Data lakes — definition, benefits, and challenges

A woman in an office learns about data lakes.

Today’s organizations have access to more market and consumer data than ever before. But before businesses can put all that valuable information to use, they must first have a means of collecting and storing it effectively. When it comes to ingesting and storing huge amounts of data, there are several methods that organizations can use — and one of the most practical is known as a data lake.

Maybe you’re an executive at a company that’s outgrown its current data management solutions or a data analyst or marketing manager who wants to improve how your organization ingests and stores information. Business leaders of all types need to understand what a data lake is, how it works, and the benefits and challenges of investing in one.

In this guide to data lakes, we’ll explore what they are, why you need one, and how they can facilitate better data management. Specifically, we’ll cover:

What is a data lake?

A data lake (sometimes written as datalake) is a location where you can store both unstructured and structured data. A data lake is also a method that you can use to organize large volumes of diverse data from various sources.

The most notable attribute of a data lake is that it functions as a centralized repository of information. As with other data storage frameworks, you can always store structured data in your data lake. However, unlike some other data storage frameworks, a data lake lets you store your data before structuring it.

Once you’ve stored your unstructured data, you can run various types of analytics on it to better understand the information within your data lake. You can visualize the data in charts or graphs, convey it into easy-to-digest dashboards, use it to power your machine-learning software, and much more. The possibilities that a data lake provides are enormous.

Why you need a data lake

Organizations in nearly every industry use data to fuel theirdecision-making processes and capitalize on growth opportunities. Using a data lake makes that possible, as it provides businesses with a reliable location for storing, managing, and interacting with all their information.

Furthermore, the information stored in a data lake is used to guide several key business processes. By harnessing the data, businesses can use machine learning software, which in turn facilitates the automation of traditionally manual workflows.

Data lakes are also an incredibly efficient means of ingesting and storing customer data. Because they can store unstructured data, businesses can focus on capturing as much information as possible during the intake process and figure out what to do with it when time permits.

A 2021 survey found that 69% of respondents said their company had implemented a data lake, 92% saw data lakes as the right solution for centralizing data and analytics going forward, and 87% of those that already use a data lake reported that it improved organizational decision-making ability.

69% survey respondents said their company implemented data lake, 90% saw data lake as the right solution, 87% had already used it.

Data lake vs. data warehouse vs. data lakehouse

Understanding the nuances of and differences between a data lake and a data warehouse will help you better use your data feeds and maximize the efficacy of your analytic processes. The most significant thing to remember is that a data lake ingests data and prepares it later. In contrast, a data warehouse prioritizes organization and structure above all else, just as a physical storehouse or distribution center would.

Think of the function and process of a data lake like rain falling into an actual lake. Any raindrops that hit the lake's surface are accumulated within the body of water, and the same basic premise applies to a data lake. Meanwhile, just as an actual warehouse would never accept a disorderly bundle of unpackaged goods or an unscheduled shipment, a data warehouse cannot receive new information unless it has already been prepared and structured.

For many business leaders, then, the question is when you should use each one.

Broadly speaking, you should use a data lake when your organization needs to gather a vast volume of data from a broad range of sources but doesn’t necessarily need that data structured right away. For instance, a company that collects general consumer data — such as how a buyer found their way to the company’s website, where its consumers reside, and the demographic information of its consumer base — would probably use a data lake.

Conversely, a distributor that requires a single source of truth to manage its inventory will likely need a data warehouse. Any structured data you place into the data warehouse will provide real-time insights about the distributor’s stock, storage capacity, and other metrics.

Given each solution’s unique use cases, it’s likely your organization will find that it actually needs to use both a data lake and a data warehouse. Recently, a new technology known as a data lakehouse has emerged. As its name suggests, a data lakehouse is a solution that combines the traits of both data lakes and data warehouses. Specifically, a data lakehouse offers the cost efficiency and flexibility of a data lake while providing the management capabilities of a data warehouse.

