group by 1

Thoughts on data systems

The hottest Substack posts of group by 1

And their main takeaways

THE WAY OF WAYS

196 implied HN points • 18 Aug 23

🕹 Technology Data Analytics Data Modeling Business Intelligence Product Analytics Data Stack

The Modern Data Stack evolved but faced challenges of cost, complexity, and sprawl.
MDS led to more focus on product analytics and consolidation of data systems.
There is still a need for innovation in data modeling to address complexity and drive value.

Data Navigators

39 implied HN points • 19 Aug 22

Dealing with uncertainty sets navigators apart from auditors.
Historical navigation methods demonstrate the importance of improving accuracy.
Data navigators play a crucial role in startups by navigating and providing context for decision-making.

Snowflake Field Notes

117 implied HN points • 13 Jul 20

Snowflake simplifies data management by reducing non-value-adding complexity for users.
Snowflake offers features like warehouses, credits, and new capabilities like zero copy clone and time travel.
Ensuring security and managing costs are crucial when deploying and using Snowflake for data analytics.

Getting into Data

58 implied HN points • 09 Aug 21

Transitioning into data analytics requires a mix of technical and soft skills.
Startups have a high demand for data roles, making it possible to transition without formal qualifications.
The roles of data analyst and analytics engineer are merging, emphasizing the need for necessary technical skills.

Dataform and dbt

58 implied HN points • 21 Jun 21

Dataform and dbt are 'indicative-of-the-future' SQL tools in data analytics.
dbt has a powerful ecosystem with integrations and open-source plugins, making it a standard for SQL transformation DAGs.
Dataform provides a user-friendly interface for quick productivity, was acquired by Google, and is now exclusively integrated with Big Query.

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Data as a Utility Tool

98 implied HN points • 07 Jun 20

Architecting data systems involves making decisions under uncertainty and pressure.
A lightweight data architecture is like a Swiss army knife, offering scalability, ease of use, and modularity.
The key components of a data stack include ingesting data, storing in scalable systems like Snowflake, transforming using SQL scripts, and distributing for business users with BI tools.