Why I No Longer Use MVC Frameworks

The worst part of my job these days is designing APIs for front-end developers. The conversation goes inevitably as:
Dev – So, this screen has data element x,y,z… could you please create an API with the response format {x: , y:, z: }
Me – Ok
I don’t even argue anymore. Projects end up with a gazillion APIs tied to screens that change often, which, by “design” require changes in the API and before you know it, you end up with lots of APIs and for each API many form factors and platform variants. Sam Newman has even started the process of institutionalizing that approach with the BFF pattern that suggests that it’s ok to develop specific APIs per type of device, platform and of course versions of your app. Daniel Jacobson explains that Netflix has been cornered to use a new qualifier for its “Experience APIs”: ephemeral. Sigh…


A successful Git branching model

If, however, you are building software that is explicitly versioned, or if you need to support multiple versions of your software in the wild, then git-flow may still be as good of a fit to your team as it has been to people in the last 10 years. In that case, please read on.


JET Blue? JET Red? oder doch Extensible Storage Engine (ESE)?


Extensible-Storage-Engine – A Non-SQL Database Engine

The Extensible Storage Engine (ESE) is one of those rare codebases having proven to have a more than 25 year serviceable lifetime. First shipping in Windows NT 3.51 and shortly thereafter in Exchange 4.0, and rewritten twice in the 90s, and heavily updated over the subsequent two decades after that, it remains a core Microsoft asset to this day.

The ESE is open source: https://github.com/microsoft/Extensible-Storage-Engine

Is this the JET database / engine?

No. Well … it depends … the question is not quite correct. Most people do not know that JET was an acronym for an API set, not a specific database format or engine. Just as there is no such thing as „the SQL engine“, as there are many implementations of the protocol, there is no „JET engine“ or „JET database“. It is in the acronym, „Joint Engine Technology“. And as such, there are two separate implementations of the JET API. This is the JET Blue engine implementation, see Notes in here. The origin of the colors have an an amusing source by the way. Most people think of the „JET engine“ as JET Red, that shipped under Microsoft Access. This is not that „JET engine“. We renamed to ESE to try to avoid this confusion, but it seems that the confusion continues to this day.https://github.com/microsoft/Extensible-Storage-Engine

JET Blue (ESE)


The Art of Code – Dylan Beattie

Dylan Beattie – programmer, musician, and creator of the Rockstar programming language – for an entertaining look at the art of code. We’ll look at the origins of programming as an art form, from Conway’s Game of Life to the 1970s demoscene and the earliest Obfuscated C competitions. We’ll learn about esoteric languages and quines – how DO you create a program that prints its own source code? We’ll discover quine relays, code golf and generative art, and we’ll explore the phenomenon of live coding as performance – from the pioneers of electronic music to modern algoraves and live coding platforms like Sonic Pi

An Inside Look at Google BigQuery

This white paper introduces Google BigQuery, a fully-managed and cloudbased interactive query service for massive datasets. BigQuery is the external
implementation of one of the company’s core technologies whose code name
is Dremel. This paper discusses the uniqueness of the technology as a cloudenabled massively parallel query engine, the differences between BigQuery
and Dremel, and how BigQuery compares with other technologies such as
MapReduce/Hadoop and existing data warehouse solutions.


Impala: A Modern, Open-Source SQL Engine for Hadoop

Implementation of an MPP SQL query engine for the Hadoop environment•Designed for performance: brand-new engine, written in C++•Maintains Hadoop flexibility by utilizing standard Hadoop components (HDFS, Hbase, Metastore, Yarn)•Reads widely used Hadoop file formats (e.g. Parquet, Avro, RC, …)•Runs on same nodes that run Hadoop processes•Plays well with traditional BI tools:exposes/interacts with industry-standard interfaces (odbc/jdbc, Kerberos and LDAP, ANSI SQL)