With a growing number of out of the box solutions with a number of external systems, TypeStream makes it easy to move and transform data with a single line of code.
Stream every insert, update, and delete from your database in real time. Connect your existing database and start streaming in minutes.
Capture changes from Apache Cassandra using the commit log.
Stream changes from IBM Db2 for Linux, UNIX, and Windows.
Capture changes from IBM Informix using the CDC API.
IncubatingCapture row-level changes from MariaDB via the binlog with a dedicated connector.
Stream document changes from MongoDB using change streams. Supports replica sets and sharded clusters.
Capture row-level changes from MySQL via the binlog.
Stream changes from Oracle Database using LogMiner or XStream.
Stream changes from PostgreSQL using logical replication. Supports all Postgres-compatible databases.
Stream changes from Google Cloud Spanner, the globally distributed relational database.
Capture changes from Microsoft SQL Server using its native CDC feature.
Capture changes from Vitess, the MySQL-compatible distributed database used by PlanetScale.
IncubatingWrite pipeline output directly to relational, document, graph, and key-value databases via Kafka Connect sink connectors.
Export streaming data to DynamoDB for serverless NoSQL workloads at any scale.
Sink data into Azure Cosmos DB for globally distributed, multi-model database workloads.
Sink data into Apache Cassandra for distributed, high-throughput writes.
Write pipeline output to MongoDB collections for document-based workloads.
Sink pipeline results into MySQL or MariaDB via the JDBC sink connector.
Sink streaming data into Neo4j for graph-based analytics and relationship discovery.
Write transformed data back to PostgreSQL or any JDBC-compatible database.
Push streaming results into Redis for low-latency caching and key-value lookups.
Stream data into warehouses and OLAP engines for real-time analytics, reporting, and business intelligence.
Export streaming data to Amazon Redshift for cloud data warehouse analytics.
Ingest streaming data into Druid for sub-second OLAP queries and real-time analytics.
Native Kafka IngestionFeed data into Pinot for user-facing real-time analytics at scale.
Native Kafka IngestionSink data into ClickHouse for real-time OLAP queries on streaming data.
Sink streaming data into Databricks Delta Lake for unified analytics and ML workloads.
Stream data directly into BigQuery for warehouse analytics and reporting.
Load streaming data into Snowflake for cloud data warehousing and analytics.
Index streaming data into search engines for full-text search, log analytics, and security workloads.
Write streaming data to cloud object stores and distributed file systems for data lake, archival, and batch processing workloads.
Write streaming data to S3 in Parquet, Avro, or JSON for data lake and archival workloads.
Write streaming data to Azure Blob Storage for cloud-native data lake workloads.
Sink data to GCS buckets in Parquet, Avro, or JSON format.
Write to Hadoop Distributed File System for big data batch processing pipelines.
Ingest traces, metrics, and logs into Kafka and route them to observability platforms for monitoring, alerting, and performance analysis.
Route logs and events to Datadog for monitoring, alerting, and observability dashboards.
Write time-series data into InfluxDB for monitoring and IoT workloads.
Ingest OTLP traces, metrics, and logs into Kafka topics for observability pipelines.
Stream events and logs to New Relic for full-stack observability and performance monitoring.
Route events to Splunk for security information and event management.
Forward events to other messaging systems and streaming platforms for cross-cloud and hybrid architectures.
Forward streaming data to Kinesis Data Streams for AWS-native pipelines.
Community ConnectorPublish events to Google Cloud Pub/Sub for GCP-native messaging workflows.
Community ConnectorPublish events to MQTT brokers for IoT and edge computing workloads.
Forward streaming data to RabbitMQ for message queue and routing workloads.
Push streaming data into vector databases for similarity search, RAG pipelines, and AI/ML embedding workloads.
Send streaming data to any HTTP endpoint or webhook for custom integrations and general-purpose connectivity.