Bulker is a tool for streaming and batching large amount of semi-structured data into data warehouses. It uses Kafka internally

Send and JSON object to Bulker HTTP endpoint, and it will make sure it will be saved to data warehouse:

• JSON flattening. Your object will be flattened - {a: {b: 1}} becomes {a_b: 1}
• Schema managenent for semi-structured data. For each fuekd, bulker will make sure that a corresponding column exist in destination table. If not, Bulker will create. Type will be best-guessed by value, or it could be explicitely set via type hint as in {"a": "test", "__sql_type_a": "varchar(4)"}
• Reliability. Bulker will put the object to Kafka Queue immidiatly, so if datawarehouse is down, data won't be lost
• Streaming or Batching. Bulker will send data to datawarehouse either as soon it become available in Kafka (streaming) or after some time (batching). Most data warehouses won't tolerate large number of inserts, that's why we implemented batching

Bulker is a 💜 of Jitsu, an open-source data integration platform.

See full list of features below

Bulker is also available as a go library if you want to embed it into your application as opposed to use a HTTP-server

• 🛢️ Batching - Bulker sends data in batches in most efficient way for particular database. For example, for Postgres it uses COPY command, for BigQuery it uses batch-files
• 🚿 Streaming - alternatively, Bulker can stream data to database. It is useful when number of records is low. Up to 10 records per second for most databases
• 🐫 Deduplication - if configured, Bulker will deduplicate records by primary key
• 📋 Schema management - Bulker creates tables and columns on the fly. It also flattens nested JSON-objects. Example if you send {"a": {"b": 1}} to bulker, it will make sure that there is a column a_b in the table (and will create it)
• 🦾 Implicit typing - Bulker infers types of columns from JSON-data.
• 📌 Explicit typing - Explicit types can be by type hints that are placed in JSON. Example: for event {"a": "test", "__sql_type_a": "varchar(4)"} Bulker will make sure that there is a column a, and it's type is varchar(4).
• 📈 Horizontal Scaling. Bulker scales horizontally. Too much data? No problem, just add Bulker instances!
• 📦 Dockerized - Bulker is dockerized and can be deployed to any cloud provider and k8s.
• ☁️ Cloud Native - each Bulker instance is stateless and is configured by only few environment variables.

Bulker supports the following databases:

• ✅ PostgresSQL
  
• ✅ Redshit
  
• ✅ Snowflake
  
• ✅ Clickhouse
  
• ✅ BigQuery
  
• ✅ MySQL
  
• ✅ S3
  
• ✅ GCS
  

Please see Compatibility Matrix to learn what Bulker features are supported by each database.

Note We highly recommend to read Core Concepts below before diving into details

• How to use Bulker as HTTP Service
  • Server Configuration
  • HTTP API
• How to use bulker as Go-lib (coming soon)

Bulker operates with destinations. Destination is database or storage service (e.g. S3, GCS). Each destination has an ID and configuration which is represented by JSON object.

Bulker exposes HTTP API to load data into destinations, where those destinations are referenced by their IDs.

If destination is a database, you'll need to provide a destination table name.

The main unit of data in Bulker is an event. Event is a represented JSON-object

Bulker can send data to database in two ways:

• Streaming. Bulker sends evens to destinaion one by one. It is useful when number of events is low (less than 10 events per second for most DBs).
• Batching. Bulker accumulates events in batches and sends them periodically once batch is full or timeout is reached. Batching is more efficient for large amounts of events. Especially for cloud data-warehouses (e.g. Postgres, Clickhouse, BigQuery).

Optionally, Bulker can deduplicate events by primary key. It is useful when you same event can be sent to Bulker multiple times. If available, Bulker uses primary keys, but for some data warehouses alternative strategies are used.

Read more about deduplication »