Databricks Zerobus Ingest

Zerobus Ingest

Build clients that ingest data directly into Databricks Delta tables via the Zerobus gRPC API.

Status: GA (Generally Available since February 2026; billed under Lakeflow Jobs Serverless SKU)

Documentation:

• Zerobus Overview
• Zerobus Ingest SDK
• Zerobus Limits

What Is Zerobus Ingest?

Zerobus Ingest is a serverless connector that enables direct, record-by-record data ingestion into Delta tables via gRPC. It eliminates the need for message bus infrastructure (Kafka, Kinesis, Event Hub) for lakehouse-bound data. The service validates schemas, materializes data to target tables, and sends durability acknowledgments back to the client.

Core pattern: SDK init -> create stream -> ingest records -> handle ACKs -> flush -> close

Quick Decision: What Are You Building?

If not specified, default to python.

Common Libraries

These libraries are essential for ZeroBus data ingestion:

• databricks-sdk>=0.85.0: Databricks workspace client for authentication and metadata
• databricks-zerobus-ingest-sdk>=1.0.0: ZeroBus SDK for high-performance streaming ingestion
• grpcio-tools These are typically NOT pre-installed on Databricks. Install them using execute_code tool:
• code: "%pip install databricks-sdk>=VERSION databricks-zerobus-ingest-sdk>=VERSION"

Save the returned cluster_id and context_id for subsequent calls.

Smart Installation Approach

Check protobuf version first, then install compatible

grpcio-tools import google.protobuf runtime_version = google.protobuf.version print(f"Runtime protobuf version: {runtime_version}")

if runtime_version.startswith("5.26") or runtime_version.startswith("5.29"): %pip install grpcio-tools==1.62.0 else: %pip install grpcio-tools # Use latest for newer protobuf

versions

Prerequisites

You must never execute the skill without confirming the below objects are valid:

1. A Unity Catalog managed Delta table to ingest into
2. A service principal id and secret with MODIFY and SELECT on the target table
3. The Zerobus server endpoint for your workspace region
4. The Zerobus Ingest SDK installed for your target language

See 1-setup-and-authentication.md for complete setup instructions.

Minimal Python Example (JSON)

import json
from zerobus.sdk.sync import ZerobusSdk
from zerobus.sdk.shared import RecordType, StreamConfigurationOptions, TableProperties

sdk = ZerobusSdk(server_endpoint, workspace_url)
options = StreamConfigurationOptions(record_type=RecordType.JSON)
table_props = TableProperties(table_name)

stream = sdk.create_stream(client_id, client_secret, table_props, options)
try:
    record = {"device_name": "sensor-1", "temp": 22, "humidity": 55}
    stream.ingest_record(json.dumps(record))
    stream.flush()
finally:
    stream.close()

Detailed guides

You must always follow all the steps in the Workflow

Workflow

0. Display the plan of your execution
1. Determinate the type of client
2. Get schema Always use 4-protobuf-schema.md. Execute using the execute_code MCP tool
3. Write Python code to a local file follow the instructions in the relevant guide to ingest with zerobus in the project (e.g., scripts/zerobus_ingest.py).
4. Execute on Databricks using the execute_code MCP tool (with file_path parameter)
5. If execution fails: Edit the local file to fix the error, then re-execute
6. Reuse the context for follow-up executions by passing the returned cluster_id and context_id

Important

• Never install local packages
• Always validate MCP server requirement before execution
• Serverless limitation: The Zerobus SDK cannot pip-install on serverless compute. Use classic compute clusters, or use the Zerobus REST API (Beta) for notebook-based ingestion without the SDK.
• Explicit table grants: Service principals need explicit MODIFY and SELECT grants on the target table. Schema-level inherited permissions may not be sufficient for the authorization_details OAuth flow.

Context Reuse Pattern

The first execution auto-selects a running cluster and creates an execution context. Reuse this context for follow-up calls - it's much faster (~1s vs ~15s) and shares variables/imports:

First execution - use execute_code tool:

• file_path: "scripts/zerobus_ingest.py"

Returns: { success, output, error, cluster_id, context_id, ... }

Save cluster_id and context_id for follow-up calls.

If execution fails:

1. Read the error from the result
2. Edit the local Python file to fix the issue
3. Re-execute with same context using execute_code tool:
   • file_path: "scripts/zerobus_ingest.py"
   • cluster_id: ""
   • context_id: ""

Follow-up executions reuse the context (faster, shares state):

• file_path: "scripts/validate_ingestion.py"
• cluster_id: ""
• context_id: ""

Handling Failures

When execution fails:

1. Read the error from the result
2. Edit the local Python file to fix the issue
3. Re-execute using the same cluster_id and context_id (faster, keeps installed libraries)
4. If the context is corrupted, omit context_id to create a fresh one

Installing Libraries

Databricks provides Spark, pandas, numpy, and common data libraries by default. Only install a library if you get an import error.

Use execute_code tool:

• code: "%pip install databricks-zerobus-ingest-sdk>=1.0.0"
• cluster_id: ""
• context_id: ""

The library is immediately available in the same context.

Note: Keeping the same context_id means installed libraries persist across calls.

🚨 Critical Learning: Timestamp Format Fix

BREAKTHROUGH: ZeroBus requires timestamp fields as Unix integer timestamps, NOT string timestamps. The timestamp generation must use microseconds for Databricks.

Key Concepts

• gRPC + Protobuf: Zerobus uses gRPC as its transport protocol. Any application that can communicate via gRPC and construct Protobuf messages can produce to Zerobus.
• JSON or Protobuf serialization: JSON for quick starts; Protobuf for type safety, forward compatibility, and performance.
• At-least-once delivery: The connector provides at-least-once guarantees. Design consumers to handle duplicates.
• Durability ACKs: Each ingested record returns a RecordAcknowledgment. Use flush() to ensure all buffered records are durably written, or use wait_for_offset(offset) for offset-based tracking.
• No table management: Zerobus does not create or alter tables. You must pre-create your target table and manage schema evolution yourself.
• Single-AZ durability: The service runs in a single availability zone. Plan for potential zone outages.

Common Issues

Related Skills

• databricks-python-sdk - General SDK patterns and WorkspaceClient for table/schema management
• databricks-spark-declarative-pipelines - Downstream pipeline processing of ingested data
• databricks-unity-catalog - Managing catalogs, schemas, and tables that Zerobus writes to
• databricks-synthetic-data-gen - Generate test data to feed into Zerobus producers
• databricks-config - Profile and authentication setup

Resources

• Zerobus Overview
• Zerobus Ingest SDK
• Zerobus Limits