This is Useless (Without Use Cases)
Sometimes I’ll write a post and the comments will say something to the effect of “this is useless.” Other times I’ll be finishing up a class and a student will ask me why I didn’t cover what they’re trying to. I’ve written example code and people will ask me why didn’t write it on something […]
The Blame Game
When a Big Data project fails, there’s plenty of blame to go around. When I do the retrospectives with teams who are failing or about to fail, their blame is often misplaced. There’s a focus on blaming the technology. The more difficult considerations of looking inwards at the team itself is often skipped. The teams […]
What It Looks Like From the Outside
I teach and mentor teams that have started or are several months into their projects. I see what happens after they’ve experienced problems. I view the teams from the outside looking in. I see the manifestations of problems and I have to figure out what the root of each problem is. These issues often come […]
Medium Data
Most companies aren’t experiencing Big Data or small data problems. They’re experiencing a witching hour of sorts. This a point in their growth where their data is too big for small data and too small for Big Data. As I’m teaching at companies, I’m finding as much as 80% of use cases are falling into […]
Beam 2.0 Q and A
Apache Beam just had its first API stable release. Now that we have an API stable release, I want to update what’s changed in the Beam ecosystem. I want to highlight the growth of Beam as a project and the increased usage of Beam in pre-production/development or production deployments. Each committer and user is sharing […]
The Difficulty of Transitioning to Data Pipelines
There’s a common difficulty that companies are having in transitioning to Big Data, especially Kafka. They’re coming from systems where everything is exposed as an RPC-esque call (remote procedure call/REST call/etc). They’re transitioning to a data pipeline where everything is exposed as raw data. These data pipelines are a brand new concept. With RPC’s, there […]
Five Dysfunctions of a Data Engineering Team
At Strata London, I premiered a new talk based on my Data Engineering Teams book. Companies are seeing great efficiency gains and ROI from using Big Data technologies. However, the vast majority of teams fail and never get something into production. I want to prevent that failure and here are the top 5 reasons why […]
How to Evaluate an Open Source Product
Open source is a great way to solve problems. Mostly we focus on the open source project from a technical and architectural points of view. In this post, I’m going to talk about it from a business point of view. Sometimes you’re look through 3-10 different open source projects on GitHub and/or Apache. If they […]
Kafka Topic Design Checklist
Designing data for consumption in a Kafka topic requires more forethought. Instead of the messages being a consumed from point to point, there are many different consumers. You will need to decide on: Name Schema Contents Key/Ordering Number of Partitions Number of Replicas Name The choice of a topic name shouldn’t be difficult. I suggest […]
The Many Meanings of Event-Driven Architecture: Kafka Edition
I spoke at GOTO Chicago last week with Martin Fowler. He gave a keynote on The Many Meanings of Event-Driven Architecture. It wasn’t tied to or specific to any particular technology. In this post, I’m applying some of his points specifically to Kafka and Big Data. Change Events Kafka is often used to event changes. […]