By Neil Kumar, Product Manager at Ocient
Let’s face it: big data is confusing enough. However, as you navigate the process of employing solutions to make sense of your data, you might come across several different definitions of various solutions that you need to understand in order to ensure that your data needs are being met. Take, for example, data lake vs data warehouse. What is the difference between the two, and what do they mean in the context of your data? Let’s evaluate these two concepts to better understand what they are and what you need to know about them in order to get more from your data.
What Is a Data Lake?
While the terms data lake and data warehouse can seem quite confusing, the reality is that their concepts and definitions are easy to grasp. Speaking to the former in the data lake vs data warehouse question, a data lake is a centralized repository of all of your structured or unstructured data. Aptly named, it’s best to think of a data lake as an actual body of water, accepting all streams of data from all sources. The data in a data lake is always raw, and there are several notable benefits of using data lakes. Data lakes are known for supporting all data types as well as all users, easily adapting to changes, and providing faster insights.
Of course, as it is with all digital solutions, there are drawbacks, with the amount of storage space needed to store and process such data being quite extensive. Within the context of data lake vs data warehouse, there’s also a third solution to be aware of: the data lakehouse. However, we’ll get into the data lake vs lakehouse conversation further down in this guide once we have a better understanding of what a data warehouse is.
What Is a Data Warehouse?
So, what is a data warehouse? Unlike data lakes, which store massive pools of raw data, a data warehouse is an application that is designed specifically to handle business intelligence activities like data analytics. Whereas the schema on data lakes is written at the time of analysis, data warehouses have a predefined schema and data structure to provide faster results with less complexity so that anyone who relies on this solution can properly analyze and interpret the data points.
Data warehouses are often defined by their relational databases, ELT solutions, reporting and mining activities, analysis and visualization tools, and more sophisticated analytical features. When researching data lake vs data warehouse solutions, learning more about the benefits can give you a better idea of what the use case for this type of solution is. Data warehouses are often lauded for their subject-oriented use to help organizations extract value from numerous data points, integration and consistency when working with disparate data sources, and simple application. That being said, the TCO is often much higher for data warehouses because of more costly storage solutions.
Comparing and Contrasting Data Lake Vs Data Warehouse Solutions
So, what exactly is the difference between a data lake and a data warehouse? To make the comparison clearer, let’s break down the key differences between the two solutions.
|Data Lake||Data Warehouse|
|Type of Data||Raw, unprocessed, non-relational data from a wide variety of sources||Relational, processed data from relevant sources|
|Schema or Purpose||Written at the time of analysis and no predefined purpose||Predetermined purpose and schema|
|Cost||Often low-cost solutions||Higher TCO|
|Prospective Users||Often leveraged by professionals like data scientists or data developers||Typically used by business analysts and other business professionals|
|Types of Analytics||Best for machine learning, data discovery, and predictive analytics||Best for business intelligence, visualizations, and batch reporting|
Those on either side of the data lake vs data warehouse conversation will highlight the benefits they personally experience. Doing your research to learn more about how these solutions are applied and where they’re relevant will give you further insight into whether or not they fall in line with the needs of your organization.
What Is a Data Lakehouse?
Above, we touched briefly on the topic of data lake vs lakehouse solutions. What exactly are data lakehouses? Data lakehouses seek to integrate the best features of data lakes and data warehouses into one easy-to-use solution that provides the benefits of both without the inherent drawbacks. As such, data lakehouses are designed to be flexible, efficient, and easy to scale while still offering data management, business intelligence, and machine learning that modern users need in order to effectively sift through and interpret numerous data points that their organization is dealing with.
For many, going about the process of deciding between data lake vs data warehouse solutions means having to carefully weigh the pros and cons of each and determining which one is best for their organization. However, this means having to make a decision and working with the disadvantages that your current solution is built with. For those looking to manage and analyze large sets of data, settling isn’t always the best solution (unless you have a clear winner for your organization). Data lakehouses merge all the best qualities of these two different solutions to provide comprehensive data analytics in the modern era. If you’re finding it difficult to choose between data lake vs data warehouse solutions, now that you know what they are and how they function, data lakehouses are a third option that you might wish to learn more about if you find that one of these two isn’t a clear winner.
Get the Data Support You Need With Ocient
Ocient is the hyperscale data analytics solution that you’ve been looking for. With use-case-driven solutions, better storage with a smaller footprint to reduce TCO, and a high-performance data warehouse platform that scales without limits, getting the support you need to store and analyze your data with ease is simple. Contact us now or schedule a demo to get started!