Ingest and Visualize CSV Files using AWS Platform For Transition from Unstructured to Structured Data

In addition to computing, storage, databases, analytics, networking, cellular, developer equipment, management equipment, IoT, security, and agency packages, AWS provides a comprehensive range of pay-as-you-go, on-demand, global cloud-based services. Accessible AWS services and solutions number over 200 and include information warehousing, directories, and content distribution. New offers can be immediately provisioned without an upfront capital cost. This makes it possible for businesses, start-ups, small and medium-sized organizations, and clients in the public sector to obtain access to the fundamentals needed to quickly adapt to changing business requirements. The technology at the core of any cloud operation is virtualization. It asserts that virtualization breaks up a physical server’s hardware resources into smaller bits. Data processing and visualization on Amazon Web Services is the process of gathering, analyzing, and visualizing large amounts of data using a variety of AWS services (AWS). Data storage, integration, analysis, and visualization are just a few of the many services that Amazon offers that may be used to manage and display data. With the help of AWS services like Amazon S3, Amazon Kinesis, and Amazon Redshift, huge amounts of data can be acquired, analyzed, and stored. Amazon S3 is a simple storage service that may be used to store and retrieve data, Amazon Kinesis is a real-time data streaming service, and Amazon Redshift is a data warehousing service. Using AWS Glue, Redshift, and QuickSight, we demonstrate a method for processing and displaying CSV files in this paper In author’s suggested approach, we extract, convert, and load data from CSV files to Redshift, a cloud-based data warehouse, using AWS Glue. The data saved in Redshift is then visualized using QuickSight, enabling quick and simple data exploration. We also go over how using AWS Glue, Redshift, and QuickSight can scale effectively and fast, automate data processing activities, and cut expenses compared to using conventional data processing and storage techniques.