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Step-by-Step Guide to Creating an AWS RDS Database Instance

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 Amazon Relational Database Service (AWS RDS) makes it easy to set up, operate, and scale a relational database in the cloud. Instead of managing servers, patching OS, and handling backups manually, AWS RDS takes care of the heavy lifting so you can focus on building applications and data pipelines. In this blog, we’ll walk through how to create an AWS RDS instance , key configuration choices, and best practices you should follow in real-world projects. What is AWS RDS? AWS RDS is a managed database service that supports popular relational engines such as: Amazon Aurora (MySQL / PostgreSQL compatible) MySQL PostgreSQL MariaDB Oracle SQL Server With RDS, AWS manages: Database provisioning Automated backups Software patching High availability (Multi-AZ) Monitoring and scaling Prerequisites Before creating an RDS instance, make sure you have: An active AWS account Proper IAM permissions (RDS, EC2, VPC) A basic understanding of: ...
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Here's to Know Data lake Vs Database

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In a data lake, data stored internally in a repository. You can call it a blob. The data in the lake a no-format data, but you need a schema for the database.  Data lake Repository Database In the database, the Schema definition you need before you store data on it. It should follow Codd's rules. Here data is completely formatted. The data stores here in Tables, so you need SQL language to read the records. Poor performance in terms of scalability. Data lake It doesn't have any format - it's just a dump. You can send this dump to the Hadoop repository for data analysis. This repository can be incremental. You can build a database. The data lake is a dump of data with no format. It needs a pre-format before it sends for analytics. Data security and encryption: You need these before you send data to Hadoop. In real-time, you need to pre-process data. This data you need to send to the data warehouse to get insights.
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