Building a data pipeline with DuckDB

In this blog post I’m going to explore how as a data engineer in the field today I might go about putting together a rudimentary data pipeline. I’ll take some operational data, and wrangle it into a form that makes it easily pliable for analytics work.

After a somewhat fevered and nightmarish period during which people walked around declaring "Schema on Read" was the future, that "Data is the new oil", and "Look at the size of my big data", the path that is history in IT is somewhat coming back on itself to a more sensible approach to things.

As they say:

What’s old is new

This is good news for me, because I am old and what I knew then is 'new' now ;)

Nov 8, 2022

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.

Oct 24, 2022

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.

Oct 20, 2022

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.

Oct 2, 2022

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 ✋.

Sep 16, 2022

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.

Sep 14, 2022

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.

Sep 14, 2022

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.

Sep 14, 2022

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.

Feb 26, 2021

Loading delimited data into Kafka - quick & dirty (but effective)

Whilst Apache Kafka is an event streaming platform designed for, well, streams of events, it’s perfectly valid to use it as a store of data which perhaps changes only occasionally (or even never). I’m thinking here of reference data (lookup data) that’s used to enrich regular streams of events.

You might well get your reference data from a database where it resides and do so effectively using CDC - but sometimes it comes down to those pesky CSV files that we all know and love/hate. Simple, awful, but effective. I wrote previously about loading CSV data into Kafka from files that are updated frequently, but here I want to look at CSV files that are not changing. Kafka Connect simplifies getting data in to (and out of) Kafka but even Kafka Connect becomes a bit of an overhead when you just have a single file that you want to load into a topic and then never deal with again. I spent this afternoon wrangling with a couple of CSV-ish files, and building on my previous article about neat tricks you can do in bash with data, I have some more to share with you here :)