Learning by Doing: Implementing Snowflake for Real-Time Data Analytics
Aravindan A
AI Engineer | Associate Data Engineer | An Ambitious and Diligent AI Initiator With Intelligent Retrival
Data Collection and Ingestion
Starting from scratch, I gathered semi-structured data in JSON format from various online sources. My first step was creating a data warehouse in Snowflake, followed by the data ingestion process. Although I encountered challenges with loading large datasets through Snowsight, I effectively utilized SnowSQL to connect to Snowflake. I set up a staging area, where I initially loaded the data before transferring it to the main database.
Data Transformation and Cleaning
The raw JSON data required transformation to become structured. I performed an ELT (Extract, Load, Transform) process, extracting useful information from the raw files and organizing it into tables for efficient processing. Using SQL queries, I cleaned the data to ensure its integrity and consistency, which involved validating data types and formats to maintain accuracy throughout the analysis.
Building Fact and Dimension Tables
To enhance my analysis, I created fact and dimension tables. This structured approach allowed for more straightforward querying and reporting, facilitating deeper insights into player performances and match statistics.
Data Validation and Visualization
After ensuring data quality, I conducted thorough data validation to confirm the accuracy of my findings. I then created a simple dashboard in Snowflake for basic visualization, which helped in deriving actionable insights from the data.
This immersive experience has deepened my understanding of essential Snowflake concepts, including:
By engaging in this hands-on learning experience, I have gained valuable insights that will undoubtedly benefit my career in data analytics. I look forward to continuing my journey by exploring more real-time analytics projects that challenge my skills and expand my knowledge in this dynamic field.