Data lakehouse vs data warehouse: In-depth comparison

According to Gartner, data quality issues cost businesses over $12M annually, highlighting a critical problem: companies struggle to trust their data. Inaccurate, outdated, or incomplete data disrupts decision-making and leads to lost revenue and damaged credibility. As data sources grow more complex, pulled from everything from applications to IoT devices, ensuring reliable data across different storage systems-whether in warehouses, lakes, or lakehouses-becomes increasingly difficult.

Each system has its own set of challenges in managing, processing, and maintaining data quality, often requiring robust data analytics services to address these complexities effectively. Without proper monitoring and governance, data inconsistencies, stale data, and incomplete records are inevitable.

This article explores the details of data lake vs data warehouse vs data lakehouse, examining their architectures, advantages and disadvantages, and use cases. Let's take a look at how these technologies align with the needs of enterprises handling complex data environments.

What is a data warehouse?

A data warehouse is a specialized system used to store large volumes of structured data from various sources. Unlike other data storage solutions, data warehouses are optimized for handling complex queries and large datasets, making them essential for businesses that rely on strategic insights.

Strengths and weaknesses of data warehouse

Let's explore how these advantages of a data warehouse can make a real difference for your organization:

1. Data warehouses excel in handling structured, organized data, making them ideal for transactional data and BI use cases. This optimization ensures that data is clean, organized, and ready for business intelligence applications.
2. Built to handle complex queries efficiently, data warehouses provide fast response times for large volumes of structured data. Predefined schemas and optimized storage formats ensure that even the most complex analytical queries are executed quickly.
3. Data warehouses enable enterprises to store historical data, allowing them to analyze trends and changes over time. This advantage is especially beneficial for industries that rely on longitudinal data, such as finance and healthcare, where forecasting and trend analysis are routine.

After discovering the benefits, it is crucial to understand the potential disadvantages of data warehouses:

1. Data warehouses are poorly suited for handling unstructured or semi-structured data such as social media content, sensor data, or logs. In turn, it makes them less adaptable for organizations dealing with diverse data types.
2. The ETL process, schema design, and ongoing data governance make data warehouses resource-intensive in terms of time and cost. Therefore, this is an important factor, especially for smaller organizations with limited resources.
3. Since data must go through an ETL process before being stored, there can be delays in making newly acquired data available for analysis. This latency can be a challenge for organizations needing near real-time insights.
4. While some data warehouses have added real-time capabilities, these are not their strong suit. Data warehouses are best for scheduled reporting and historical analysis but fall short when it comes to real-time insights.

Facing the challenge of managing massive volumes of on-premise, siloed data, our client partnered with us to migrate their data infrastructure to Google Cloud Platform (GCP). Our team seamlessly transferred over 70 data sources, consolidated four data warehouses, and integrated a data lake into a unified, centralized platform on GCP.

We adopted an ELT approach, standardizing diverse data formats and enabling automated, consistent reporting across their client base. This transition saved the client over 17,000 manual work hours annually, eliminated costly third-party reporting tools, and significantly cut operational expenses by decommissioning over 20 servers.

Read more: Automation, cloud migration, and cost optimization for a global tech company

To understand a data lakehouse fully, we first need to look at the data lake's concept.

What is a data lake?

A data lake is a centralized repository that stores vast amounts of raw, unprocessed data in its native format. Unlike traditional databases or data warehouses, data lakes enable organizations to ingest structured and unstructured data without predefined schemas or strict transformations.

The essential purpose of a data lake is to provide a scalable environment where data can be stored as-is, allowing for multiple types of analysis-including machine learning, real-time analytics, and big data processing-without the need for immediate structure.

Strengths and weaknesses of a data lake

Data lakes benefit enterprises that manage vast, complex datasets and need agile data solutions. Here's a look at the core benefits of a data lake:

1. Data lakes are built to scale, providing a cost-effective way to store massive amounts of structured and unstructured data. Whether a business handles terabytes or petabytes, a data lake can expand without high storage costs.
2. Unlike traditional storage solutions, data lakes accept data types, from structured transactional data to unstructured images, logs, and sensor data. Comparing data lake vs data warehouse, the flexibility of lake structure is essential for organizations working across various data sources and formats.
3. Data lakes support both real-time data ingestion and batch processing. This dual capability allows businesses to analyze streaming data for immediate insights and retain historical data for trend analysis and reporting.

