Warning: preg_replace(): Compilation failed: invalid range in character class at offset 4 in /home/dataxflo/public_html/blog/wp-content/plugins/crayon-syntax-highlighter/crayon_langs.class.php on line 340
DataOps – Page 2 – dataflow analytics blog Press "Enter" to skip to content

Posts published in “DataOps”

Cloud Analytics: Utilizing Composable to turn your organization into a Data-Driven Company

Published September 16, 2016 by Andy Vidan

Cloud Analytics: Utilizing Composable to turn your organization into a Data-Driven Company

Major transformations are shaping the global economy, and it is now clear that organizations with the resources and know-how to leverage the large volume of both internal and external data…

Hadoop is Dead. DataFlow is Alive!

Published September 27, 2014 by Lars Fiedler

Hadoop is Dead. DataFlow is Alive!

We’ve given Hadoop almost 10 years to mature, invested billions, and very few companies are seeing the return on investment. Several companies have tried to make Hadoop a real-time analytical…

Leverage existing Hadoop cluster with CA

Published August 10, 2014 by Lars Fiedler

Leverage existing Hadoop cluster with CA

With the Hive ODBC driver and Composable Analytics, users can perform ad-hoc queries on their data living in the HDFS. Hive will take a sql-like query and translate it into…

Want to analyze Twitter data?

Published August 10, 2014 by Lars Fiedler

Want to analyze Twitter data?

Twitter analysis typically starts with grabbing a bunch of tweets via Twitter’s REST API. Fortunately this is easy to do with Composable Analytics. Just use the Twitter Search module, and…

Can Edge Analytics Become a Game Changer?
Edge analytics is considered to be the future of sensor handling, and this article discusses its benefits and architecture of modern edge devices, gateways, and sensors. Deep Learning for edge analytics is also considered along with a review of experiments in human and chess figure detection using edge devices.
The Big Bad NLP Database: Access Nearly 300 Datasets
Check out this database of nearly 300 freely-accessible NLP datasets, curated from around the internet.
Hands on Hyperparameter Tuning with Keras Tuner
Or how hyperparameter tuning with Keras Tuner can boost your object classification network's accuracy by 10%.
Decision Tree Intuition: From Concept to Application
While the use of Decision Trees in machine learning has been around for awhile, the technique remains powerful and popular. This guide first provides an introductory understanding of the method and then shows you how to construct a decision tree, calculate important analysis parameters, and plot the resulting tree.
New Poll: When Will AutoML Replace Data Scientists (if ever)?
Take part in the latest KDnuggets poll by weighing in on when you think AutoML and Automated Data Science will replace humans — if ever.
Introducing fastpages: An easy to use blogging platform with extra features for Jupyter Notebooks
This article introduces the easy to use blogging platform fastpages. fastpages relies on Github pages for hosting, and Github Actions to automate the creation of your blog, and contains extra features for Jupyter Notebooks.
Top KDnuggets tweets, Feb 19-25: 9 lessons learned during my first year as a #DataScientist
Also The Next Decade in #AI: Four Steps Towards Robust #ArtificialIntelligence; Perl, yes, but will R be dead by 2030? #rstats Programming Languages You Won’t Use by 2030; Will AutoML eliminated Data Scientists? Let's do some tests to find out...Leaders, Changes, and Trends in Gartner 2020 #MagicQuadrant for #DataScience
Data Science Curriculum for self-study
Are you asking the question, "how do I become a Data Scientist?" This list recommends the best essential topics to gain an introductory understanding for getting started in the field. After learning these basics, keep in mind that doing real data science projects through internships or competitions is crucial to acquiring the core skills necessary […]
Image Recognition and Object Detection in Retail
“According to Gartner, by 2020, 85% of customer interactions in the retail industry will be managed by AI.”
Python and R Courses for Data Science
Since Python and R are a must for today's data scientists, continuous learning is paramount. Online courses are arguably the best and most flexible way to upskill throughout ones career.