After a week’s holiday ("vacation", for y’all in the US) without a glance at anything work-related, what joy to return and find that the DuckDB folk have been busy, not only with the recent 1.3.0 DuckDB release, but also a brand new project called DuckLake.

Here are my brief notes on DuckLake.

Not got time for all this? I’ve marked 🔥 for my top reads of the month :)

SQL. Three simple letters. Ess Queue Ell. /ˌɛs kjuː ˈɛl/.

In the data world they bind us together, yet separate us.

As the saying goes, England and America are two countries divided by the same language, and the same goes for the batch and streaming world and some elements of SQL.

Another year, another Current—another 5k run/walk for anyone who’d like to join!

In my last post I wrote all about watermarks in Apache Flink. As you’ll have realised—or been aware of already—it can get fairly hairy. That’s why I’m keen to see how Confluent Cloud for Apache Flink deals with watermarks, to see if it makes life any easier for the developer.

Whether you’re processing data in batch or as a stream, the concept of time is an important part of accurate processing logic.

Because we process data after it happens, there are a minimum of two different types of time to consider:

So. Many. Interesting. Links. Not got time for all this? I’ve marked 🔥 for my top reads of the month :)

Confluent Cloud for Apache Flink gives you access to run Flink workloads using a serverless platform on Confluent Cloud. After poking around the Confluent Cloud API for configuring connectors I wanted to take a look at the same for Flink.

Using the API is useful particularly if you want to script a deployment, or automate a bulk operation that might be tiresome to do otherwise. It’s also handy if you just prefer living in the CLI :)

The problem with publishing February’s interesting links at the beginning of the month and now getting around to publishing March’s at the end is that I have nearly two months' worth of links to share 😅 So with no further ado, let’s crack on.

I wanted to post a Carousel post in LinkedIn, but had to wade through a million pages of crap in Google from companies trying to sell shit. Here’s how to do it simply.

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:

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

DuckDB added a very cool UI last week and I’ve been using it as my primary interface to DuckDB since.

One thing that bothered me was that the SQL I was writing in the notebooks wasn’t exportable. Since DuckDB uses DuckDB in the background for storing notebooks, getting the SQL out is easy enough.

I wrote a couple of weeks ago about using DuckDB and Rill Data to explore a new data source that I’m working with. I wanted to understand the data’s structure and distribution of values, as well as how different entities related. This week DuckDB 1.2.1 was released and that little 0.0.1 version boost brought with it the DuckDB UI.

Here I’ll go through the same process as I did before, and see how much of what I was doing can be done in DuckDB alone now.

Much as I love kcat (🤫 it’ll always be kafkacat to me…), this morning I nearly fell out with it 👇

😖 I thought I was going stir crazy, after listing topics on a broker and seeing topics from a different broker.

😵 WTF 😵

Some would say that the perfect blog article takes the reader on a journey on in which the development process looks like this:

The UK Government publishes a lot of its data as open feeds. One that I keep coming back to is the Environment Agency’s flood-monitoring API that gives access to an estate of sensors that provide information about data such as river levels and rainfall.

The data is well-structured and provided across three primary API endpoints. In this blog article I’m going to show you how I use Flink SQL to explore and wrangle these into the kind of form from which I am then going to build a streaming pipeline using them.

There was a useful question on the Apache Flink Slack recently about joining data in Flink SQL:

Let’s imagine we’ve got a source of data with a nested array of multiple values. The data is from an IoT device. Each device has multiple sensors, each sensor provides a reading.