Category: Data

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.

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.

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.

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.

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.

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

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.


Getting started with Apache Spark

Earlier this year I attended GOTO Conference which had a special track on distributed computing. One of the talks described the evolution of big data processing frameworks. It was really interesting when a presenter mentioned that Hadoop’s MapReduce is a first generation network, Apache Storm and Apache Tez are second generation, where as Apache Spark is

Designing for New & Maturing Tech – 2016 Edition

Mobile, web, and interactive design has matured a lot since last year. This is driven by tech that has become open to developers. Voice, Wearables, IoT, Auto, and TV have become the focus of most major technology companies in 2016.  This has sparked a fury of new ideas around the future of apps and how they fit into our daily lives. With these technologies, the design standards have also become refined and standardized. Users have come to expect a premier experience on whatever platform they are using. In this post we will highlight a few of the new and exciting trends and how design is leading the charge.

Architectural detail of a shopping mall in Frankfurt

Why Architecture Matters for Data and Analytics

The reality is that the data architecture of most organizations are complicated. Data systems are now a complex, ubiquitous and critical component of modern firms in contrast to most simplistic data models one would find when it comes to designing an enterprise data systems. The most important task of a data architect is having an end-to-end vision of the flow of information within an organization. Data architecture defines what data is important to the organization and how it will be effectively delivered and managed. It is the term for standards, metadata and models to ensure an organization’s data strategy is in alignment with overall strategic decision making needs.