- Chapter 4 of the 2018 National Climate Assessment looks at the potential climate impacts on the US energy system.
- Flow of Flows — Orchestrating ELT with Prefect and dbt. More exploration of how to build data processing pipelines using open source tooling.
- Orchestrating Airbyte data connection tasks with Prefect. Official integrations for Airbyte connectors as Prefect tasks.
- Data cleaning IS analysis, not grunt work. A longish post exploring what we really get out of doing data cleaning, and why it’s more valuable and complex than it often gets credit for.
- Peer learnings about what it means to become an open data steward, from the 2021 ODI Open Data Summit. Videos and responses from participants on many facets of stewarding open data, especially as a business / organization.
Oil and gas companies operating in the arctic and other areas impacted by climate change have been adapting their operations and infrastructure planning to the melting permafrost and other long-term impacts of their pyromania for decades, even while spreading disinformation about the same processes publicly. But are electric utilities doing the same kind of planning?
We’ve been thinking a bit about the ways in which the energy system in the US West is exposed to potential climate risks, in the context of long term utility resource adequacy and operational planning. We posted a short thread on Twitter and got some references from the #EnergyTwitter hive mind.
Modern Data Stack, Etc.
- ETL vs. ELT — a comparison of two data pipeline architectures from the folks at Fivetran.
- What is the Modern Data Stack? another post from Fivetran, attempting to define the different components of data engineering pipelines as they appear to be coming together in the last few years.
- A good interactive introduction to SQL from Mode. You can even use your own data as you work through it, if it’s in a database online. Broken down into beginner, intermediate, and advanced sections.
- Hex is another platform that seems similar to Mode, for collaborating on data analyses using notebooks and a combination of straight SQL and python. Again, you load your own data directly via an online DB connection. I admit that after seeing it mentioned for months I only clicked through after realizing it was named after the magic / science hybrid technology depicted in Arcane.
- Cookiecutter Data Science is a cookie-cutter repo and a set of guidelines for standardizing data science projects to be more easily replicated and parsed by other people.
- Thou Shalt Scale Sustainably: some thoughts (commandments…) on how to scale social enterprises (especially when dependent on foundation funding) from the Shuttleworth Foundation. Not related to the so-called Modern Data Stack.
Some good technical long reads from the last couple of weeks:
(Postgre)SQL for Data Analysis
Before the Tidyverse and Pandas, there was SQL. There’s still SQL, and as Vicki Boykis often points out: every data-centric framework that hangs around long enough tends toward SQL. It’s got almost half a century of careful thinking and optimization behind it. It seems entirely possible that it’ll still be around after another half century.
In this extensive post Haki Benita explores a bunch of data analysis that can be done directly with PostgreSQL in particular. It can be used either as an efficient preprocessing step before handing off to other tools, or to generate final products. It covers basic data selection, random selection, sampling, splitting data into training & testing sets, descriptive statistics, aggregations, regressions, interpolation, binning and much more. It’s almost more of a pocket guide to data analysis in SQL than a blog post.
Data (Error) Generation Processes
In this post Emily Riederer explores how conceptualizing data (and error!) generation processes can help you do better data validation. What does the data represent in the real world? How is it being collected? How does it move from where it’s collected to where it’s processed? What kinds of transformations operate on it before you look at the outputs? Understanding these steps and their contexts makes it easier to imagine how things can go wrong along the way and what errors to check for. It also makes it easier to debug errors when you find them.
On Pair Programming
A guide to pair programming from Birgitta Böckeler and Nina Siessegger. They look at both how and why to do it, and some of the challenges that it brings up. I had no idea that this has been a practice going back as far as the women who programmed ENIAC.
The authors explore several different styles of pair programming and the logistical planning required to make it work. They touch on the extra challenges of doing remote pairing which seems extra relevant these days. They cover productive and destructive social dynamics that come up, and a whole lot more. The article is long, but it’s definitely worth a read if you’ve thought about trying pair programming and been reluctant, or have tried it and been dissatisfied.