Couple kissing outdoors — Lovers on a romantic date at sunset,girls stands on tiptoe to kiss her man

When taking analytical architectures into production, it is always about performance of this system. When working agile, the first Minimum Viable Product (MVP) might be of rather low accuracy; when taking more time before going live or when working “in Waterfall,” the quality of the predictions might be more accurate. In the end in both systems, I’d like to be able to deploy an improved version of my model into production. There is one well-known approach to do so: the pattern called Blue-Green Deployment.


Couple kissing outdoors — Lovers on a romantic date at sunset,girls stands on tiptoe to kiss her man

When taking analytical architectures into production, it is always about performance of this system. When working agile, the first Minimum Viable Product (MVP) might be of rather low accuracy; when taking more time before going live or when working “in Waterfall,” the quality of the predictions might be more accurate. In the end in both systems, I’d like to be able to deploy an improved version of my model into production. There is one well-known approach to do so: the pattern called Blue-Green Deployment.


In today’s modern software projects, there is basically two kinds of projects we face. We could take over an existing project with an existing code base or start from an empty scratch. Applying good practices to your software project is hard work and demands discipline and proficiency. But what am I supposed to do when I face an uphill race? What if the existing code sets me up for failure due to a lack of documentation, unit tests, possibly bad code quality and missing architecture sketches? I’ll try to suggest some means I tend to apply in this short post.


Status Quo: Transitioning from Waterfall-only to Agile Projects
In today’s IT world, we are seeing ever more Agile projects being started. Usually those new, mostly Scrum-based IT projects keep replacing the elder approach, which is called “Waterfall.” This is due to its staging of Requirements Engineering first, planning second, architecture, code development, testing, documentation, etc. — all of it happens in sequence. The idea in earlier days was, to have one stage completed and then it’s respective output “pours” into the next stage — like a series of waterfalls.

Those Waterfall projects had their sound proof and reasonable logic behind…


We’re back on July 9, 2016: Within the next 24 hours, the soccer world will be heads over heels for the upcoming final game of this season’s championship of UEFA Euro 2016 in France. Like usual for such big events, I am curious to know the team’s performances over all. So compare two teams that never played against each other. Additionally, I’d like to get the know the probable winner in advance. According to my predictions, France will win this final with odds 2:1 (about 63.5%).

Data

To be fair, the data is fairly sparse. I only took matches from this…


Cassandra als Datenbank nutzt zwischen den einzelnen Serverknoten ein Peer-to-Peer-Protokoll namens Gossip zur Kommunikation in einem sich daraus aufspannenden Graphen

Bei besonders hohen Anforderungen an Skalierbarkeit, Performance und Hochverfügbarkeit gibt es neben diversen Hadoop-Distributionen kaum einander ebenbürtige Kontrahenten. Eine Ausnahme bildet DataStax Enterprise. Die Wahl zwischen Hadoop- und DSE-Distribution hängt jedoch stark vom Anwendungsfall und persönlichen Geschmack ab.

Die Gartner-Analysten bewerten DataStax in ihrem aktuellen Magic Quadrant für operative Datenbanksysteme als führend. Die wesentliche Technik der DSE zur Datenhaltung ist die NoSQL-Datenbank Apache Cassandra. Aus historischen Gründen wird sie auch mit C* bezeichnet, um auf “Eventual Consistency” hinzuweisen. …


“Big Data is the Elephant in the room” — wie es im Englischen heißt

Vor ein paar Jahren sagte man noch, dass Big Data wie Sex-Gespräche unter Teenagern sei: Alle sprechen darüber, viele wollen es erleben, doch niemand weiß wie es geht. Diese Punkte haben sich zunehmend geändert.

In einer kürzlich erschienenen Studie von Gartner zum Zustand von Big Data wurde festgestellt, dass Hadoop selbst unter forschungsnahen Großunternehmen in zwei Jahren in lediglich jedem zweiten vertreten sein werde. Konkret gaben die Hälfte dieser Unternehmen an, weder bestehende Hadoop-Systeme zu haben noch derartige Pläne für die kommenden zwei Jahre zu verfolgen. …

Daniel Schulz

Delivery Architect & Strategic Alliance Manager focusing on Excellence in Cloud-native & Fast Data Architectures

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