A centralized repository that is used to store structured and unstructured data at a large scale is called a cloud data lake. There are different types of uses of a cloud data Lake. The first and foremost among them is data processing that can also be extended to data analytics and data reporting. Other uses of cloud data lakes include the storage of data and even data generation at various instances of time. It needs to be noted that the traditional data lakes were constructed using various on-premises clusters. Gradually, we started to move away from this trend and the present practice relies on the functioning of data lakes in the cloud environment for which infrastructure-as-a-service can be effectively utilized.
The working mechanism
The working of a cloud data lake can be understood from the perspective of computing capabilities and storage capacities. Computing capabilities refer to the ability to process huge volumes of data with very low latency. In addition to this, the working of cloud data lakes also involve simultaneous processing of data without slowing down the processes in operation. By virtue of its centralized application, a cloud data lake enables us to process the entire information irrespective of time and place.
The operating process
For understanding the structure and the journey of a cloud database, we rely on four different processes. The first among them is called uptake of data. In this process, data ingestion takes place which involves the upkeep of structured and unstructured data types. After this, data collected from various types of sources is fed into the data lake for further formatting. Soon after, scaling of data can be undertaken as per our requirements without the need for complex data structures.
The simplicity of storage and transfer of data has incentivized different organizations around the world to maintain a variety of data lakes and to segregate sensitive data from non-sensitive one. The next process is related to storage wherein the selected datasets are stored in a central repository. All transformations to data are avoided before this step. The simplicity of the storage system allows businesses to work with huge volumes of data with the advantage of auto-scaling and affordability. The processing of data is the third step where we convert data from its raw state into a processed state. After the data has been processed, it can be sent for further analysis and deriving insights. Although analytics is the final step in the data journey, it is intimately related to the processing stage. The processed data in the much-transformed format is sent to data scientists who carry out research of these data sets in a holistic manner.
The distinguishing spectrum of platforms
There are three main cloud data lake platforms. These include the Google cloud platform, Amazon Web Services, and Microsoft Azure. All these data platforms differ from each other in a considerable manner. When it comes to the uptake of data, Microsoft Azure makes use of Azure stream whereas Amazon Web Services make use of Amazon snowball. The Google cloud platform service makes use of data flow storage. When it comes to the storage of data, Microsoft Azure makes use of ADLS Gen 2 whereas Amazon Web Services makes use of Amazon S3. Google cloud platform has Google cloud storage at its disposal. Speaking about the processing capabilities, Microsoft Azure makes use of HD Insight Storm while Amazon web services make use of AWS Glue Amazon Glacier. Google cloud platform makes use of Cloud Datalab. For analytics, Microsoft Azure relies on data Lake analytics. Amazon Web Services carries out analytics with the help of Amazon Redshift. Google makes use of a big query cloud to carry out analytics. There are two other analytics tools that are at the disposal of the Google cloud platform. These include a big table and a data processing cloud.
Concluding remarks: The innumerable benefits of cloud data lakes
The benefits of building data lakes in the cloud are numerous and have vastly benefited various stakeholders. Ranging from the storage capacity that is associated with the cloud to the benefit of cost efficiency, a cloud data lake incorporates it all. A cloud data lake also serves as a central repository for storing information in different compartments that is to be used by various teams. Not only does the segregation of data simplify the complexity of operations but it also enables data engineers to probe data in a surgical manner. Finally, when it comes to data security, the onus lies on organizations to protect their sensitive data sets. This is where companies show utmost trust in various data services of cloud data lakes.