Fluidity: The key to continuous confidence in cloud analytics
No one wants to be overtaken by events. Adaptability is the confidence that we can roll with the flow of changing circumstances and also proactively shape the future before it’s too late. Modern businesses know that data analytics is a key enabler for continuous, proactive adaptation to changing circumstances.
The cloud is the key to business agility, and a well-designed cloud analytics platform can provide the confidence that every user, decision and business process is fluidly taking the best course of action based on the most relevant data, the most predictive analytic models and the most comprehensive context at all times. The power of data-driven agility, which we see implemented, for example, in the data-driven decision-automation clouds that power modern multichannel retail, increasingly depends on cloud analytics of unprecedented sophistication and flexibility.
Fluidity is the degree to which your cloud data analytics resource can be rapidly and cost-effectively repurposed and reconfigured to respond to and proactively drive change in a dynamic world. The principal characteristics of a fluid cloud data resource include:
- Ability to rapidly, transparently and securely query and perform higher-order analytics on any cloud data resource from anywhere in the cloud
- Ability to distribute cloud data analytics resources across multiple data centers, public cloud providers, private and public cloud data centers and other platforms, nodes, clusters and zones
- Ability to reconfigure and re-optimize your cloud data analytics resources rapidly, easily and automatically without recoding and without impacting users, applications and service levels
- Ability to architect your cloud data analytics resource to take advantage of distributed hybrid deployments, maximize openness and interoperability, eliminate constraints and bottlenecks to growth and throughput and avoid lock-ins to particular platforms and locations
Why is fluidity in cloud data analytics so important?
For starters, avoiding lock-in addresses the reality that the data platform you deploy today might not be the best choice in the future. Cloud platforms and markets evolve. Being able to relocate all or part of your data layer to new platforms allows you to keep it best in class.
Also, fluidity helps you to take advantage of continual improvements in the economics of cloud data analytics. Public cloud pricing is very competitive, and having a fluid cloud environment allows you to shift data management to different cloud providers in order to benefit from fluctuations in cloud pricing and exchange rates.
Furthermore, regulatory factors can affect where you locate your data layer. For example, evolving data provenance laws in some countries require data to be hosted on “native soil.” In those cases the fastest path to compliance might be to extend your data layer to a public cloud data center within the country, rather than establishing your own data center there.
Fluidity also enables greater business continuity, hence confidence. After all, servers fail. Network connections fail. Data centers go offline. In order to keep serving users and generating value, a fluid data layer includes replicas of data in different locations that can handle data requests in the event of failures in other parts of the data layer.
There are plenty of other benefits to cloud data analytics fluidity, such as its ability to boost data mobility and 24/7 global reach of data-driven applications.
The data warehouse (DW) is a centerpiece in any cloud fluidity strategy. Fundamentally, the DW can deliver maximum business benefit as a decision-support asset if it delivers 24/7 fluidity in data acquisition, transformation, loading, access, query and analysis functions. Without this fluidity, your users would soon grow frustrated by bottlenecks that prevent them from making evidence-driven decisions.
For an enterprise DW to support fluid delivery of data-driven insights, the enabling infrastructure needs to be engineered with simplicity, scale, speed, interoperability and usability in order to eliminate any obstacles to maximum value. In the drive to modernize their DWs and address emerging requirements, enterprises may risk adding complexity that inadvertently impacts the productivity of DW users, administrators and other stakeholders.
The fluidity of the logical DW depends on core interfaces, infrastructure and tooling that span the entire architecture, no matter how complex the underlying hybrid assortment of relational, Hadoop, NoSQL and other data platforms. Chief among these enablers of logical-DW fluidity is SQL, the data access, query and manipulate lingua franca of databases everywhere. SQL now pervades the Hadoop market thanks to initiatives and interfaces such as IBM Big SQL.
A fluid query layer that spans the entire logical DW would eliminate several obstacles to user and administrator productivity. It would avoid the need for users to query two or more separate data platforms and then either manually combine the results or have someone in IT implement a “data munging” tool to do that in a more automated fashion. If the unified query interface is combined with a fluid ability to move data back and forth between relational and Hadoop platforms to ensure optimal utilization of available LDW capacity, queries and all the supporting back-end data movement and transformation processes can operate much faster and more efficiently.
With that in mind, I urge you to check out the recently launched IBM Fluid Query, which demonstrates that this dream is now a reality. This new solution gives DW administrators new power to choose the underlying data platform, PDA or Hadoop, which is best suited for each type of query, data and workload.
If you’re an existing PureData System for Analytics customer, download IBM Fluid Query today.