Databases, warehouses and search stores SPSync copies SharePoint lists into

SPSync connects to many destinations, relational databases, warehouses and lakes, search and vector stores, and the same lists can be replicated to more than one of them, with one replication for each. If yours is not on this list, we are happy to add a custom destination or source for you, book a call and tell us what you need.

For where the data comes from, see sources: SharePoint Online lists and document libraries.

Still deciding whether a replica is the right route at all? SharePoint list to a database compares every way of getting a list into a database, from Excel links and flows to Data Factory and Fabric mirroring.

Destinations at a glance

The licence column names the destination family a licence covers. Embeddings on sync means SPSync writes a vector for each row as it copies. In the last column, listed means the store is on Microsoft's list of Copilot Studio real-time knowledge connectors, a preview feature when we checked on 22 October 2026.

Destination Family Status Licence Embeddings on sync Copilot Studio real-time knowledge
Google AlloyDB Relational databases Shipped Relational databases No Not listed
Amazon Aurora Relational databases Shipped Relational databases No Not listed
Amazon RDS Relational databases Shipped Relational databases No Not listed
Azure Database for MySQL Relational databases Shipped Relational databases No Not listed
Azure Database for PostgreSQL Relational databases Shipped Relational databases No Not listed
Azure SQL Database and Managed Instance Relational databases Shipped Relational databases No Listed
Dataverse Relational databases Shipped Relational databases No Listed
Google Cloud SQL Relational databases Shipped Relational databases No Not listed
Heroku Postgres Relational databases Shipped Relational databases No Not listed
MariaDB Relational databases Shipped Relational databases No Not listed
MySQL Relational databases Shipped Relational databases No Not listed
Neon Relational databases Shipped Relational databases No Not listed
Oracle Database Relational databases Shipped Relational databases No Listed
PostgreSQL Relational databases Shipped Relational databases No Not listed
SQL Server Relational databases Shipped Relational databases No Not listed
SQLite Relational databases Shipped Included with every product No Not listed
Supabase Relational databases Shipped Relational databases No Not listed
Amazon Redshift Warehouses and lakes Later, Parquet today Warehouses and lakes No Not listed
Amazon S3 Warehouses and lakes Shipped Warehouses and lakes No Not listed
Azure Blob Storage Warehouses and lakes Shipped Warehouses and lakes No Not listed
Azure Data Lake Storage Gen2 Warehouses and lakes Shipped Warehouses and lakes No Not listed
Azure Synapse Analytics Warehouses and lakes On the roadmap, Parquet today Warehouses and lakes No Not listed
Google BigQuery Warehouses and lakes Shipped Warehouses and lakes No Not listed
ClickHouse Warehouses and lakes Shipped Warehouses and lakes No Not listed
Databricks Warehouses and lakes Shipped Warehouses and lakes No Listed
Delta Lake tables Warehouses and lakes Shipped Warehouses and lakes No Not listed
DuckDB and MotherDuck Warehouses and lakes Shipped Warehouses and lakes No Not listed
Apache Iceberg tables Warehouses and lakes Shipped Warehouses and lakes No Not listed
Microsoft Fabric Lakehouse and OneLake Warehouses and lakes On the roadmap, Parquet today Warehouses and lakes No Not listed
Parquet files Warehouses and lakes Shipped Warehouses and lakes No Not listed
Snowflake Warehouses and lakes On the roadmap, Parquet today Warehouses and lakes No Listed
Azure AI Search Search and vector stores Shipped Search and vector stores Yes Listed
Elasticsearch Search and vector stores Shipped Search and vector stores No Not listed
LanceDB Search and vector stores Shipped Search and vector stores No Not listed
OpenSearch Search and vector stores Shipped Search and vector stores No Not listed
Postgres with pgvector Search and vector stores Shipped Search and vector stores Yes Not listed
Qdrant Search and vector stores Shipped Search and vector stores No Not listed
Weaviate Search and vector stores Shipped Search and vector stores No Not listed

How to choose a destination

Start with whoever reads the data. When a Power BI report, an internal app or an agent with a SQL tool reads it, the usual answer is a relational database your team already runs. That tends to be SQL Server or Azure SQL in a Microsoft shop, and Postgres or MySQL elsewhere. Lookups arrive as key columns and multi-value columns as child tables, so a report joins two lists the way it joins any other tables.

A warehouse or a lake makes sense when the lists feed a data platform you already have. BigQuery, Databricks, ClickHouse and DuckDB take the tables directly. Parquet is the route into the platforms SPSync does not write to yet. Each run replaces a current.parquet file per table, which Snowflake, Synapse, Fabric and Redshift can load or query today, and each of their pages shows how.

Pick a search or vector store when people, or an agent, look for rows by what the text means. Postgres with pgvector and Azure AI Search are the two where SPSync writes the embedding itself. If your managed Postgres offers the pgvector extension, the pgvector destination can write to it. Elasticsearch, OpenSearch and the other search stores suit keyword and filtered search over the same columns.

For a Copilot Studio agent, the last column of the table matters most. A copy in a store Microsoft lists can be added to the agent as knowledge through that connector. For a copy anywhere else, a custom agent can query it with a read-only SQL tool, the route the AI and agents hub walks through. Either way the agent reads the copy with a database login and needs no Graph permission of its own. Each destination page covers the setup, the type mapping and the limits for that store.

Relational databases

Warehouses and lakes

Search and vector stores

Postgres with pgvector and Azure AI Search receive the replicated columns and an embedding per row that SPSync generates on each sync, from OpenAI, Azure OpenAI or a local model. Elasticsearch, OpenSearch, LanceDB, Qdrant and Weaviate receive the replicated columns and no vector; they fit keyword and filtered search, and a vector you add belongs to your own pipeline.