While data lakes provide extensive benefits, they also come with specific challenges and limitations that can impact their effectiveness:

1. Without robust governance, data lakes can quickly become disorganized, leading to a "data swamp" where finding and verifying data is challenging. The flexibility in data ingestion can result in inconsistent formats, making quality control essential yet challenging to maintain.
2. Managing the diverse data in a lake requires careful planning and skilled data teams. The lack of a standardized structure means organizations must invest in metadata management, lineage tracking, and efficient search capabilities to make the data lake usable.
3. Data lakes are not optimized for fast querying on structured data. As a result, they may need to catch up in performance when handling complex queries, especially compared to data warehouses.

N-iX has supported Lebara in a full-scale digital transformation. We collaborated with Lebara to develop a comprehensive data lake solution that centralized their data across multiple departments, allowing for near real-time analytics and reporting. This Azure-based data lake now streams data from sources across six countries, facilitating timely reports and insights crucial for sales, finance, and marketing.

In transforming their legacy systems with data lake consulting, N-iX implemented a multi-cloud strategy and a data lake architecture to replace outdated infrastructure, which previously struggled with delayed reporting and scalability issues.

Read more: Faster time to market with full-scale digital transformation in telecom

What is a data lakehouse?

A data lakehouse is a modern data architecture that integrates the best features of data lakes and warehouses. It provides the flexibility and scalability of a data lake, which stores raw, unstructured, and semi-structured data while incorporating a data warehouse's data management, querying, and governance features. This hybrid approach allows organizations to store and process large amounts of diverse data types- from structured transaction data to unstructured media files-without sacrificing traditional robust analytical capabilities in data warehouses.

Strengths and weaknesses of a data lakehouse

The data lakehouse model is increasingly popular for enterprises needing flexible, scalable data solutions. However, like any architecture, it has certain advantages to consider.

• With cloud-native support, lakehouses use low-cost object storage for raw data. Decoupling computing from storage allows companies to scale processing power on demand without increasing storage costs.
• Comparing data lakehouse vs data lake, the first solution supports real-time data ingestion and analytics, which is crucial for time-sensitive applications. This capability eliminates the need for ETL processes between data lakes and warehouses for real-time analysis.
• Lakehouses provide a central repository for raw data, which is ideal for training machine learning models.

While the data lakehouse architecture brings powerful capabilities, it also comes with a few key challenges that organizations must consider carefully.

1. While data lakehouses support high-speed querying, their performance for complex analytical queries may lag compared to a traditional data warehouse.
2. The lakehouse ecosystem is still maturing, with tools and standards continuously evolving. As a result, enterprises may face challenges integrating specific analytics or data governance tools, particularly those built for traditional warehouses.
3. Applying strict governance to unstructured data in a lakehouse can be challenging. While metadata layers offer some control, enforcing lineage, access policies, and data quality rules on raw data sources.

Key differences between data lakehouse vs data warehouse

A data warehouse and data lakehouse represent different data management architectures tailored to specific use cases. Each has unique strengths and challenges in storing, processing, and managing data. Let's discover how data lakehouse vs data warehouse differ.

Data sources and type

Regarding the types of data each architecture can handle, data warehouses do best in environments with structured data. This includes information from CRM systems, ERP applications, and other transactional databases, where data is highly organized, consistent, and ready for analysis. Data warehouses are well-suited for conventional business reporting and BI tasks but can struggle with semi-structured or unstructured data. And, as businesses increasingly leverage diverse data sources like social media feeds, IoT sensors, and machine logs, these limitations can create roadblocks.

In contrast, data lakehouses are built to handle various data types, from structured relational data to raw, unstructured content like text and images. This adaptability enables businesses to explore diverse use cases, particularly in AI and advanced analytics, where unstructured data uncovers hidden insights.

Data processing and integration

Data warehouses rely on the traditional ETL approach regarding data processing. Data is cleaned, formatted, and processed before storage with high data integrity and quality. This pre-storage transformation process is beneficial for static BI reporting and historical trend analysis, as it creates a well-organized database ready for fast querying. However, ETL can slow down processes, especially when dealing with large, fast-growing datasets or data requiring constant updates.

Data lakehouses take a different route with an ELT (Extract, Load, Transform) model. Data is ingested in its raw form and transformed when needed, allowing for real-time data ingestion and reducing time-to-storage. This approach is more adaptable to unstructured and semi-structured data.

