Modern Data Stack

The modern data stack automates data collection using cloud-based tools to connect to various sources and pull data into a central location in near real-time. This approach provides a more efficient and accurate way to collect and transform data compared to traditional data stacks, which often involve manual processes and separate tools for data integration and transformation.

The modern data stack uses cloud-based integration tools to handle large volumes of data from various sources in real-time. It also allows for the integration of cloud-based sources that may not have been accessible in the traditional stack, providing a more flexible and efficient approach to data integration.

The modern data stack uses cloud-based data warehouses that are more scalable, flexible, and cost-effective than the traditional stack's on-premises hardware. Cloud-based data warehouses also offer advanced analytics capabilities, providing a more efficient way to store, process, and analyze data.

The modern data stack's cloud-based data transformation tools utilize the ELT paradigm to push the processing duties to the cloud data warehouse. These transformations can be accomplished via no-code/low-code workflows that provide a more user-friendly interface for faster data transformation or modularized SQL/Python that gives advanced data engineers more control. They also offer built-in data validation and lineage tracking to ensure data quality and traceability. This is in contrast to the traditional data stack, where data transformations are typically done inside legacy ETL tools which require more hardware and therefore cost more.