# Databricks Zerobus Ingest

> Build Zerobus Ingest clients for near real-time data ingestion into Databricks Delta tables via gRPC. Use when creating producers that write directly to Unity Catalog tables without a message bus, working with the Zerobus Ingest SDK in Python/Java/Go/TypeScript/Rust, generating Protobuf schemas from UC tables, or implementing stream-based ingestion with ACK handling and retry logic.

- Skill: `gabrielmoreira/databricks-zerobus-ingest` (Agent Skill, multi-file: 9 files)
- Install (CLI): `npx skillmds@latest add gabrielmoreira/databricks-zerobus-ingest`
- Raw SKILL.md: https://api.skillmd.com/api/skills/gabrielmoreira/databricks-zerobus-ingest/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: gabrielmoreira (https://skillmd.com/u/gabrielmoreira)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/gabrielmoreira/databricks-zerobus-ingest

---


# Zerobus Ingest

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

**Status:** Generally Available. Charges are billed against the Jobs Serverless SKU. Check the [Zerobus overview](https://docs.databricks.com/ingestion/zerobus-overview) for the current status of specific features (some, such as Streaming-table targets and Arrow Flight, may be in Beta).

**Documentation:**
- [Zerobus Overview](https://docs.databricks.com/ingestion/zerobus-overview)
- [Zerobus Ingest SDK](https://docs.databricks.com/ingestion/zerobus-ingest)
- [Zerobus Limits](https://docs.databricks.com/ingestion/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?

| Scenario | Language | Serialization | Reference |
|----------|----------|---------------|-----------|
| Quick prototype / test harness | Python | JSON | [references/2-python-client.md](references/2-python-client.md) |
| Production Python producer | Python | Protobuf | [references/2-python-client.md](references/2-python-client.md) + [references/4-protobuf-schema.md](references/4-protobuf-schema.md) |
| JVM microservice | Java | Protobuf | [references/3-multilanguage-clients.md](references/3-multilanguage-clients.md) |
| Go service | Go | JSON or Protobuf | [references/3-multilanguage-clients.md](references/3-multilanguage-clients.md) |
| Node.js / TypeScript app | TypeScript | JSON | [references/3-multilanguage-clients.md](references/3-multilanguage-clients.md) |
| High-performance system service | Rust | JSON or Protobuf | [references/3-multilanguage-clients.md](references/3-multilanguage-clients.md) |
| Schema generation from UC table | Any | Protobuf | [references/4-protobuf-schema.md](references/4-protobuf-schema.md) |
| Retry / reconnection logic | Any | Any | [references/5-operations-and-limits.md](references/5-operations-and-limits.md) |

If not specified, default to python.

---

## Common Libraries

These libraries are essential for Zerobus data ingestion and are typically NOT pre-installed on Databricks:

- **databricks-zerobus-ingest-sdk**: Zerobus SDK for high-performance streaming ingestion
- **databricks-sdk**: Databricks workspace client for authentication and metadata
- **grpcio-tools**: only needed to compile a `.proto` for Protobuf serialization

Install them through the **job/cluster library configuration** (see [Installing Libraries](#installing-libraries) below) rather than pip-installing at runtime — the SDK cannot pip-install on serverless compute.

### grpcio-tools and protobuf compatibility

`grpcio-tools` must match the runtime's `protobuf` version. If proto compilation fails with a version error, pin a compatible build (for older `protobuf` 5.26/5.29 runtimes, `grpcio-tools==1.62.0` works); otherwise use the latest release.

---

## 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 [references/1-setup-and-authentication.md](references/1-setup-and-authentication.md) for complete setup instructions.

---

## Minimal Python Example (JSON)

```python
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:
    # Pass a dict for JSON streams; the SDK serializes it.
    record = {"device_name": "sensor-1", "temp": 22, "humidity": 55}
    offset = stream.ingest_record_offset(record)
    stream.wait_for_offset(offset)  # Block until durably written
finally:
    stream.close()
```

---

## Detailed guides

| Topic | File | When to Read |
|-------|------|--------------|
| Setup & Auth | [references/1-setup-and-authentication.md](references/1-setup-and-authentication.md) | Endpoint formats, service principals, SDK install |
| Python Client | [references/2-python-client.md](references/2-python-client.md) | Sync/async Python, JSON and Protobuf flows, reusable client class |
| Multi-Language | [references/3-multilanguage-clients.md](references/3-multilanguage-clients.md) | Java, Go, TypeScript, Rust SDK examples |
| Protobuf Schema | [references/4-protobuf-schema.md](references/4-protobuf-schema.md) | Generate .proto from UC table, compile, type mappings |
| Operations & Limits | [references/5-operations-and-limits.md](references/5-operations-and-limits.md) | ACK handling, retries, reconnection, throughput limits, constraints |

