Category: Data

Twisted, flexible, silver ventilation pipe

Data Science: Back to Basics – It’s about being flexible

Learn how the flexible or inflexible characteristics of machine learning or statistical model can impact its performance and predictive capabilities.

READ MORE >
developers working

Azure Integration Part III: Azure Storage Offerings

When developing your integration solutions in Azure, there is no doubt that there will be discussions on which storage offerings you will want to use.  Depending on the requirements, there will be an offering in Microsoft Azure that will be available. In Part 3, we will focus on some of these different storage options that are available for any solution integrating with Microsoft Azure.

READ MORE >
Mountains and a bright, cloudy sky reflected in a clear lake

Azure Data Lake Store – Service-to-Service Authentication

In this blog post, I explain how to set up service-to-service authentication while working with Azure Data Lake Store and Data Lake Analytics.

READ MORE >
On a windswept hill a squatting woman peers through binoculars.

Data Science: Back to Basics – Don’t forget Data Exploration

Data Science is one of the hottest fields today. But as people take on the Data Scientist job title, it appears data exploration has taken a back seat within the data science process. In this blog, let’s break down what data exploration is, and how it is an important step of the data science process.
READ MORE >

Colorful circles on a white background

A Guide to Using R with Power BI

The integration of Power BI and R has greatly extended Power BI’s capabilities. However, it can be challenging to know the best times to use R. Using R and Power BI within the context of the Power BI service and Power BI desktop does have its limitations, mainly that R output must result in an R graphic object. Therefore, this blog post provides a brief list of guidelines and examples to help determine when to leverage R functionality inside of Power BI.

READ MORE >
data architecture

Why Architecture Matters for Data and Analytics – Part II

Following on from our first article on the aforementioned article, we demonstrated how Data Architecture provides an understanding of what data exists, where it is stored and how it flows throughout the organizations and/or systems. We also demonstrated how understanding system mapping, connection and configuration is key to formulating and deploying a data architecture that fits into the company’s objectives.

READ MORE >
Data migration to the Cloud with Talend

Smooth Move – Taking Data to the Cloud with Talend

As cloud computing continues to be a hot topic, with interest shown across all levels of organization. Its adoption is becoming rapid and showing no sign of slowing down. As this solution become cheaper and more widely utilized, cross database conversion is becoming prevalent. Industry leading relational database engines are very similar to one another, however, they are not identical in their supported data types, metadata or internal data manipulation capabilities. You might need to extract data from a cloud based storage for processing on-prem and load back into the cloud.

READ MORE >
Advanced Analytics Data Scientist

Introduction to Data Science: Math + Tech = Business Smarts

Industries today are combining technology and advanced analytics to help make increasingly intelligent decisions. This is known as data science.

READ MORE >
mountains and lakes

It’s No Longer a Data Lake. It’s a Data Dump

This is the world of Big Data where the volume of digital data is going to double every two years for the foreseeable future. By 2020 there will be eight billion people on earth, using 20 billion devices and communicating with 100 billion connected things. As Big Data blossomed, organizations began to store the endless
READ MORE >

The Challenge of Exploring Big Data Technology

The main challenge in today’s big data world is not the big data but exploring big data technology. Nowadays automatically generated data (Ex: stock market data) is likely to be more analyzed and used for making the greater level of decisions rather than the user’s generated and enterprise generated data.

READ MORE >