SingleStore + Kafka
Connect SingleStore and Kafka for Real-Time Data Pipelines at Scale
Stream, ingest, and act on high-velocity data by integrating SingleStore's distributed SQL engine with Kafka's battle-tested event streaming platform.
Why integrate SingleStore and Kafka?
SingleStore and Kafka are a natural pairing for organizations that need to move fast on massive volumes of data. Kafka excels at capturing and transporting real-time event streams from dozens of producers, while SingleStore has the analytical horsepower to query and act on that data in milliseconds. Together, they form the backbone of modern real-time data architectures — closing the gap between events happening and insights being available.
Automate & integrate SingleStore & Kafka
Use case
Real-Time Operational Analytics Ingestion
Stream Kafka topic messages from clickstream events, IoT sensors, or application logs directly into SingleStore tables as they arrive. Teams can run sub-second analytical queries on live data without batch delays or ETL windows.
Use case
Event-Driven Microservices Synchronization
Use Kafka events published by microservices to keep SingleStore as the authoritative operational data store. When a service publishes an order-placed or user-updated event, tray.ai updates SingleStore instantly and notifies downstream services.
Use case
Fraud Detection and Anomaly Alerting
Ingest high-frequency transaction or behavioral events from Kafka into SingleStore, then run continuous queries to detect fraud patterns or anomalies in real time. Alerts and remediation workflows fire the moment suspicious activity is identified.
Use case
Customer 360 Profile Enrichment
As customer interaction events flow through Kafka — web visits, purchases, support tickets — tray.ai continuously upserts enriched customer profiles in SingleStore. Marketing and customer success teams always have a complete, up-to-date view of every customer.
Use case
Log Aggregation and Observability Pipeline
Centralize application, infrastructure, and security logs by streaming them through Kafka and landing them in SingleStore for fast querying. Engineering and SRE teams can run ad-hoc SQL queries against live log data without spinning up dedicated log management infrastructure.
Use case
Machine Learning Feature Store Updates
Feed real-time Kafka event streams into SingleStore as a live feature store for ML models. As new events arrive — user actions, sensor readings, market data — features are computed and stored in SingleStore so models always score against the freshest inputs.
Use case
Change Data Capture (CDC) Replication
Use Kafka as the CDC transport layer to capture database change events and replicate them into SingleStore for reporting and analytics workloads. This offloads heavy analytical queries from production databases while keeping SingleStore tables continuously synchronized.
Get started with SingleStore & Kafka integration today
SingleStore & Kafka Challenges
What challenges are there when working with SingleStore & Kafka and how will using Tray.ai help?
Challenge
Managing Schema Evolution Across Kafka and SingleStore
Kafka producers frequently change message schemas — adding fields, renaming keys, or changing data types — which can silently break SingleStore ingestion pipelines and corrupt downstream tables without proper handling.
How Tray.ai Can Help:
tray.ai's visual data mapper lets teams define flexible field mappings with default values and type coercion rules. When Kafka schemas change, mappings can be updated in the UI without redeploying code, and built-in alerting flags schema drift before it causes data loss.
Challenge
Handling High-Throughput Kafka Topics Without Data Loss
Kafka topics in production environments can produce millions of messages per minute. Naive polling approaches create lag buildup, cause consumer group rebalances, and risk losing messages during pipeline failures or restarts.
How Tray.ai Can Help:
tray.ai manages consumer group offsets reliably, supports configurable micro-batch sizes to balance throughput and latency, and provides automatic retry logic with exponential backoff so no messages are lost even during transient SingleStore outages.
Challenge
Deduplicating Events Before Writing to SingleStore
Kafka's at-least-once delivery guarantee means the same message can arrive multiple times, leading to duplicate rows in SingleStore tables if the ingestion layer doesn't implement idempotent writes.
How Tray.ai Can Help:
tray.ai templates use SingleStore's native upsert (INSERT ... ON DUPLICATE KEY UPDATE) semantics and let teams configure primary key or composite deduplication keys in the workflow, ensuring exactly-once effective writes without custom code.
Challenge
Securely Connecting to Kafka Clusters in Private Networks
Enterprise Kafka clusters are typically deployed inside private VPCs or behind strict network policies with SASL/SSL authentication requirements, making it hard to connect external automation platforms without security compromises.
