In this R data science project, we will explore wine dataset to assess red wine quality. The objective of this data science project is to explore which chemical properties will influence the quality of red wines.
One of the broadest uses of Snowflake is building a data warehouse platform or enhancing the existing data lake. It offers all sorts of services to build an efficient Data warehouse with ETL capability and support for various external data partners. Slowly Changing dimensions are a common database modeling technique used to capture data in a table and show how it changes over time. The slowly changing dimension of the warehouse dimension is said to rarely change. However, when they change, there should be a systematic approach to capturing that change. Examples of SCDs are customer and products information. This project explains how to build a Slowly Changing Dimension (SCD) using Snowflake’s Stream functionality and how to automate the process using Snowflake’s Task functionality.
This is a typical Big Data ETL visualization project implemented in AWS cloud using cloud native tools like Glue which is used to Spark jobs without maintaining cluster infrastructure, Step Functions which is used to schedule jobs based on dependency ,Redshift which is the ultimate petabyte scale data warehouse solution in AWS and Quicksight which is AWS managed Visualization tool to create business reports