![]() Rob, a data engineer, needs to store and model several terabytes of data in Fabric. Susan decides to use a data warehouse, which allows the team to interact primarily with T-SQL, while also allowing any Spark users in the organization to access the data. Thinking about the larger team, the primary consumers of this data are also skilled with SQL and SQL analytical tools. Susan has spent many years building data warehouses on relational database engines, and is familiar with SQL syntax and functionality. After review of the details in the previous table, the primary decision points are the available skill set and the need for multi-table transactions. They are ready to get started cleaning, modeling, and analyzing data but need to decide to build a data warehouse or a lakehouse. Susan, a professional developer, is new to Microsoft Fabric. Review these scenarios for help with choosing between using a lakehouse or a data warehouse in Fabric. Queued ingestion, Streaming ingestion has a couple of seconds latency ![]() Time Series native elements, Full geospatial storing and query capabilitiesįull indexing for free text and semi-structured data like JSON Yes, query across KQL Databases, lakehouses, and warehouses with shortcuts Yes, query across lakehouse and warehouse tables query across lakehouses (including shortcuts using Spark) Yes, query across lakehouse and warehouse tables Row level,table level (when using T-SQL),none for Spark Object level (table, view, function, stored procedure, etc.),column level,row level,DDL/DML Unstructured, semi-structured, structuredĬitizen Data scientist, Data engineer, Data scientist, SQL engineer
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