How to get going with Modern Analytics Sandpit Environment using Azure in no time…


The objective behind referring to this environment as Modern Analytics Environment is due to the fact that it covers all types of Analytical projects whether Big Data, Modern BI/DW, Data Science and Advance Analytics.

It will be 3 blog post series, starting with the purpose then details of the approach and lastly a working example / Solution (using ARM) covering all the aspects discussed in this blog post series to get going on the Modern Analytics journey.

By no means this approach is recommended for production environments as it’s merely to get organisations started with a sandpit environment for doing mainly experimentation around Data Science and Advance Analytics related use cases.

I have been thinking of writing this blog for a while reason being in all my recent customer interactions irrespective of organisation size or sector there has been a single biggest challenge stopping them to get going mainly on their Data Science and Advance Analytical journey is “How do we get started”?

“How do we get started” question can very quickly unfold into an extensive debate seeking all eventualities, hence, in a lot of cases leading to inconclusive outcomes. Just to give a flavour of those, discussion could be in the form of queries like “How do we do different analytical projects within same environment?”, “How do we ingest data?”,”How do we store data keeping our organisation and source of data in mind?”, “How to catalog data?”, “How could Data Scientist access the relevant data and do exploratory analysis with ease to find key insights?” “How to ensure secure access to the data and manage azure data services spend?”, “How could the environment follow DevOps process?” and the list could go on and precisely for this very reason, to just “get going”, I have adopted a rather simplistic still solid enough approach in building an effective Modern Analytics sandpit environment which allows organisations to start transforming raw data into intelligent action and reinvent their business processes in a very quick efficient manner. And, once the organisation maturity level starts improving the environment and processes can be transformed for much coherent Modern Analytics projects delivery / management practices because the foundations of the environment will still still be intact.

The key and sole purpose of this blog is to provide a Modern Analytics sandpit environment boilerplate keeping industry best practices and basic enterprise readiness requirements in perceptive i.e. Data Governance, Security, Scalability, High Availability, Monitoring, Lower TCO and most importantly Agility.


Here is the background to rather simplistic still solid approach for the proposed Modern Analytics Sandpit environment:

  1. Information Management Approach
  2. Data Lake Framework
  3. Architectural Components or Azure Building blocks for the Environment
  4. Example Project (Cisco Meraki)
  5. Key Personas (mainly Data Science and Advance Analytics)
  6. Modern Analytics Project Delivery Process (mainly Data Science and Advance Analytics)

The environment code will bet available via a Github repository later so watch this space.


Now, before we go into details of each one of the core areas of the above mentioned approach, here is the high level view of the proposed Sandpit environment using Azure Data & Analytics offering to keep things in perspective for next blog post:


Also, just to highlight here that the above high level architecture diagram represents the logical grouping of resources using Azure Resource Groups as an example implementation, the above implementations can be different depending on Organisation/Teams structure, Security/compliance etc.

Azure Resource groups are logical containers that allow to group individual resources such as virtual machines, storage accounts, websites and databases so they can be managed together.

The key benefits of the Azure Resource Groups are in the form of Cost Management, Security, Agility, Repeatability etc.

For complete benefit details please click on the following link:

Majority of the Services used in the proposed high level design are either PaaS or Managed Services to provide lower TCO and greater agility in deriving key actionable insights.

All PaaS services include cloud features such as scalability, high-availability, multi-tenant capability and Resiliency

Resource Group Details Technologies to be used
1 – rgCommonDev Shared resources to be used by all types of projects mainly for Information Management perspective and most important as well
  • Azure Data Lake Store
2 – rgAnalyticsTeamDev Development resources required for the Big Data, Data Science and Analytics Projects
  • Data Science Virtual Machines times no. of Data Scientists/Engineers
  • Azure Blob Storage
3 – rgMerakiDev Resources required for delivering the Meraki project which is to demonstrate a type of Advance Analytics example project
  • Web App
  • Event Hub
  • Stream Analytics
  • Azure SQL
  • Power BI
  1. rgCommonDev

In this resource group, the only resource will be Azure Data lake Store as that’s the most important and critical part of the implementation. Any data ingested from multiple data sources will be stored in here in an organised manner based on best practices / patterns.

More details will be added in the next series of this blog post Data Lake Framework process regarding the structure of the Data Lake.

2. rgAnalyticsTeamDev

rgAnalyticsTeamDev (Resource Group) contains 1 Data Science Virtual Machine but this is bare minimum setup and could be altered based on number of Data Scientists and Data Engineers usage. Regarding, Blob storage it will be used for storing ad-hoc data files, artefacts related to development environment.

Mainly, DSVMs will be used by the Data Scientists to perform exploratory data analysis by accessing data stored within Azure Data Lake Store, finding patterns and creating predictive models.

Talking explicitly around Azure Machine Learning & AI Platform portfolio the following image shows landscape and can be employed depending on organisation needs but to keep things simple have gone with DSVMs (details in next blogpost).

AI Platform Stack

For further options regarding machine learning offerings see here

3. rgMerakiDev

 The sample project to be deployed in this environment is Meraki. I have blogged earlier regarding this project here. This project helps demonstrating:

a) How different analytical projects can be deployed in Modern Analytics Environment side by side with completely separate governance framework

b) It’s also got the Lambda Architecture implementation which helps in showcasing the underlying Data architecture and framework whilst using Azure Data Lake store.

