The whole system which defines how raw data is collected, stored, and used for analysis is designed and built through a process called data engineering. Raw data comes from different sources and in different formats, making it nearly impossible to analyze without the ecosystem built by data engineers.

Among the tools and processes required to complete data engineering tasks, a data engineer needs a large, centralized repository to store data that has been processed and refined. This repository of information is what is known as data warehouse.

In this article, we help you dive deep into tasks involved in data engineering, differentiate between data warehouse, data lake, and data mart, as well as get to know a few examples of data engineering and warehousing tools.

Data Engineering Tasks

In their day-to-day work, a data engineer deals with tasks such as…

• Acquiring datasets based on business needs

• Identifying and correcting any errors or inaccuracies in data

• Standardizing data format

• Interpreting data in multiple ways when possible

• Eliminating redundant copies of data

• Building, testing, and maintaining data pipeline architectures

• Storing processed data for easy retrieval

Data Warehouse, Data Mart, or Data Lake?

Data warehouse, data mart, and data lake are all data storage and management solutions, but they have different architectures and purposes.

Data Warehouse

Data warehouse is a centralized repository where data is collected from various sources, organized, and stored in a structured format. It is designed to support business intelligence and reporting activities.

Although data warehouses require significant effort to set up and maintain, it offers speedy query performance and data consistency.

Data Mart

Data mart is a subset of data warehouse designed to serve a specific business function or department. It contains data from data warehouse that has been organized in a way that is optimized for a particular group of users or business process.

Data marts are typically created for reporting and analysis purposes since they allow companies to improve data access and speed up query performance by providing pre-aggregated and filtered data.

Data Lake

Data lake is also a centralized repository of information, but instead of processed data, it stores all types of raw and unstructured data in their original formats. Unlike a data warehouse, a data lake does not enforce a specific data schema or structure, and it can handle large volumes of data that are difficult to structure.

Data lakes are used for exploratory analysis and data science, and they allow companies to store and process data from different sources without the need to define a schema upfront.

Data Engineering & Warehousing Tools

A few popular data engineering and warehousing tools you can try:

• Amazon Redshift - set up cloud-based data warehouse

• Apache Spark - perform large-scale data processing

• PostgreSQL - code using open-source relational database

• Snowflake - scale using virtual warehouses

• Google BigQuery - set up cloud-based data warehouse with machine learning capabilities