Goals

Q2 2026 objectives

Objective 1: Self serve ducklings

• Motivation: It is PostHog's pattern to allow self serve of all features. Be scalable
• Goal: Complete an open beta where teams given access to beta can self-serve Ducklings end to end — provisioning, event data bootstrapping, and external source connector data landing in the warehouse
• What we'll ship:
  • Multitenant Control Plane deployment of duckgres/duckling
  • Data Pipelines to move PostHog data into customer ducklings
  • Metrics
  • UI in PostHog app to allow for provisioning
  • Pricing scheme for duckling
  • System for attributing usage costs

Objective 2: Warehouse used across PostHog

• Motivation: Better experience for modeled/external data across PostHog
• What we'll ship:
  • Support data modeling
  • Support API Endpoints
  • UI to select "golden" tables that should be available in Product Analytics & Experiments
  • Pipe to copy those "golden tables" from duckling to "vanilla S3 parquet" store that CH can query

Objective 3: Docs and dev rel

• Motivation: Socialize the capabilities, limitations, and features of Ducklings, Duckgres, DuckHog, and DuckLake
• What we'll ship:
  • Docs for system and usage pattern
  • Onboard product marketer
  • Do "talks and demos" for teams inside PostHog
  • More demos externally as well

Handbook

Who are we building for?

Personas

• Primary Personas:
  • Data/Analytics Engineer
    • These are the engineers building and maintaining the whole data stack, and are also responsible for providing the best tooling for their company.
    • They want flexibility and control of every part of the stack to ensure correct reporting downstream.
  • Data Analysts/Product Managers (technical type)
    • They are responsible for providing insights for their teams/company. If a question can be answered by data, they will write complex SQL models to get to the answer.
    • Data analysts and product managers are the power-users of data modeling and business intelligence (BI). They have the desire and the time to go significantly deeper into the data.
• Limited focus:
  • Product engineer
• Not a focus but should be usable by:
  • All other engineers, e.g. they should have access to the data they need, and easily query it

Feature support by persona

Data/Analytics Engineers

What works today:

• Duckling provisioning and event data bootstrapping
• External source connector data landing in warehouse

What we need to ship:

• Tune compute allotment (scale up/down CPU and memory for workloads)
• Connect to modern data warehouse tooling
• Standard SQL interface (JDBC/ODBC drivers) for compatibility with existing pipelines
• Role-based access control and credential management
• Query performance monitoring and resource usage dashboards
• Support for custom materialized views and incremental models

Data Analysts/Product Managers

What works today:

• SQL querying of PostHog event data in Duckling
• "Golden tables" available in Product Analytics and Experiments

What we need to ship:

• Connect to BI tools (Metabase, Looker, Tableau, Power BI, Hex)
• Connect to data modeling tools (dbt)