Let’s quickly outline the main bullet points about the integral part of any data repository – a data catalog. A good data governance starts with the business glossary. It’s obvious that for the data to make sense first we need to define what each data element means. Methodology and choosing the approach to catalog your …
Continuing on the topic of the data storage evolution, let’s shift our focus to the data warehouses, data lakes, lakehouses and their pros and cons. History of data warehouses and data lakes Going back to the 80-ies we can recall the high level evolution of information of the storage architecture. In the 80-ies data warehouses …
There are many scenarios that showcase the pains organizations are going through with their data platfoms. Of course, everyone is different, but we wanted to draw your attention to just three use cases that may cover big chunk of the business landscape. Can we use our Netezza instance to store a little bit of transactional …
The challenges of the data marts and data warehouses Read More »
A typical question in a world of data-driven business: what do I need to do to start leveraging my data for decision making, for growth, to get ahead of competition? While each customer’s situation is different, there are some basic truths that needs to be followed i order to set up a stage for the …
What’s the difference between data and information and when one turns into the other? One of the data challenges taht organizations have to deal with is managing what has been collected, organizing it and making it available for analysis and decision making. Like anything in the life of an organization, data has its own lifecycle, …
What are the characteristics that make a skilled data modeler? In short, it always helps not only to know technology well, but also to have experience in both working with data and understanding business side of information stored. So the first thing that an aspiring modeler needs to do is to stay very close to …
Let’s take a look at DMS problems from the perspective of the DW data concerns: Data Accuracy, Ease of Accessibility, Security etc. The typical problems of many organizations stem from not staying true to the primary objectives of the data storage. Many copies of the same data, with sometimes conflicting attribute values. It’s not uncommon …
What are the typical problems with data management systems? Read More »
Normalization is a process of modelling data and organizing it into tables. The goal of normalization is a high quality data model.The motivation for Normalization is to avoid unwanted redundancies in the table design, and eliminate modification anomalies.There are use cases when you strongly need normalization (transaction processing db) and when you avoid it (business …
Success of a project and its data model is determined by multiple factors, and one of the critical components of a project is collection of requirements and data flow modeling. Data flow modeling starts with collecting requirements for the New system and documenting the Current system’s data flows. Current physical model is documented first and …
Data flows, Data modeling, Functional and non functional requirements Read More »