site map

--------------------------------------------------------------------------------------------------------------------------

Comments

Post a Comment

Popular posts from this blog

Top 10 Data Engineering Mistakes

A large fraction of big data projects fail to deliver return of investment, or take years before they do so. The reasons are typically a combination of project management, leadership, organisation, available competence, and technical failures. In this presentation, I will focus on the technical aspects, and present the most common or costly data engineering mistakes that I have experienced when building scalable data processing technology over the last five years, as well as advice for how to avoid them. The presentation includes war stories from large scale production environments, some that lead to reprocessing of petabytes of data, or DDoSing critical services with a Hadoop cluster, and what we learnt from the incidents. EVENT: #bbuzz 2018 SPEAKER: Lars Albertsson PERMISSIONS: Original video was published with the Creative Commons Attribution license (reuse allowed). CREDITS: Original video source: https://www.youtube.com/watch?v=mv7PL...
Read more

Optimizations in Syntax Highlighting

Image
Optimizations in Syntax Highlighting Visual Studio Code version 1.9 includes a cool performance improvement that we've been working on and I wanted to tell its story. TL;DR  TextMate themes will look more like their authors intended in VS Code 1.9, while being rendered faster and with less memory consumption. Syntax Highlighting Syntax Highlighting usually consists of two phases. Tokens are assigned to source code, and then they are targeted by a theme, assigned colors, and voilà, your source code is rendered with colors. It is the one feature that turns a text editor into a code editor. Tokenization in VS Code (and in the  Monaco Editor ) runs line-by-line, from top to bottom, in a single pass. A tokenizer can store some state at the end of a tokenized line, which will be passed back when tokenizing the next line. This is a technique used by many tokenization engines, including TextMate grammars, that allows an editor to retokenize only a small subset of the line...
Read more

Machine Learning Services (R, Python) in SQL Server 2017

                Machine Learning Service (R,Python )in SQL Server 2017   SQL Server 2017 Machine Learning Services is an add-on to a database engine instance, used for executing R and Python code on SQL Server. The feature includes microsoft R python packages  for high-performance predictive analytics and machine learning. Code runs in an extensibility framework, isolated from core engine processes, but fully available to relational data as stored procedures, as T-SQL script containing R or Python statements, or as R or Python code containing T-SQL. If you previously used , Machine Learning SQL server 2016 R service  in SQL Server 2017 is the next generation of R support, with updated versions of base R, RevoScaleR, MicrosoftML, and other libraries introduced in 2016. In Azure SQL Database machine learning service with R is currently in public preview.  Bring  compute power to the data The key value proposi...
Read more