Data Engineering

In today’s data-driven world, data is fuel for organizations. Data can be information-driven only when it is structurally organized. Here, comes data engineering for the rescue. With the increasing rate of data, it’s tedious to extract information from it, so, the requirement of skilled data engineers also increased. Let’s first understand Data Engineering in detail.

What is Data Engineering?

Data Engineering is an engineering field that designs, builds, and arranges data pipelines that process, transform, and transport data in the required format. Data Engineers work on large amounts of data and develop scalable data architectures to organize it. They extract a large amount of unstructured and corrupt data, clean the data, and transform it to be stored in databases that can be used for analytics and business intelligence.

With the rise of big data, data engineers’ roles and responsibilities have become crucial. Data Engineers do ETL (Extract, Transform, and Load) processes with modern and advanced tools that can handle the large volume of data.

Data Engineering Foundation

Data engineers set the platform for analytics and business intelligence teams to analyse and visualize data for their organizational growth.

The Importance of Data Engineering?

The Data Engineer job is critical. Data is scattered everywhere on digital platforms and is available in an unorganized and unstructured format. Data can be corrupt as well. Such data is not appropriate for the analysis.

Before any analysis of data, it is the first step to gather data from various sources and organize it in a database or warehouse. After gathering the data, data cleaning, and transformation steps like filtering, grouping, and aggregation are done. Transformation is the heart of a data engineer job, as it translates raw data into analysis-ready datasets. After organized data is loaded into the analytical database, analysis, visualizations, and predictions are performed.

Without data engineering jobs, data science tasks cannot be executed. It set a roadmap for the machine learning and artificial intelligence fields.

Skills needed to become a Data Engineer

Data Engineering is a broad field and requires a spectrum of skill sets for successful execution. Below are the skills needed to become good in this area:

  • Knowledge of SQL and NoSQL databases is a must
  • Hands-on programming experience for data processing. The current preferred language is Python.
  • Knowledge of big data processing tools like Apache Spark or Hive
  • Scheduling tool knowledge is a must, like Apache Airflow.
  • Good understanding of RESTful APIs for client-server communication.
  • Hands-on with Data Visualization and BI tools like Tableau or PowerBI
  • Knowledge of Cloud Platforms like AWS, Azure, or Google

Roll up your sleeves and get ready to become a data engineer!!

Stay Tuned

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Keep learning and keep implementing!!

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