In the age of big data, we have established a new world on the cloud. In lieu of the various benefits offered by the cloud ecosystem, data lakes are migrating to the cloud.
A background research analysis
A few years ago, dark clouds loomed large on the future of Hadoop and it was believed that Hadoop would become an artifact in the future. A survey by Forrester concluded that Hadoop had witnessed one-third of the growth in the last 3 years. The qualitative and quantitative research yielded contrasting results. These results became stories of change for Hadoop. By revising its expectations and chalking out a new plan for analyzing a voluminous amount of data, Hadoop had taken the first step towards its redemption. It started to provide cloud technologies with a high degree of flexibility at the lowest possible cost. The other service providers like Amazon and Google started feeling the Hadoop wave in the market. Technological giants like Google and Amazon started to analyze this shift of data lake to the cloud and the number of benefits that it could bring. In this article, we take a look at different reasons due to which data lakes are moving to the cloud.
The ease of operation and the cost factor
The erstwhile ecosystem of Hadoop was relatively difficult to manage. At the same time, it was expensive as well. It required knowledge about the programming language of java. There were other complications as well and these halted effective operations. That said, different types of projects needed inputs from data scientists and data engineers. This put the organizations in a very difficult situation. In addition to this, the inefficiency of operation with small data sets puts additional pressure. Lack of security and performance issues added to organization woes. The situation demanded a cloud data lake that would require relatively lesser technical dependencies and would be easy to manage.
It was also observed that companies invested heavily in hardware for storing and processing data. The cloud-based environment could do away with the need for making investments in hardware and businesses may choose from a gallery of products and services by just paying for the products and services they use. Other types of maintenance costs would also be avoided as they would become the responsibility of the service provider. The computing costs and latency rate would also come down. The testing of new software in the cloud data lake would increase efficiency and efficacy while simultaneously reducing operational costs.
The technological premise
In the present times, data is segregated across various platforms. It is very difficult to move data using the traditional tools which rely on extract, transform, and load methodology. The increase in the response time and an increasing number of delays make it clear that cloud solutions are the need of the hour.
As the cloud of big data is starting to inflate, mature cloud technologies are taking birth. These evolving technologies enable data integration and transformation in real-time and with a low latency rate.
At a time when the technological premise of the modern world rests on artificial intelligence, a cloud data lake can prove to be a suitable option due to its adaptive capabilities for machine learning and deep learning applications.
Data privacy and security cannot be compromised in a time when sensitive data is a lifeline for businesses. The authentication of data and its right to access needs to be managed in a customized manner. Different types of cloud service providers are taking security and governance architecture much more seriously than ever before. Robust encryption technologies are adding new security features to the data architecture.
The dimensions of scalability
If we want to configure and accommodate more data in the on-premise data lakes, it would be a cumbersome task. The accommodation of additional users would also prove to be difficult. However, we don’t experience such problems in the cloud environment. The public cloud providers offer the option of scalability which enables expansion at any point in time. It has been observed that auto-scaling features are also provided by different service providers which enable us to rope in a large number of users at very minimal costs. Hence, the scalability of cloud infrastructure not only supports a large number of technologies but also handles a voluminous amount of data.
The erstwhile model of on-premise data lake has not been abandoned but has become redundant. So, a movement of data lakes to the cloud environs is what modern technologies are aiming at.