A few years back I worked for a client that was implementing cell level security on every data structure within their data warehouse. They had nearly 1,000 tables and 200,000 columns — yikes! Talking about administrative overhead. The logic was that data access should only be given on a need-to-know basis. The idea would be […]
Posts Tagged ‘BI Generic Architectures’
One Cluster To Rule Them All!
In the Hadoop space we have a number of terms for the Hadoop File System used for data management. Data Lake is probably the most popular. I have heard it called a Data Refinery as well as some other not so mentionable names. The one that has stuck with me has been is the Data […]
The Modern Data Warehouse Will Augment Hadoop
The data warehouse has been a part of the EIM vernacular for nearly 20 years. The vision of the single source of the truth and a single repository for reporting and analysis are two objectives that have resulted in a never-ending journey. The data warehouse never has had enough data and the quality required for […]
Realizing Agile Data Management …
Years of work went into building the elusive single version of truth. Despite all the attempts from IT and business, Excel reporting and Access databases were impossible to eliminate. Excel is the number one BI tool in the industry and for the following good reasons : accessibility to the tool, speed and familiarity. Almost all […]
DevOps Considerations for Big Data
Big Data is on everyone’s mind these days. Creating an analytical environment involving Big Data technologies is exciting and complex. New technology, new ways of looking at the data which is otherwise remained dark or not available. The exciting part of implementing the Big Data solution is to make it a production ready solution. Once […]
Virtualization – THE WHY?
The speed in which we receive information from multiple devices and the ever-changing customer interactions providing new ways of customer experience, creates DATA! Any company that knows how to harness the data and produce actionable information is going to make a big difference to their bottom line. So Why Virtualization? The simple answer is […]
Cloud BI use cases
Cloud BI comes in different forms and shapes, ranging from just visualization to full-blown EDW combined with visualization and Predictive Analytics. The truth of the matter is every niche product vendor offers some unique feature which other product suite does not offer. In most case you almost always need more than one suite of BI […]
Governing the Cloud Analytics…
The new trend in the Analytics world, Cloud Analytics is slowly becoming a norm. Except for the Cloud tag, companies have used Cloud or External Analytics for a long time. Historically Campaign Management has been part outsourced, part managed by Marketing, using external data besides ‘Enterprise Data’. Traditional Data Vendors / Credit Score providers like […]
Myths & Realities of Self-Service BI
Myths & Realities of Self-Service BI The popularity of Data Visualization tools and the Cloud BI offerings are new forces to reckon with. I find it interesting to see how the perception Vs usage of these tools in reality. Traditionally IT likes the control and centralized management for obvious reasons of accountability and quality of […]
PaaS from the Big Blue!
I heard about Bluemix recently and I decided to give it a try. It is amazing that the DevOps environment is free for trying out (30 days) and makes perfect sense for start-ups and individual developers. It also makes sense for corporations trying to check out a new technology and don’t want to wait for […]
Data Staging and Hadoop
Traditionally, in our information architectures we have a number of staging or intermediate data storage areas / systems. These have taken different forms over the years, publish directories on source systems, staging areas in data warehouses, data vaults, or most commonly, data file hubs. In general, these data file staging solutions have suffered from two […]
Seven Deadly Sins of Database Design
This is a summary of an article from Database Trends And Applications; dbta.com. The author addresses fundamental mistakes that we do or we live with in regards to our database systems. 1. Poor or missing documentation for databases in PRODUCTION We may have descriptive table names and columns to begin with, but as workforce turns […]