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Machine learning and artificial intelligence have become one of the most prominent technologies over the past few years. These technologies use specific algorithms to predict future states based on the input data. Data engineers need prior knowledge of these algorithms to work and organize data based on the company’s requirements.
Data Engineering Skills for structures and algorithms (DSA)
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Data engineers are responsible for organizing and handling unstructured data and transforming it into a usable format. This data must be stored somewhere accessible so that it can be used conveniently. Data structures are simple storage units that come in handy for the same. Although data engineers are mainly responsible for organizing and filtering data, they should know how to see if an algorithm is working efficiently.
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As a data engineer, you cannot find your way around SQL-based schemas and their syntax, given SQL is a widely used language. Many cloud-based systems like Amazon QuickSight and Athena are compatible with SQL-like interfaces. Additionally, NoSQL database technologies have gained popularity over the last few years, especially for storing unstructured or semi-structured data. These databases store data in key-value pairs and object formats like JSON or Parquet.
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You must be an efficient developer if you wish to be a data engineer. Almost all data engineer positions require you to know programming languages like Python. As a data engineer, you will also have to write scripts and glue codes because everything nowadays involves coding. There is infrastructure as code, pipelines as code, etc. Hence, having a robust programming background and interest in finding data patterns is vital.
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Hyper Automation is one of the very obvious data engineering skills and refers to a business-driven approach to automating as much as possible. The process incorporates several technologies, tools, and platforms to perform value-added tasks like scheduling events, running jobs, etc. It leads to enhanced work quality, faster business processes, and decision-making agility. .
Data Visualization as One of the Data Engineering Skills
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Data engineers are often expected to perform exploratory data analysis (EDA) while working with data to ensure that the required ETL/ELT task is completed. EDA analyzes specific trends, patterns, or graphical representations of the data. A data engineer should know how to visually present and analyze data using statistical knowledge with tools like SSRS, Tableau, Azure Synapse, Excel, Power BI, etc.
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The model is called multi-cloud computing when an enterprise utilizes many cloud combinations simultaneously. It could be two or more public or private clouds or a combination of public, private, and edge-based clouds. Shifting to a multi-cloud model enables companies to avail themselves of better data security features and cost savings simultaneously.
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Data engineers must have experience with application programming interfaces (APIs) in addition to standard data engineering skills because part of their job is to build APIs (application user interfaces) in databases to enable data analysts and scientists to send queries. These interfaces share data infrastructure for real-time analysis.
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ETL stands for extract, transform, and load, and the term describes the entire process of extracting data from a source system, transforming it into a required format, and loading it to the desired location. Since data engineers are responsible for organizing and processing data, it is relevant to know ETL tools. When handling vast amounts of data, engineers perform batch processing and ensure that the data remains relevant to the specific requirement.
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Apache Hadoop is an open-source library that enables distributed procession of large datasets. It is a cluster of frameworks that support data integration, making it suitable for big data analytics. Hadoop is a popular framework as it is compatible with gigabytes to petabytes of data by strategic clustering.