Data warehouse vs data lake: Key differences and how to choose

When designing the company’s data management strategy, the first crucial step is choosing how and where to store the data. Some prefer a structured repository in a Data Warehouse (DWH), while others prefer the simplicity of having vast amounts of unprocessed structured and unstructured data in a Data Lake (DL).

In this article, you’ll learn what a data warehouse and a data lake are, the key differences, and how to choose which one is the right choice for you.

• What is a data warehouse? 
• What is a data lake? 
• Which is best for you: data warehouse vs data lake
• How the low-code data science tool, KNIME helps to: 
  • Connect to any data source. 
  • Automate ETL/ELT pipelines. 
  • Implement business metrics with ease.

Let’s dive right in and start by understanding the fundamentals of data warehouses and data lakes.

A data warehouse is a large, centralized, and organized storage system for data. Data warehouses are like big libraries where all the data is neatly categorized and easy to find. The data in a warehouse has already been cleaned and pre-processed, making it ready for quick analysis. 

Data warehouses are built to handle large amounts of information quickly, so when someone asks a question, like "How many product returns did we have last month?" they get an answer fast. 

A data warehouse typically includes a metadata repository, which provides essential information about the data structure, including schema details and relationships, such as detailing the organization of tables, columns, data types, relationships, and constraints. A metadata repository also tracks data lineage by providing insights into the data's origin, transformations, and movements throughout the data pipeline to ensure quality, transparency, and compliance. This helps users understand and navigate the data. 

Here are the key features of a data warehouse: 

• Central repository. A data warehouse serves all departments within a company and is designed to streamline data querying. Since the data is already pre-processed and aggregated, queries are quicker and easier to perform, making it ideal for reporting and analysis.
• Integrate multiple data sources: A data warehouse can store a diverse set of aggregated data from multiple sources like transactional databases, log files, CRM systems, ERP systems, web services, data lakes, and other data sources.
• Historical data. A key role of data engineering is to collect data over time. While many data sources only provide current data (e.g., daily, hourly, weekly), data warehouses include historical data and make it available to end users. 
• Integrated data. The data in a data warehouse must be structured and organized for user-friendly access. For example, contracts from a CRM system could be processed to provide several aggregated measures that better describe the customer’s habits and values. Similarly, data on medical visits can be transformed into healthcare KPIs to illustrate patient health and progress.

• Subject-oriented: Data warehouses focus on specific subjects, such as sales, customers, and patients, and aggregate data for specific tasks.
• Time-variant: Data in a data warehouse is periodically updated to reflect changes over time.
• Non-volatile: Once the data is stored in a data warehouse, it cannot be updated or deleted during transactional processing, which is essential for preventing data loss.

To build a data warehouse, data is retrieved from various sources, transformed to create relevant business metrics, and then loaded into the data warehouse. This process is commonly referred to as ETL (Extract, Transform, Load).

Some of the aggregated data is stored in small reserved tables, called Data Marts, which focus on specific areas of the business, such as sales, customers, campaigns, and projects.

For data storage, a variety of commercial solutions can be used including SQL, no-SQL, cloud-based, and on-premise options, each of which operates on different schemas. 

The primary advantage of data warehouses is the availability of structured, aggregated data, that provides a higher-level view of the business, organization, or research process.

The organized structure of a data warehouse simplifies data access for business analysts by making the data ready for analysis and visualization. However, it requires significant data preparation and data engineering efforts to store all data in an immediately consumable format. Companies must weigh the tradeoff between easily accessible, informative data and the investment needed to design and implement the data architecture, a decision often informed by data engineers and shared across departments.

The data warehouse approach is particularly beneficial for companies with repetitive data operations across different departments. As an example, if several departments require regular reports based on customer loyalty KPIs, it would be time-efficient to store and update the KPI once in the DWH rather than recalculating it every time for each report across all departments.

A data lake (DL) is a central repository that stores data in its raw, natural format from various data sources. A data lake can include structured data from relational databases, semi-structured data like CSV files or XML/JSON objects; unstructured data such as emails, documents, or PDF files; and binary data such as images, audio, and video.

For data storage, various commercial solutions are available, both cloud-based and on-prem. A data catalog is used in both DLs and DWHs to provide information about data storage locations and how data is organized. This includes a metadata repository that helps users understand what information is in the data lake or data warehouse. 

The data pipeline for a data lake starts by ingesting data from the various data sources directly into the data storage in its raw format. The end user then extracts this data, transforms it into the desired format, and visualizes it in reports. The initial phase of the data pipeline is referred to as ELT (Extract, Load, Transform), as the loading occurs before the transformation.

Here are the key features of a data lake: 

• Central repository: A data lake can ingest data in its original form, allowing for the storage of:
  • structured data from relational databases
  • semi-structured data like CSV files and JSON objects
  • unstructured data such as emails and documents
  • binary data including images and audio files. 
• Storage solutions: Data lakes can be implemented using various commercial solutions, available both on-premise and in the cloud. Its open formats prevent lock-in to restricted systems like a data warehouse.