How Tray.ai Can Help:
tray.ai supports SASL/PLAIN, SASL/SCRAM, and SSL/TLS authentication for Kafka, and works with private network configurations through secure credential storage and IP allowlisting, so teams never have to expose their Kafka brokers to the public internet.
Challenge
Monitoring Pipeline Health and Recovering from Failures
Without end-to-end observability, it's nearly impossible to know when a Kafka-to-SingleStore pipeline has fallen behind, produced bad data, or silently stopped processing — which means stale dashboards and missed business events.
How Tray.ai Can Help:
tray.ai provides built-in workflow execution logs, error step highlighting, and configurable alerting so teams are immediately notified of pipeline failures. Consumer lag metrics and row-count reconciliation checks can be wired into the workflow to catch issues before they impact business operations.
Start using our pre-built SingleStore & Kafka templates today
Start from scratch or use one of our pre-built SingleStore & Kafka templates to quickly solve your most common use cases.
SingleStore & Kafka Templates
Find pre-built SingleStore & Kafka solutions for common use cases
Template
Kafka Topic to SingleStore Table — Continuous Ingestion
Automatically consumes messages from one or more Kafka topics and upserts them into a target SingleStore table, handling schema mapping, deduplication, and error logging out of the box.
Steps:
- Subscribe to a specified Kafka topic and poll for new messages on a configurable interval
- Parse and transform message payloads, mapping Kafka fields to SingleStore column definitions
- Upsert records into the target SingleStore table using primary key deduplication
Connectors Used: Kafka, SingleStore
Template
SingleStore Query Result Published to Kafka Topic
Runs a scheduled or trigger-based SQL query against SingleStore and publishes the result set as structured messages to a downstream Kafka topic, so other consumers can act on aggregated or filtered data.
Steps:
- Execute a parameterized SQL query against SingleStore on a schedule or webhook trigger
- Serialize each result row as a JSON or Avro message
- Publish serialized messages to the designated Kafka topic for downstream consumers
Connectors Used: SingleStore, Kafka
Template
Kafka Dead Letter Queue Handler with SingleStore Logging
Monitors a Kafka dead letter queue (DLQ) for failed messages, logs them to a SingleStore audit table with error context, and triggers alerting or retry workflows based on configurable thresholds.
Steps:
- Consume messages from the Kafka dead letter queue topic
- Insert each failed message along with error metadata into a SingleStore error log table
- Evaluate retry eligibility rules and re-publish qualifying messages to the original topic or trigger an alert
Connectors Used: Kafka, SingleStore
Template
Real-Time Fraud Alert Pipeline — Kafka Ingest to SingleStore Detection
Ingests transaction events from Kafka, writes them to SingleStore, executes a fraud-scoring SQL query, and routes high-risk transactions to a Kafka alert topic or external notification service.
Steps:
- Consume transaction events from a Kafka topic and insert them into SingleStore in micro-batches
- Run a configurable fraud-scoring SQL query against the newly inserted records
- Publish flagged transactions to a Kafka alert topic or trigger a downstream notification workflow
Connectors Used: Kafka, SingleStore
Template
CDC Event Stream from Kafka into SingleStore Replica Table
Processes change data capture events (inserts, updates, deletes) from a Kafka CDC topic and applies them to a mirrored SingleStore table, keeping the replica synchronized in near real time.
Steps:
- Consume CDC events from the Kafka topic, identifying operation type (INSERT, UPDATE, DELETE)
- Transform event payloads into SingleStore-compatible SQL operations
- Apply changes to the SingleStore replica table and log any schema drift or errors
Connectors Used: Kafka, SingleStore
Template
Kafka Event-Triggered SingleStore Stored Procedure Executor
Listens for specific Kafka events — such as a completed order or a user tier change — and executes a corresponding SingleStore stored procedure to update aggregates, trigger rollups, or enforce business rules.
Steps:
- Subscribe to a Kafka topic and filter messages matching a defined event type or key pattern
- Extract relevant parameters from the Kafka message payload
- Call the target SingleStore stored procedure with the extracted parameters and log the execution result
Connectors Used: Kafka, SingleStore