Introduction
In the recent past, we have witnessed unprecedented trends in the data analytics market and these trends have revolutionized the data market in 2021. Two important business goals for organizations in 2021 include the implementation of advanced data architectures and the improvisation of present data technologies. These modern trends are making cloud data lake even more popular than before. This is because a cloud data lake analyses, extracts, and derives deep insights from unstructured data sets at a very low cost.
Computing and storage capabilities
The computing and storage capabilities of an organization are at the center of its operations. So, the efficacy of computing and storage capabilities determine the scope of its operations. To carry out major upscaling of operations is a herculean task given the infrastructural constraints. This is where migration to the cloud environments comes in handy. Not only do the cloud environments provide limitless storage and computing capabilities but they also provide an advanced level of analytics.
Some of the most prominent examples of data storage include Azure Data Lake Storage and Amazon S3. They provide the dual advantage of elasticity and efficiency when it comes to computing resources.
Weaning away from traditional warehouses
The cloud data lake provided the capabilities of computing and storage that the traditional data warehouses could not provide. The improved scalability offered by the separation of storage from computing has enabled cloud data lakes to gain prominence in a short span of time. The low-cost storage facilities provided by cloud data lake have made them a center of attraction for many organizations that are looking to boost their performance and meet their business needs. Modern cloud data lake engines allow for the direct migration of huge sets of data stored in data warehouses to the cloud environs without affecting performance.
Warehouse versus Data Lake
While data lake stores vast amounts of data in raw format, the warehouse stores it in structured and filtered format. Data lakes and data warehouses are distinguished on basis of four prime parameters. The first parameter is the structure of data while the second parameter is the accessibility of data. The data stored in a data lake is highly accessible and can be updated more easily as compared to a data warehouse where it is difficult to make quick changes.
Another important property of data lake is that it is able to retain different types of data and supports various genres of data types. It is important to observe the formation of structured data types in a warehouse. When a data warehouse is developed, the data distribution is subject to deep analysis before profiling vast datasets. In this way, we get to make appropriate choices related to data types that need to be stored in the warehouse, unlike a data lake.
However, when we look at the functionalities of a data lake and a data warehouse, we find them to be quite similar.
Data privacy and governance
As the reliance of different types of users is growing on data, they are becoming increasingly concerned about the privacy and security aspects. This is leading to the generation of new rules and strengthening of the existing ones when it comes to data governance.
The most important trend that we are seeing related to warehouses and data lakes is the strengthening of security infrastructure. Various kinds of open source technologies are emerging which are helping enterprises to not only govern their data but also manage it in a secure environment. One of the leading examples of this is project Nessie which is working with new data products and tools related to security and privacy.
As the need for Data Analytics is growing, concerns of privacy are becoming more important than before. Right from the extraction of data to its processing and insights, supervision and compliance are becoming more important than ever before. However, new governance challenges are emerging and data scientists are working on new tools, methodologies, and processes to counter such challenges. Be it the fabrication of secure data platforms or the conceiving of stringent policy norms, all the stakeholders and now recognizing the power of data and the need to regulate it.
Conclusion
As far as the discussion on data lakes and data warehouses is concerned, data lakes maintain a likely upper edge over the warehouses. This is because data lakes are easily adapting to the new changes and various requirements of business intelligence and data analytics. The capability of data lakes to generate insights is far superior to warehouses. It is highly recommended to switch to cloud data lakes for enhanced efficiency in the future.
