Data warehouses, data lakes and data lakehouses are different types of data management solutions with different functions:

• Data warehouses aggregate, clean and prepare data so it can be used for business intelligence (BI) and data analytics efforts. 

• Data lakes store large amounts of raw data at a low cost. 

• Data lakehouses combines the flexible data storage of a lake and the high-performance analytics capabilities of a warehouse into one solution.

Because these solutions have different features and serve different purposes, many enterprise data architectures use 2 or all 3 of them in a holistic data fabric:

• An organization can use a data lake as a general-purpose storage solution for all incoming data in any format.

• Data from the lake can be fed to data warehouses that are tailored to individual business units, where it can inform decision-making.

• A data lakehouse architecture can help data scientists and data engineers more easily work with raw data in a data lake for machine learning (ML), artificial intelligence (AI) and data science projects.

Data lakehouses are also popular as a modernization pathway for existing data architectures. Organizations can implement new lakehouses without ripping and replacing their current lakes and warehouses, streamlining the transition to a unified data storage and analytics solution.

A data warehouse aggregates data from disparate data sources—databases, business applications and social media feeds—in a single store. The defining feature of a data warehousing tool is that it cleans and prepares the data sets it ingests. 

Data warehouses use an approach called “schema-on-write,” which applies a consistent schema to all data as it is written to storage. This helps optimize data for business intelligence and analytics.

For example, a warehouse for retail sales data would help ensure that details such as the date, amount and transaction number are formatted correctly and assigned to the right cells in a relational table. 

A data mart is a type of data warehouse that contains data specific to a particular business line or department rather than an entire enterprise. For example, a marketing team might have its own data mart, human resources might have one, and so on. 

A typical data warehouse has 3 layers:

• The bottom layer, where data flows into the warehouse from various sources through an extract, transform and load (ETL) process. In many warehouses, data is stored in a relational database or similar system. 

• The middle layer is built around an analytics engine, such as an online analytical processing (OLAP) system or an SQL-based engine. This middle layer enables users to query data sets and run analytics directly in the warehouse. 

• The top layer includes user interfaces and reporting tools that enable users to conduct ad hoc data analysis on their business data.  

Early data warehouses were hosted on-premises, but many are now hosted in the cloud or delivered as cloud services. Hybrid approaches are also common. 

Because traditional data warehouses rely on relational databases systems and strict schema, they are most effective with structured data. Some modern warehouses have evolved to accommodate semistructured and unstructured data, but many organizations prefer data lakes and lakehouses for these types of data.

Data warehouses are used by business analysts, data scientists and data engineers to conduct self-service analytics efforts.  

Applying a defined schema to all data promotes data consistency, which makes data more reliable and easier to work with. Because a data warehouse stores data in a structured, relational schema, it supports high-performance structured query language (SQL) queries.

Organizations can use built-in or connected BI and data analytics tools to analyze transactional data and historical data, generate data visualizations and create dashboards to support data-driven decision-making.

Warehouses can be costly to maintain. Data must be transformed before it is loaded into a warehouse, which requires time and resources. Because storage and compute are tightly coupled in traditional warehouses, scaling can be expensive. If data is not properly maintained, query performance can suffer. 

Because they can struggle with unstructured and semistructured data sets, data warehouses are not well suited to AI and ML workloads.

Data lakes are low-cost data storage solutions designed to handle massive volumes of data. Data lakes use a schema-on-read approach, meaning they do not apply a standard format to incoming data. Instead, schemas are enforced when users access the data through an analytics tool or other interface.

Data lakes store data in its native format. This allows a data lake to store structured data, unstructured data and semistructured data all in the same data platform.  

Data lakes emerged to help organizations manage the flood of big data unleashed by Web 2.0 and the rise of cloud and mobile computing in the late 2000s and early 2010s. Organizations found themselves dealing with more data than ever, much of it in unstructured formats—such as free-form text and images—that traditional warehouses cannot easily manage.

Early data lakes were often built on the Apache Hadoop distributed file system (HDFS). Modern data lakes often use a cloud object store, such as Amazon Simple Storage Service (S3), Microsoft Azure Blob Storage or IBM Cloud® Object Storage.

Data lakes separate data storage from compute resources, which makes them more cost-effective and scalable than data warehouses. Organizations can add more storage without scaling compute resources alongside it. Cloud storage supports further scalability, as organizations can spin up more storage without expanding on-premises resources.

To process data in a data lake, users can connect external data processing tools such as Apache Spark. Unlike a data warehouse, these processing tools are not built into the lake.

Data lakes are a popular choice for general-purpose data storage because of their low cost, scalability and ability to store data of any format.

Organizations often use data lakes to maintain backups and to archive old and unused data. Organizations can also use lakes to store all incoming new data, including data without a defined purpose. The data can stay in the lake until the organization has a use for it.

Organizations also use data lakes to store data sets for ML, AI and big data analytics workloads, such as data discovery, model training and experimental analytics projects.  

