Wed.Jun 20, 2018

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Cassandra vs. HBase: twins or just strangers with similar looks?

ScienceSoft

Similar at first glance, Cassandra and HBase actually are quite different in terms of architecture, performance and data models. What are these differences and how do they influence the tasks that HBase and Cassandra perform? It’s all here.

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Brittleness and incremental improvement

DMBS2

Every system — computer or otherwise — needs to deal with possibilities of damage or error. If it does this well, it may be regarded as “robust”, “mature(d), “strengthened”, or simply “improved” * Otherwise, it can reasonably be called “brittle” *It’s also common to use the word “harden(ed)” But I think that’s a poor choice, as brittle things are often also hard. 0.

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What Are Data Trends and Patterns, and How Do They Impact Business Decisions?

Smarten

In this article, we will focus on the identification and exploration of data patterns and the trends that data reveals. The business can use this information for forecasting and planning, and to test theories and strategies. Let’s look at the various methods of trend and pattern analysis in more detail so we can better understand the various techniques.

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Brittleness, Murphy?s Law, and single-impetus failures

DMBS2

In my initial post on brittleness I suggested that a typical process is: Build something brittle. Strengthen it over time. In many engineering scenarios, a fuller description could be: Design something that works in the base cases. Anticipate edge cases and sources of error, and design for them too. Implement the design. Discover which edge cases and error sources you failed to consider.

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From Developer Experience to Product Experience: How a Shared Focus Fuels Product Success

Speaker: Anne Steiner and David Laribee

As a concept, Developer Experience (DX) has gained significant attention in the tech industry. It emphasizes engineers’ efficiency and satisfaction during the product development process. As product managers, we need to understand how a good DX can contribute not only to the well-being of our development teams but also to the broader objectives of product success and customer satisfaction.

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Data democratization is driving self-service analytics

IBM Big Data Hub

Data democratization allows data to be accessed across the organization and empowers individuals to use the data in their decision making and gain critical business insights. Data democratization is fast becoming a game changer as it’s moving towards a user centric micro-services based architecture.