To effectively utilize the growing amount of data generated globally, enterprises need better ways of not only collecting data but also storing and organizing it for future retrieval.
While exploring solutions for these purposes, they are often faced with the choice between data lakes and data warehouses. Understanding the differences between these will help business leaders to take the right approach in terms of data storage strategy.
The global business landscape is increasingly becoming a digitally driven one. In fact, not just business but every facet of civilization is increasingly being augmented and even replaced by digital processes. It stands to reason, then, that the most valuable commodity in this era is data. The organizations that have more of it are the ones that have the strongest leverage over their processes, their market, and consequently, over their competition. And, unlike other physical commodities, data isn’t scarce in supply. On the contrary, new data is generated in gargantuan quantities every day — as much as 2.5 million terabytes per day, according to one estimate. And that number keeps increasing with every year and every iteration of digital innovation. The introduction of technologies like IoT analytics is set to multiply the volume and variety of data generated by the traditional big data sources, adding further to the existing cornucopia of data. The development of newer means of accelerated data generation calls for better ways of not only using the data but also storing it. And this need has led to the adoption of high-capacity data storage systems like data lakes and data warehouses. However, organizations that seek to incorporate such systems into their organizational IT infrastructure often fail to understand the differences between data lakes and warehouses. This leads to them choosing the suboptimal alternative for their purposes. This article aims to help organizational leaders to understand the key differences between data lakes and data warehouses, and make a more informed choice as to which one suits their purpose better.
Data lakes and data warehouses are the results of organizations’ hunt for ways to harness the constant, unbridled flux of data around them. They use these storage architectures to keep data handy for their data analysts, scientists, and researchers to pore through and find solutions to the problems that keep emerging and the changes that keep happening in the ever-evolving market and economy. Although data lakes and data warehouses are similar in function or what they do — keeping data ready for access when needed — they have major differences in how they do it. So, it is important to understand what data lakes and warehouses actually are and the fundamental differences between the two, before determining the best alternative for an organization.
Data warehouses are centralized repositories of highly structured data around which their business intelligence processes are built. A data warehouse contains purposefully collected and organized information that is highly relevant to business operations and is immediately usable by organizational members to guide decision-making and other core activities. Every business that gathers data and uses analytics for business intelligence to drive their operations uses some form of a data warehouse for data storage, organization, sharing, and retrieval.
Data lakes are vast, unified repositories of raw, generally unorganized enterprise data stored in their native formats. A data lake essentially serves as a storage reservoir of all kinds of data, structured, semi-structured, or unstructured for which appropriate applications may or may not have been specifically defined. The data stored may include relational databases of structured data, system logs, documents, images, videos and other kinds of binary information. This means that the data from a data lake is not always in an immediately usable state for analytics and business intelligence. The basic difference between a data warehouse and a data lake can be discerned from their names themselves. While a data lake, like a natural lake, contains a broad miscellany of elements in their natural, untouched form, a data warehouse is like an actual warehouse containing an organized, classified collection of objects that are stored for well-defined purposes. Organizations may be inclined to view a data warehouse as the easy choice due to its usability and familiarity. But, using a data lake also has numerous benefits. Exploring the differences between these two types of data storage systems will give a clearer understanding of the capabilities, the different applications, and benefits offered by both. This will give organizations a better idea as to which approach may fit their organization, their goals, and their existing capabilities better.
Data lakes and data warehouses are more different than they are alike. Following are the key differences between the two that may help leaders in deciding the ideal fit for their organization.
While data warehouses only store data that is highly pertinent to an organization’s core business processes, data lakes are more inclusive in that respect. The greater inclusivity of data is due to the fact they are used by organizations to store data that they think may someday in the future be of utility. Thus, data lakes include both data that is immediately relevant to the business and data that has no apparent value, at least in the present. A data lake also includes data that a business hasn’t learned how to use. For instance, a business’s data may contain a lot of social media posts and other kinds of customer communications that they have been unable to use for analytics due to the absence of natural language processing (NLP) capability. However, when they do incorporate NLP into their analytics architecture, they can use social media data to identify patterns from the data that may help in improving their offerings and business processes. Thus, a data lake may hold data that can potentially solve problems that are not yet identified by a business, while data warehouses store data that is essential for the existing operations of an enterprise.
Since a data lake is essentially a dump of unorganized, unstructured information it is easy to access and modify. However, as a consequence, it also lacks the strong security that comes with enterprise data warehouses. A data warehouse is more ordered, inflexible, and secure when compared to a data lake. Thus, when it comes to maintaining the security and integrity of data, data warehouses have the edge, while data lakes are more accessible and malleable.
The data stored in data lakes is unprocessed and contains all the attributes and metadata from the time of its generation intact. This means that the indexing data for this type of data storage hasn’t been adapted to be consistent with the data stored on the enterprise’s information systems. The fact the data hasn’t been cleaned and processed means there is still a lot of associated information that can be used to gain deeper insight using the same data. However, the data stored in data warehouses are processed to fit the purpose they were initially collected and retained for, making them fit for immediate use to drive enterprise operations.
Data warehouses purport to provide business executives and all the other organizational personnel with the information they need to perform their functions with efficiency. The data helps the personnel to respond effectively to the situations that arise during the day-to-day operations. The data stored in data lakes have no specific purpose and hence is left unprocessed. That is unless the enterprise senses the need for such data. Cleaning and processing data from data lakes are often done by data scientists, who are proficient at making sense of raw data and making it usable. This data can be used by data scientists to either solve newly emerged problems or even identify more profound questions that need to be asked and answered to achieve huge leaps in terms of innovation and organizational growth. Due to the ease of using the data stored in data warehouses, business professionals and executives may favor that approach over data lakes. But the potential utility offered by data lakes in terms of making a deeper, larger scale, and strategic impact on enterprises make them an appealing prospect for visionary leaders and data science professionals. However, it is important to note the caveat that comes with having data lakes.
Building and maintaining a data lake filled with unusable, unsorted information makes it prone to turning into a data swamp, which, as the name might suggest. is hardly useful. Thus, based on what your organization needs now and the capability it possesses in terms of talent and infrastructure should give you clarity while deciding between a data lake and data warehouse.