Data quality and governance

Data quality and governance are critical for organizations with vast data volumes across regulatory boundaries. Data warehouses offer mature governance frameworks with built-in quality controls from their schema-on-write model. This model requires data to meet strict quality criteria before storage.

In contrast, data lakehouses provide a different approach by balancing flexibility with control. Metadata management and data catalogs are governance tools, allowing organizations to track data lineage and quality without enforcing strict structure at the ingestion stage. While governance in data lakehouses is less rigid than in data warehouses, they incorporate modern quality checks and lineage tracking.

Performance and querying

Data warehouses are built for speed with optimized, high-performance querying on structured data. By applying schemas at ingestion, data warehouses reduce the need for extensive querying transformations, making them ideal for real-time analytics and business reporting that require fast response times.

Data lakehouses blend the fast querying capabilities of data warehouses with the flexibility of data lakes. They provide high-performance analytics for structured data while supporting exploratory data analysis on unstructured data through open storage formats. Advanced indexing and caching techniques enable the lakehouse to handle a range of queries efficiently, whether for structured SQL-based reporting or exploratory Machine Learning analysis.

Data structure and schema

Data structuring is a foundational difference between these two architectures. Data warehouses are schema-on-write systems where data is transformed to fit a specific schema before it's stored. This approach supports organized, structured data that's easily accessible for reporting. However, schema rigidity means that adapting to new data types or sources can be time-intensive.

In comparison, data lakehouses adopt a schema-on-read approach, which stores data in its native form and applies structure only when accessed. This flexibility allows enterprises to ingest data from different sources without pre-defining a schema, making it possible to adapt to new data types without substantial reengineering.

When to choose data lakehouse vs data warehouse?

Here are some critical use cases where choosing a data warehouse makes sense:

• Does your organization require a dependable foundation for Business Intelligence? A data warehouse offers the consistency and reliability needed for companies where data-driven solutions are essential.
• Do you need to analyze historical trends and forecast outcomes? Data warehouses excel when tracking performance over time, and identifying trends is critical. They retain historical data, allowing organizations to conduct in-depth time-series analysis and make data-driven predictions.
• Is consolidating data from multiple systems a challenge? If your organization gathers data from various sources-like CRM systems, ERP software, and marketing platforms-data warehouses are designed to merge these datasets into a unified format, making analysis far more streamlined.
• Do you run complex, high-volume data queries regularly? Data warehouses handle intensive queries with ease. A data warehouse provides the infrastructure to process queries efficiently for businesses that rely on data-heavy analyses, such as customer segmentation or profitability studies.
• Are strict data governance and consistency requirements part of your operational needs? Data warehouses are built with rigorous data governance capabilities, ensuring that only cleaned and validated data is available.

If you're considering a data lakehouse, it's likely because your organization has diverse data needs and wants the efficiency of a single platform. Here are some specific scenarios where a data lakehouse is the ideal choice:

• Are you integrating IoT and sensor data with transactional records for predictive maintenance? A data lakehouse can manage unstructured IoT sensor data and structured transactional data, creating a unified repository for real-time analytics.
• Do you need to analyze customer interactions across multiple channels in real time? Data lakehouse lets you capture and analyze diverse customer data sources-such as social media, web interactions, and purchase histories-in real-time.
• Are you developing Machine Learning models using raw historical data and structured operational data? For businesses building AI models, a data lakehouse provides the infrastructure to store raw historical data alongside structured, cleaned data.
• Are you running real-time risk assessments using structured reports and unstructured data sources? A data lakehouse can consolidate these sources, enabling real-time analysis considering various risk indicators.

Final thoughts

The architecture you choose today will set the stage for tomorrow's growth, innovation, and competitive advantage. The correct data infrastructure can drive your organization's analytics capabilities and build a foundation for scaling AI, Machine Learning, and real-time insights.

The choice of whether to go with a data warehouse, data lake, or data lakehouse isn't just a technical decision-it's a long-term strategic move that can streamline operations, enable more intelligent decision-making, and open doors for future innovation. Each architecture has unique strengths, but the best option is the one that aligns with your organization's specific needs, growth goals, and data ambitions.

Choosing between a data warehouse vs data lake vs data lakehouse can be challenging, but you don't have to go it alone. At N-iX, we help enterprises design data strategies tailored to their unique objectives. Let us help you pinpoint the best data solution for your goals and needs.