4 Things You Should Know About Cloud Services and Big Data
Fortunately, today’s cloud management services can leverage new tools and processes to seamlessly support the needs of big data. These challenges can manifest in different ways depending on the cloud strategy of the enterprise. By taking a closer look at the pressing big data needs and the available options, organizations can see how cloud services are adapting to meet the need of the data center industry.
Big Data Transfer Solutions for the Cloud
To become a practical option for big-data management, processing and distribution, cloud services must provide high-speed transport mechanisms that address two main bottlenecks:
- Distance-based WAN transfer speed degradation due to traditional transfer protocols
- The “last foot” bottleneck inside cloud data centers caused by the HTTP interfaces to the underlying object-based cloud storage
To tackle these challenges requires an acknowledgement that the data needs of every business are different. That’s why there are cloud services that can provide transfer solutions capable of solving WAN and cloud I/O bottlenecks by facilitating large data set transfers in and out of the cloud. Moreover, these solutions make it possible to attain bi-directional transfers at line speed from any location in the world.
These high-speed software bridges can transfer data at line speed, from the source directly into cloud storage with no hops or stops in between. With strong partnerships with cloud services providers, these PaaS providers can streamline and simplify fast global connectivity.
Modern fully managed data transfer solutions can provide capabilities such as content availability, synchronization, backup/restore and disaster recovery while minimizing latency and providing secure data transfer. In addition, these services are designed for scaling to accommodate large volumes of data.
Cloud service providers and their cloud management services can deliver granular control of transfer rates and bandwidth sharing in addition to bandwidth utilization visibility for enterprises. These transfer rates can be guaranteed as a part of SLAs regardless of the network distance or its condition.
With the varied roles of big data in the data center, analytics capabilities due to IoT as well as meeting regulatory compliance for privacy needs must be adhered to by SMBs and global enterprises. This has prompted cloud providers to ensure that complete security is baked in, which can include:
- Secure endpoint authentication
- On-the-fly data encryption
- Integrity verification
The move toward multi-cloud and hybrid cloud frameworks have increased agility as well as lowered potential CAPEX and OPEX for organizations looking to minimize their data center footprint. Big data needs can fit into those frameworks in ways that work for near and long-term operational efficiencies and business case agility.
Big Data in Multi-Cloud Frameworks
Businesses are increasingly moving to multi-cloud strategies to make the most of data flows
rather than data that is just processed and deposited into a database. Cloud and colocation are providing business with comprehensive access to multiple kinds of computation and data from many sources, which creates a foundation for building these large-scale, flow-based systems. Cloud services providers are now concentrating on solutions that focus data-in-motion and data-at-rest needs for multi-cloud computation.
Companies with a global footprint can take advantage of cloud computing services that are enabled by incoming IoT data from countless endpoint locations while having a single management point that combines authorization, user management, and access control. More companies are seeing the advantages of content ingest and sharing for private, public, hybrid clouds with complete flexibility in data placement and transport.
These multi-cloud approaches require comprehensive user and server administration capabilities and enterprise- grade security and optional encryption of the file content over the wire and at rest. Cloud management providers can deliver the framework for coordinating and managing a variety of user interfaces and applications to send and receive these digital deliveries.
Big Data Cloud Management Options
Cloud services providers are now making it easier to make the most of serverless cloud as part of big data architectures. This can include on-demand smart application deployment and IoT data analysis. The growth in the use of smart process applications such as AI-driven BPM that can provide analytics for IoT-generated data is spurring cloud computing services providers to deliver the needed responsiveness via serverless cloud architecture.
Serverless cloud adoption brings the benefits of CAPEX and OPEX reductions of server utilization maximization along with the reduced management and maintenance costs associated with IT personnel needs. When taken with the compute and processing agility that big data analytics requires, serverless computing delivers efficiencies that drive data center industry and business innovations.
Benefits of Cloud Management Services for Big Data
The use of cloud management services can take much of the burden off of organizations when it comes to big data by removing the need for building a team to manage your own data center or private cloud. As cloud-based big data solutions converge into integrated offerings through cloud management services, organizations reap the benefits of agility while reducing complexity and accelerating time to value. For example, more solution providers are delivering standardized APIs for:
- Simplifying access
- Accelerating development
- Enabling more comprehensive administration throughout their big data solution stacks
- Strengthening encryption and security
Other are providing cloud-based, end-to-end managed services that are designed to accommodate big data warehousing. These approaches include agile connectivity, encryption, and low latency data streaming solutions between on premise data centers and cloud data centers. By removing bottlenecks, these services pave the way for effective big data deployments.
Another challenge faced by organizations is a lack of in-house expertise for provisioning and maintaining complex Big data platforms and technologies. Even those organizations with large IT teams must find a way for them to concentrate on other pressing core business projects. That means finding a way to devote less time to running technology to deliver insights from big data analytics projects.
The shift from big data to fast data and continuous data-driven applications through emerging cloud services will enable continuous data ingestion and use by a wide variety of sources that may be scattered across the globe. This sets the stage for analytics that can aggregate, contextualize, and enrich data in real-time via AI models. The end result for organizations is the ability to gather immediate user response generations that drive informed business and market actions.