A beginner’s guide to data engineering concepts, tools, and responsibilities.

This content originally appeared on DEV Community and was authored by John Otienoh

Data Engineering Concepts

Data engineering is the process of designing, building, and maintaining the infrastructure for storing, processing, and retrieving large datasets. It involves creating systems that efficiently collect, transform, and store data, making it usable for analysis and decision-making.

Tools for data engineers.

1. Scripting and programming language: python is the commonly used language in data engineering due to its simplicity and extensive libraries , which is used in transformation and data cleaning. There are other languages that are used such as ruby, Scala among others.
2. Databases: data engineers use various databases like MySQL to store and manage structured data for analytics and reporting.
3. Data visualization tools: to gain insights and patterns from data, a data engineer should be familiar with various tools used in visualization such as tableau and Power BI.
4. Data warehousing and storage tools: the commonly used tool in managing data is snowflake, snowflake is a cloud data warehouse that allow one to store and manage data, snowflake is very flexible since it works with some programming languages such as python

Responsibilities:

• Designing data pipelines to collect and process data from various sources

• Developing data warehouses and lakes to store structured and unstructured data.

• Ensuring data quality, integrity, and security.

• Building scalable and high-performance data processing systems.

• Collaborating with data scientists and analysts to understand their data needs.

• Implementing data governance policies and metadata management.

This content originally appeared on DEV Community and was authored by John Otienoh

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