What is Data Lake?

A data lake is a centralized repository that allows you to store all kinds of structured and unstructured data at absolutely any scale. It stores data in its raw form so that it can be saved so that the ability for processes can be regulated according to needs for specific purposes. Below are some features and benefits of data lakes.

Characteristics of Data Lakes

• Scalability: These platforms can store enormous quantities of data ranging from terabytes to petabytes, empowering a business with the capacity to handle big data.
• Flexibility: They can accept different kinds of data like structured, semi-structured, and unstructured.
• Raw Data Storage: This stores data in its original format; this leads to in-depth analysis and various uses.
• Schema-on-Read: While traditional databases follow the convention of schema-on-write, data lakes perform schema-on-read. In other words, the data structure is applied only when read and not when stored.

Benefits of Data Lakes

• Cost-Effective: Data lakes can be more cost-effective as data lakes require commodity hardware and pure open-source software.
• Analytics and Insights: Enable next-generation advanced analytics encompassing big data analytics, machine learning, and real-time analytics.
• Integration: Data lakes can easily be integrated with other systems and data sources to get a complete view of data across the organization.
• Data democratization: It allows for broader access, so different teams within an organization can do their analyses.

Use cases of data lakes

• Processing big data: Handling large volumes of data from sources including IoT devices, social media, logs, and more.
• Machine learning: Providing raw data to train the machine learning model being developed.
• Data Archiving: Storing historical data and any particular file repository is mainly for future access and analysis purposes.
• Data Exploration and Discovery: Providing users with exploring and finding out new information through iterative search and investigation without predefined data structures.

Today, in the age of digital supremacy, data has shifted into a strategic asset that drives business decision-making, innovation, and competitive advantage. Now, organizations collect significant amounts of data from different sources, so the task of efficient management, storage, and proper information analysis arises as a significant challenge. That is where a data lake comes into play.

A data lake is a centralized repository for storing structured and unstructured big data at any scale. Unlike traditional data warehouses, data lakes do generally not require making the data’s structure evident in advance. The flexibility associated with raw data in terms of type and content format brings immense opportunities for diverse data analytics, machine learning, and real-time processing of data.

In this guide, we are going through a process to build a data lake on AWS using Terraform, we will cover critical concepts while defining major terminologies and take you step-by-step to help design and build a scalable and maintainable solution for your data lake. Whether you are a data engineer, cloud architect, or IT professional, this guide will provide you with knowledge and tools for harnessing data lakes on AWS.