Monthly Archives: September 2015

Cassandra’s “Repair” Should Be Called “Required Maintenance”

One of the bigger challenges when you go Eventually Consistent is how to reconcile data not being replicated. This happens if your using Oracle and multi-data centers with tech like Golden Gate and it happens if you’re using async replicas … Continue reading 

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Scale is a Dish Best Served Eventually Consistent

A lot of people new to Cassandra find the data modeling required tedious and outrageously hard. They’ll long for their RDMBS, if only insert favorite vendor or project lead here would make their RDBMS scale like Cassandra they could tell their bosses … Continue reading 

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Lambda+ or Event Sourcing with TTLs

Some people are worried about the data volume that a strategy like Lambda+ or Event Sourcing implies. As a disclaimer, by giving up the historical data you have, you risk losing useful data layer and you lose the ability to have … Continue reading 

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Lambda+: Cassandra and Spark for Scalable Architecture

UPDATE: For some background on Spark Streaming and Cassandra please consult some of my previous blog post on the subject. Many of you have heard of and a few of you may have used the Lambda Architecture. If you’ve not heard … Continue reading 

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Data Density! Destroyer of Scalability

UPDATE: I’d incorrectly attributed a practice to Netflix about scaling down daily . I cannot find any reference to using today, and I’ve been unable to find the previous reference to it. So I’ve just removed the point. I’ll cover cluster … Continue reading 

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Real Time Analytics With Spark Streaming and Cassandra

Spark Streaming is a good tool to roll up transactions data into summaries as they enter the system. When paired with an easily idempotent data store like Cassandra you get a high performance low hassle approach to getting your work done. … Continue reading 

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Retry not Rollback: Idempotent Data Models in Cassandra

Naive Consistency Often the first error handling code I see from new Cassandra users is the client side rollback in an attempt to replicate database transactions from the ACID world. This is typically done when a write to multiple tables … Continue reading 

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Event Sourcing and System of Record: Sane Distributed Development In The Modern Era

No matter the message queue or broker you rely on whether it be RabbitMQ, JMS, ActiveMQ, Websphere, MSMQ and yes even Kafka you can lose messages in any of the following ways: A downstream system from the broker can have … Continue reading 

Posted in Cassandra, Event Sourcing, Lambda | Tagged , , | 1 Comment

DataStax a Love Letter

Today is my last day at DataStax and what an amazing ride it was. This is easily the best job and the best group of people I’ve ever worked with and I’m very sad to go, but I had an … Continue reading 

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