The key components of the project are: Web App, Event Hub, Stream Analytics, Azure SQL DB and Power BI. All these resources are already wrapped in ARM template within a Visual Studio Solution to automate the deployment process and ease of repeatability reasons which can be accessed separately by going to Github repository.

This project will also help in developing better understanding regarding the environment and processes involved in managing it going forward.

Details of the Modern Analytics approach mentioned above to follow in next blog post…

Just to give a quick glance of the Modern Analytics Pipeline in Azure with multiple offerings in each stage.

Azure Modern Analytics

Some relevant blogposts:

Buck Woody’s DevOps for Data Science series

Making sense of the swamp




IoT in Action – In Store Location Analytics on Microsoft Cloud

In store Internet of Things (IoT) analytics is a key area for retail organisations.  The nature of retail stores and the importance of maximising investment of staff and retail space makes this one of the leading areas in the IOT space.  However, getting started can seem daunting for many IT teams:  “What equipment do we need?” , “How do we collate the IoT data streams?”, “What should we measure?”.  This post introduces a Microsoft Cloud (Azure) based GitHub IoT project which acts as an end to end example of an IoT in store analytics implementation as well as demonstrating how Azure PaaS (Platform as a Service) Services can be used to quickly implement Enterprise class IoT solutions.

GitHub project

If you want to get straight into the project it’s available on GitHub as “Azure PaaS Implementation using Lambda Architecture of Cisco Meraki In-Store Location Analytics“.  The project is fully documented and self contained.

Overview of Solution

Almost any public place can become a “smart building“, retail stores, universities, hospitals can all benefit from implementing IoT devices & sensors.  This particular solution solves a common retail IoT problem which can be expressed as:

“Provide a chart of customer footfall in real time across selected areas in store” 

However, although that specific issue is addressed this solution more broadly demonstrates the art of the possible in IoT Analytics on Azure.  The implementation can be extended to apply to any building equipped with WAPs (Wireless Access Points) and a number of analytics objectives including:

  • Staff Optimisation
  • Store Layout Optimisation
  • Product Recommendation

As well as many other advanced use cases. The project delivers an end to end Azure based solution for IoT analytics from initial capture of events via the in store WAPs, real time analysis of the event stream, archiving of events onto a persistent storage layer and finally visualization of the real time & historical combined results.  Throughout, Azure PaaS services (in conjunction with Cisco Meraki Cloud) are used to provide a scalable, robust, extensible IoT analytics platform.The outcome of the solution is the result shown below, a foot fall chart operating in real time:

chart 1

Note: Azure PaaS & Cisco Meraki Cloud – Azure PaaS and Cisco Meraki cloud are the 2 main technology stacks used in this solution.  Cisco Meraki Location Analytics displays real-time location statistics to improve customer engagement and loyalty across sites, and is built in to Cisco Meraki Access Points with no additional cost or complexity.

Cisco Meraki does offer some insights out of the Box, however, this approach extends more in-depth analysis by having the flexibility to correlate other data sources on top of events data for richer / deeper actionable insights. Also, this solution can be extended to in-corporate other Vendors similar to Cisco Meraki in similar fashion.

Azure PaaS Services are part of the Microsoft Azure cloud platform.  They offer robust and extensible capabilities which are cloud first in design, meaning they are scalable, serverless (no patching or maintenance of VMs is required) and typically operate on a “pay as you go” model.


The diagram below gives the high level architecture for the approach, which follows the established Lambda Architecture.  Briefly, this architecture approach divides data processing into  “speed” (near real time) and “batch” (historical, cleansed, aggregated) layers.  This design is well established and is a relatively common implementation pattern on Azure.ArchitectureThe general workflow is as follows:

Note: This  solution comes prepackaged with sample Cisco Meraki event data which is sufficient for end to end testing & evaluation.  However the solution can easily be integrated into a working Meraki installation.

Cisco Meraki tracks events as customers/visitors move around the building – each customer will lose one Wireless Access Point & acquire another and this loss/acquisition translates into movement around the building.  These events are passed in real time (1) to Azure and collated using an Azure Event Hub instance (2).  In turn, the event hub forwards the events to Azure Stream Analytics, the azure real time event processing engine which can operate on individual events or aggregated events over a rolling time Stream Analytics workflow

Following the Lambda architecture the Stream Analytics engine now routes its output to the “speed” (5) & “batch”  (4) processing streams.  In the “batch” stream events are captured for historical analysis on both Blob storage and an Azure SQL Database instance – from here, events can be cleansed, merged with other data or aggregated for further analysis.Meanwhile the output of the real time analytic processes is also forwarded directly to a Power BI Dashboard.  Here it be displayed as a real time data stream however it can also be combined with data from the historical store (6).As mentioned above, the outcome is a real time chart showing footfall for the selected area in the building.

Deployment & Outcome

As mentioned the GitHub solution is fully self contained and documented.  It includes all necessary code, sample “real time” Meraki Cloud data and provides step by step deployment guidance.

Azure PaaS Implementation using Lambda Architecture of Cisco Meraki In-Store Location Analytics

Example guidance

The upfront requirements (Azure subscription, Power BI etc) are listed in the documentation.  At the end of the deployment (which should only take a few hours) you’ll have a full end to end Lambda compliant Azure Cloud based IoT solution.  You’ll also have worked with some of the key event & IoT processing engines within Azure as well as the Power BI visualization tool.  Together this should provide a solid foundation to build richer and more complex IoT analytics as outlined in the introduction.