Key drivers of the unprecedented data explosion
The big data era is upon us. The large amount of data being generated, captured and processed is due to several simultaneous trends.
First, there have been many noteworthy innovations in the technology space. With unprecedented maturity and stability, technology adoption is consistently rising across industry verticals. Second, the device ecosystem is growing steadily to support both personal and professional needs in everyday life, resulting in the mass manufacture of both generic and purpose-specific interconnected devices with high cognition levels.
Edge technologies (such as sensors, controllers and chips) are capturing the imagination as never before. The digitization movement is becoming extremely pervasive, while disruption and transformation capabilities are on the upswing thanks to the smart usage of a number of groundbreaking technologies.
Big data challenges and opportunities
Organizations realize that big data has become widely recognized as a strategic asset. Big data can create data-driven predictive insights in multiple enterprises across the globe, and businesses are banking on these insights. Increasingly personalized insights are important as well. Aside from analytics, data virtualization, visualization, storage and mining are processes that help collect data in large quantities. In knowledge-based areas, insight-driven decision making has taken on greater influence. Specifically, cognitive data analytics is capable of uncovering better approaches and solutions for a variety of ongoing economic and social problems.
Pragmatic solutions
Big data offers multiple challenges that require practical solutions. Traditional data management systems are being revamped to accommodate big data-induced streaming issues, with clustered, distributed, parallel and analytical SQL databases emerging and evolving. Alternatively, newer database options such as NoSQL and NewSQL help simplify and streamline big data storage and analytics. For real-time and streaming data, there are standards-compliant and industry-strength solutions such as in-memory databases and data grids. For high-performance analytics, in-database processing platforms such as IBM Netezza have been successful. Hardware appliances, expertly integrated systems, converged infrastructures and specially engineered systems are part of the big data mix as well. For streams of business events and multimedia files, complex event processing engines are being recommended to unearth all kinds of hidden patterns. Enterprise data warehouses are another way to enable data analytics. Furthermore, powerful parallel file systems can comfortably store oceans of data. The mature Hadoop concept is being used to ingest and crunch massive data using proven parallelization techniques and commoditized hardware modules together. Finally, data marts and cubes—along with dashboards, consoles, report-generation tools and other knowledge-visualization platforms—are leading the journey from data to information to knowledge.
The onset of the cloud paradigm
The cloud paradigm is sweeping the entire IT industry. The remarkable adoption indicates that cloud is the most useful technology for establishing and sustaining IT infrastructure optimization. Web-scale and enterprise-class IT environments are meticulously tuned to be programmable, service-oriented, and then securely shared using cloud-centric technologies and tools. Other hallmarks of the cloud include high accessibility, and programmatically converged and simplified IT for greater and deeper consumption.
Why data analytics in public clouds?
Cloud technologies can give businesses an advantage when processing data analytics:
Scalability, agility and affordability: IT infrastructures for analytics are highly variable, so they must be automatically scalable (scale-out and scale-in). However, making a capital investment in on-premises IT may not be feasible; cloud is usually superior because of its flexibility, affordability and so on.
Hadoop platforms in clouds: Almost all the established Hadoop platforms (generic or specific, open or commercial-grade) are available in the cloud, so performing big data analytics using any Hadoop platforms is quick and simple.
NoSQL and NewSQL databases and data warehouses are in clouds.
WAN optimization technologies: WAN optimization products and platforms are available for efficiently and quickly transmitting data over an Internet infrastructure.
Social networking and media sites such as Facebook, Twitter and Netflix are running in cloud environments.
Prominent business workloads are steadily moving into public clouds.
Cloud-based object storage is very popular.
Purpose-specific clouds—such as sensor, device, mobile, knowledge, storage and science—are publicly available.
Clouds are software defined to be workload-aware, extremely extensible and adaptive.
Cloud integrators, brokers and orchestrators: There are products and platforms that promote seamless interoperability among distributed systems, services and data across geographically different clouds.
Cloud-based data management systems
More businesses and their IT teams are embracing cloud-based database systems. In larger organizations, it can take several weeks for a database system instance to be provisioned for a new development project, which limits innovation and flexibility. Database as a service (DBaaS) enables instant provisioning of the data layer, so businesses can begin their development work when necessary. DBaaS solutions provide and guarantee a specific level of data layer performance and uptime, which helps eliminates the risk of service delivery failure.
A second option is data warehouse as a service (DWaaS). IBM dashDB is a powerful data warehousing solution on the cloud that puts an analytics powerhouse at businesses’ fingertips. It helps reduce any infrastructure constraints and quickly boosts business agility. dashDB can help extend an existing infrastructure into the cloud or help businesses start new data warehousing self-service capabilities. It is powered by high-performance in-memory and in-database technology. dashDB provides the simplicity of an appliance with the elasticity and flexibility of the cloud for any size organization.
Considering the ongoing challenges of big data and real-time data, organizations must make sure they have a high-performance data management system in place.
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