Launched in 2022, the Current conference hosted by Confluent has established itself as one of the leading conferences in the data streaming space. Stemming from Kafka Summit originally, it’s broadened to reflect Confluent’s product portfolio including most notably Apache Flink.

At Current 24 a few of us will be going for an early run (or walk) on Tuesday morning. Everyone is very welcome!

Delta Lake (or Delta, as it’s often shortened to) is an open-source project from the Linux Foundation that’s primarily backed by Databricks. It’s an open table format (OTF) similar in concept to Apache Iceberg and Apache Hudi. Having previously dug into using Iceberg with both Apache Flink and Decodable , I wanted to see what it was like to use Delta with Flink—and specifically, Flink SQL.

So you’ve built your first real-time ETL pipeline with Decodable: congratulations! Now what?

You’d think once was enough. Having already written about the trouble that I had getting Flink SQL to write to S3 (including on MinIO) this should now be a moot issue for me. Right? RIGHT?!

Amazon Managed Service for Apache Flink (MSF) is one of several providers of hosted Flink. As my colleague Gunnar Morling described in his recent article , it can be used to run a Flink job that you’ve written in Java or Python (PyFlink). But did you know that this isn’t the only way—or perhaps even the best way—to have your Flink jobs run for you?

Sometimes it’s not possible to have too much of a good thing, and whilst this blog may look at first-glance rather similar to the one that I published just recently , today we’re looking at a 100% pure Apache solution. Because who knows, maybe you prefer rolling your own tech stacks instead of letting Decodable do it for you 😉.

One of the things that I love about SQL is the power that it gives you to work with data in a declarative manner. I want this thing…go do it. How should it do it? Well that’s the problem for the particular engine, not me. As a language with a pedigree of multiple decades and no sign of waning (despite a wobbly patch for some whilst NoSQL figured out they actually wanted to be NewSQL 😉), it’s the lingua franca of data systems.

Apache Iceberg is an open table format. It combines the benefits of data lakes (open standards, cheap object storage) with the good things that data warehouses have, like first-class support for tables and SQL capabilities including updates to data in place, time-travel, and transactions. With the recent acquisition by Databricks of Tabular—one of the main companies that contribute to Iceberg—it’s clear that Iceberg is winning out as one of the primary contenders in this space.

Welcome to the Checkpoint Chronicle, a monthly roundup of interesting stuff in the data and streaming space. Your hosts and esteemed curators of said content are Gunnar Morling and Robin Moffatt (your editor-in-chief for this edition). Feel free to send our way any choice nuggets that you think we should feature in future editions.