Tag Archives: as a service

Blended Data Warehouse SW/HW Solutions Phased Into the Cloud

Relational Database Solutions “In a Box”

Several of the relational database software vendors, such as IBM, Oracle, and Teradata have developed proprietary data warehouse software to be tightly coupled with server hardware to maximize performance.  These solutions have been developed and refined as “on-prem” solutions for many years.

We’ve seen the rise of “Database (DW)  as a Service” from companies like Amazon, who sell Redshift services.

Amazon Redshift is a fast, fully managed data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing Business Intelligence (BI) tools.  It allows you to run complex analytic queries against petabytes of structured data, using sophisticated query optimization, columnar storage on high-performance local disks, and massively parallel query execution. Most results come back in seconds.

RDB Complex Software/Hardware Maintenance

In recent times, the traditional relational database software vendors shifted gears to become service providers offering maximum performance from a solution hosted by them, the vendor, in the Cloud.    On the positive side, the added complexity of configuring and tuning a blended software/hardware data warehouse has been shifted from the client’s team resources such as Database Administrators (DBAs), Network Administrators,  Unix/Windows Server Admins,… to the database software service provider.  The complexity of tuning for scalability, and other maintenance challenges shifts to the software vendor’s expertise, if that’s the abstraction you select.  There is some ambiguity in the delineation of responsibilities with the RDBMS vendor’s cloud offerings.

Total Cost of Ownership

Quantifying the total cost of ownership of a solution may be a bit tricky, especially if you’re trying to quantify the RDBMS hybrid software/hardware “on-prem” solution versus the same or similar capabilities brought to the client via “Database (DW) as a Service”.

“On-Prem”, RDB Client Hosted Solution

Several factors need to be considered when selecting ANY software and/or Hardware to be hosted at the client site.

  • Infrastructure “when in Rome”
    • Organizations have a quantifiable cost related to hosting physical or virtual servers in the client’s data center and may be boiled down to a number that may include things like HVAC, or new rack space.
    • Resources used to maintain/monitor DC usage, there may be an abstracted/blended figure.
  • Database Administrators maintain and monitor RDB solutions.
    • Activities may range from RDB patches/upgrades to resizing/scaling the DB storage “containers”.
    • Application Database Admins/Developers may be required to maintain the data warehouse architecture, such as new requirements, e.g. creating aggregate tables for BI analysis.
  • Network Administrators
    • Firewalls, VPN
    • Port Scanning
  • Windows/Unix Server Administrators
    • Antivirus
    • OS Patches

Trying to correlate these costs in some type of “Apples to Apples” comparison to the “Data Warehouse as a Service” may require accountants and technical folks to do extensive financial modeling to make the comparison.   Vendors, such as Oracle, offer fully managed services to the opposite end of the spectrum, the “Bare Metal”, essentially the “Infra as a Service.”  The Oracle Exadata solution can be a significant investment depending on the investment in redundancy and scalability leveraging Oracle Real Application Clusters (RAC). 

Support and Staffing Models for DW Cloud Vendors

In order for the traditional RDB software vendors to accommodate a “Data Warehouse as a Service” model, they may need to significantly increase staff for a variety of technical disciplines, as outlined above with the Client “On-Prem” model.  A significant ramp-up of staff and the organizational challenges of developing and implementing a support model based on a variety of factors may have relational database vendors ask: Should they leverage a top tier consulting agency such as Accenture, or Deloitte to define, implement, and refine a managed service?  It’s certainly a tall order to go from a software vendor to offering large scale services.  With corporate footprints globally and positive track records implementing managed services of all types, it’s an attractive proposition for both the RDB vendor and the consulting agency who wins the bid.  Looking at the DW Service billing models don’t seem sensical on some level.  Any consulting agency who implements a DW managed service would be responsible to ensure ROI both for the RDS vendor and their clients.  It may be opaque to the end client leveraging the Data Warehouse as a Service, but certainly, the quality of service provided should be nothing less than if implemented by the RDB vendor itself.  If the end game for the RDB vendor is for the consulting agency to implement, and mature the service then at some point bring the service in-house, it could help to keep costs down while maturing the managed service.

Oracle Exadata

Here are URLs for reference to understand the capabilities that are realized through Oracle’s managed services.

https://cloud.oracle.com/en_US/database

https://cloud.oracle.com/en_US/database/exadata/features

https://www.oracle.com/engineered-systems/exadata/index.html

Teradata

https://www.teradata.com/products-and-services/intellicloud

https://www.teradata.com/products-and-services/cloud-overview

Teradata
Teradata

DB2

https://www.ibm.com/cloud/db2-warehouse-on-cloud

IBM Mainframe
IBM Mainframe

Note: The opinions shared here are my own.

