Technology is like the ocean, and it entirely relies upon you how much resources you can use from it. If you are not obsessed or attentive enough to use resources from it, the ocean will follow its natural course and others will take its advantage.
Putting this context in a concise manner, if your business is not going with the flow of technology or not flexible enough to be mold as per the wave of innovation, then you could be left behind forever in this competitive marketplace.
Giving a more meaningful narrative to this context, the technology we are going to discuss is data, or more specifically data lake. Actually, the data lake is a kind of data reservoir of large quantities of data in the native form to satiate your business requirement with every imaginable insight. Data lake is all about data management, and a highly recommendable tool to driving business value and enhance the decision-making ability of businesses in this data-driven culture.
So to capitalize on these unprecedented business opportunities, you need to have a scalable data management for your organization, flexible enough to support emerging data needs. But the question is— from where you can get that scalable data management, which can satiate your data needs and provide you easy access to all data sets without any loss of information along the way? That’s why the concept of data lake has been emerged to give you a respite from the data management concern.
It is an emerging concept equipped to unleash the business significance of relevant data for detailed analysis. It works both ways with equal efficiency, within and outside an organization. But from whence its need arises? And why it has replaced all the traditional data warehouses and emerged as the modern data warehouse.
It is because Data Lake allows businesses to leverage more data types and enable faster turnaround time, even with the large volume of data, which was out-of-bounds with a traditional data warehouse. Furthermore, it delivers this optimal performance at reasonable costs and creating new possibilities for traditional businesses for process reinvention. Due to the compatibility with multiple data sources, such as—Web, Mobile, and other connected devices along with text, graphs have further paved the way for the augmentation of data management and the need for Data Lake.
Specifically, talking in terms of cost, then the overall cost in implementation and operating a data lake is ten times lower than using the SQL-based traditional data warehouse. So from a business perspective, especially when it comes to scalability and efficiency in data management, Data Lake can create substantial business value and have become quintessential in this digital business landscape.
Giants like Amazon, Netflix, Capital One, and others are already thriving on the power of data by using data lake infrastructure. No industry is left behind to feel the influence of this phenomenon—Energy, Healthcare, Finance, Mining, Education, Telecom, and others are using the data analytics feature in different forms.
Even startups like Arundo Analytics, Civis Analytics, Formation & Gemini Data are making data a more valuable asset for businesses and making a big voice in this industry.
Hereby mentioning some business values which organizations have already begun to reap from data lakes—
Data Lake acts as an enabler for faster big data analytics—Data lakes are customized for fast big data analytics, indeed, for real-time analytics. It can leverage exploding quantities of data with certain algorithms to drive analytics.
Store and mix— structured unstructured in one Data Lake—Comes with the unique ability to acquire, mix, integrate multiple data types, irrespective of their format and source.
Data lakes—comes with the flexibility to scale your growing data—Presently, data lakes are highly scalable and flexible. The infrastructure is automated to deal with the exploding volume of data.
Saving enterprise data warehouse resources—Data Lake can act as the staging area for the enterprise data warehouse, in which it only passes the query-based relevant data to the warehouse.
Stringent Data security features—The security features with Data Lakes enable the organization to provide limited access to information. It also includes access to the original source content.
Centralized system to store Data—It is made in a suitable manner that it stores data with all the attributes. It uses a flat architecture to store data instead of store it in files and folders.
Faster insights every time—Since the data is assigned with a unique identifier and tagged metadata, it empowers to extract and process data at a higher speed.
Easy integration with the Internet of Things (IoT)—Multiple data such as IoT device logs and telemetry can be collected and analyzed. We have examined this whole process when we developed a prototype fitness app, Fitplus, and gathered all the fitness data and sync it with the different fitness machines to control with a smart app. Click now the entire model of functioning (https://bit.ly/33Rxeqd).
Seamless integration with Machine Learning (ML)—Data lake has a schemaless structure and ability to a large amount of data. So it is perfect when we integrate machine learning algorithms to large data. Like we did in Videobomb to create seamless Augmented Reality (AR) experiences that enable a user to scan the popular tied up products and plays with their latest videos and objects.
Flexibility—Its schemaless structure support analysis supports the analysis of data from social networks and mobile devices. Going one step ahead, it supports large heterogeneous, multiregional, and microservices environments
Agility—Data lakes are good for analyzing data from different, diverse sources, and yes it can easily decode complex patterns.
Cloud Offerings—All the prominent & trusted players in cloud technology, including AWS, Google, Cloud Platforms, and Azure, offer managed data lake solutions. As it ensures speedy information and empowering businesses with marketing insights from multiple data sets. Major cloud providers tend to offer data lakes rather than data warehouses, given data lakes integrate better with organizations’ systems and are better optimized for cloud environments.
In the current scenario, the data lake has delivered all its promises on scalable and efficient data management. It ensures speedy information and giving specific business insights from multiple data sets through data science and machine learning. From a data management point of view, there is immense potential in industrial applications of Data Lake, and the benefits we have mentioned above are just the beginning.