SQL for Data Warehousing and Analytics

In the first part ( Why SQL is the natural language for data analysis ) of this series of blog posts I explained why analytics is important to the business, how the volume of data along with the types of data is continuing to expand and why that makes it vital that you select the right language for data analysis. Many of us work with SQL every day and take for granted many of its unique features, its power, flexibility and the richness of the analytics. This familiarity with SQL means that sometimes we are a bit slow at preventing some of our projects investing in other big data languages such as MapReduce, Impala, Spark, Java and many of the new generation of SQL-like open source projects. While many of these new languages are considered “ cool ”, it is very easy to end up building new proprietary data silos or investing in a language that eventually is replaced by another open source project or eventually fails to deliver the required analytics. One of the aims of this series of blo...

Analytics is a must-have component of every corporate data warehousing and big data project. It is the core driver for the business: the development of new products, better targeting of customers with promotions, hiring of new talent and retention of existing key talent. Yet the analysis of especially “big data environments”, data stored and processed outside of classical relational systems, continues to be a significant challenge for the majority companies. According to Gartner, 72% of companies are planning to increase their expenditure on big data yet 55% state they don’t have the necessary skills to make use of it. A report by Accenture and GE ( How the Industrial Internet is Changing the Competitive Landscape of Industries ) found that 87% of enterprises believe big data analytics will redefine the competitive landscape of their industries within the next three years and 89% believe that companies that fail to adopt a big data analytics strategy in the next year risk losing marke...