A huge amount of current and historical data is full of unrivaled yet hidden potential – and this is where data warehouse concepts come into play.
A data warehouse simply lets an organization collect, cleanse, analyze, report, and present information coming in terabytes and from different sources in a useful and reliable way.
It’s because it is a core component of many Business Intelligence systems, helping make sound judgments and take the right business decisions based on meaningful insights and analytical reports created. This way, organizations may eventually gain a competitive advantage.
In this article, we’ll discuss the following issues:
Data warehouse – what it’s like today, and how it all began
Data Warehouse may be defined as a data management system that consolidates pieces of information from multiple heterogeneous resources. What’s important is that a large amount of data from numerous sources get aggregated and stored electronically as a single, central and consistent data repository. The main reason for creating it and choosing over a standard database is the ability to analyze data in enormous amounts like petabytes.
Although data management has an even longer story, data warehouse concepts date back to 1988 when they were first introduced by IBM researchers Barry Devlin and Paul Murphy. Three years later, data warehouse implementation was made easier thanks to publishing the book „Building the Data Warehouse” by W. H. Inmon.
Dubbed the bible of data warehousing, and the classic bestseller, it provides „the most comprehensive introduction to the core concepts and methods of data warehousing”. This book is thought to have launched the data warehousing industry, and its author, „the father of the data warehouse”, was called one of the ten people who influenced the first 40 years of the computer industry the most by Computerworld magazine.
Today, data warehouses are used immensely in many industries, including the retail, healthcare, finance, and insurance sectors. In general, relational data that land in data warehouses come from sales, marketing, or other operational systems and transactional databases but also from flat files, introduced in the early 1970s by IBM, where data is stored as records in a table, in a two-dimensional database, in a plain text format.
However, as time goes by, pulling together new data types from different sources has become more complex, demanding, sophisticated, and time-consuming. Also, hosting methods have evolved from the on-premises to cloud solutions model and have been enhanced with additional analytical and presentation tools, identity and access management procedures, and many more.
The problem is that data challenges are evolving, and modern data requirements often cannot be met by traditional data warehouses because of their rigid structure and complex, inflexible architecture. They are not adaptable nor agile, and even a tiny change to a data model may require weeks or months to be implemented.
Building a data warehouse – best practices and concepts in 2021
When thinking about how to build a data warehouse, it’s best to implement some of the data warehouse best practices that will make data warehouse reporting the most comprehensive, full and useful, and data storage – secure, reliable, and easily managed.
Some of them include:
1. Adopting an Agile approach
The times they are a-changin’, as are businesses, markets, consumer habits, and general conditions we are living in. To make the newly-created data warehouse universal enough, but also best fitted to a particular industry or business, you’d better consider implementing it by an Agile approach.
Its advantages are well-known and appreciated within the IT world. Its trademarks like timeboxed sprints or iterations and short development cycles make data warehouse creators and operators track issues at an early stage or simply adapt the solution to changing needs or circumstances. It’s important as sometimes it takes years for a data warehouse to be built. Also, continuous feedback that being Agile offers very often means a more human approach that may have some other benefits.
2. Using architecture that enables scalability
It’s reasonable to assume that the organization the data warehouse is built for may need more impressive processing abilities in the future than it does now as well as to reduce them, e.g. for cost optimization purposes. For this reason, it is usually worth adopting cloud-based solutions offering on-demand scalability, as well as architectures or systems that rely on immense parallel processing. It’s crucial as scalability may also contribute to increasing querying speed.
3. High quality of data put first
Building a data warehouse for any kind of organization is a time-consuming and expensive task. What may be overlooked within this process is simply ensuring that data provided to the system are of good quality – accurate, usable, available and secure. That’s why it may be worth implementing a data governance process to handle this task. Anyway, the data warehouse system itself has some possibilities to flag bad data, and make sure that codes and descriptions are consistent.
4. ELT procedure’s accessibility
The data set within the data warehouse should also be easy to access, and it is so within the ELT (standing for Extract, Load, Transform) approach used to copy data from multiple sources, as well as build and run the data warehousing system. Any new pieces of information, including raw or unstructured ones, may be stored and retrieved easily with ELT tools.
Data warehouse best practices – final thoughts
When making decisions on applying some of the data warehouse best practices, it’s good not to omit or overlook other important issues. Some of them include securing the data, logging and metadata management, planning data loading frequency, and – first and foremost – making a profound decision whether to implement a cloud storage solution or an on-premises model.
Taking into account all the above-mentioned factors, concepts, and data warehouse structure, you may create a comprehensive solution proper not only for the year 2021 but also for the upcoming decades and ever-changing market and social conditions.
And you, what data warehouse best practices can you trace? Do you have experience building a data warehouse? Have you faced any problems with data warehouse implementation?