Reconstructing Woolly Mammoth (in the cloud)

The idea behind the Woolly Mammoth Project lies in re-making genes, building blocks of life, and rebuild a monolith. A similar approach can be adopted in re-hosting an application Monolith by decomposing it into micro-services and reconstructing that application functionality in a manner that is more scalable, efficient and repeatable.

Let’s take a closer look at the ‘story’. You start with a legacy application that has grown out of manageable proportions or is even outdated. Then you identify critical portions that can behave autonomous. Identify cross-cutting services like logging, caching, monitoring which can be re-purposed as individual API-exposed services. And for the core application logic – few considerations can be:

  • Aggregation
  • Data services
  • Orchestration
  • Messaging
  • Integration

Such a ‘Peel & Replace’ approach can now help your organization resurrect The Monolith. While the Woolly Mammoth project may reduce carbon emissions, this new micro-services architecture can

  • Help build product /releases incrementally
  • Build upon existing capabilities by adding new features
  • Enable faster data and quality checks
  • Easily integrate any third-party products

Project Smart Cloud

What happens when you add cognition to your cloud resources? This can be similar to asking what an ideal scenario for optimized cloud may look like.  Sometimes it’s good design, often good operations and seldom anti-pattern.

SmartCloud

 

 

 

 

 

 

One can add cognition to your cloud by-

  • Adding context driven analytics leveraging engines from IBM Watson, Google analytics or custom models.
  • Bring in Deep learning to unravel patterns otherwise easily missed by only relying on single source of truth i.e. data.
  • Combine structured and unstructured dark data for analysis to get associations and relationships helpful to your business.
  • Incorporating multiple streams of data like IoT sensors to social and mobile data feeds into the “learning” algorithm.

 

 

To embrace big data, stop obsessing on small data

As monitoring and alerting matured, the focus was on gathering & logging health metrics to the smallest quantum of time or other units. This led to increased awareness of state of a system in real-time. However that became insufficient to deliver innovative or disruptive business ideas required to establish market leadership.

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Much of an organizations efficiency lay in its ability to deliver operational excellence. This was typically supported by technologies which monitor, alert and report data on ‘state-of-the-company’. However those who analyzed and learned from these data points became more resilient and soon their ecosystems functioned at the ‘next level’. When their tools & technologies acquired this habit, it became a machine learning experience. When they added speed, they learned faster. When they added non-correlated data sources (e.g. social chatter), they learned newer ways of reinventing the way they did business. As a result almost anything on the planet became a data source. Slowly data had acquired volume, velocity and variety.

The goal-post was moving from capturing small data points to getting overwhelmed with big data sets. To embrace this reality and be the next leader in disruption, one has to have the right tools, references and processing stages working on data. Some in telecom arena may liken this deluge to extracting “signal” from “noise”, the difference being “signal” was unknown and waiting to be discovered.

One can help connect these dots of big data by getting smart on-

  • Using right tools for data gathering. Different cloud providers provide services for ingestion of data whether it is transactional, real-time or batch.
  • Using right storage to save data. Data may need to be stored structured/raw, transient/long-term, distributed/columnar.
  • Using right processing tools. Data may come in batches or streaming fashion and each sample will provoke questions and produce answers (predictions).

How good is your digital platform? (read PaaS)

If 2015 is predicted as year of PaaS, it may be worthwhile to know why phenomenons like network effects, social interactions, inbound servicing or long tail distribution can alter the way we do business in a digital economy.

By understanding what constitutes your platform business model, one can ascertain how ready it is to serve in a digital economy.

 

One of the key tenets in a platform world is building interactions FIRST. If your business connects producers (i.e. developers, testers etc.) to consumers (Business leaders etc.) in multitude of different ways like access from any device, self-served provisioning etc. then you have natural probability to reduce cycle time between idea and execution. When your cloud platform allows multiple interactions it increases enterprise flexibility, drives structural change and fosters real-time go-to-market products. Decoding the “PaaS” landscape requires enterprises to not just host applications in cloud but rather transform them before hosting.

A non-exhaustive list for PaaS stack could contain-

  • Loosely coupled services (i.e. micro-services) between tiers. Each micro-service can have its own dependencies. This is in contrast to traditional applications which may not be agile.
  • Responsive and reuse of applications or services – having automated deployments enables faster response to market demands and scaling as needed. Load-balancing and sharing promotes reuse of common services across business units improving applications that use these services.
  • Polyglot experience – The more languages, databases and operating systems a PaaS platform supports, easier it becomes for legacy applications to take the leap.

“Things” is the new XaaS

Viewing the Digital and physical world as one or as they call it having a Yin and Yang for completeness is the new wave in ‘Internet of Things’ (IoT) revolution. It is a structural change to which whoever adapts shall succeed in unlocking greater value from the physical and digital universe.

IOT

By using location and device independence characteristics of cloud computing, one can attempt to expand definition of XaaS to encompass IoT components like communication equipment, infrastructure devices, data and platforms.

A world of physical objects (industrial, medical, automotive etc.) whose digital data is seamlessly integrated into your information network then stored, processed and intelligently consumed makes the case for IoT in the cloud.

IoT has many layers that can feed data –

  • Actual objects which have a digital adaptor to transmit data
  • Sensors which are attached to objects to convert and transmit analytical measurements such as temperature, voltage etc.
  • Communication path equipment such as wireless modem chips (3G/4G), USB, HDMI etc.
  • Data aggregators and compute machinery which may reside in a public or private cloud

Combining agility and expansiveness of cloud ecosystems, one can expect IoT to drive

  • Greater efficiency
  • NEW services and solutions
  • Increased customization leading to digital personalization