Best practices for data ingestion and table creation vary based on data volume to balance performance and convenience:

• Large amount of data requires a Manual approach. Manually defining the schema (e.g., using CREATE TABLE DDL) is critical to precisely optimize data types, partitioning, distribution, and indexing. This level of control is essential for ensuring query performance and storage efficiency at scale.
• Small amount of data is best handled using Import data wizards. Tools like the "Get Data" experience in Microsoft Fabric provide maximum convenience by automatically inferring the schema and loading the data, which is ideal for quick, ad-hoc analysis where performance is not the primary concern.
• Medium amount of data benefits from a Discovery process. This implies a hybrid approach. You use schema discovery or profiling tools to inspect the data's structure first, then refine the suggested schema before a full import. This balances the convenience of inference with the need for better-than-default performance.

Manual (Large data): Microsoft Learn. "Guidance for designing distributed tables in Azure Synapse Analytics." This documentation details the importance of manually selecting table distribution methods (e.g., hash, round-robin) and column data types, which are fundamental Manual design decisions required for optimizing large-scale data warehouses.

Manual (Large data): Microsoft Learn. "Data types for tables in Azure Synapse Analytics." This guide emphasizes that "Minimizing row size by using the smallest data type that works" is a key principle for improving query performance. This optimization is only possible through a Manual DDL approach, not automated inference.

Import data (Small data): Microsoft Learn. "Get data from a file into a KQL database." This document describes the wizard-based "Get Data" experience in Microsoft Fabric, which "guides you through the process... [including] creating a new table, mapping the data, and ingesting the data." This automated flow is the definition of the Import data method, perfect for small, simple ingestion tasks.

Discovery (Medium data): Microsoft Learn. "Schema drift in mapping data flow." (Azure Data Factory documentation). This document discusses the feature of "schema discovery," which allows a developer to "discover the schema of your data source on the fly" and "see the changing columns." This concept of inspecting and adapting to a schema, rather than just blindly importing or fully manually creating it, aligns with the Discovery approach for medium-sized or evolving datasets.