---

You must always follow all the steps in the Workflow

## Workflow
0. **Display the plan of your execution**
1. **Determine the type of client** and serialization (JSON for prototypes/simple schemas; Protobuf for production/type safety)
2. **Get schema**: for Protobuf, generate the `.proto` per [references/4-protobuf-schema.md](references/4-protobuf-schema.md); for JSON, ensure record keys match the target table columns
3. **Write Python code to a local file** following the instructions in the relevant guide (e.g., `scripts/zerobus_ingest.py`)
4. **Upload to workspace**: `databricks workspace import-dir ./scripts /Workspace/Users/<user>/scripts`
5. **Execute on Databricks** using a job or notebook
6. **If execution fails**: Edit the local file, re-upload, and re-execute

---

## Important
- Install the SDK through the job/cluster library configuration, not by pip-installing at runtime in a notebook.
- **Serverless limitation**: The Zerobus SDK cannot pip-install on serverless compute. Use classic compute clusters, or use the [Zerobus REST API](https://docs.databricks.com/ingestion/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.

---

### Execution Workflow

**Step 1: Upload code to workspace**

Get your workspace username for the `<user>` path segment, then upload:
```bash
USER=$(databricks current-user me --output json | jq -r .userName)
databricks workspace import-dir ./scripts "/Workspace/Users/$USER/scripts"
```

**Step 2: Create and run a job**
```bash
databricks jobs create --json '{
  "name": "zerobus-ingest",
  "tasks": [{
    "task_key": "ingest",
    "spark_python_task": {
      "python_file": "/Workspace/Users/<user>/scripts/zerobus_ingest.py"
    },
    "new_cluster": {
      "spark_version": "16.1.x-scala2.12",
      "node_type_id": "i3.xlarge",
      "num_workers": 0
    }
  }]
}'

databricks jobs run-now JOB_ID
```

**If execution fails:**
1. Read the error from the job run output
2. Edit the local Python file to fix the issue
3. Re-upload: `databricks workspace import-dir ./scripts /Workspace/Users/<user>/scripts`
4. Re-run: `databricks jobs run-now JOB_ID`

---

### Installing Libraries

Databricks provides Spark, pandas, numpy, and common data libraries by default, but the Zerobus SDK is **never pre-installed** — always add `databricks-zerobus-ingest-sdk` to the job/cluster library config. Only add other libraries if you hit an import error.

Add to the job configuration:
```json
"libraries": [
  {"pypi": {"package": "databricks-zerobus-ingest-sdk>=1.0.0"}}
]
```

Or use init scripts in the cluster configuration.

## Timestamp Format

A Delta `TIMESTAMP` column maps to a Protobuf `int64` of **epoch microseconds** (see the type mappings in [references/4-protobuf-schema.md](references/4-protobuf-schema.md)) — supply an integer, not a string:

```python
from datetime import datetime, timezone

event_time = int(datetime.now(timezone.utc).timestamp() * 1_000_000)  # epoch microseconds
```

---

## 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**: Ingestion is acknowledged when records are durably written. Use `ingest_record_offset()` + `wait_for_offset(offset)` for offset-based tracking, an `AckCallback` for asynchronous confirmation, or `flush()` to ensure all buffered records are durably written.
- **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

| Issue | Solution |
|-------|----------|
| **Connection refused** | Verify server endpoint format matches your cloud (AWS vs Azure). Check firewall allowlists. |
| **Authentication failed** | Confirm service principal client_id/secret. Verify GRANT statements on the target table. |
| **Schema mismatch** | Ensure record fields match the target table schema exactly. Regenerate .proto if table changed. |
| **Stream closed unexpectedly** | Implement retry with exponential backoff and stream reinitialization. See [references/5-operations-and-limits.md](references/5-operations-and-limits.md). |
| **Throughput limits hit** | Max 100 MB/s and 15,000 rows/s per stream. Open multiple streams or contact Databricks. |
| **Region not supported** | Check supported regions in [references/5-operations-and-limits.md](references/5-operations-and-limits.md). |
| **Table not found** | Ensure table is a managed Delta table in a supported region with correct three-part name. |
| **SDK install fails on serverless** | The Zerobus SDK cannot be pip-installed on serverless compute. Use classic compute clusters or the REST API (Beta) from notebooks. |
| **Error 4024 / authorization_details** | Service principal lacks explicit table-level grants. Grant `MODIFY` and `SELECT` directly on the target table — schema-level inherited grants may be insufficient. |

---

## Related Skills

- **[databricks-python-sdk](../databricks-python-sdk/SKILL.md)** - General SDK patterns and WorkspaceClient for table/schema management
- **databricks-pipelines** - Downstream pipeline processing of ingested data
- **[databricks-unity-catalog](../databricks-unity-catalog/SKILL.md)** - Managing catalogs, schemas, and tables that Zerobus writes to
- **[databricks-synthetic-data-gen](../databricks-synthetic-data-gen/SKILL.md)** - Generate test data to feed into Zerobus producers
- **databricks-core** - CLI install, profile selection, authentication

## Resources

- [Zerobus Overview](https://docs.databricks.com/ingestion/zerobus-overview)
- [Zerobus Ingest SDK](https://docs.databricks.com/ingestion/zerobus-ingest)
- [Zerobus Limits](https://docs.databricks.com/ingestion/zerobus-limits)