Kaggle-Databricks-Snowflake: End-to-End Hands-on ETL demo

There has been a notable shift in focus, primarily towards the collection and storage of data, due to advancements in artificial intelligence and machine learning. The goal is to gather as many data points as possible. But it’s crucial to understand that data collection is just the tip of the iceberg. The complexities required in building effective and accountable data pipelines to derive meaningful insights and stage them for consumption are where the true complexity lies. In the current technological landscape, data is amassing and being stored in data lakes and cloud storage solutions at an astounding rate. The primary goal is to achieve millisecond latency for data access. Data gathering and storage used to be a simple process involving integration and service offerings. Data has, nevertheless, become a vital tool and an asset in recent years. Data is now a crucial component of monetization frameworks, and businesses are relying heavily on data to fuel their success and achieve a competitive advantage. It’s critical to note that data is valuable only when it is in the appropriate format, is in the right shape, and offers insightful information. While accomplishing this can be difficult, it is not impossible. It is now much simpler to clean up and organize data to satisfy the needs of downstream applications and machine learning models using in-memory data processing technologies like Apache Spark. <a href="https://jay-reddy.medium.com/kaggle-databricks-snowflake-end-to-end-hands-on-etl-demo-e9cfd52bb1e3">Visit Now</a>