Data lakes explained — definition, benefits, and challenges.

A data lake is a centralized repository that allows storage of large volumes of structured, semi-structured, and unstructured data in its raw form without a predefined schema. It enables flexible data ingestion from various sources and supports analytics, visualization, and machine learning for valuable insights.

What is a data lake?

A data lake is a repository where you can store both unstructured and structured data. Data lakes allow for the storage of large volumes of structured, semi-structured, and unstructured data — in its native format, at any scale.

The purpose of a data lake is to hold raw data in its original form, without the need for a predefined schema or structure. This means that data lakes can ingest data from a wide variety of sources and store it in a more flexible and cost-effective way.

How does a data lake work?

Data lakes ingest and store raw data in its original format. The process typically begins with data ingestion from multiple sources, such as IoT devices, social media feeds, enterprise systems, and databases. This data is then stored in a scalable storage solution, often on cloud-based platforms.

Unlike a data warehouse, data in a data lake remains in its raw, unstructured form until needed. At that point users can process, query, and transform the data into structured formats for various types of analytics, reporting, or visualization. Data lakes also support advanced functions such as machine learning and artificial intelligence by providing a vast pool of raw data to fuel these applications.

Why do you need a data lake?

Businesses in every industry use data to fuel their decision-making process and capitalize on growth opportunities. Using a data lake makes that possible by providing businesses with a reliable location for storing, managing, and analyzing vast amounts of data.

• Big data processing. Data lakes handle massive datasets, from terabytes to petabytes, enabling businesses to process large volumes of structured, semi-structured, and unstructured data. They support distributed computing frameworks, which facilitate scalable data processing and advanced analytics. These are essential for handling big data at scale.
• Unstructured data handling. Data lakes efficiently store unstructured data like videos, audio, images, and text, enabling businesses to analyze raw content. This is especially useful for industries like media, healthcare, and social media, allowing advanced analytics such as sentiment analysis and image recognition on non-tabular data.
• Real-time analytics. Data lakes enable real-time analytics by integrating with tools like Adobe Analytics, allowing businesses to monitor live data and make immediate decisions. This is crucial for industries like ecommerce, finance, and manufacturing, where timely insights and quick decision-making are essential.

By 2030, the global market for data lakes is projected to reach $45.8 billion dollars, according to research published in 2024. In a 2021 survey of IT professionals, 69% said their company had already implemented a data lake — a number that’s likely to have risen.

When do you need a data lake?

• Big data processing. If you have large volumes of data that need to be processed and analyzed, a data lake can provide a scalable and cost-effective solution.
• Unstructured data. If your organization works with unstructured data, such as video, audio, images, and text files, a data lake can be an ideal solution. Data lakes can store data in its raw form, allowing you to run various analytics and artificial intelligence (AI) models to extract insights.
• Real-time data processing. If you need to process data in real time, a data lake can help you capture and process data quickly and build real-time analytics dashboards.
• Cost-effective storage. Data lakes can be a cost-effective way to store large volumes of data. Since data is stored in its raw form, you don’t need to spend time and resources structuring or cleaning it before storing it.
• Collaboration. Lakes also allow you to centralize data from various departments within an organization, making it easier for teams to collaborate and share data. Different stakeholders can access the data, including data analysts, data scientists, and business leaders, allowing them to perform their analyses and make data-driven decisions.

Data lake vs. data warehouse.

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.

When should you use a data lake or data warehouse?

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, companies that collect general consumer data would probably use a data lake to understand:

• How a buyer found their way to the company’s website
• Where its customers live
• The demographic information of its customer base

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 needs to use both a data lake and a data warehouse.

The data lakehouse: The best of both worlds.

The data lakehouse model combines the best features of both data lakes and data warehouses. It offers the cost efficiency and flexibility of a data lake, allowing the storage of vast amounts of raw, unstructured data (such as logs, videos, and social media content) without predefined schemas, making it ideal for organizations with large volumes of data.

At the same time, data lakehouses incorporate the structured data capabilities of a data warehouse, enabling analytics and business intelligence (BI) tasks. This allows businesses to process both structured and unstructured data while running SQL-like queries and machine learning models for valuable insights.

Data lakehouses provide unified data management, reducing silos and improving data accessibility. They are also highly compatible with AI, supporting data science and machine learning applications. Data lakehouses deliver a scalable, modern solution using open-source technologies like Apache Spark or Delta Lake.  This unified approach enhances efficiency, accelerates insights, and reduces overall costs for organizations.

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.

