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For analysts, engineers or data scientists, Query Stash makes organising and finding your SQL code and snippets easier.

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QueryStash Organise Your SQL

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Save SQL snippets

An easy way to store useful SQL snippets or entire queries, in a taggable and searchable stash. Making it easier to find the query you need quickly.

Organise into collections

Break your stash up into organised collections. Create groups of queries around your own specific projects or realted snippets its up to you.

Share your queries

Sharing tips, learnings or useful queries with the world or just your team, you can pick and choose what to share and with whom

Discover popular snippets

Browse the public stash from all users for interesting querys and view the most popular right now.

An actual SQL snippet example...

Outlier Detection using Z-Scores

Public
stats outliers

QueryStash

Created: Sep 29th 2020

View this actual public query to see the full detailed version here.

Explore some example SQL queries...

QUALIFY by ROW_NUMBER for cleaner queries

Using QUALIFY you can greatly simplify your query when dealing with RANK and ROW_NUMBER, and avoid having multiple sub queries.

QUALIFY by ROW_NUMBER for cleaner queries

Spike Detection for Website Visits

To demonstrate one approach to spike detection, we are exploring website visitor data from the BigQuery Public Google Analytics sample data.

Spike Detection for Website Visits

Merge Statements BigQuery

The combination of insert and update in a merge statement simplifies data processing where we don't know if a record already exists.

Merge Statements BigQuery

Create a Persistent Function on BigQuery

Persistent user-defined functions allow users to create custom functions that can be used across multiple queries and shared amongst users.

Create a Persistent Function on BigQuery

BigQuery Create ML Regression Model

Creating ML models has become even easier with BigQuery ML, create a new model in a few lines of SQL.

BigQuery Create ML Regression Model

BigQuery Create Array of Structs

Working with flattened data might not always be the optimal way to work with data in BigQuery, use arrays to group data together into a single field.

BigQuery Create Array of Structs
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