A Step-by-Step Roadmap to Data Engineering
Tags: dataengineering, datascience
Published: 2023-11-09 20:18:43

### Introduction
Data engineering is the practice of designing and building systems for collecting, storing, and analyzing data as scale. 
Data engineers work in a variety of settings to build systems that collect, manage and convert raw data into usable information for data scientists and business analysts to interpret. They make data accessible so that organizations can use to evaluate and optimize performance.

### 1.Basic programming languages
.Knowledge in python programing and scala.
.Knowledge of SQL and database programming. Know how to design and implement data models and database using tools such as PostgreSQL and MySQL.

### 2. Learn Linux commands and Git
.Knowledge in version control using git, this allows you to manage changes t workloads and basic Linux commands. 

### 3. Data integration, ETL and ELT
Data integration is the process of combining data from multiple sources into cohesive view. It involves gathering data from various sources, cleaning and transforming the data to make it consistent and compatible then storing it.

ETL(Extract, Transform and Load) is a process in data data warehousing and business intelligence that involves extracting data from various sources, transforming it into a format that is suitable for analysis and reporting then loading it into a data warehouse or other data repository.
  .Experience ETL tools like Apache Kafka, Talend.
  
ELT(Extract, Loading and Transformation) a process that involves moving raw data from a source system to a destination resource such a data warehouse.

Learn about ELT and ETL and when is the best time to use which method.

### 4. Data storage and warehousing
Data warehousing enables organizations to store, organize, and analyze data from various sources in a centralized location, providing a more complete view of the organization's data. 

  .Learn data modelling and schema design for data warehouse.
  .Learn data warehousing concepts and tools like Snowflake, Redshift and 
  amazon.

### 5. Data Pipelines
A method in which raw data is ingested from various data sources and then moved to a data store, like a data lake or a data warehouse for analysis. Data can be sources form API's, SQL and NoSQL databases.

 .Learn about Batch processing and Streaming data.
 .Learn the data pipeline architecture.
 .Understanding of workflow management tools such as Apache Airflow, AWS and 
  Azure Data Factory.
 . Familiarize with containerization technologies such as Docker and Kubernetes for managing and deploying data pipelines.

### 6.Cloud Computing
The cloud computing platforms offer an advantage to data engineers including scalable infrastructure and a range of tools for data processing and analysis.
  .Platforms like AWS, Google Cloud Platform and Microsoft Azure.

### 7.Data governance and security
Data governance ensures data is managed by legal and regulatory requirements. Another important function of data governance is helping protect data from unauthorized access, theft, and misuse. This is critical for data engineers, who are in charge of designing and maintaining secure data systems.