Data Lakes and Analytics on AWS
What is Data Lake? With recent technological advancements, there is a high demand for a solution for data storage and analytics that provides greater agility and flexibility than conventional data management systems. This is where Data Lakes comes in handy for most AWS customers. Because it enables businesses to handle numerous data types from a wide range of sources and store this data, both structured and unstructured, in a centralized repository, a data lake is a novel and more common method of storing and analyzing data. You can run several sorts of analytics, from dashboards and visualizations to big data processing, real-time analytics, and machine learning to help you make better decisions, without first structuring your data. In other words, it maintains data in its original format and provides tools for analyzing, querying, and processing.
Architecture Now lets talk about the internals of the Data Lake. The architecture is essentially a collection of tools that are used to create and operationalize such specific data approach. It starts with event processing tools, goes to ingestion, transformation pipelines and reaches the analytics and query tools. Based on business needs there are many various combinations of these tools to build a complete Data Lake. We will cover some of the possible combinations in the sections below.