An Introduction to the Data Lakehouse for Data Warehouse Users

Data warehouses have long been the gold standard for data analysis and reporting. Developed in the 1980s, the data warehouse concept was introduced to provide an architectural model for data flows from operational systems to decision support systems.  

Although data warehouses remain extremely popular, especially with cloud offerings like Snowflake, Amazon Redshift, and Google BigQuery, customers are increasingly looking at newer and more open paradigms for data management such as the data lakehouse. The modern lakehouse architecture combines the key benefits of data lakes and the data warehouse. And it’s growing increasingly popular: according to a 2024 Dremio survey, 65% of organizations now run the majority of their analytics on lakehouses, with 42% having moved from a cloud data warehouse to the data lakehouse in the last year.  

If you’re coming from a background in data warehouses and have found that the concept of the ‘lakehouse’ confusing or alien, don’t fret — we’re going to dive into both concepts below and explore how they work together.  

What is a Data Lakehouse?  

A data lakehouse is an open data management architecture that takes the structure and performance of data warehouses and combines them with the cost-efficiency, flexibility, and scalability of data lakes.  

Lakehouses allow data teams and other users to apply structure and schema to the unstructured data that would normally be stored in a data lake. This means that data consumers can access the information they need more quickly, without adding overhead for data producers. In addition, because the data lakehouse is designed to store both structured and unstructured data, they can remove the need to manage a data warehouse and a data lake.  

These attributes make data lakehouses ideal for storing big data and performing analytics or data science operations. They also provide a rich data source for data teams to meet a broad spectrum of analytical needs, such as training machine learning models or performing advanced analytics, and serve as a source for business intelligence operations.

Data Lakehouse vs Data Warehouse: What’s the Difference? 

The primary difference between a data lakehouse and a data warehouse is the former's native support for all data types. As we’ve already mentioned, data lakehouses support both structured and unstructured data by leveraging cloud object storage alongside open-source formats such as Apache Parquet and Apache Iceberg. Data warehouses, in contrast, are designed for structured data and are highly optimized for performing queries on it, often relying on proprietary file formats to do so. 

Although this optimization enables fast and complex queries over large datasets, it’s nowhere near flexible or scalable enough to meet the demands of advanced analytics, artificial intelligence, or machine learning applications that digest vast amounts of unstructured data and need direct access to it.  

However, which of the two is most suitable for your organization boils down to your use case. A data warehouse is likely the most appropriate option if you need structured business intelligence and reporting emphasizing data quality and governance. If, on the other hand, you need the flexibility to handle a high volume of varied unstructured data types and intend to leverage advanced analytics and AI, you’ll be served better by a data lakehouse. In some cases, you might end up with both (in the short term) with specific use cases or workloads running on each to optimize costs while delivering the right performance. 

It’s also important to be mindful of potential cost and resource constraints. While data lakehouses can be more cost-effective for storing large data volumes, they can also require more resources for upkeep and management. This is especially true if data quality and governance are important to your use case; but it tends to cancel out when you are looking to store or process large volumes of data. So, the key for lakehouses would be to automate the data quality and governance and handle dynamic optimization of the underlying tables, so as to drive more value and performance from your lakehouses.

Why Choose a Data Lakehouse Over a Data Warehouse? 

The data lakehouse is quickly becoming the go-to architecture for delivering analytics as dev teams move away from solely relying on data warehouses as the backbone of their infrastructure.  There are multiple reasons to make this transition: 

• Broader access to data with more flexible architecture. By using open standards for data storage and a decoupled architecture, organizations can avoid the vendor lock-in that inevitably occurs when your data is stored in locked-down, proprietary file formats that only your data warehousing vendor holds the key to open. Instead, they can make full use of their data by building on scalable object storage and using open formats such as Apache Iceberg, which allow the data to be queried using different engines and support a broad set of use cases (see below).  

• Warehouse-like simplicity on data lake storage: Managed lakehouse offerings such as Qlik Open Lakehouse have closed the governance gap that used to exist between raw and consumable data in traditional data lakes (which rely on unstructured storage). This enables a data management style in line with those seen in data warehouses, which is now achievable using data lake technology and all its benefits, making the lakehouse suitable for regulated industries. 

• Support for diverse data types: Data warehouses are typically designed to store structured, table-based data. The flexibility of object storage makes it better suited to store any kind of data including structured, semi-structured data such as telemetry or event streams or unstructured data. 

• Flexible schema management: Modern table formats used in data lakehouses (e.g., Iceberg — we’ll cover this shortly) allow for schema evolution and enhanced functionality when amending data in a table.  

