Presentation
Efficient Data Preparation for Better Analytics and AI Models
Structuring and preparing data effectively is crucial for successful data analysis and AI models. In fact, Data Scientists spend up to 80% of their time on data preparation. However, this process can be significantly simplified with the right methods and tools.
In Data Engineering, cloud technologies and a modern data stack offer a powerful approach to data processing. Data Engineering combines best practices from software engineering with smart data management, resulting in a robust and scalable infrastructure that enables businesses to process data efficiently and reliably.
The cloud provides incredible flexibility. With a cloud data warehouse or lakehouse, companies can scale their data processing based on demand, adapting it to their individual needs.
The modern data stack is revolutionizing data processing, with the ELT approach (Extract, Load, Transform) at its core. Unlike traditional ETL processes, where data is transformed before loading, ELT allows maximum flexibility by loading raw data first and transforming it later. This enables faster data integration, analysis, and adaptability to changing requirements.
At Sunfire, we demonstrate how tools like dbt (data build tool) and Databricks support this approach. dbt ensures efficient and traceable data transformations directly in the data warehouse, while Databricks provides a powerful platform for data processing and analysis. This combination allows us to build a flexible, scalable, and future-proof data infrastructure.