SQL Server to Spark
real-time replication
TwinCommit replicates Microsoft SQL Server to Apache Spark in real time by reading the SQL Server transaction log and applying every committed change exactly once and in commit order. Use it to process every committed SQL Server change in real time with Spark, with no triggers or capture tables on the source.
SQL Server
log reader
commit-ordered · exactly-once
Spark
native writer
How capture works on SQL Server
TwinCommit reads the active and backed-up transaction log directly, so you do not need SQL Server Agent CDC jobs or change tables.
Prerequisites
- ✓FULL recovery model
- ✓Primary key on each replicated table
- ✓VIEW SERVER STATE and db_owner on the source database
How apply works on Spark
The TwinCommit Spark connector exposes each pipeline as a Structured Streaming source with offsets mapped to log positions, so your jobs get exactly-once semantics with Spark checkpoints.
Requirements
- ✓The twincommit-spark connector JAR on the driver class path
- ✓Network access from the driver to the TwinCommit change feed endpoint
Setup
Set up SQL Server → Spark in four steps
- 1
Prepare SQL Server
Run twincommit check. It verifies full recovery model and the other prerequisites, and prints the exact fix for anything missing.
- 2
Point at Spark
Add a target block for Spark. TwinCommit can generate target DDL with twincommit ddl -apply.
- 3
Snapshot
twincommit snapshot copies each table under a brief share lock and records the exact log position.
- 4
Stream
twincommit run streams every committed change from that position, exactly once and in commit order.
source:
type: sqlserver
dsn: ${SOURCE_DSN}
target:
type: spark
listen: 0.0.0.0:7443
tls: true
tables:
- source: SALES.ORDERS
- source: SALES.ORDER_LINES
exclude_columns: [INTERNAL_NOTES]SQL Server to Spark replication FAQ
How does TwinCommit replicate SQL Server to Spark?
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TwinCommit reads changes from the SQL Server transaction log, buffers them per transaction, and applies each committed transaction to Spark in commit order. The TwinCommit Spark connector exposes each pipeline as a Structured Streaming source with offsets mapped to log positions, so your jobs get exactly-once semantics with Spark checkpoints.
Does SQL Server to Spark replication need triggers or capture tables?
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No. TwinCommit reads the SQL Server log directly and creates no objects in the source database.
Is SQL Server to Spark replication exactly-once?
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Yes. TwinCommit commits its checkpoint in the same Spark transaction as the data, so a crash or restart never loses or duplicates a transaction.
What SQL Server versions are supported?
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SQL Server 2016–2025 · Azure SQL Managed Instance · Azure SQL Database (target).
Stream SQL Server to Spark today.
Download the free Developer edition, or have one of our replication engineers walk you through a migration plan.