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Previously, I wrote about the two-tiered data lakehouse with an analytical sandbox and a curated data warehouse in the second layer – the one for productised BI and the other for data science work. I was therefore intrigued when coming across a Forbes article titled ‘Three Keys to a Harmonious Relationship between Data Science and …
As you may have gathered from my previous post, I have become very interested in cloud-based data lakehouses. It was therefore with a keen interest that I read the article ‘Five Effective Ways to Build a Robust Data Lake Architecture’ on Enterprise Talk. While initially I was hoping that the author was going to review …
Long-time readers of my blog can likely recall my scepticism around data lakes when they first emerged. However, a lot of water has flowed into the space since then, motivating me to start investigating the area in-depth. My early criticisms against Hadoop-based data lakes were that they were too batch-oriented to cater for business analytics …
In my blog post last month, I started looking at the concept of data fabrics to get an understanding around what it is all about. This month, I continue with the discussion, focussing on the advantages of data fabrics. The points I have outlined below are based on a very good article written by Lori …
Seeing as I’m currently working at large for a federated organisation with significantly different and siloed business streams that are managed through a plethora of different systems – ranging from 30-year-old mainframes to modern in-cloud platforms – the topic of data fabrics is very interesting to me. Even more so given how I’m coming from …
You may have picked up from my last few blog posts that I have paid quite a bit of attention to the theme of data governance. With a passion for the theme, I recently picked up on some research specifically around governance of the cloud data warehouse. This caught my interest and reminded me that …
The term ‘data lake’ has started to take a strong place in the Information Management architectures and processes of many organisations. In fact, over the last two years, I have seen more and more data managers developing data lakes in an effort to assist their organisations who need to store information that the business may …
Some days I get really flexed out of shape about the rate at which new terms become spontaneously combusted in the BI and analytics industry. The latest I came across is the term “elastic data warehouse.” So what are we doing here – bending the rules, stretching the imagination or flexing our muscles here? Now …
In order to cater for the demands of a modern data-centric and analytics-driven organisation, one needs a more extensive ecosystem for analytics than what a traditional simple “data source – ETL – data warehouse – dashboard” environment can cater for. This is especially relevant considering the more complex data types and structures and the more …
The increased use of analytics, coupled with an exponential growth in the volume and complexity of data, has changed the business intelligence landscape forever. Business users’ sophistication has extended beyond descriptive reports and dashboards into data exploration, predictive modelling and aspects of cognitive computing. Traditional requirements-driven ETL, data warehouse and reporting approaches do not meet …