Research from 2022 found that 66% of surveyed organizations were using a data lakehouse, with half of the respondents citing improved data quality as the primary reason to do so.

A data lakehouse also allows organizations to apply machine learning and business intelligence software to their vital information.

A data lake is a foundational component of data science, providing the information you need to fuel your analytics tools. Therefore, it’s vital that your organization implements processes to efficiently create and manage its data lake.

Data lake architecture

There are multiple ways to assemble a data lake, but you should base your data lake’s architectural framework and makeup on the unique needs of your business.

The three main data lake architectures

In terms of construction, there are three main architectures people typically associate with data lakes.

  1. Hadoop. Hadoop is best defined and categorized as an open-source framework that’s useful for facilitating scalable, distributed computing. In other words, it’s a queryable database that you can tailor to the needs of your business.
  2. Amazon WorkSpaces. A subsidiary of Amazon, Amazon WorkSpaces provides end-to-end cloud computing services to businesses of all sizes. The platform includes more than 200 different services and features, including a database and several app management frameworks.
  3. Microsoft Azure. Like Amazon WorkSpaces, Azure is a more comprehensive cloud computing solution than Hadoop. In addition to being a queryable database, it also serves as an integrated infrastructure that can support mobile-related activities, analytics processes, and computing functions.

A data lake is an essential component of data science, providing the information you need to fuel your analytics tools. Therefore, it’s vital that your organization implements processes to efficiently create and manage its data lake.

Data lake architectural principles

Regardless of the foundation you use to build out your data lake, you will need to adhere to the following three core architectural principles:

  1. Do not turn away data. The first rule of forming a data lake is also the most important. Never turn away data, regardless of whether you currently plan to use it. Harvest as much information as possible, add it to your lake, and figure out the “how” later.
  2. Leave data in its original state. The data lake method prioritizes efficiency and scalability over all else. Therefore, it’s critical that you leave data in its original state during the collection stage. Otherwise, you will offset the efficiency benefits of the strategy.
  3. Data can be transformed later to fit analytics needs. If you want to fuel your business intelligence, machine learning, and artificial intelligence (AI) technologies, you need data — and lots of it. But remember that there is no need to figure out what requirements a particular set of data is filling at the time of its collection. You can always transform it later to best suit your analytics.

Data lake structural tiers

Now that we’ve covered the architectures and principles, let’s look at the actual structure of data lakes. No matter the intent behind your data lake, it must include the following tiers:

Data lake must include ingestion, insights, storage, security, distillation, processing, and unified operations.

All of these tiers are essential to the function and performance of your data lake. Any inefficiency or underperformance along a single touchpoint could hinder your data analysis processes and keep you from harnessing the power of all of your information.

Benefits of a data lake

Data lakes serve as the foundation for data science and analytics processes. Creating data lakes will help organizations:

Additionally, data lakes promote better information visibility across your entire organization. Since data lakes eliminate the need to structure data before storing it, these pools of information also expedite data collection processes and help your organization gather more information about its customers and market.

Data lake challenges

Despite the many benefits, data lakes are not without their challenges. When developing and managing a data lake, some of the hurdles you may encounter involve the following:

Fortunately, familiarizing yourself with these challenges before you invest in data lake architecture will help you navigate them and get the most out of your data management tool.

Evaluate a platform to build your company’s data lake

A data lake is an essential component of data science, providing the information you need to fuel your analytics tools. Therefore, it’s vital that your organization implements processes to efficiently create and manage its data lake.

If you’re ready to transform the way your business accumulates and stores information by using data lakes, Adobe Experience Platform can help. It’s a supercharged engine that’s finely tuned to make experiences hum. Delivering personalized experiences at scale requires a centralized and connected data foundation. Experience Platform is that foundation, and it’s powering the next generation of customer experiences.

To learn more about Adobe Experience Platform, watch the overview video or request a demo.