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Azure Modern Data Warehouses / Platform

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?

With data driving the current business landscape, data analytics and algorithms have become essential to the livelihood and competitiveness of organisations. Data management has drastically changed in the past few years thanks to Big Data and traditional methods of data management are now unable to keep up with it. Traditional data warehouses often store data locally and undertake inefficient batch reporting to process incoming data which is simply not viable for Big Data. The 3 V’s of Big Data mean that huge volumes of data are being produced daily that encompass many different formats.  

Modern data warehouses can meet organisations’ growing needs due to their flexibility and scalability. They can store data in its raw form instead of the previous multilayer formats and handle Big Data whilst providing fast queries so that your staff can quickly perform their necessary tasks. Modern data warehouse’s ability to handle high volumes of data makes them especially useful for the increased adoption of Internet of Things applications where real time data is consistently being generated.  

Types of Data Warehouses

There are three main types of data warehouses, which are: 

Enterprise Data Warehouse

Enterprise Data Warehouses are central databases that gathers data from various sources and makes them readily available for analysis and usage across the organisation in a consolidated manner. It works by unifying data across various business functions and classifying the data according to specific subjects before giving access to the respective divisions – marketing, finance, HR etc. Data from all time periods – current and historical – can all be housed here, providing employees with a one stop shop for data analysis and business intelligence.  Antares helped deliver a seamless Enterprise Data Warehouse for GWA Group that helped them improve data accuracy and data processing.

Operational Data Store

Operational Data Stores (ODS) are used as an intermediary in synchronising data from unrelated and disparate systems for operational reporting. It acts as a source of data for Enterprise Data Warehouses by focusing on the operational needs of a business process and assists with decision making at the tactical and strategic levels. It is a volatile database that frequently updates and overrides existing data without keeping records of historical changes. As opposed to traditional data warehouses, an ODS captures data in real time and only performs simple queries, meaning the data is always current, up to date and lesser in volume.

Data Mart

Data marts are a subset of data warehouses that is oriented towards specific organisational functions, departments or subject areas. They make it easier and quicker for particular divisions to access key information without having to navigate the entirety of the data warehouse for the right data. As the information housed within individual data marts pertains to a singular department, this isolates each divisions’ use, access and manipulation of each other’s 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:  


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 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.

Contact us to find out more about the Azure Modern Data Warehouse

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 third party is responsible for all the hardware, software, support, and maintenance making it a cost-efficient method of storage

  • Low entry threshold

    As there is no upfront investment needed, organisations can reap the full benefits of elastic storage and scalability with a minimal entry cost. Traditional warehouses required organisations to shell out hundreds of thousands of dollars on servers, administrators and physical infrastructure making it a non-viable option for organisations on a tight budget.

  • Flexible nature

    Traditional methods of analysing large data sets would often involve incredible amounts of both computing power and resources, making it difficult and inefficient for evaluation. The beauty of cloud computing is that they are designed to handle data in a variety of formats and process it in a speedy manner thanks to Massively Parallel Processing (MPP).

  • Increased scalability

    Data is constantly being produced, at unprecedented speeds, and in a variety of formats. Due to the elastic nature of the cloud, modern data warehouses can accommodate changes in demands so that organisations will not be restricted by limits and miss out on growth opportunities.

  • Potential for new insights

    Data is power and modern data warehouses have the capabilities to both store and collect extensive volumes of data for analysis. Larger data sets can enable organisations to undertake more advanced forms of analytics such as preventive and predictive analytics which can greatly improve their competitive position.  By analysing past trends and forecasting the future, organisations are well positioned to minimise uncertainty and make better informed decisions.

  • Increased usability

    Rather than being used for the sole purpose of storing data, modern data warehouses are equipped with the capabilities to undertake data analysis and extract valuable insights. They provide considerable value to organisations by enabling them to quickly and easily understand what’s going on in the current environment and give directions for next steps.

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. 


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-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.  

Why Antares?

Not sure where to start with your modern data warehouse journey? Antares offers a host of Microsoft cloud services and can help you determine the right architecture and data model that can help you achieve your organisational goals. As a Microsoft Solution Partner, we have the experience in delivering modern data warehouses that meet your needs, enable you to perform data driven decisions, and improve your processes.  

Frequently asked questions

  • What is MPP?

    Massively parallel processing is a structure that involves many interconnected nodes that work independently of one another. The data and processing are split across many nodes where each node has its own operating system and memory. By working in parallel with one another, these small homogeneous nodes can process large datasets at the same time resulting in faster analytical queries. MPP is the common underlying architecture of modern data warehouses due to their efficient processing power and ability to analyse large datasets quickly.

  • What are the three data warehouse models?

    In traditional data warehouse architecture, there are three common models – data mart, virtual warehouse, and enterprise data warehouse.
    Data mart models are oriented towards specific business functions such as marketing or finance. They draw information from a few sources, storing data that is relevant and used by that organisation department.

    A virtual warehouse is another term for a modern data warehouse. It is a set of disparate databases that stores both structured and unstructured data in a singular location for easier user access and analysis.

    An enterprise data warehouse is a central repository that gather and stores an organisation’s data from multiple sources. By gathering enterprise data into a single location, users have ready access and availability to conduct analysis and gain data driven insights.

  • Which data warehouse is best?

    An Azure Modern Data Warehouse that leverages Microsoft’s Azure Synapse Analytics will provide you with a fast, secure, and agile cloud data warehouse that can easily scale according to your business needs. With Synapse, you will be empowered to unlock the full value of your data by applying machine learning and business intelligence tools. Microsoft’s advanced security standards also ensures your modern data warehouse is armed with the latest data protection measures. Data residing in Dynamics 365 and Office 365 can all be seamlessly integrated into your data models for a unified analytics experience. If you wish to diverge from the Microsoft suite, there are also other powerful integrated solutions such as the Oracle Modern Data Warehouse or AWS validated modern data warehouse architecture, Amazon Redshift, which deliver a complete end to end data analytics process.

  • Is the data warehouse dead?

    Data warehouses are not dead nor becoming obsolete. Data has become increasingly valuable in generating growth and valuable insights that drive core decisions for organisations. The need for data warehouses is only ever increasing as they streamline reporting processes, gather and clean data from multiple sources, and stores everything in a central location so that it can be readily used for analysis when needed. Whilst data warehouses will not be dead, there will be a considerable shift towards cloud-based ones built with agile methodologies such as those using Azure modern data warehouse reference architectures. The cloud delivers flexibility and scalability that enables your organisation to respond to changes in the external environment faster.

  • How is modern data warehouse architected?

    A modern data warehouse is commonly designed with three tiers – top, middle, and bottom. The bottom tier is the relational database where data is stored, cleansed, and loaded. The middle layer comprises of one or more OLAP server that provides an abstract view of the database. It acts as a mediator between the bottom and top layer by processing data and analysing it for use. The top layer is the user facing tier that consists of tools that allow individuals to conduct data mining, analysis, reporting, query processing, and more.

  • How does a data warehouse work?

    A data warehouse works by collecting, storing and managing data from various sources by providing a central platform for access and reporting needs. Data warehouse architecture can be fairly complex and is constructed using either single tier, two tier or three tier. The most commonly used architecture is three tier where the bottom tier cleans and normalises raw data so that it is formatted to a consistent structure before storing it in the warehouse itself. The middle tier contains an OLAP server that allows for fast querying between the end user and the data warehouse. The top tier is the front-end layer that provides the access tools for users in retrieving the data that they are after for reporting, analysis and data mining purposes.

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