At the heart of lakehouses are open table formats such as Apache Iceberg. Iceberg represents a seismic shift in data handling, offering solutions to three critical challenges: managing expansive data sets, cost-effectiveness, and supporting diverse use cases.

Learn more about the basics of Apache Iceberg and why it matters.  

Lakehouse Use Cases 

The data lakehouse architecture is well-suited for a wide range of use cases across industries. Some common examples include: 

1. Managing Expansive Data Sets 

Data volumes are growing exponentially. This, coupled with its sheer diversity, is fast outpacing the capabilities of traditional data management systems. Iceberg offers a robust framework designed to cost-effectively handle petabytes of data across different environments seamlessly.  

This, coupled with its scalability and ability to handle ACID transactions, incremental updates, schema evolution, and partition evolution without downtime or performance degradation, makes it ideal for dev teams that are wrangling with growing volumes of unstructured data.  

2. Cost-Effectiveness  

Data warehouses can become very expensive, particularly when scaling up is needed. Iceberg-based lakehouses, on the other hand, promote cost efficiency through storage diversification and an inherent flexibility that decouples compute and storage layers. 

By doing so, Iceberg enables dev teams to utilize cost-effective storage and compute without being tethered to a single vendor’s ecosystem. This approach helps reduce storage and compute costs (by up to 50%) at a time when businesses are under pressure to do more with less and allows dev teams to tailor their data infrastructure to their specific needs, optimizing both performance and cost. 

3. Supporting Diverse Analytics Needs 

Modern data applications are extremely versatile, necessitating data management infrastructure that can support a wide array of use cases without compromising on efficiency. From real-time analytics and machine learning models to batch processing and historical data analysis, the requirements are as diverse as they are demanding. 

Iceberg’s ability to support multiple data models (such as batch, streaming, and bi-temporal queries) within the same platform simplifies the data architecture, reducing the need for multiple systems and the complexity of managing them.  

More importantly, Iceberg enables organizations to unlock their data by storing it once, while providing the ability to query that data using any engine -- as opposed to being tied to a specific platform or engine. This makes it easier for dev teams to deploy and scale data-driven applications and ensures that data remains consistent, accessible, and secure across all use cases. 

Unlocking the Modern Lakehouse with Apache Iceberg 

Apache Iceberg is an open table format that can help dev teams bridge the gap between their structured data warehouses and their vast unstructured data lakes by enabling the execution of high-performance SQL queries directly on the data lake. Leading data platforms including Snowflake and Databricks, and all Cloud vendors have already adopted Iceberg to address control, cost, and ecosystem challenges faced by customers.

One of the key capabilities that Apache Iceberg enables is interoperability. Although the Iceberg table format has capabilities and core functionality similar to SQL tables, the difference is that those in Iceberg are fully open and accessible, enabling multiple engines to operate off the same dataset. Popular open-source platforms or query engines  supported include Apache Presto / Trino, Apache Spark, and Apache Flink; Iceberg is also supported by SaaS and leading managed cloud platforms including Amazon Athena, Google BigQuery, Databricks, and Snowflake. 

Iceberg can also be used to simplify crucial data management challenges, such as managing high-velocity incoming data streams of up to five million events per second. This highlights the platform’s scalability and adaptability to various data ingestion rates and underscores the importance of tailored optimization strategies for different use cases. 

Powered by modern table formats such as Apache Iceberg, data lakehouses support structured and unstructured data to meet the demands of operations that digest vast amounts of data, such as artificial intelligence and machine learning applications. This makes it easy to manage expansive data sets and support diverse use cases while benefitting from a cost-effective storage solution that isn’t tethered to a single vendor. 

Build and Scale Iceberg-based Lakehouses with Qlik Open Lakehouse 

Exploring data lakehouse possibilities? See the benefits of adopting an open Iceberg architecture with an independent offering such as Qlik Open Lakehouse. Ingest your streaming or batch data into Iceberg tables on cloud storage in real time, use any query engine with automatically-optimized Iceberg tables, and simplify data governance to ensure accuracy, compliance, and accessibility.  

Qlik’s Adaptive Iceberg Optimizer technology continuously monitors tables and determines the ideal optimizations, compactions, and clean ups to execute based on each table’s unique characteristics, delivering an unmatched performance boost (up to 2.5x - 5x improvement in performance), and up to a 50% reduction in costs — all without writing a single line of code.  

Learn More: 

• Learn more about Qlik Open Lakehouse 

• Watch our webinar on Build on Apache Iceberg with Qlik Open Lakehouse