AWSDataEngineeringOnlineTraining

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 simplifies the setu

AWS Data Engineering with Data Analytics Online Recorded Demo Video Mode of Training: Online Contact +91-9989971070 Visit: https://www.visualpath.in/aws-data-engineering-with-data-analytics-training.html WhatsApp: https://www.whatsapp.com/catalog/917032290546/ Subscribe  Visualpath channel  https://www.youtube.com/@VisualPath Watch demo video@ https://youtu.be/DWMbfMrGVW4?si=oefmStspz8hAPwn6

  What is Amazon DynamoDB? Amazon DynamoDB   is a fully managed NoSQL database service provided by Amazon Web Services  ( AWS ).  It is designed to deliver high performance at any scale, offering seamless scalability, low latency, and a flexible data model. DynamoDB is ideal for applications that require consistent, single-digit millisecond response times for any scale of workloads. Key Features of Amazon DynamoDB 1.   Fully Managed Service : AWS handles administrative tasks like hardware provisioning, setup, configuration, replication, software patching, and cluster scaling.  AWS Data Engineer Training 2.     Performance at Scale : DynamoDB can handle high requests and large datasets, providing consistent low latency. 3.   Flexible Data Model : Supports document and key-value store models, allowing developers to store structured or semi-structured data. 4.     Built-in Security : Offers encryption at rest, secure endpoints, and fine-grained access control using AWS Identity and Access

  Differences Between Big Data and Hadoop? Introduction Big Data  and  Hadoop   are two integral concepts within the data management and processing realm. While they are often mentioned together, they represent different aspects of the data landscape. Understanding their differences is crucial for leveraging their respective strengths effectively. Big Data 1.       Definition: o   The term  "big data"  describes the enormous amounts of organized, semi-structured, and unstructured data that come from various sources and are produced quickly. It encompasses the challenges and opportunities associated with processing and analyzing these large datasets.  AWS Data Engineering Training 2.       Characteristics: o      Volume:  The sheer amount of data generated. o      Velocity:  The speed at which data is generated and processed. o      Variety:  The different types of data (text, images, video, etc.). o      Veracity:  The uncertainty or reliability of data. o      Value:  The in

AWS Data Engineering with Data Analytics Online Recorded Demo Video Mode of Training: Online Contact +91-9989971070 Visit: https://www.visualpath.in/aws-data-engineering-with-data-analytics-training.html WhatsApp: https://www.whatsapp.com/catalog/917032290546/ Subscribe  Visualpath channel  https://www.youtube.com/@VisualPath Watch demo video@ https://youtu.be/FRMyXYbsjiw?si=h0ANGTqN7EehJVGe

              Amazon QuickSight   is a powerful, cloud-based business intelligence (BI) service that  Amazon Web Services  (AWS) provides. It enables organizations to create and deliver interactive data visualisations, perform ad hoc analysis, and gain insights from their data. Below is an  overview  of Amazon QuickSight visualisation, detailing its features, capabilities, and benefits.  AWS Data Engineer Training Overview of Amazon QuickSight Visualization Key Features and Capabilities 1.       Data Integration : o     Multiple Data Sources : Amazon QuickSight supports a wide range of data sources, including  AWS  services like Amazon RDS, Amazon Redshift, Amazon S3, and external sources such as Salesforce, MySQL, and PostgreSQL. This flexibility allows users to connect to virtually any data source, whether in the cloud or on-premises. o    SPICE Engine : QuickSight's Super-fast, Parallel, In-memory Calculation Engine (SPICE) allows users to perform rapid data analysis. SPICE can