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.
https://nvie.com/posts/a-successful-git-branching-model/
James Nicholas Gray (1944 – declared dead in absentia 2012) was an American computer scientist who received the Turing Award in 1998 „for seminal contributions to database and transaction processing research and technical leadership in system implementation“
https://en.wikipedia.org/wiki/Jim_Gray_%28computer_scientist%29
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
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
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
Details from here https://semver.org/
This white paper introduces Google BigQuery, a fully-managed and cloudbased interactive query service for massive datasets. BigQuery is the external
https://cloud.google.com/files/BigQueryTechnicalWP.pdf
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.
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)
http://cidrdb.org/cidr2015/Slides/28_CIDR15_Slides_Paper28.pdf
Delta Lake is an open-source storage layer that brings ACID
https://delta.io/
transactions to Apache Spark™ and big data workloads.
All data in Delta Lake is stored in Apache Parquet format enabling Delta Lake to leverage the efficient compression and encoding schemes that are native to Parquet.
Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes.
Presto was designed and written from the ground up for interactive analytics and approaches the speed of commercial data warehouses while scaling to the size of organizations like Facebook.
https://prestodb.io/