words Alexa Wang
In the field of Big Data, the temptation to turn to “As-A-Service” is increasingly encouraged and fueled by the proliferation of ready-to-use Big Data Cloud offers with payment to usage.
After a first wave that saw large companies invest “on-premise” in Big Data technologies, mid-size companies also attracted by the sirens of Big Data, seduced by these first convincing feedbacks, are wondering about the reverse on the opportunity to outsource their “Data Factory”, not having internal means, resources and skills.
This temptation to turn to “As-A-Service” is increasingly encouraged and fueled by the proliferation of “ready-to-use” Big Data “Cloud” offers with “pay-per-use”. . This formula finds a natural echo with these companies, allowing them to overcome this complexity and gradually initiate their investments. These “As-A-Services” offers range from traditional IaaS (Infrastructure-As-A-Service) to the ambitious “Analytics-As-A-Service”, often verticalized to a business area (Marketing Analytics, Video Analytics ) through the classic PaaS offers (Platform-As-A-Service) which provide an “off-the-shelf” Big Data “stack” comprising the standard ecosystem (Hadoop-As-A-Service).
But the “As-A-Service” concept now goes further. It touches on the previously exclusively “on-premise” nerve centre of companies: data via “Data Lab-As-A-Service” type offers intended for players. Business lines, including some Big Data development services and a virtualized Lab environment, make its data speak. Some offers even go so far as to outsource the Data Science activity (Data Science-As-A-Service).
Despite this plethora of offers, today, there is no complete industrial managed service offer to outsource the activities of its Data Factory to a single third party in a logic of monthly cost, maintenance and support. Big Data uses, whether operational, decision-making / analytical, or even experimental. Is it so unique? Not that much. For a “Data Factory-As-A-Service” offer to be viable for all stakeholders, many ingredients, sometimes still missing, are necessary. Diceus.com is the one of the best website that can provide big data outsourcing services .
The Lack Of Maturity Of The Underlying Technologies
To ensure cost control, service commitments and backward compatibility in the face of technological developments, the maturity of Big Data has not yet been reached. However, the essential technologies, initiated by the data giants (GAFA) and which partly benefit from the “Open Source” model, are remarkably reliable and can be satisfied with “commodity hardware”.
In addition, the “Data Factory-As-A-Service” offer would not be complete without industrialization tools making it possible, among other things, to mask the plurality of technologies, to manage multi-tenant / multi-instance, to unify the management of data access security and centralized log management. A tool that remains today the weak link of Big Data outsourcing. Numerous industrialization components have to be specifically developed, almost across the entire Big Data value chain.
Efficient and secure network interconnections to absorb data flows are also essential if the Big Data software base cannot be projected onto the company’s infrastructures. Collocation of data remains a significant subject to limit transfer costs and transit times, particularly when the uses require real-time.
Indispensable Conditions To Be Offered By Publishers And Service Providers Choosing
Diceus is one of the best company that can provide Big Data solution and the right provider of big data outsourcing. Thus, the service provider must offer a catalogue of unmarked and industrialized usage models to control production costs throughout the value chain (acquisition, transformation, etc.). The “licensing” model of Big Data software publishers must also be adapted to pay-per-use, knowing that this type of offer already exists on the infrastructure part. However, many of them, mainly financed by venture capital, has not yet reached the breakeven point and are, in fact, not necessarily inclined to join this type of rental model, except to offer themselves “managed services” around their software offer.
. Must also integrate robust and efficient mechanisms (in volume and speed) for integrating flows, services and APIs to interoperate with existing systems, partners and the company’s authentication systems into the service offering. Finally, the ability to potentially offer freedom in choosing certain peripheral software components such as analysis tools appears essential for a successful transition from decision-making to analytics. Reversibility clauses must also appear in “As-A-Service” contracts to authorize the possible re-internalization of all or part of the Big Data activity, for example, to be able to re-internalize data management, carry out specific uses unmarked.
