Big data dramatically expands IT’s scope of responsibility with new data types, new methods of analysis, new storage and processing platforms, and new user expectations. Now that we have almost a decade of experience with big data, a set of best practices has emerged that can be described in detail. Download Ralph’s white paper in […]

The enterprise data warehouse (EDW) community has entered a new realm of meeting new and growing business requirements in the era of big data. Common challenges include: Extreme integration Semi- and unstructured data sources Petabytes of behavioral and image data accessed through MapReduce/Hadoop Massively parallel relational database Structural considerations¬†for the EDW to support predictive and […]

Industry-standard data models are an appealing concept at first blush, but they aren’t the time savers they are cracked up to be. What’s more, these prebuilt models may inhibit data warehouse project success. Vendors and proponents argue that standard, prebuilt models allow for more rapid, less risky implementations by reducing the scope of the data […]

We live in a world of extreme status tracking, where our customer-facing processes are capable of producing continuous updates on the transactions, locations, online gestures, and even the heartbeats of customers. Marketing folks and operational folks love this data because real-time decisions can be made to communicate with the customer. They expect these communications to […]

Whether you are developing a new dimensional data warehouse or replacing an existing environment, the ETL (extract, transform, load) implementation effort is inevitably on the critical path. Difficult data sources, unclear requirements, data quality problems, changing scope, and other unforeseen problems often conspire to put the squeeze on the ETL development team. It simply may […]

This article describes six key decisions that must be made while crafting the ETL architecture for a dimensional data warehouse. These decisions have significant impacts on the upfront and ongoing cost and complexity of the ETL solution and, ultimately, on the success of the overall BI/DW solution. Read on for Kimball Group’s advice on making […]

The employee dimension presents one of the trickier challenges in data warehouse modeling. These five approaches ease the complication of designing and maintaining a ‘Reports To’ hierarchy for ever-changing reporting relationships and organizational structures. Most enterprise data warehouses will eventually include an Employee dimension. This dimension can be richly decorated, including not only name and […]

A student attending one of Kimball Group’s recent onsite dimensional modeling classes asked me for a list of “Kimball’s Commandments” for dimensional modeling. We’ll refrain from using religious terminology, but let’s just say the following are not-to-be-broken rules together with less stringent rule-of-thumb recommendations. Rule #1: Load detailed atomic data into dimensional structures. Dimensional models […]

In today’s economic climate, business intelligence (BI) faces two powerful and conflicting pressures. On the one hand, business users want more focused insight from their BI tools into customer satisfaction and profitability. Conversely, these same users are under huge pressure to control costs and reduce risks. The explosion of new data sources and new delivery […]

It’s no secret that the US and global economies are facing difficult times. If the economic pundits are correct, we are now working through the most challenging economic decline of most of our lifetimes. Many of your organizations have already made significant reductions in staffing and spending. The data warehouse/ business intelligence (DW/BI) sector seems […]