What Is Data Lake Architecture?

Key takeaways

Data lake architecture enables the storage of large amounts of diverse data in a single location, thereby eliminating silos and enhancing security.

• A recent survey conducted by S&P Global Market Intelligence found that data lakes increased the competitive advantage for 33 percent of responding organizations [1].

• When implementing data lake architecture in your company, three platforms you can consider are Amazon Web Services (AWS), Microsoft Azure, and Cloudera.

• You can pursue various careers that involve working with data lake architecture, such as a data scientist, a data architect, or a data engineer.

Learn more about data lake architecture, its advantages and disadvantages, and its various use cases. If you’re considering a career in data science, enroll in the IBM Data Science Professional Certificate, where you can learn about data analysis, data visualization, and generative AI.

What is a data lake?

A data lake is a repository that enables the effective storage of vast amounts of data in a centralized location. Not only can you deposit and keep structured, unstructured, and semi-structured data in a data lake, you can also perform various forms of analytics on that information, such as visualizations, big data processing, real-time analytics, and machine learning. The insights from this analysis can help you make better, data-driven decisions for your organization. For example, companies offering a streaming media service gather data based on customer behavior to more accurately recommend shows to their viewers.

What is the difference between data lake and data warehouse architecture?

Data lake architecture is meant for storing structured, semi-structured, and unstructured data that doesn’t yet have a defined purpose. In contrast, data warehouse architecture cleans and organizes data in preparation for the analytics process. However, data scientists may still use the raw data in a data lake for a variety of reasons, such as real-time analytics, machine learning (ML) models, and artificial intelligence (AI). Data warehouse architecture stores structured data that has been processed for a predefined purpose, like learning about products, customers, or employees. 

What is the need for data lake architecture?

If you can glean value from your organization’s data, you can use it to outperform the competition. For example, S&P Global Market Intelligence conducted a survey regarding the benefits of data lakes, and 33 percent of responding organizations stated that data lakes increased their competitive advantage [1]. Furthermore, the ability to improve regulatory compliance increased by 34 percent, business agility by 36 percent, and product and service development by 38 percent [1]. 

Data lakes also enable you to consolidate raw data at a reasonable cost, keeping it secure in a single location and eliminating the need for silos. This makes the information easily accessible so users can perform structured query language (SQL) queries, streaming analytics, and text mining.

Examples of data lake architectures on AWS, Azure, and Cloudera

When considering data lake architecture implementation to store your organization’s data, three of your options are AWS, Microsoft Azure, and Cloudera. Take a closer look at these three possibilities and a real-world example for each one:

AWS data lake architecture

Georgia-Pacific, a materials manufacturing company, utilizes an AWS data lake to enable advanced analytics as a means to gain insights and simplify the data collection process. Using AWS services, the organization can stream—in real-time—information from its manufacturing equipment directly into the data lake, allowing for the analysis of both structured and unstructured data. Georgia-Pacific can then learn the optimal operating speed for its machines.

Azure data lake architecture

FedEx scans millions of packages daily as each one travels to its destination, producing large amounts of helpful logistics data. After combining that information with traffic and weather data, FedEx transfers it all to a Microsoft Azure data lake. The company can then analyze the data to determine methods for improving efficiency and delivery speeds.

Cloudera data lake architecture

Keck Medicine of USC loads several data sets, including pharmacy orders and patient health records, into a Cloudera data lake for future analysis, aiming to surface issues and create actionable insights. Gathering the data in one place allows the organization to ensure the accuracy of the information while preparing and storing it. The Keck Medicine of USC Controlled Substance Committee could then, for example, identify any irregular patterns regarding opioid prescriptions.

Pros and cons of data lake architecture

While data lake architecture offers several benefits, such as versatile data storage and scalable storage, it also presents a few challenges, such as governance to prevent disorganization and longer data queries. Explore the various advantages and disadvantages to gain a better understanding of data lake architecture:

Data lake architecture pros

• Centralized data storage: Whether your organization collects structured, unstructured, or semi-structured data, you can store it all using data lake architecture, which means fewer silos and more accessibility across departments.

