Every organization is debating the issue of migrating data lakes from on-premise to cloud. And, just like any other infrastructural concern, there are various pros and cons involved in this transition process, such as cost, scale and availability of technology. For organizations, it is indeed quite a challenging task to plan, build, merge and migrate.
However, despite all these obstacles, there are multiple reasons that prompt enterprises to migrate data lakes to the cloud.
- Cloud data lakes provide quick solutions to organizations to gather big data that can be duplicated and later used by developers, data experts, analysts etc.,
- They help organizations to enhance business value and avoid reliance on building and maintaining infrastructure, thereby, facilitating the optimal use of their engineering resources to create unique functionalities for business value.
- Cut engineering costs as organizations can develop data pipelines easily and efficiently using data lakes. The entire process is pre-integrated and very effective. Thus, saving a lot of time and effort.
- It helps organizations to scale up easily.
- They are agile, thereby, helping organizations to meet instant requirements. They will give you complete flexibility to re-think and modify your data lakes.
- Data Lake is the latest technology to store data. One key advantage of cloud-based data lakes is that they always automatically update and provide the latest available technology to organizations. Moreover, they are flexible and tailored made to suit your requirements as you can just add cloud services once they are available to you, and you don’t need to modify your architecture.
- Data lakes are dependable. It is another area where Cloud providers obviously enjoy some advantages. They could help to avoid a lot of functional hassles like stockpiling of disposable data spread over diverse servers as well as disturbances in services. For instance, Amazon S3, offers an incredible durability of ‘11 nines’ for data.
However, in a data lake, data is saved in an unstructured format, making it too complex for normal use. To overcome this challenge and to make the data useful, organizations need to process and make it available for analysis. This is a big handicap for those organizations that lack the necessary engineering resources for big data analysis.
Let us discuss in detail about the main challenges an organization must overcome to deploy data lakes in the cloud.
- Data Ingestion: The mere thought of loading volumes of data into the cloud is enough to give nightmares to most of the organizations since most of the cloud data is being stored in ‘immutable’ storage systems causing organizations to continuously reload very huge tables into data lakes, which is a very slow and time consuming process. It is even more tough to do the same in the cloud with the help of less human intervention.
- Gap in Data Pipelines: The Huge gap between ad hoc and data pipelines that are production ready. Some of the initial setbacks or delays in migrating to the cloud might be due to bad planning or other reasons. While some organizations have been depending on open source solutions to reduce expenditure, these tools are not free of any flaws. Unfortunately, often these tools end up incurring more expenses for companies.
- Portability of Data Pipeline: It is a completely obsolete idea to relegate data to either cloud environment or on-premise now. Meanwhile, to meet the challenges of contemporary market needs, organizations need to be willing to either switch or work out a strategy of multiple cloud vendors, new formats and changing their needs. Making data portability at the initial stage will help the data teams to control and ensure quantity, formats, data use etc.
- Cross-Cloud and Hybrid Environment: Organizations must create and oversee data workflows that could be used across entire cloud environments in order to sort out issues in security, privileges, metadata, etc., since they function in multi-hybrid situations.
- Maintenance Cost: One of the main factors that force organizations to stay away from cloud data lakes is the huge upfront investment required for purchasing the storage equipment and servers. Also, the operation of an on-premise data lake may escalate the expenses of management and operating costs.
- Scalability: Scaling up of the data lake requires manual addition and configuration of servers. It will require organizations to focus on better utilization of resources. Further, additional servers will increase operating and maintenance costs.
Migrating data lakes from on-premise to the cloud is not an easy task. However, it is essential for organizations to take advantage of the cloud to stay ahead of the curve in a data-driven world. Gemini Consulting & Services can help you migrate your data lake from on-premise to the cloud and gain insights from it. With our solutions, you can identify opportunities and attract and retain customers in a better manner by making use of the data lake. To know more click here.