---
title: "Discussion on \"Point-in-Time Joins in SQL: Building ML Training Datasets Without Data Leakage\" | Hashnode"
description: "Discussion on \"Point-in-Time Joins in SQL: Building ML Training Datasets Without Data Leakage\". How to build temporally correct training datasets using streaming SQL — without a dedicated feature store."
url: "https://hashnode.com/posts/point-in-time-joins-ml-training-sql/69d452a05432c08dcb4c7ab0"
---

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# [Point-in-Time Joins in SQL: Building ML Training Datasets Without Data Leakage](https://risingwave.com/blog/point-in-time-joins-ml-training-sql?utm_source=hashnode&utm_medium=feed)

How to build temporally correct training datasets using streaming SQL — without a dedicated feature store.

Apr 7 · 10 min read · risingwave.com  
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-   [#data-leakage](https://hashnode.com/tag/data-leakage)
-   [#feature-engineering](https://hashnode.com/tag/feature-engineering)
-   [#machine-learning](https://hashnode.com/tag/machine-learning)
-   [#mlops](https://hashnode.com/tag/mlops)
-   [#sql](https://hashnode.com/tag/sql)

## Written by

[RisingWave Labs](https://hashnode.com/@RisingWaveLabs)

@RisingWaveLabs · Bay Area, California

  
[View profile](https://hashnode.com/@RisingWaveLabs)

RisingWave is an open-source real-time event streaming platform.

RisingWave is a real-time event streaming platform designed to offer the simplest and most cost-effective way to process, analyze, and manage real-time event data — with built-in support for the Apache Iceberg™ open table format. It provides both a Postgres-compatible SQL interface and a DataFrame-style Python interface. RisingWave can ingest millions of events per second, continuously join and analyze live streams with historical data, serve ad-hoc queries at low latency, and manage data reliably in Apache Iceberg™ tables.

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