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 3 main data lake architectures.

1. Hadoop.

Apache Hadoop is an open-source tool that helps manage and process large-scale data across multiple servers.

2. Amazon WorkSpaces.

Amazon WorkSpaces provides end-to-end cloud computing services to businesses of all sizes.

3. Microsoft Azure.

Microsoft Azure serves as an integrated infrastructure that supports analytics processes and computing functions.

The 3 main data lake architectural principles.

1. Don’t 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 AI technologies, you need data — and lots of it. But you don’t need to figure out a particular dataset’s requirements at the time of its collection. You can gather and store data without having a specific purpose for it or schema definition at the outset. You can always transform it later to best suit your analytics.

Data lake structural tiers.

Data lake structural tiers are the different phases of data organization and management within a data lake architecture, beginning with data ingestion.

• Ingestion. The ingestion tier will likely appear on the left side of your workflow. Data can be loaded in real time or submitted in batches.
• Insights. The insights tier is located on the right and reveals what actionable information is derived from your data lake.
• Storage. Storage is not necessarily a tier in the data lake architecture, but you should know where your information is kept when it’s not in use, such as on a cloud server.
• Security. The security of data lake architecture is interwoven into every other tier, as you are responsible for maintaining the integrity of the information you collect.
• Distillation. During the distillation tier, information is taken from storage and transformed into structured data.
• Processing. The processing tier is the phase in which your analytics team will run algorithms on your data to prepare it for analysis.
• Unified operations. The unified operations tier functions as a governance and auditing workflow so that you can monitor and optimize data management processes.

All 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 your information.

Data lake benefits.

• Centralized data. When data is housed in a disjointed array of platforms and applications, it can become siloed. Aggregating data in a data lake helps remove silos by making all critical business information accessible through a centralized location.

  Amazon centralizes customer data, inventory, and sales metrics in a data lake, enabling teams to track preferences, manage supply chains, and optimize pricing in one place, eliminating data silos.

• Scalable and cost-effective storage. To thrive, businesses need data — and lots of it. Without a data lake, organizations have to structure their data during the intake process, which can discourage them from harvesting enough information to fuel analytics. Data lakes remove this hurdle because they allow organizations to store data in any format.

  Netflix stores vast user data, viewing habits, and streaming behavior in a data lake, allowing scalable, cost-effective storage of raw data to drive personalized recommendations and content creation.

• Accelerates analytics. Data lakes are essential to data analytics. Thanks to these massive pools of information, businesses can use modern analytic processes to access real-time market insights and guide decision-making.

  Tesla collects real-time data from its electric vehicles in a data lake to improve vehicle performance, enhance self-driving capabilities, and analyze battery usage, driving innovation and predictive maintenance.

• Improves collaboration. Consolidating data from various sources into a single, centralized repository enables organization-wide access. By removing barriers between isolated systems, data lakes foster collaboration between teams across departments.

  Coca-Cola aggregates sales, customer feedback, and social media insights in a data lake, fostering collaboration across marketing, sales, and R&D teams to improve product development and regional marketing efforts.

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

Data lake challenges.

• Security. The more data you accumulate, the harder it can be to manage, classify, and protect. Amassing data also makes you a target for hackers, as threat actors are always looking to get their hands on valuable consumer information.
• Data quality. Without proper data quality controls, data lakes can become cluttered with low-quality or irrelevant data that is difficult to manage and analyze.
• Technology overload. Data lakes can ingest information far faster than data warehouses, but if your other technological resources cannot keep up, they may become overloaded and experience a dip in performance.
• Data governance. Depending on your industry, you may have to adhere to strict data governance protocols to ensure compliance and avoid fines, which can prove difficult when dealing with an entire data lake of information.
• Data integration. Without proper integration, data may be poorly organized, leading to difficulties in searching, querying, and analyzing the data.
• Data management. Data lakes require effective data management strategies to ensure that the data is properly organized, labeled, and tagged to make it easy to search, retrieve, and analyze.

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.

Choosing the right platform to build your company’s data lake.

Data lakes provide a scalable, flexible solution to unify and analyze vast amounts of data, enabling advanced analytics and AI applications.

When selecting a data lake solution for your organization, consider the following factors:

• Scalability. Can the platform handle your organization’s growth?
• Integration. Does it work with existing systems and analytics tools?
• Cost efficiency. What are the upfront and operational costs?
• Security features. Does it offer built-in encryption and access controls?

To see how Adobe Experience Platform can help your business make the most of a data lake, request a demo.