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The Data-Driven CPO

Hey there,

I'm Alexander, a veteran tech lead with a healthy obsession for data and a knack for building products that people actually want to use. I've spent years in the trenches, leading product teams through the chaos of the tech world, from scrappy startups to massive corporations. I build, have built and contribute to data open source projects, mainly Hadoop and IoT focussed, founded startups, won and lost. At the moment I'm an "entrepreneur in residence" - means I had some exits, enjoy now the Meds, take care about our 12 yrs old Frenchy (Heinrich), getting more into watersports and sailing and drive my food passion - Vegan cooking :)  

This blog is where I share my hard-won insights, strategies, and (occasionally) rants about everything product-related. It's my opinion and experience, maybe it's useful for the one or another.

What You'll Find Here:

  • Open Source Stuff: I'm a developer turned systems architect. I blog here about that.
  • Data-Driven: I write about using data and analysis to make decisions so profound as they can be, paired with gutt feeling and experience.
  • Team Setup: Build collaborative, cross-functional teams that actually deliver.
  • AI Stuff: I research since years, and test here and there.
To reach out to me use either my LinkedIn or X profile, you can also send a message over the contact page.

Popular posts from this blog

Deal with corrupted messages in Apache Kafka

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Under some strange circumstances, it can happen that a message in a Kafka topic is corrupted. This often happens when using 3rd party frameworks with Kafka. In addition, Kafka < 0.9 does not have a lock on Log.read() at the consumer read level, but does have a lock on Log.write(). This can lead to a rare race condition as described in KAKFA-2477 [1]. A likely log entry looks like this: ERROR Error processing message, stopping consumer: (kafka.tools.ConsoleConsumer$) kafka.message.InvalidMessageException: Message is corrupt (stored crc = xxxxxxxxxx, computed crc = yyyyyyyyyy Kafka-Tools Kafka stores the offset of each consumer in Zookeeper. To read the offsets, Kafka provides handy tools [2]. But you can also use zkCli.sh, at least to display the consumer and the stored offsets. First we need to find the consumer for a topic (> Kafka 0.9): bin/kafka-consumer-groups.sh --zookeeper management01:2181 --describe --group test Prior to Kafka 0.9, the only way to get this inform
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Hive query shows ERROR "too many counters"

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A hive job face the odd " Too many counters:"  like Ended Job = job_xxxxxx with exception 'org.apache.hadoop.mapreduce.counters.LimitExceededException(Too many counters: 201 max=200)' FAILED: Execution Error, return code 1 from org.apache.hadoop.hive.ql.exec.MapRedTask Intercepting System.exit(1) These happens when operators are used in queries ( Hive Operators ). Hive creates 4 counters per operator, max upto 1000, plus a few additional counters like file read/write, partitions and tables. Hence the number of counter required is going to be dependent upon the query.  To avoid such exception, configure " mapreduce.job.counters.max " in mapreduce-site.xml to a value above 1000. Hive will fail when he is hitting the 1k counts, but other MR jobs not. A number around 1120 should be a good choice. Using " EXPLAIN EXTENDED " and " grep -ri operators | wc -l " print out the used numbers of operators. Use this value to tweak the MR s
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AI's False Reality: Understanding Hallucination

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Artificial Intelligence (AI) has leapfrogged to the poster child of technological innovation, on track to transform industries in a scale similar to the Industrial Revolution of the 1800s. But in this case, as cutting-edge technology, AI presents its own unique challenge, exploiting our human behavior of "love to trust", we as humans face a challenge: AI hallucinations. This phenomenon, where AI models generate outputs that are factually incorrect, misleading, or entirely fabricated, raises complex questions about the reliability and trust of AI models and larger systems. The tendency for AI to hallucinate comes from several interrelated factors. Overfitting – a condition where models become overly specialized to their training data – can lead to confident but wildly inaccurate responses when presented with novel scenarios (Guo et al., 2017). Moreover, biases embedded within datasets shape the models' understanding of the world; if these datasets are flawed or unreprese
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