databricks-zerobus-ingest

What this skill does

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

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## Quick Decision: What Are You Building?

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

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](1-setup-and-authentication.md) for complete setup instructions.

---

## Minimal Python Example (JSON)

```python
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

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

---

### 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`: "<saved_cluster_id>"
   - `context_id`: "<saved_context_id>"

**Follow-up executions** reuse the context (faster, shares state):
- `file_path`: "scripts/validate_ingestion.py"
- `cluster_id`: "<saved_cluster_id>"
- `context_id`: "<saved_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`: "<cluster_id>"
- `context_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.

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## Key Concepts

- **g