Data Engineering 101: Introduction to Data Engineering
Tags: datascience
Published: 2022-08-19 17:49:00

With the tremendous growth of technology throughout the World, Data handling has been a big challenge especially when the data has to be moved from storage to users, from different original formats to new formats without losing its value.
To handle this data, Data Engineering is essential.

## **What is Data Engineering?**
Data Engineering is the art of building/architecting data platforms, designing and implementing data stores and repositories, data lakes and gathering, importing, cleaning, pre-processing, querying, analyzing data, performance monitoring, evaluation, optimization, and fine-tuning the processes and systems. It makes Data available for analysis and efficient Data-driven decision-making.

## **What is the Role of a Data Engineer?** 
It’s the role of a data engineer to store, extract, transform, load, aggregate, and validate data. 
This involves:

- Building data pipelines and efficiently storing data for tools that need to query the data. 

- Analyzing the data, ensuring it adheres to data governance rules and regulations.

- Understanding the pros and cons of data storage and query options.

**Data Engineers deliver:**
- The Correct data.

- In the Correct Form.

- To the Right People.

- As Efficiently as Possible.

**Data Engineers are Responsible for:**

- Ingesting Data from different Sources.

- Optimizing Databases for Analysis.

- Removing Corrupted Data.

- Develop, Construct, test, and Maintain Data Architecture.

 
## **Why is Data Engineering important?**
The Data Engineering lifecycle consists of building data platforms, designing and implementing Data Stores, Repos, and Data Lakes, and gathering, imparting, cleaning, preprocessing, querying, analyzing data, performance monitoring, evaluation, optimization, and tuning the System. 
Companies of all sizes have huge amounts of disparate data to comb through to answer critical business questions. Data engineering is designed to support the process, making it possible for consumers of data, such as analysts, data scientists, and executives, to reliably, quickly, and securely inspect all of the data available.

## **Data Engineering Tools and skills**
Data Engineers use many tools to work with Data. They use a specialized skill set to create end-to-end data pipelines that move Data from Source systems to target destinations.
Data Engineers work with a variety of tools and technologies, such as:

- **ETL Tools**: ETL (extract, transform, load) tools move data between systems. They access data, then apply rules to “transform” the data through steps that make it more suitable for analysis.

- **Cloud Data Storage**: Including Amazon S3, Azure Data Lake Storage (ADLS), Google Cloud Storage, etc.

- **Query Engines**: Engines run queries against data to return answers. Data engineers may work with engines like Dremio Sonar, Spark, Flink, and others.

- **Python:** Python is a general programming language. Data engineers may choose to use Python for ETL tasks.

- **SQL**: Structured Query Language (SQL) is the standard language for querying relational databases.

## Data Engineering Versus Data Science


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