• Scalable: When working with vast amounts of data, your company can use data lake architecture to store it without incurring extra expenses. Because a data lake utilizes cloud-based or on-premises infrastructure for storage, it can expand depending on how much you use it. Some providers offer pay-as-you-go models.

• Advanced analytics: Since data lake architecture can store large data sets of unstructured data, ML models, AI algorithms, and real-time analytics operations can easily access this information for purposes such as training, uncovering insights, and making predictions. With this technology and these processes, you can build predictive models to tailor experiences for more personalized customer interactions.

• Real-time and historical analysis: Unlike traditional databases, you can include both real-time and historical data in data lake architecture, which gives you the ability to examine live data streams while also analyzing previous trends. From this simultaneous analysis, you can gain insights into present operations while also developing plans for the future.

Data lake architecture cons

• Requires management: You need to manage the information inside data lake architecture in terms of cataloging and metadata to avoid a “data swamp,” a situation in which users are unable to locate important data sets. A mismanaged data lake can be filled with irrelevant and outdated information.

• Slower data queries: A traditional database utilizes predefined schemas and organizational methods to increase query speed, but a data lake contains raw data, which needs more processing before analysis. This can slow down query speeds, especially if you’re working with a huge data set. 

• Security challenges: Because a data lake can hold so much data, you need powerful security measures to keep your sensitive information safe and adhere to compliance rules. 

How to create data lake architecture

If you’ve decided to use data lake architecture to store your organization’s data, you can start by following a few steps, beginning with establishing clear objectives and ending with building for collaboration. 

1. Establish clear objectives: Know exactly why you need a data lake.

2. Ensure data quality: Ensure that you cleanse and validate your data, and include metadata for easy retrieval.

3. Design data lake architecture: Select a platform that guarantees scalability, security, and compliance, such as AWS, Azure, or Google Cloud.

4. Establish governance: When dealing with petabytes of data, you need a governance framework that grows alongside your data.

5. Implement metadata frameworks: For data scientists to understand, trust, and analyze data effectively, they require metadata frameworks that provide context for the information.

6. Perform regular maintenance: Once you’ve built your data lake architecture, you need to continually monitor its performance, prune the data, and update the protocols based on your business’s evolving needs.

7. Build for collaboration: Constructing a data lake architecture designed for data scientists and business analysts enables collaboration across various departments within your organization, helping everyone extract value from it.

Who uses data lake architecture?

A variety of industries utilize data lake architecture to improve products and services, business agility, and regulatory compliance, including finance, health care, streaming services, travel, and driving services: 

• Finance: Data lake architecture helps investment firms access the most current market data to effectively oversee portfolios. 

• Health care: The health care industry uses data to learn about and take better care of patients. 

• Streaming media: Your streaming service improves its recommendation algorithm by studying all the information the data lake acquires about your viewing habits. 

• Travel: To improve the customer experience and establish the best prices, Airbnb gathers large amounts of information in its data lake for the company’s ML models. 

• Driving services: Uber can analyze information in its data lake to identify optimal driving routes and pricing strategies.

How to get started with data lake architecture careers

To begin a career that involves data lake architecture, such as a data scientist, you typically need to earn a bachelor’s degree in mathematics, statistics, computer science, or a related field. Certain employers prefer a master’s degree, and others may even require it. Other options for starting this career path are boot camps and online courses.

You also need to develop a technical skill set that involves programming, statistics, machine learning, visualization, and data wrangling. Workplace skills that can help you in this career are communication, critical thinking, problem-solving, teamwork, and empathy. Three other roles utilizing data lake architecture are cloud data engineers, data architects, and data engineers.

What is the difference between data lake and lakehouse architecture?

While a data lake is a cost-effective storage system capable of handling vast quantities of diverse, raw data sets, data lakehouse architecture utilizes both the storage flexibility of a data lake and the highly effective analytics capabilities of a data warehouse. With a data lakehouse, your organization can implement ML, business intelligence, and predictive analytics.

Stay current with data science using free resources

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• Study the terminology: Data Science Terminology and Definitions

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