Airflow is a powerful platform to automate and manage workflows. There are several options to deploy Airflow on AWS, including MWAA, ECS OR EKS:

  1. Deploying Airflow on AWS Managed Workflow for Apache Airflow (MWAA): This option provides a fully managed service for Apache Airflow and is a good choice for those who want a quick and easy way to get started with Airflow on AWS.

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In our last post, we explored the topic of the Data Platform on AWS. This post continues the discussion by offering an in-depth look into the central component of the data platform, the data lake, which serves as the single source of truth.

A data lake is a centralized repository for storing structured and unstructured data at any scale. It helps organizations effectively store, manage, and analyze growing amounts of data. Building a data lake on AWS offers cost-effective, secure storage and real-time analysis using scalable infrastructure, robust security, and analytical tools for making data-driven decisions and improving business value.

The proposed architecture is presented as below with 5 main components Ingestion, Storage, Processing, Meta Data & Governance and Orchestration.

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As AI continues to impact the world, the importance of data in business decision making has become increasingly apparent. Data also offers the potential to deliver greater value with less effort. To fully realize these benefits, it is essential to prioritize the development of a robust data platform architecture.

This series begins with the goal of constructing a comprehensive data platform on AWS, designed to meet the diverse needs of companies from startups to enterprises. Our objective is to create a platform that is scalable, reliable, secure, flexible, and cost-effective.

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