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Gone are the days when business leaders would follow their instincts or guess randomly in making a majority of their business decisions. In recent years, organizations, even small and medium businesses are placing a high priority on data-driven, informed decisions which are more accurate leading to the healthier top and bottom lines. Such decision-making is based on correct facts and figures extracted from the data which is collected across several sources and analyzed. New business opportunities are also discovered with innovation and the creation of a newer range of products and services, thereby expanding the organizational growth and establishing a competitive edge. For organizations to harness the data to uncover insights, it has to be stored first. Earlier, data warehouses were leveraged for this purpose but they only store structured data, hence limited in scope. Today, gaining more attention are data lakes which are a repository of structured, semi-structured, and unstructured data in their native format retrieved from several sources – social media, mobile apps, or IoT environments. These data lakes power data analytics, machine learning, and business intelligence solutions. Furthermore, these reservoirs or information can be stored on-premise, in the cloud, or be included in a hybrid solution.
With surging volumes of data accumulated from various sources in organizations, it becomes very crucial to manage and extract maximum value from it and at the same time be cost-efficient. Data lakes are seen as an economical alternative to data warehousing, where an additional process of computation of data is involved in the latter increasing the cost. There is a significant growth in the Data Lakes Market and is estimated to increase further in the coming years. According to ResearchAndMarkets.com, this market which was valued at USD 3.74 billion in 2020, is expected to reach USD 17.60 billion by 2026, at a CAGR of 29.9% over the forecast period 2021-2016. An increase in the adoption of IoT devices is one of the drivers for the growth of the data lakes market as data retrieval is quicker with data lakes as compared to data warehouses. This aspect can reduce the time taken by Data Scientists on the basic operation of extraction, cleaning, and exploration of data and make them focus their time and efforts on data analytics and modeling, thereby increasing the process efficiency. The design of data lakes can support data analytics and address the data silo challenge seen in big data.
To suit the growing demands, the data lake architecture is also evolving. The key components of data lake architecture are data ingestion, data storage, data analytics, data security, and data management and governance. The data ingestion layer provides connectors to extract data from various sources and ingests it into the lake. A low-cost and highly scalable and available data storage layer supports any data format. Data analytics and machine learning help in quick analysis to derive valuable and actionable insights. Data security is achieved through data protection, authorization, role-based access, and multi-factor authentication.
After establishing the data lake architecture, it has to be integrated with the existing infrastructure to reap the benefits.
Data analysts and related professionals dealing with data can in a short period create optimized data architectures, where more data can be harnessed. Stream data can be tapped for real-time analytics by data programmers with data lakes that provide low-cost storage of data. With data lakes being scalable and flexible, the system and processes can be scaled too as needed.
Furthermore, this data is required by the entire organization for gaining the maximum benefits for all departments and not just a select few. With data lakes, the democratization of data is made possible thereby saving time and leading to faster decision-making.
The data warehouse in general supports SQL for doing basic analytics, however for today’s advanced use cases, there is a requirement for more ways to analyze data. Here data lake can come into the picture and help by providing support for several other languages as well for advanced requirements.
With its several business benefits, data lakes are leveraged by organizations across all industry verticals, including, healthcare, transportation, media and entertainment, and more. The convergence of data lakes and data warehouse platforms is already happening in many enterprises today. However, data lakes are a relatively new concept and have to be built strategically. It is the responsibility of both IT and business leaders with support from the business unit heads to do that and cannot be implemented in isolation. Best practices also have to be followed to build a robust data lake. Going forward, businesses will become essentially digital where it will be critical to access data, and so will the speed of development and deployment. Data lake which is the cornerstone of modern big data architecture promises to deliver immense business value as it evolves.
(The author is Mr. Rahul S Kurkure, Founder and Director, Cloud.in and the views expressed in this article are his own)