# Overview

The rest of the Pinot documentation covers individual features: schema design, ingestion modes, indexes, query syntax, and operational knobs. These playbooks stitch those features together into **complete, production-ready patterns** for the workloads adopters ask about most often.

Each playbook covers:

| Section                      | What it answers                                        |
| ---------------------------- | ------------------------------------------------------ |
| **When to use this pattern** | Is this the right fit for my workload?                 |
| **Architecture sketch**      | Which Pinot components are involved and how data flows |
| **Schema and table config**  | Concrete JSON you can adapt                            |
| **Ingestion setup**          | Stream and/or batch job configuration                  |
| **Indexing strategy**        | Which indexes to enable and why                        |
| **Query patterns**           | Representative SQL and performance tips                |
| **Operational checklist**    | Monitoring, scaling, and common pitfalls               |

## Playbooks

| Playbook                                                                                                    | Typical use case                                                                             |
| ----------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------- |
| [Real-Time Product Analytics](https://docs.pinot.apache.org/workload-playbooks/real-time-product-analytics) | Sub-second dashboards over Kafka event streams (page views, clicks, transactions)            |
| [CDC / Upsert Pipeline](https://docs.pinot.apache.org/workload-playbooks/cdc-upsert-pipeline)               | Keep Pinot in sync with a transactional database via Debezium and upserts                    |
| [Hybrid Real-Time + Offline](https://docs.pinot.apache.org/workload-playbooks/hybrid-offline-realtime)      | Combine low-latency streaming with high-quality batch backfills in a single logical table    |
| [Multi-Tenant Analytics](https://docs.pinot.apache.org/workload-playbooks/multi-tenant-analytics)           | Serve many customers from one Pinot cluster with resource and data isolation                 |
| [Text Search Analytics](https://docs.pinot.apache.org/workload-playbooks/text-search-analytics)             | Full-text search over logs, product catalogs, or support tickets alongside OLAP aggregations |

## How to use these playbooks

1. **Pick the playbook** closest to your workload.
2. **Copy the configs** and adjust field names, topic names, and resource sizes for your environment.
3. **Cross-reference the feature docs** linked inside each playbook for deep dives on any single capability.
4. **Check the operational checklist** before going to production.

{% hint style="info" %}
These playbooks target Pinot 1.x and assume familiarity with [Pinot's architecture](https://docs.pinot.apache.org/architecture-and-concepts/concepts/architecture) and [table types](https://docs.pinot.apache.org/architecture-and-concepts/components/table). If you are new to Pinot, start with the [10-Minute Quickstart](https://docs.pinot.apache.org/start-here/ten-minute-quickstart) first.
{% endhint %}