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Over the past few years, data-driven enterprises have succeeded with the Extract Transform Load (ETL) process to promote seamless enterprise data exchange. This indicates the growing use of the ETL process and various ETLtools and techniques across multiple industries.
Understanding SQL You must be able to write and optimize SQL queries because you will be dealing with enormous datasets as an Azure Data Engineer. To be an Azure Data Engineer, you must have a working knowledge of SQL (Structured Query Language), which is used to extract and manipulate data from relationaldatabases.
The most common data storage methods are relational and non-relationaldatabases. Understanding the database and its structures requires knowledge of SQL. Data is moved from databases and other systems into a single hub, such as a data warehouse, using ETL (extract, transform, and load) techniques.
The duties and responsibilities that a Microsoft Azure Data Engineer is required to carry out are all listed in this section: Data engineers provide and establish on-premises and cloud-based data platform technologies. Relationaldatabases, nonrelational databases, data streams, and file stores are examples of data systems.
Data engineers must thoroughly understand programming languages such as Python, Java, or Scala. Relational and non-relationaldatabases are among the most common data storage methods. Learning SQL is essential to comprehend the database and its structures.
Image Credit: altexsoft.com Below are some essential components of the data pipeline architecture: Source: It is a location from where the pipeline extracts raw data. Data sources may include relationaldatabases or data from SaaS (software-as-a-service) tools like Salesforce and HubSpot.
Top 100+ Data Engineer Interview Questions and Answers The following sections consist of the top 100+ data engineer interview questions divided based on bigdata fundamentals, bigdatatools/technologies, and bigdata cloud computing platforms.
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