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Data Lake vs Data Warehouse vs Data Lakehouse, Which is Best for You?

Updated: Jul 7, 2023

The Only Guide You’ll Need for Selecting the Right Data Analytics Architecture.


Many organizations miss out on the powerful insights that come from correctly organizing, transforming, and modeling data. This is due to not taking advantage of the correct data analytics architecture tool. Data analytics have so much power and influence on any organization, and knowing how to store data with the right tool is crucial to business success and ROI (return on investment).


Data architecture selection can seem daunting and overwhelming because there are so many technical options out there. Your organizational data could be stored in separate locations, injected at a rapid pace, not congregating seamlessly, or not being properly categorized into a format that is easy to turn into reporting analytics. Whatever the data pain point you experience, your organization can turn data into golden, proprietary insights by selecting the right analytics solution.

Learn about the three repository solutions of data lakes, data warehouses, and data lakehouses to find out which one most aligns with your organizational objectives. How your organization uses, generates, or evaluates data determines which data architecture solution is best. Explore the differences between the three data architecture solutions below.


What is a Data Lake?

A data lake is a large, centralized repository that stores raw, unstructured data in its native format. This unstructured data format can come from a variety of sources at once, and its main purpose is to store and process substantial amounts of data. The data in a data lake is different from that in a data warehouse as it is raw, unaltered data that contains more detailed information. Typically, data lakes are better for long-term use since they save the original data.


What is a Data Lake Used For?

Since data lakes store data in its original form, it can be processed and used for various analytical needs. The original raw format makes information more readily available across multiple user groups to analyze as needed. Some of those include reporting and analytics, big data processing, social media, cloud and IOT data movement, and on-prem data movement. Efficiently store data long-term and use it for diverse needs with a data lake.


Data Lake Pros and Cons

Pros:

  • Consolidates structured and unstructured data in one location

  • Data adaptability, meaning data lakes can store any type of data format

  • Cost-effective, cheaper than a data warehouse or a data lakehouse

  • Works seamlessly with machine learning to turn processed data into meaningful insights

Cons:

  • Can sometimes be hard to organize data and connect with business intelligence and reporting platforms

  • Lack of data consistency makes it difficult for optimal security and reliability

  • Can be overrun with useless information if not governed properly

data lake vs date lakehouse

Types of Organizations That Would Benefit Most From a Data Lake?

Those looking to store and evaluate data according to their specific needs as they change over time would benefit most from a data lake. A data lake is a great solution for several types of organizations and industries that generate large amounts of raw data. These organizations can use this raw data to generate reporting analytics based on various datasets but require manual sorting when creating these detailed reports.


A data lake could be used for any organization that is storing large amounts of raw data, connecting with various machine learning, IOT devices, and data workloads to create actionable insights. This is the most cost-effective of the three, good for organizations across industries that store various types of data, and connect that data with additional analytical platforms to do a lot of the work.


What is a Data Warehouse?

A data warehouse is a centralized repository used to store and manage data from multiple sources much like a data lake, but has structured and integrated data according to a “schema”. This means the data is cleaned, transformed, and organized in a way that makes it far easier to query, analyze, and connect with other data technologies. This reduces some of the need for resources to manually sort and categorize the data into reporting and analytical tools, as it categorizes the data for you.


What is a Data Warehouse Used for?

A data warehouse is used to store clean, organized data before it is loaded into a reporting and analytics tool. Data warehouses are trusted for their ability to store reliable and consistent data, making it easy to transfer that data into a data analytics tool. The data in a warehouse is constantly growing, but is not as fast at running processes, like a data lake.


Data Warehouse Pros and Cons

Pros:

  • Enhances the consistency, quality, and standardization of data

  • Provides advanced business insights

  • Amplifies the capabilities and velocity of data analytics and business intelligence operations

  • Simplifies the decision-making process with data to back it up

Cons:

  • Limits the data adaptability

  • Expensive to implement and fairly time-consuming to manage

  • Can be slow to run processes


Types of Organizations That Would Benefit Most From a Data Warehouse?

Those that rely on data for most of their business processes would benefit most from a data warehouse. A data warehouse is a great solution for organizations who are constantly storing and managing new substantial amounts of data from multiple sources, performing complex analysis, and generating reports.


This could be large healthcare, financial services, manufacturing, or other organizations that collect large amounts of data and need to turn it into reporting metrics to showcase increased ROI. Leaders can take desired data from their data warehouse and turn it into actionable items to report on to improve performance.


data source vs data warehouse

What is a Data Lakehouse?

A data lakehouse combines the best features of both a data lake and a data warehouse, hence the name “lakehouse.” It allows for the storage and management of both structured and unstructured data in a single location. Data lakehouses allow organizations scalability and flexibility by storing raw data long-term in a data lake, while also providing the security and performance reporting found in a data warehouse.


What is a Data Lakehouse Used for?

Data lakehouses are the most versatile of the three solutions, allowing for the most flexibility in a data management tool. It provides a unified view of data which leads to quicker data-based decisions, and improved business operations, potentially leading to new revenue streams quicker than the other two solutions.


A data lakehouse is an open standards-based modern data analytics solution that is multifaceted in nature. It can address the needs of data scientists and engineers who conduct deep data analysis and processing, as well as the needs of traditional data warehouse professionals who curate and publish data for business intelligence and reporting purposes.


Data Lakehouse Pros and Cons

Pros

  • Cost-effective as it provides low-cost storage options, as well as is one consolidated solution rather than requiring multiple

  • Provide direct and easy connections to data analytics and visualization tools like Power BI and Tableau

  • Ideal for data governance, versioning, and security

Cons

  • New to the market! Fewer case studies and experiments about data lakehouses have been shared to learn from as it is a relatively new option for data analytics


Types of Organization's That Would Benefit Most From a Data Lakehouse?

Large to enterprise-level organizations tend to benefit most from a data lakehouse. As far as industries go, any industry and organization wanting to combine the quality of data potential from a data lake and the consistency of a data warehouse would benefit from a data lakehouse. Data lakehouse users have access to machine learning, business intelligence, and data transferring capabilities in one storage solution.


Which Data Analytics Architecture is Best for Your Organization?

While data lakes, data warehouses, and data lakehouses are all excellent data analytics platforms, each are used in different ways depending on the desired outcome. Depending on how your organization queries and manages data will determine which solution is best. Evaluate what you want your data to do for your business, and the processes required to get you there. Turn your data into insights and actionable reports to make necessary improvements to your organization with the right data platform in place.


To learn more about data engineering and the Microsoft tools that make it easy, contact a JourneyTEAM data specialist today.