Because they do not enforce a strict schema and lack built-in processing tools, data lakes can struggle with data governance and data quality. They are also less suited to the day-to-day BI and data analytics efforts of business users.

Organizations often need separate tools—such as a comprehensive data catalog and metadata management system—to maintain accuracy and quality. Without such tools in place, data lakes can easily become data swamps.

A data lakehouse merges the core features of data lakes and data warehouses into one data management solution. 

Like a data lake, a data lakehouse can store data in any format—structured, unstructured or semistructured—at a low cost. 

Like a warehouse, a data lakehouse supports fast querying and optimized analytics.

A data lakehouse combines previously disparate technologies and tools into a holistic solution. A typical lakehouse architecture includes these layers:

As in a data lake, compute and storage resources are separate, allowing for scalability.

Data lakes rely heavily on open source technologies. Data formats such as Apache Parquet and Apache Iceberg enable organizations to freely move workloads between environments. Delta Lake, an open source storage layer, supports features that help users run analytics on raw data sets, such as versioning and ACID transactions. "ACID" is short for atomicity, consistency, isolation and durability; key properties that help ensure integrity in data transactions.

Organizations can build their own lakehouses from component parts, or use prebuilt offerings such as Databricks, Snowflake or IBM® watsonx.data™.

Data lakehouses can help organizations overcome some of the limits and complexities of warehouses and lakes.  

Because data warehouses and lakes serve different purposes, many organizations implement both in their data stacks. However, that means users need to straddle 2 disparate data systems, especially for more advanced analytics projects. This can lead to inefficient workflows, duplicated data, governance challenges and other problems.

Lakehouses can help streamline analytics efforts by supporting data integration. All data, regardless of type, can be stored in the same central repository, reducing the need for duplication. All kinds of business users can use lakehouses for their projects, including BI, predictive analytics, AI and ML.

Data lakehouses can also serve as a modernization pathway for existing data architectures. Because open lakehouse architectures easily slot in alongside existing lakes and warehouses, organizations can start transitioning to new integrated solutions without a disruptive rip and replace.

While lakehouses can streamline many data workflows, it can be complicated to get one up and running. Users might also experience a learning curve, as using a lakehouse can differ from the warehouses they are used to. Lakehouses are also a relatively new technology and the framework is still evolving.

Data warehouses, data lakes and data lakehouses serve different business and data needs. Many organizations use 2 or all 3 of these systems in combination to streamline data pipelines and support AI, ML and analytics.   

By way of analogy, consider a commercial kitchen. Every day, this kitchen receives shipments of ingredients (data) arriving on trucks (transactional databases, business apps, and so on.)  

All ingredients, regardless of type, land on the loading dock (the data lake). Ingredients are processed and sorted into refrigerators, pantries and other storage areas (data warehouses). There, the ingredients are ready to be used by the chefs without any additional processing.  

This process is fairly efficient, but it does expose some of the challenges of traditional data lakes and data warehouses. Like ingredients on a loading dock, data in a data lake can’t be used without further processing. Like ingredients in the kitchen, data in a data warehouse must be properly prepared and delivered to the right place before it can be used.

A data lakehouse is a bit like combining a loading dock, pantry and refrigerator into one location. Of course, this combination might be unrealistic in the realm of commercial kitchens. However, in the world of enterprise data, it enables organizations to get the same value from data, while reducing processing costs, redundancies and data silos.

• Data warehouses store cleaned and processed data, whereas data lakes house raw data in its native format. 

• Data warehouses have built-in analytics engines and reporting tools, whereas data lakes require external tools for processing.

• Data lakes have cheaper, flexible and scalable storage. Data warehouses offer optimized query performance.

• Warehouses are best suited for supporting the business intelligence and data analytics efforts of business users. Data lakes are best suited for operations that require large volumes of data in various data formats, such as artificial intelligence, machine learning and data science. 

• Warehouses support ACID transactions. Data lakes do not.

• Lakehouses and warehouses have similar analytics and querying capabilities, but lakehouses can better support complex AI and ML workloads than warehouses can.

• Lakehouses offer cheaper, flexible and scalable storage for all types of data. Warehouses mainly support structured data.

• Warehouses use ETL, while lakehouses can use ETL or ELT.

• Lakehouses can handle batch and streaming data. Warehouses work in batches.  

• Both data lakes and lakehouses can support large data volumes and various data structures. Both use similar data storage systems typically cloud object storage. 

• Data lakes do not apply schemas to ingested data. Data lakehouses have the option to apply schemas.

• Both data lakes and lakehouses can support AI and ML workloads, but lakehouses offer better support for BI and data analytics efforts than data lakes do.

• Lakehouses have built-in analytics tools or are tightly integrated with analytics frameworks. Data lakes require external tools for data processing.  

• Lakehouses have stronger data governance, integrity and quality controls than data lakes.  

• Lakehouses support ACID transactions; data lakes do not.

• Data lakes are often built for batch processing and might not support streaming data. Lakehouses can support batch and streaming data.