Data Warehouses
The ever increasing torrent of data being captured by companies and organisations presents new ongoing challenges in terms of how its gathered, stored and analysed: the decision as to how to set up data storage and analytics is an important one to get right. Unsure where to start? Download our guide to find out the steps when starting a typical data analytics project.
The Data Storage and Analysis Challenge
You may have heard and be familiar with terms such as ‘data warehouse’ or ‘data lake,’ but what roles do they play in data use?
In a nutshell a data warehouse is designed to offer a repository to not only store data but enable meaningful analysis, so performing more than simply a data storage function.
The data warehouse incorporates some or all of these typical elements:
- A relational database to store and manage data
- A solution for extracting and transforming data so preparing it for analysis
- Data mining, reporting and statistical analysis capability
- Analytics tools to present data in easily digestible form – for example clear visuals
- Other applications to produce meaningful insights through using tech such as AI (Artificial intelligence) and machine learning-based algorithms
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Benefits of Data Warehouses
What are the advantages and benefits of a data warehouse for your business?
- Centralised data… Consolidates information for easy access, analysis, and reporting, improving decision-making.
- Enhanced analytics… Optimises complex queries, enabling deeper insights and comprehensive data analysis.
- Historical perspective… Stores extensive historical data, facilitating trend analysis and forecasting for informed decisions.
- Data quality… Ensures consistency and reliability, enhancing trust and accuracy in decision-making processes.
- Scalability… Adaptable to handle large volumes of data and evolving business needs for sustained relevance.
Data Warehouse Solutions: Not Just Data Storage
So, it is evident that ‘data warehouse’ isn’t simply data storage but could be more accurately described as ‘data warehouse solutions’. Data is centralised and consolidated from multiple sources and provides powerful analytical capabilities to allow your business or organisation to gain important and highly useful insights to improve long or short-term decision making and policy development.
A ‘data lake’ by comparison is a more basic solution: here unfiltered and uncategorised data from various sources – for instance social media, apps and IoT (Internet of Things) devices – is more loosely stored for later use.
The format and structure of the various data sets contained in the ‘lake’ are determined at the time of analysis – which could be some time later – as opposed to being categorised straight away as is the case with the data warehouse.
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Making the Most of Data Warehouse Solution
While Big Data itself is relatively new in terms of the sharp increase in data capture from more and varied sources, data warehouse itself dates back to the 1980s although the scope of data being gathered, stored, and analysed has developed considerably since.
For businesses and organisations a key decision is in how best to set up their data warehouse solutions or, if they have this facility already, how best to develop it and ensure its meeting their current needs and its suitability to handle future demands.
To this end consulting with data warehouse services professionals is a worthwhile move for your business to create a solution to make the most of their data relevant to specific needs. The architecture of a data warehouse they would create for you is based on your specific requirements – not a simple ‘cookie cut’ solution that everyone else uses.
There are various options in terms of commercially available systems to build your data warehouse on: you may have heard of labels such as ‘Azure data warehouse’ or ‘Azure SQL data warehouse’ – these refer to Microsoft’s data warehouse solutions packages.
Microsoft’s solution is a SQL data warehouse using, as its name suggests, the familiar SQL database language.