AWSDataEngineeringOnlineTraining

  Overview of AWS Data Modeling Data modeling in AWS involves designing the structure of your data to effectively store, manage, and analyze it within the Amazon Web Services (AWS) ecosystem. AWS provides various services and tools that can be used for data modeling, depending on your specific requirements and use cases. Here's an overview of key components and considerations in AWS data modeling AWS Data Engineer Training Understanding Data Requirements: Begin by understanding your data requirements, including the types of data you need to store, the volume of data, the frequency of data updates, and the anticipated usage patterns. Selecting the Right Data Storage Service: AWS offers a range of data storage services suitable for different data modeling needs, including: Amazon S3 (Simple Storage Service): A scalable object storage service ideal for storing large volumes of unstructured data such as documents, images, and logs. Amazon RDS (Relational Database Service):

  Data Management Architectures for Analytics Data management architectures for analytics typically involve various components and layers to handle data ingestion, storage, processing, and analysis. Here's a high-level overview of common components in such architectures AWS Data Engineering Training Institute Data Sources: These are systems or applications where data originates. Sources can include databases, cloud services, IoT devices, and external APIs. Data Ingestion Layer: This layer is responsible for extracting data from sources and ingesting it into the data management system. It may involve ETL (Extract, Transform, Load) processes to clean and prepare the data. Data Storage Layer: Data is stored in this layer for further processing and analysis. Common storage solutions include data lakes (for raw data) and data warehouses (for processed and structured data).                                                                                   - AWS Data Enginee

  Data Engineering: AWS Prescriptive Guidance AWS (Amazon Web Services) offers a plethora of services and tools that facilitate the collection, storage, processing, and analysis of data. AWS provides prescriptive guidance to help users effectively utilize its services for building robust data engineering pipelines. Here's a high-level overview of prescriptive guidance for data engineering on AWS AWS Data Engineering Online Training Understanding Requirements: Before diving into implementation, it's crucial to have a clear understanding of your data engineering requirements. This involves determining the sources of data, types of data (structured, semi-structured, unstructured), expected volume, velocity, and variety of data, as well as the intended use cases. Choosing the Right Services: AWS offers a wide array of services for data engineering, including: Data Ingestion: AWS Glue, Amazon Kinesis, AWS DataSync Data Storage: Amazon S3, Amazon Redshift, Amazon RDS,

  AWS Data Engineering Complete Roadmap AWS data engineering requires a combination of skills in cloud computing, data management, and programming. Here's a comprehensive roadmap to guide you through the essential steps AWS Data Engineering Training Institute Foundation: Understanding Cloud Computing: Learn the basics of cloud computing concepts, especially focusing on AWS services. AWS Fundamentals: Familiarize yourself with core AWS services like EC2, S3, IAM, VPC, etc. Basic Programming: Acquire proficiency in at least one programming language like Python or Java. Data Fundamentals: Data Structures and Algorithms: Develop strong skills in data structures and algorithms, which are fundamental for efficient data processing. Databases: Learn about different types of databases (relational, NoSQL, etc.) and how they are used in data engineering.                                                             - Data Engineering Course in Hyderabad AWS Core Services:

  AWS Managing duplicate objects Managing duplicate objects in AWS typically involves identifying and removing duplicate data to optimize storage and ensure data consistency. Here are some common approaches AWS Data Engineering Training Institute Identifying duplicates: Use AWS services like S3 Inventory, AWS Glue, or Athena to scan and identify duplicate objects based on criteria such as file name, size, or content. Removing duplicates: Manual deletion: Identify and delete duplicates manually using the AWS Management Console, AWS CLI, or SDKs.                                                       - Data Engineering Course in Hyderabad Automated deletion: Use AWS Lambda functions triggered by S3 events to automatically identify and delete duplicates based on predefined rules. Preventing duplicates: Implement data validation checks to prevent duplicate uploads. Use unique identifiers or metadata to track and manage objects to avoid duplicates.                       

  Data Engineering using Databricks on AWS AWS Data Engineering involves designing and implementing data processing systems on the Amazon Web Services (AWS) cloud platform. It includes tasks such as ingesting, storing, processing, and analyzing data to derive insights and support decision-making. AWS Data Engineering Online Training Set up Databricks: Provision a Databricks workspace on AWS. Databricks provides a unified analytics platform that allows you to collaborate on data engineering tasks using Apache Spark. Data Ingestion: Ingest data from various sources into Databricks . This can include structured data from databases, semi-structured data from sources like JSON or XML, and unstructured data like text files or images. Data Transformation: Use Databricks notebooks to transform the ingested data. This can include cleaning, aggregating, and transforming the data to make it suitable for analysis.                - AWS Data Engineering Training Ameerpet Data Storage: