With data becoming the driving force behind making important decisions that stimulate organisational growth, it is now more critical than ever that the integrity and accuracy of it is not compromised. Modern data warehouses can store data in various formats, from multiple sources, and bring it together in a unified fashion to derive useful business insights.
Solutions such as the Microsoft modern data warehouse can handle the challenges of big data, apply advanced analytics tools, and deliver real time insights in an efficient manner. Hence, unlocking the true value of your organisational data can be the key difference in attaining an edge in this increasingly competitive landscape.
A modern data warehouse is a unified platform that can store and handle data in multiple forms.
By storing data in an organised and easily accessible manner, modern data warehouses enable users to undertake analytics to draw conclusions and insights for reporting. The primary purpose of a modern data warehouse is to undertake analytics and focus on value rather than transactional processes.
Why do you need a Modern Data Warehouse?
In today’s data-driven business world, data analytics and algorithms are critical to the success and competitiveness of organisations. With the rise of Big Data, traditional data management methods have become outdated and are no longer sufficient. Legacy data warehouses, which often store data on-premises and rely on slow batch reporting, are not equipped to handle the sheer scale of Big Data. The 3 V’s—volume, variety, and velocity—mean vast amounts of data in multiple formats are being generated every day.
Modern data warehouses offer the flexibility and scalability required to meet these growing demands. Unlike their traditional counterparts, they store data in its raw form and can efficiently manage Big Data while delivering fast query results, allowing teams to access insights and perform tasks more effectively. Their capacity to manage large volumes of data makes them particularly valuable for the increasing use of Internet of Things (IoT) applications, where real-time data is constantly being produced.
Types of Data Warehouses
There are three primary types of data warehouses:
Enterprise Data Warehouse (EDW)
An Enterprise Data Warehouse serves as a centralised repository that aggregates data from various sources and makes it readily accessible for analysis across the entire organisation. It unifies data from different business areas, categorising it by subject (e.g. marketing, finance, HR) to ensure relevant teams have the information they need. EDWs store both current and historical data, providing employees with a comprehensive hub for business intelligence and data analysis. For example, Antares helped GWA Group implement an Enterprise Data Warehouse that improved data accuracy and streamlined processing.
Operational Data Store (ODS)
An Operational Data Store functions as an intermediary for synchronising data from diverse systems, focusing on operational reporting and real-time decision-making. It feeds data into Enterprise Data Warehouses and supports tactical and strategic decisions by capturing real-time updates. Unlike traditional data warehouses, an ODS is a dynamic system that frequently updates data without retaining historical changes. It handles simpler queries and provides up-to-date, lower-volume data for immediate use.
Data Mart
A Data Mart is a smaller, focused subset of a data warehouse that serves specific organisational functions or departments. It allows divisions to quickly access the key data they need without navigating the entire data warehouse. By isolating data for particular departments or subject areas, Data Marts prevent unnecessary overlap in data access and manipulation between teams, ensuring each division can work independently with their own relevant data.
Characteristics of a Modern Data Warehouse
- Capable of handling and processing large volumes of data in various formats
- Governed access and usage of data by authorised users
- Able to manage incoming streams of real time data
- Integrated with various storage technologies and cloud applications
- Support for undertaking a variety of advanced analytics
- Unrestricted access to data for different kinds of users
- Fast processing and monitoring of data for real time access and analytics
- Can evolve to meet changing demands and support multidimensional data models
- Multi platformed architecture that balances performance, scalability, and elasticity
- Supports many users performing actions simultaneously
Key Components of a Modern Data Warehouse
Data warehouses perform a variety of functions from storing data to maintaining it to ensuring it’s optimised for analysis. These are the five main components of a typical data warehouse:
- Database: The database is the core component of a data warehouse that stores all the data from various sources by making it usable for analysis and reporting purposes.
- Extraction, transformation and loading tools (ETL): ETL are a three in one tool that assists with pulling and extracting data from various sources, transforming it into a suitable format and loading it onto the target database.
- Metadata: Metadata is simply data that describes data. It governs the data warehouse architecture by providing structure in building, maintaining, handling and utilising the data warehouse. There are two types of metadata – technical and business. Technical metadata refers to the warehouse information that can be used by data warehouse designers and administrators when executing warehouse development and management duties. Business metadata consists of information that provides users an easily comprehensible view of the information stored within the data warehouse.
- Access Tools: As end users are typically unable to interact with databases directly, access tools can help them make sense of the data and use it to fulfil their business needs. The four main tools include query and reporting tools, application development tools, data mining tools, and OLAP tools. Query and reporting tools assist users with producing reports for analysis such as spreadsheets and visualisations. Application development tools can integrate with common OLAP tools and other database systems to create custom reports for specific interpretation use. Data mining tools enable users to identify meaningful relationships, trends and patterns between datasets by sifting through large volumes of data using statistical modelling methods. OLAP tools allows analysis of data from multiple viewpoints by organising the data in a multidimension model.
- Data Marts: Data marts are designed to serve a particular function of a business and provides an access level to move the data to users. It is often used as a partition of data and is created for a specific set of users for easier and faster access.
Traditional vs Cloud Based Data Warehouses
Whilst traditional data warehouses are still able to adequately house structured data for data analytics, they are limited in their architecture and how they accommodate organisational growth. Their lack of scalability and flexibility inhibits an organisation’s ability to keep up with the data that they are generating, and in the long run this can restrict overall performance. Benefits of a modern data warehouse include:
- Reduced Hardware Costs: With cloud-based data warehouses, the responsibility for hardware, software, support, and maintenance is handled by the provider, making it a highly cost-effective storage solution.
- Low Entry Barrier: Cloud data warehouses eliminate the need for significant upfront investment, allowing organisations to benefit from elastic storage and scalability with minimal initial costs. In contrast, traditional warehouses required substantial expenditures on servers, administrators, and infrastructure, making them unfeasible for budget-conscious businesses.
- Flexibility: Traditional methods of analysing large datasets often required immense computing power and resources, leading to inefficiencies. Cloud computing, with its ability to handle data in various formats, offers rapid processing thanks to Massively Parallel Processing (MPP), making data evaluation much more efficient.
- Enhanced Scalability: As data production grows rapidly in both volume and variety, cloud data warehouses offer the flexibility to scale up or down based on demand. This ensures that organisations aren’t constrained by capacity limits and can seize growth opportunities without hindrance.
- Unlocking New Insights: Modern data warehouses not only store large volumes of data but also enable more advanced analytics, such as predictive and preventive analysis. This allows organisations to forecast trends, reduce uncertainty, and make well-informed decisions, significantly improving their competitive edge.
- Improved Usability: Beyond simply storing data, modern data warehouses are equipped to perform data analysis and extract actionable insights. They empower organisations to quickly understand their current environment and provide strategic guidance for future decisions, offering immense value.
Modern Data Warehousing Options
Infrastructure as a Service
IaaS is a form of cloud computing that requires the provider to manage all the hardware and infrastructure whilst you purchase and configure the software. This model enables organisations to cut costs and enjoy the flexibility required to accommodate changing demands in data.
Platform as a Service
Just like IaaS, PaaS uses a pay as you go model whereby organisations are able to access data warehouse services via an internet connection. The service provider delivers a complete platform for developers to create custom software and hosts everything – servers, storage, hardware – whilst the customer is responsible for managing their own data and applications. This option is perfect for organisations looking to develop software unique to their own needs without shelling out grand amounts and undertaking the heavy lifting.
Software as a Service
The SaaS model allows organisations to access a complete software solution via an internet connection. The cloud provider is responsible for the maintenance of all the underlying infrastructure, hardware, software, data, and applications. Organisations are able to get up and running quickly with this model with minimal upfront costs or effort.
What are the Characteristics of Modern Data Warehouse Architecture?
- Automated: Modern data warehouses automatically profile and tag data as it enters the system through a process called metadata injection. As data flows continuously into the warehouse, data cataloguing kicks in to sort the data, detect changes and anomalies before alerting the relevant individual of the irregularity.
- Real time data: Organisations need the latest data to make the most informed decisions. Outdated insights produce expired decisions which do not meet the current changes and needs of the external environment. Modern data architecture can encapsulate real time data and perform validation, classification, and management automatically so that organisations can leverage the most up to date insights to support their business decisions.
- Collaborative: Unlike traditional data architectures which required the IT department to be responsible for all aspects of data related procedures, modern ones allow individuals from different business units to access and use data as they need. Data analysts and data scientists can prepare reports and undertake analytics as required without having to funnel procedures via IT.
- Governance: Modern data architecture revolve around the idea of self service and define different levels of authority according to user roles and needs. Each user type is assigned a level of permission so that they can gain access to the data necessary for them to carry their tasks out but automatically locked out of entry for anything beyond that.
- Elastic: In the age where data rules everything, organisations need an elastic architecture that can accommodate and adapt to changing data requirements. Modern data architecture allows organisations to benefit from on demand scalability at affordable prices without having to shell out grand amounts for upfront investments.
- Data integration: The beauty of modern data architecture is that it can integrate with existing legacy applications without the need for replacements. You can continue to use your pre-existing systems and enable data to be easily optimised for sharing across organisations and locations.
- Secure: Modern data architecture provides ready access to authorised individuals whilst also keeping unwanted threats and intruders at bay through data encryption. By masking sensitive data and tracking audit trails, organisations are better protected against hacks and security breaches.
- Resilient to changes and demand: With data residing in the cloud, modern data architecture needs to be resistant against server outages and disasters. Many cloud providers offer disaster recovery options and data backup capabilities so that your data is proofed against potential threats and risks.
Choosing Where to Store Your Data Warehouse
With data growing at an astonishing rate, data warehouses have grown to become a critical necessity to the modern organisation. However, should your data warehouse be stored on-premise, in the cloud or on a combination of both to best serve your needs? The answer isn’t a simple this or that. Instead, you should be considering multiple aspects of your organisation such as your data security, data volume, support required, control and scalability. Each comes with its own benefits and drawbacks, so be sure to weigh them against your organisation’s requirements before deciding.
On-premise
An on-premise data warehouse, as the name suggests, involves data being collected, stored and managed on-site at your organisation. Whilst this gives your organisation complete control and visibility into your data, it also requires a pretty hefty upfront front. You’ll be responsible for purchasing all of the hardware that is required as well as having to put together and train a team of staff to manage the servers. On-premise data warehouses are a more expensive option in managing your organisational data but you may choose to use this option if you want greater compliance and security. Scaling up your on-premise data warehouse is also much more difficult and time-consuming as it consists of purchasing additional hardware and installing them.
Whilst the cloud does offer tight data security, many organisations find it safer to store sensitive information on site to reduce chances of data leakage. Data governance and regulatory compliance are also easier to obtain with on-site data warehouses as you have complete transparency in where your data is located and making sure it adheres to company policies. On-premise data warehouses usually offer faster access to key information than cloud-based ones as they aren’t susceptible to network latency and potential wait times for server responses.
Cloud
Cloud-based data warehouses are often seen as a more attractive option for storing and collecting data due to their cost effectiveness and flexibility. With cloud-based data warehouses, there are minimal upfront and long-term costs as the model offers an on-demand pricing where you only pay for what you use. This gives your organisation on demand scalability and flexibility with the advantage of accessing a data warehouse solution at a low entry cost. You can scale your data up or down instantly with minimal hassle and associated costs. Aside from the cost benefits, the cloud also offers integration with other cloud services that can process multiple forms of data including semi-structured and unstructured data that on-premise warehouses are traditionally unable to. If your organisation has offices in different geographies, cloud data warehouses can also simultaneously process and serve data streams entering from disparate locations.
However, cloud data warehouses also come with their own set of drawbacks. As the cloud is operated by a third-party service, there are increasing concerns in regard to sensitive business information being exposed to risks with data being stored remotely and managed by an external party. The repercussions can be severe if the cloud’s security is breached by cyber attacks and the sensitive information to be stolen. As such, it is recommended that organisations operating with individuals’ confidential information such as banks and the government to keep their data stored on premise rather than on the cloud.
Hybrid cloud data warehouse architecture
By using a mix of the two forms of data warehouses, you’ll be enjoying the best of both worlds. By choosing to store both sensitive and frequently accessed information on premise, you’ll be able to ensure complete control over the data’s security and increase the speed of access. For organisations looking to work in an agile and fast paced manner, a hybrid data warehouse architecture meets the needs of scalability, flexibility and cost effectiveness that comes with unpredictable demands. You’ll be able to access data from both data warehouses and enable applications from both sources to be integrated for accelerated business analytics.