Human Evolution: Technology Continues to Transform Socieities for Generations

In the last 20 years, I’ve observed technology trends, and Tech achievements have risen and fallen from the mainstream.  Tech has augmented our lives, and enhanced our human capabilities.  Our evolution will continue to be molded by technology and shape humanity for years to come.

Digital Asset Management (DAM)

Everything you might find on your computer from emails to video are digital assets.  Content from providers, team collaboration,  push and/or pull asset distribution, and archiving content are the workflows of DAM.

DAM solutions are rapidly going main stream as small to medium sized content providers look to take control of their content from ingestion to distribution.  Shared digital assets will continue to grow rapidly.  Pressure by stockholders to maximize use of digital assets to grow revenue will fuel initiatives to  globally share and maintain digital asset taxonomies.  For example, object recognition applied to image, sound and video assets will dynamically add tags to assets in an effort to index ever growing content.  If standard taxonomies are not globally adopted, and continually applied to assets, digital content stored will become, in essence, unusable.

The Internet of Things (IoT)

All devices across all business verticals will become ‘Smart’ devices with bidirectional data flow.  Outbound ‘Smart’ device data flow is funneled into repositories for analysis to produce dashboards, reporting, and rules suggestions.

Inbound ‘Smart’ device data can trigger actions on the device. Several devices may work in concert defined by ‘grouping’ e.g. Home: Environmental. Remote programming updates may be triggered by the analysis of data.

  • AI Rules Engine runs on ‘backend’.  Rules defined by Induction,  through data analysis, and human set parameters,  executed in sequence
  • Device optimization updates, presets on devices may be tuned based on ‘transaction’ history, feedback from user, and other ‘Smart’ devices.
  • Grouped ‘Smart’ devices, e.g. health monitors’ data uploaded, analyzed, and correlating across group.  Updated rules, and notifications triggered.
  • Manual user commands, ad hoc or scheduled

… as a Service

Cloud ‘Services’ enables scalability on demand, relatively lower cost [CapEx] overhead, offsite redundancy, etc.  Provides software solutions companies to rapidly deploy to Dev., Test, and Prod. environments.  Gaming, storage, and virtual machines are just a few of the ‘…as a service’ offerings.  IoT analysis may reveal a new need for another service.

Human Interface

  • Augmented Reality A.R.

Integrates user to surrounding environment with overlay images to your eyes to REpresent anything, e.g. Identifies surrounding people with Twitter handle/user name above their heads.  Interacts with smartphone for Inbound and outbound data flow.  May allow App and OS programmers to enable users to interact with their ‘traditional’ software in new ways, e,g. Microsoft Windows 8+, current interaction with ’tiles’, may shift from a two to three dimensional manipulation and view of the tiles.  Tiles (apps) pop up when, through object recognition, predefined characteristics match, e.g.  Looking at a bank check sent to you from the mail?  Your Bank of America tile / app may ask if you want to deposit the check right now?

  • Virtual Reality, V.R.

As more drones, for example, collect video footage, may be used for people to experience the landscapes, beaches, cities, mountains, and other features of a potential destination, which may lead to tourism.  In fact, travel agencies may purchase the V.R. Headsets, and subscribe to a library of V.R. content.  Repository platform would need to be created.  Specs for the ‘How To’ on collecting V.R. Video footage should be accessible.  Hathaway real estate offers a V.R. tour of the house, from their office.

Autonomous  Vehicles (Average Consumer or hobbyist)

  • Cars 
  • Drones
  • Satellites 

Social Media Evolution

Driving forces to integrate with society puts pressure on individuals to integrate with the collective social conscious.  As digital assets are published, people will lunge at the opportunity to self tag every digital asset both self and community shared assets.  Tagging on social media platforms is already going ahead.   Taxonomies are built, maintained and shared across social media platforms.  Systematically tagged [inanimate] objects occur using object recognition. Shared, and maintained global taxonomies not only store data on people and their associated meta data, (e,g,  shoe size, education level completed, HS photo,etc.) but also store meta data about groups of people, relationships and their tagged object data.

The taxonomies are analyzed and correlated, providing better, more concise demographic profiles.  These profiles can be used for 

  • Clinical trials data collection
  • Fast identification of potential outbreaks, used by the CDC
  • The creation and management of AI produced Hedge Funds
  • Solicitation of goods and services

Out of Compliance

These three dreaded words you are guaranteed to see more and more often.  As all aspects of our lives become meta data on a taxonomy tree, the analysis of information will make correlations which drive consumers and members of society ‘out of compliance’.  For example, pointers to your shared videos of you skydiving will get added to your personal taxonomy tree.  Your taxonomy tree will be available and mandatory to get life insurance from a tier 1 company.  Upon daily inspection of your tree by an insurance AI engine, a hazardous event was flagged. Notifications from your life insurance company reminding you ‘dangerous’ activities are not covered on your policy.  Two infractions may drive up your premiums.