of utmost importance. Team player skills are critical in such scenarios.
Data engineers are frequently required to present their work to other specialists in teams. Public speaking and presentations skills come in handy for summarising and sharing the work in teams.
The data engineering space is expanding faster and offers ample opportunities for newcomers and laterals. One may easily leverage the opportunities by gaining the required skills. Online courses are helpful to train individuals to become promising data engineers. Lessons are suitable for both newcomers and laterals as curated based on the experience and understanding of candidates. The recommendation is to take a course and start the journey.
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