Blog Writing for Developers
Writing is one of the most powerful forms of communication, and it’s useful in a multitude of roles and contexts. As a blog-writing, documentation-authoring, twitter-shitposting DevEx engineer I spend a lot of my time writing. Recently, someone paid me a very nice compliment about a blog I’d written and asked how they could learn to write like me and what resources I’d recommend.
Never one to miss a chance to write and share something, here’s my response to this :)
What Does This DevEx Engineer Do?
This was originally titled more broadly “What Does A DevEx Engineer Do”, but that made it into a far too tedious and long-winding etymological exploration of the discipline. Instead, I’m going to tell you what this particular instantiation of the entity does 😄
Authoring Wordpress blogs in Markdown (with Google Docs for review)
Wordpress still, to an extent, rules the blogging world. Its longevity is testament to…something about it ;) However, it’s not my favourite platform in which to write a blog by a long way. It doesn’t support Markdown to the extent that I want. Yes, I’ve tried the plugins; no, they didn’t do what I needed.
I like to write all my content in a structured format - ideally Asciidoc, but I’ll settle for Markdown too. Here’s how I stayed [almost] sane whilst composing a blog in Markdown, reviewing it in Google Docs, and then publishing it in Wordpress in a non-lossy way.
Building Better Docs - Automating Jekyll Builds and Link Checking for PRs
One of the most important ways that a project can help its developers is providing them good documentation. Actually, scratch that. Great documentation.
Using Delta from pySpark - java.lang.ClassNotFoundException: delta.DefaultSource
No great insights in this post, just something for folk who Google this error after me and don’t want to waste three hours chasing their tails… 😄
Quickly Convert CSV to Parquet with DuckDB
Here’s a neat little trick you can use with DuckDB to convert a CSV file into a Parquet file:
COPY (SELECT *
FROM read_csv('~/data/source.csv',AUTO_DETECT=TRUE))
TO '~/data/target.parquet' (FORMAT 'PARQUET', CODEC 'ZSTD');
Making the move from Alfred to Raycast
It all started with a tweet.
Aligning mismatched Parquet schemas in DuckDB
What do you do when you want to query over multiple parquet files but the schemas don’t quite line up? Let’s find out 👇🏻
Looking Forwards, and Looking Backwards
As we enter December and 2022 draws to a close, so does a significant chapter in my working career—later this month I’ll be leaving Confluent and onto pastures new.
It’s nearly six years since I wrote a 'moving on' blog entry, and as well as sharing what I’ll be working on next (and why), I also want to reflect on how much I’ve benefited from my time at Confluent and particularly the people with whom I worked.
Data Engineering in 2022: ELT tools
In my quest to bring myself up to date with where the data & analytics engineering world is at nowadays, I’m going to build on my exploration of the storage and access technologies and look at the tools we use for loading and transforming data.
Data Engineering in 2022: Wrangling the feedback data from Current 22 with dbt
I started my dbt journey by poking and pulling at the pre-built jaffle_shop demo running with DuckDB as its data store. Now I want to see if I can put it to use myself to wrangle the session feedback data that came in from Current 2022. I’ve analysed this already, but it struck me that a particular part of it would benefit from some tidying up - and be a good excuse to see what it’s like using dbt to do so.
Data Engineering in 2022: Exploring dbt with DuckDB
I’ve been wanting to try out dbt for some time now, and a recent long-haul flight seemed like the obvious opportunity to do so. Except many of the tutorials with dbt that I found were based on using Cloud, and airplane WiFi is generally sucky or non-existant. Then I found the DuckDB-based demo of dbt, which seemed to fit the bill (🦆 geddit?!) perfectly, since DuckDB runs locally. In addition, DuckDB had appeared on my radar recently and I was keen to check it out.
Current 22 - Session Analysis with DuckDB and Jupyter Notebook
At Current 2022 the audience was given the option to submit ratings. Here’s some analysis I’ve done on the raw data. It’s interesting to poke about it, and it also gave me an excuse to try using DuckDB in a notebook!
Data Engineering in 2022: Architectures & Terminology
This is one of those you had to be there moments. If you come into the world of data and analytics engineering today, ELT is just what it is and is pretty much universally understood. But if you’ve been around for …waves hands… longer than that, you might be confused by what people are calling ELT and ETL. Well, I was ✋.
Current 2022 - 5k Fun Run
At Current 22 a few of us will be going for an early run on Tuesday morning. Everyone is very welcome!
Data Engineering in 2022: Exploring LakeFS with Jupyter and PySpark
With my foray into the current world of data engineering I wanted to get my hands dirty with some of the tools and technologies I’d been reading about. The vehicle for this was trying to understand more about LakeFS, but along the way dabbling with PySpark and S3 (MinIO) too.
I’d forgotten how amazingly useful notebooks are. It’s six years since I wrote about them last (and the last time I tried my hand at PySpark). This blog is basically the notebook, with some more annotations.
Data Engineering: Resources
As I’ve been reading and exploring the current world of data engineering I’ve been adding links to my Raindrop.io collection, so check that out. In addition, below are some specific resources that I’d recommend.
Data Engineering in 2022: Storage and Access
In this article I look at where we store our analytical data, how we organise it, and how we enable access to it. I’m considering here potentially large volumes of data for access throughout an organisation. I’m not looking at data stores that are used for specific purposes (caches, low-latency analytics, graph etc).
The article is part of a series in which I explore the world of data engineering in 2022 and how it has changed from when I started my career in data warehousing 20+ years ago. Read the introduction for more context and background.
Stretching my Legs in the Data Engineering Ecosystem in 2022
For the past 5.5 years I’ve been head-down in the exciting area of stream processing and events, and I realised recently that the world of data and analytics that I worked in up to 2017 which was changing significantly back then (Big Data, y’all!) has evolved and, dare I say it, matured somewhat - and I’ve not necessarily kept up with it. In this series of posts you can follow along as I start to reacquaint myself with where it’s got to these days.
