AWSDataEngineeringOnlineTraining

  What is Amazon Athena in AWS? Amazon Athena in AWS: A Comprehensive Overview Amazon Athena is an interactive query service provided by  Amazon Web Services  (AWS) that allows users to analyze data directly in Amazon Simple Storage Service (S3) using standard SQL. It is serverless, meaning there is no infrastructure to manage, and users only pay for the queries they run. This makes Athena a powerful and cost-effective solution for quickly analyzing large datasets stored in S3.  AWS Data Engineer Training Key Features of Amazon Athena 1.       Serverless Architecture : o     No Infrastructure Management : With Athena, there is no need to manage servers or data warehouses.  AWS  handles all the necessary infrastructure, ensuring high availability and performance. o     Scalability : Athena automatically scales based on the amount of data and the complexity of queries, ensuring consistent performance without m...

  AWS Key Management Service  (KMS)  is a managed service that enables you to create, manage, and control cryptographic keys used to encrypt and decrypt data in AWS. KMS is integrated with many AWS services and provides a high level of security to protect your data. It allows you to manage encryption keys for your applications and control their use across a wide range of AWS services and in your applications.  AWS Data Engineer Training Key Features of AWS KMS 1.   Centralized Key Management : KMS provides a centralized key management system for managing encryption keys across various AWS services and your applications. 2.    Integrated with AWS Services : KMS is integrated with various  AWS services  such as S3, EBS, RDS, Lambda, and more, making it easy to encrypt data across your AWS environment. 3.    Secure Key Storage : AWS KMS uses Hardware Security Modules (HSMs) to protect the security of your keys. The HSMs are certified u...

Step-by-Step Guide to ETL on AWS: ETL  (Extract, Transform, Load) is a critical process in data engineering, enabling the consolidation, transformation, and loading of data from various sources into a centralized data warehouse.  AWS offers  a suite of tools and services that streamline the ETL process, making it efficient, scalable, and secure. This guide will walk you through the steps of setting up an ETL pipeline on AWS, including the tools, techniques, and tips to optimize your workflow.  AWS Data Engineer Training Step 1: Extract Data 1. Identify Data Sources Begin by identifying the data sources you need to extract data from. These could be databases, APIs, file systems, or other data repositories. 2. Use AWS Data Extraction Tools AWS Glue : A fully managed ETL service that makes it easy to move data between data stores. It automatically discovers and profiles your data using the Glue Data Catalog. AWS Database Migration Service (DMS) : Helps you migrate datab...

Tools Used for AWS Data Engineering Amazon Web Services  (AWS)  offers comprehensive tools and services tailored for data engineering. These tools help data engineers collect, store, process, and analyse large volumes of data efficiently. Below is an overview of the key AWS tools used in data engineering, along with their functionalities and use cases.  AWS Data Engineer Training 1. Amazon S3 (Simple Storage Service) Overview : Amazon S3 is a scalable object storage service used for storing and retrieving any amount of data at any time. Key Features : Durability and Availability : Designed for 99.999999999% durability and 99.99% availability. Scalability : Automatically scales to handle any storage demand. Security : Provides strong security features like data encryption and access control. Use Cases : Data lake creation Backup and restore Big Data Analytics  AWS Data Engineering Training in Hyderabad 2. Amazon RDS (Relational Database Service) Overview : Amazon RDS ...