8-minute read

Quick summary: An overview of data warehouses, data lakes, and data lakehouses, plus key insights on how to choose the option that best fits your organization’s data needs

In the fast-paced world of data management, a lively debate has emerged around data warehouses, data lakes, and data lakehouses—and which is “best” for organizations seeking to derive the greatest benefit from their data. Today, many data teams are eager to move past the hype and uncover the true distinctions between these approaches.

While the term “data warehouse” has been a buzzword since the 1980s, its cloud-based evolution offers new possibilities. The more flexible “data lake” emerged in the early 2010s, and today’s versions serve as crucial components for machine learning and agile data exploration. The “data lakehouse,” a term coined by Databricks, presents a bold vision merging a data lake’s flexibility with a data warehouse’s structure. So, who needs which platform and for what purposes?

We’ll explore the nuances of these three options, examining their histories, typical users, and technical architectures. We’ll also address the ideal use cases for each platform, look at the current provider landscape, and share insights on how to choose the best option(s) for your organization.

A lively debate has emerged around the virtues of data warehouses versus data lakes versus and data lakehouses. Time to move past the hype and uncover the true distinctions between these approaches.

Where the data warehouse, data lake, and data lakehouse came from … and where they are now

Origins of the data warehouse trace back to the 1970s and 1980s, when the concept first emerged as a response to the growing need for centralized storage and efficient retrieval of business data. The first data warehouses were on-premises and characterized by structured, tabular formats. With the advent of cloud computing, data warehousing underwent a transformative evolution, leading to the rise of cloud data warehouses. Today, data warehouses play a pivotal role in business intelligence, analytics, and decision-making processes, offering a centralized repository for structured data and enabling advanced data-driven insights.

The concept of a data lake emerged as a response to escalating challenges posed by the growing volume, variety, and velocity of data. These storage repositories hold vast amounts of raw data in its native format until it is needed, providing a more flexible and scalable approach than data warehouses. Major players in the industry, along with innovative open-source tools, continue to refine and enhance the data lake paradigm, making it a cornerstone for extracting valuable data-driven insights.

Data lakehouses, spearheaded by early innovators like Databricks, emerged as a bold solution to the limitations of siloed data storage. Initially focused on simplifying cloud-based data management, the ecosystem evolved to merge the flexibility of data lakes with the structured query power of data warehouses, creating a unified hub for all data needs. Today, data lakehouses stand at the forefront of data democratization, empowering a broad range of users to unlock the insights hidden within ever-growing stores of information.

The key is to understand your organization’s specific needs and choose the solution that best enables your data-driven goals.

Use cases for data warehouses, data lakes, and data lakehouses

Given the highly structured nature of data warehouses, they are ideally suited for use cases such as

  • Business intelligence and reporting: Data warehouses excel at handling structured, historical data and pre-defined queries. Business users rely on them for fast retrieval of specific data points or aggregates for generating reports, dashboards, and business intelligence insights.
  • Complex data aggregation and analysis: Built-in data modeling and transformation capabilities allow users to integrate data from multiple sources into a central, consistent format, facilitating complex data analysis involving multi-dimensional joins, calculations, and historical comparisons.
  • Regulatory compliance and data governance: Data warehouses shine in delivering data security and access control through features such as granular user permissions, audit trails, and encryption for sensitive information, which are crucial for maintaining data integrity and complying with regulations.

In contrast to the highly structured nature of data warehouses, data lakes offer a versatile platform for scenarios where data is diverse and dynamic, requiring flexible processing. Key use cases for data lakes include

  • Advanced analytics and machine learning: Data scientists can harness raw and varied data—structured, semi-structured, and unstructured—to develop and deploy sophisticated algorithms, fostering innovation in predictive analytics, pattern recognition, and other advanced analytical endeavors.
  • Scalable storage for big data: Data lakes provide a cost-effective, scalable solution for storing vast amounts of diverse data, particularly beneficial for organizations dealing with the challenges of handling big data and seeking efficient storage solutions that meet their specific needs.
  • Data exploration for new insights: The agility of data lakes allows businesses to explore data for new insights and discoveries. Data engineers and analysts can investigate uncharted territory without predefined schemas, fostering creativity and enabling valuable understandings to emerge.

In combining the advantages of data warehouses and data lakes, data lakehouses thrive in business use cases such as

  • Unified analytics: Data lakehouses are ideal for scenarios that require advanced analytics, machine learning, and business intelligence under a unified architecture, allowing for seamless integration and analysis of diverse data types for comprehensive insights.
  • Real-time data processing: Unlike traditional data warehouses, data lakehouses offer the advantage of processing real-time data, which is crucial for applications that require up-to-the-minute insights and decision-making based on the most recent information.
  • Collaborative data science projects: With a unified platform encompassing both historical and real-time data, data lakehouses facilitate collaboration among data scientists, analysts, and business users, promoting joint exploration and decision-making on data science projects.

Of course, the data landscape is in a continuous state of flux, and hybrid approaches combining elements of data warehouses, data lakes, and data lakehouses are also becoming increasingly common. The key is to understand your organization’s specific needs and choose the solution that best enables your data-driven goals.

The current provider landscape: a quick overview

Data storage is a cornerstone of any modern digital infrastructure, and choosing the right platform(s) can significantly affect costs, performance, and agility. Currently, three of the leading players in data storage layers are Snowflake, Databricks, and Microsoft Azure Fabric.

Here’s a quick look at each provider as its offering stands today, although each is rolling out new and updated capabilities on a regular basis:

  • Snowflake: Offers cloud-native ease and multi-cloud flexibility for structured and semi-structured data, but costs can climb with heavy workloads.
  • Databricks: Its unified platform for all things data (warehousing, engineering, and ML) is built on open-source Apache Spark for ultimate flexibility, but it requires more technical expertise.
  • Microsoft Azure Fabric: Cost-effective and tightly integrated with the Azure ecosystem, Fabric offers diverse storage options and hybrid/multi-cloud capabilities, but managing various Azure services can become complex.

Ultimately, the best fit for your organization depends on data volume and types, analytics needs, budget, technical expertise, and cloud preferences.

Solution architectures

The growing demand for data has led to a landscape populated by vendors offering various data storage solutions, each promising distinct advantages to augment a company’s data platforms. The pivotal question centers on which storage capability is best poised to support timely and effective business decisions.

Data warehouse

In the realm of data warehousing, also known as enterprise data warehousing (EDW), the emphasis is on reporting and analytical needs, making it a core component of business intelligence. Tailored for structured data defined by specific schemas, data warehouses provide a consolidated storage unit and processing hub for data. Leveraging SQL queries, these warehouses, exemplified by players like Amazon Redshift and Google BigQuery, offer integrated solutions with pre-built functionalities, making them a sensible choice for platforms primarily focused on data analysis and reporting.

Data lake

Flexible in accommodating data ranging from highly structured to loosely assembled, data lakes empower data engineering teams to choose technologies based on unique requirements. By decoupling storage and compute, data lakes facilitate cost savings, real-time streaming, and distributed computation, with vendors like Databricks’ Delta Lake and tools such as Hive and Apache Spark playing crucial roles.

Data lakehouse

This hybrid model enables enterprises to benefit from unified storage akin to data warehouses without sacrificing the analytical flexibility inherent in data lakes. Offering scalable storage and processing capabilities, the data lakehouse becomes a strategic choice for organizations aiming to avoid siloed systems, providing a single source of truth, eliminating redundant costs, and ensuring data freshness. File formats like Parquet, alongside technologies like Delta Lake and Apache Hudi, enhance query performance and introduce ACID functionality to data lake files.

Which option is best for your organization?

The choice between data warehouse, data lake, and data lakehouse hinges on distinct use cases. Business Intelligence users may gravitate towards the structured, schema-based environment of data warehouses, while data scientists working with raw, unfiltered information might opt for the flexibility of a data lake. Enter the data lakehouse, attempting to strike a balance by offering the advantages of both worlds—raw data storage akin to a data lake and the option for preprocessed, structured data reminiscent of a warehouse. This innovative combination promises improved processing time and efficiency without compromising flexibility, potentially serving as a cost-effective solution for a broad spectrum of data usage scenarios.

The data lakehouse, while a relatively recent innovation, is gaining traction, with major vendors like Microsoft, Snowflake, and Databricks investing significantly in its enhancement. As the demand for real-time analytics fueled by data streaming rises, this hybrid approach is becoming increasingly popular and relevant across industries in the years to come.

Navigating the data landscape

In the dynamic field of data management, an educated discourse on “data warehouse versus data lake versus data lakehouse” is essential for discerning the nuances between these approaches. As data warehouses evolve in the cloud era, data lake solutions become more versatile, and the concept of a “lakehouse” gains traction, the quest to understand the ideal platform for specific users and purposes will only intensify.

The choice among data warehouse, data lake, and data lakehouse is contingent on distinct use cases, with the latter positioned as an innovative, cost-effective, and flexible solution catering to a wide spectrum of data usage scenarios. The future lies in understanding organizational needs and selecting a solution that best aligns with data-driven goals in this ever-evolving data landscape.

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Mick Wagner
Mick Wagner is a Senior Solutions Architect in Logic20/20’s Advanced Analytics practice.
Mick Wagner
Syed Zaidi is an Architect in Logic20/20’s Advanced Analytics practice.