Moving the data lake into cloud is known to have a wide array of benefits which are inclusive of agility and cost effectiveness. If you want to reap the benefits of your data lake, you need to find the right option to structure the Data Lake architecture in the cloud. It is different, as compared to the regular architecture.
In addition to this, it is not possible to accomplish the movement to the multi-cloud environment or cloud-based data lake in one go. It is essentially a journey which happens over the due course of time. Here are few reasons why it is recommended to consider moving the modern data architecture in the cloud:
Dynamic processing or the agile pay for use
Flexibility and agility are regarded as the primary features of a good cloud environment. As you opt for cloud, you require making payment only for the compute that you use. For instance, you can begin with a cluster of 20 nodes after which you can increase that to 100 nodes, in accordance with the change in the requirements.
It is also possible to scale down, in accordance with the requirements as well. With the other models, it requires making payment for a particular time period alone. For instance, if you are looking for computing for just two hours, with an eye on the back end, you need to make the payment for the time session of two hours only.
Cost efficient compute and storage of data
Speaking of compute and storage of data, the cloud is known to stand apart from the on-premise data lake. With the on-premise data lake, you will find that the compute and the storage are the same nodes, regardless of whether the cluster is in Map R, Cloudera or HortonWorks.
Therefore, in case you have a cluster of hundred nodes, it plays a vital role in the storage of the data. It is beneficial to perform computation. This means you are on the path to having a separate computing service and storage in the cloud.
It is owing to the fact that the storage involves a reduced cut off from the pocket in the cloud whereas the compute burns a huge hole in the pocket. Thus, for such an operation, you need to think differently for the architecture of the data lake.
Infrastructure agility
In addition to on-demand processing, you can avail the on-demand infrastructure for the cloud. Thus, you will be capable of starting small, grow in accordance with your requirements. In addition to this, if you come across a scenario, where it is crucial to cut back, it is quite easy to do this as well.
The latest technologies
The cycles to refresh the upgrade may be long as you need to plan different dependencies and aspects. The cycles which should be refreshed for the update are inclusive of software, operations and infrastructure, to name a few.
However, there are a plethora of cloud service providers who have earned a high reputation in offering services from the vendors owing to which it becomes really easy to upgrade without having any effect on the entire solution.
There are service providers having data lake architectures which are cloud based and they are capable of upgrading to the latest version of Hadoop within the time duration of few days.
Regulatory and compliance requirements
Privacy and security of the cloud is recognized to be a major concern for enterprises. The clients wonder whether the cloud is really secure or not. They also wonder if it is possible to share or trust the data, present in the cloud.
The movement of data lakes of the healthcare and financial companies to the cloud might result in pushing the vendors of cloud technology for achieving different certifications for privacy and security over the time. At present, you will find that majority of the regulatory and compliance requirements are baked in already and are offered by the leading cloud vendors.
Geographic replication
One of the primary reasons to move to the clouds is that the vendors of the cloud are equipped with the cross-regional, cross country data recovery strategies as and have applications to do this in place as well. It is an indication that you do not need to maintain the data center to ensure the resiliency in case of a disaster. Movement of a Data Lake into the cloud is certainly not an overnight process and it should not be so as each business has unique challenges of its own. A wide array of business firms move through four phases of the journey from the regular architecture to the modern one. It helps in leveraging multi cloud, Greenfield, hybrid, and full cloud.
In the Greenfield phase, the best choice is to start with the smaller use cases set, present in a certain line of business after which the infrastructure will be moved to the cloud. Once success is demonstrated with the use cases, it is possible to use the same for getting extra buy-ins from the management with an eye to move to the next phase.
In the second hybrid stage, you need to have a certain data percentage in the cloud whereas the remaining part should be present on the premise. You require an ample amount of time to figure out the strategy for the team to process and speed of movement into the new environment. You require building the integration with the on-premise platform and the cloud for bestowing satisfaction to the requirements of the client and the user.
In the third phase, you require applying the learning from the hybrid experience for getting the data lake in the cloud fully and seeking all the benefits. After moving to the cloud, the journey is not known to be static at the phase as the technology is always innovating and changing.
The multi cloud environment happens to be the final phase in which you have to find out ways to make the platform agnostic to allow the data movement between the technologies and the cloud providers.
