Data Lakes - Time to Look to The Future
Originally published: December 12, 2019 10:51:45 AM
Meanwhile, the whole world is overwhelmed by the practicality and versatility of cloud hosting, data lakes are also moving to the cloud. But let’s before establish the whole concept of data lakes.
The data lake is a central storage repository that holds big data from many sources in raw and granular format. The data can be stored in other forms like semi-structured or unstructured, this means data will be available in the future in a more flexible format. Therefore, the data lake is a single physical repository of all the organization’s data. All kinds of data either internally or externally generated by interactions from the third party and publicly available sources. But enough of the definitions you can get plenty of them on Google, the main concern is what it means to a business.
In contrast to data warehouse which stores data in hierarchical form, data lakes store data in a flat architecture format. The unique identifier is assigned to every single data element and is tagged with a set of extended metadata tags.
Traditionally, data lakes are associated with Hadoop oriented object storage before people realized the immense benefits of cloud hosting data lakes. So Hadoop is a software designed for storing and processing large volumes of data. But it’s placed in the data ecosystem has shifted in the wake of competitive cloud hosting offerings that definitely delivers more flexibility, lower cost, and uncomplicated development. Although Hadoop is scalable, low-cost and offers good performance with its inherent advantage of data locality, but still there are some challenges that Hadoop brings in –
Space- Data lakes implemented on-premises of Hadoop clusters started costing high as their servers are bulky and occupy real estate.
Setup- Setting up data centers is time taking and tiresome and many a time it takes a couple of months to take off.
Scalability- For scaling up the storage capacity, it takes a lot of time and effort to increase the space requirement and get the cost approvals.
Estimating Requirements- Since any further changes become difficult on-premises and hence estimation of all the hardware requirements have to be done beforehand.
Cost- Cost estimations are another big concern than what it is with cloud alternatives.
Cloud Data Lake
Earlier when data lakes were built on Hadoop Distributed File System clusters the growth was not as prominent as it is in the cloud as an infrastructure-as-a-service. It is so designed so that it can take advantage of the separation of computing and storage. It helps in scaling each element when necessary which proves to be the prime benefit of keeping data over the cloud. The logic of the cloud data lakes can be understood by following the structure of the path that data takes-
Ingestion- The structured or unstructured data is collected and is transferred to the data lake in its original format.
Storage- The second in the line after ingestion comes the storage part. The data is stored in its original form before any transformations.
Processing- In the third step in which the data in the original form is converted into a form that is consistent with multiple data types.
Analytics- The concluding step in the data journey is the analysis of the stored, processed data with the help of data scientists.
Now let’s endorse the advantages of building data lakes in the cloud-
Capacity building- There is no more tension of size expansion for your growing size of the files. With data lakes built on cloud storage, along with the small files, one can easily expand their size according to the size of the files. And when there are no worries for the storage size your valuable workforce can put their stress towards important things.
Simplified Central Operation- As the data is all centrally located, all the complexities with the data access and object stores are reduced to the quite lower level. Repository being the central one makes the setup common for every team of the organization.
Cost Competency- Cloud storage providers bring their package in so many different and variety of pricing options. Hence you can buy the package that suits your business module and therefore paying for exactly as much you are using.
Data Security- Data is extremely secured within the cloud-based data lakes that include sensitive information like financial records or any other confidential details related to the customer.
Auto-Scaling- Working within the cloud environment there is no worriment regarding the scaling of any immediate functionality and no payment for any supplementary hardware.
Challenges with Data Lakes in the Cloud
Everything comes with challenges as part and parcel with itself. Hence building data lakes on cloud environment also comes up with some minor challenges –
Migration- The migration process of introducing the data into the cloud environment is intimidating and complex at the same time. Not only this, the process is expensive and the expenses increase to exponential levels when the process happens repeatedly.
Storage Costs- The main obstacle in the costs of cloud data lakes is that cloud providers charge for storage based on time more than size.
Data Swamping- Data lakes on the cloud are capable of supporting all types of data, hence maintaining the spruceness of data lakes becomes a difficult task. A data swamp that is full of errors and formats is similar to waste for the business.
Self-Service Analytics- Transforming, combining and organizing data sources together requires a robust analytics solution. Though most of the cloud providers offer analytics solution the ability to perform at that level only sounds easy.
Despite these challenges, building data lakes on the cloud is highly preferable.
Swapnil is a writer by hobby and fortunately also by her profession. She loves to play badminton and is greatly enthusiastic about dancing. Swapnil has written short stories, blogs, and snippets for a number of blogs (including her own). She has an active appearance on CloudOYE, always ready for the enthusiast technical approach.
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