• Data catalog: A data lake uses a data catalog to help users understand where data is stored and how it is organized. It includes a metadata repository that provides essential information about the data. It also helps to eliminate data silos, allowing users to easily access and catalog all data sources.

The main advantage of a data lake is its ability to store raw data from various sources without the need for preprocessing or predefined schemas. This eliminates the requirement of complex data architecture and data preparation, resulting in lower upfront costs compared to a data warehouse. However, when creating reports, all KPIs, metrics, and aggregated quantities must be recalculated from scratch, which can be time-consuming when considering the workload across all departments. 

Note that if data lakes are not properly managed, they can become "data swamps", making it difficult to find the necessary data you need amid vast amounts of uncurated raw data.

The data lake approach is often recommended for companies that have yet to define their data strategy, lack the resources to process their data efficiently, or need to quickly collect large volumes of data without worrying about structure. For example, in IoT applications, large amounts of fast unstructured data are collected from a wide range of sources. The rapid influx of this data makes it difficult to use traditional DWH.

A data warehouse can serve as a central data hub for analysts and data scientists, but it requires careful planning to design its content and implement it error-free. In contrast, a data lake is easier to set up as it doesn’t require any data processes. However, this can lead to issues like data storage redundancy and inefficient data processing. There is no one-fit-all solution and each company must make a choice based on its specific business needs and the type of data it handles. 

In a data lake, the approach is often described as "bottom-up." Data is ingested in its raw form, and transformations occur only when needed for analysis, following the Extract, Load, Transform (ELT) process. 

In contrast, a data warehouse typically uses a "top-down" approach, where the schema of the data is defined in advance. This requires data to be transformed to fit the schema before being loaded into the data warehouse, aligning with the Extract, Transform, Load (ETL) process.

The answer depends on your company's specific needs, data strategy, and maturity in data management. Companies rarely choose one approach exclusively. Instead, many opt for a hybrid solution, that incorporates a data warehouse for frequently used aggregated metrics and a data lake for infrequently used data or data that doesn't fit the data warehouse architecture. 

Modern commercial software solutions often provide both options for effective data handling. So the question often isn’t one of either/or but rather which data should be stored in a pre-processed form for regular use and quick querying, and which data can be stored in a data lake. 

Key feature comparison: Data warehouse vs. data lake

KNIME Analytics Platform is free and open-source software that you can download to access, blend, analyze, and visualize data, without any coding.

KNIME offers versatile solutions that can adapt to both storage architectures and help streamline data operations. KNIME Analytics Platform, with its visual approach to building data operations pipelines, can be a valuable tool for data engineers.

You can use KNIME to easily:

• connect to all types of data sources
• assemble automated data pipelines for both ETL and ELT processes
• implement the necessary metrics for your business requirements.

KNIME offers an extensive collection of nodes designed for seamless interaction with various data sources and for building and maintaining both data warehouses and data lakes—whether on-premises or on the cloud. This includes SQL and no-SQL database connectors, file-handling nodes, big data connectors, and nodes to access web services, among others.

For more detailed information, check the KNIME Connectors cheat sheet. The video “Connectors with KNIME Analytics Platform” also provides deeper insights into the 300+ available KNIME Connectors.

In this article, we've introduced you to the concept of data warehouses and data lakes and outlined their pros and cons in a specific business context. While data lakes are favored for their flexibility, some companies prioritize well-structured data, making data warehouses a better fit. 

Often, companies choose a hybrid approach, with data warehouses used for certain business functions and data lakes for others.

For further learning, you can explore the self-paced KNIME learning path for data engineering courses, available for free on the KNIME Learning Center. This learning path covers everything from the basics of data literacy to the productionization and best practices of data engineering, all through visual programming, without any prior coding experience needed.

Here are answers to some of the most frequently asked questions about data warehouses and data lakes:

No, data lakes and data warehouses can co-exist but are fundamentally different. Data lakes store unprocessed data, whereas data warehouses store pre-processed data. You can decide whether you have a data lake, a data warehouse, or a mixture of the two based on this difference. 

They are designed to meet different requirements.

• Data warehouses are optimized for fast querying and reporting. The data here is already processed and cleaned, which means that querying data is easier and faster. Note, however, that entering data into the data warehouse takes longer because it has to be processed before it is entered.
• Data lakes are designed to enable a business to store higher volumes of data. The data is stored in its raw format until a team decides to process and transform it for analysis. This means that entering the data is easy, but retrieving the data can be more time-consuming.

A data warehouse is a large, central repository that stores data from various sources across an entire organization. It is designed to support many departments and processes – or even the whole organization.

A data mart is a smaller, more focused subset of a data warehouse, tailored to meet the needs of a specific department or business function, like marketing or sales.

Data warehouse layers are stages in the data processing pipeline. Data warehouse layers include the data source layer for collecting raw data, the staging layer for temporary storage, the ETL layer for transforming data, the storage layer for structured data storage, the access layer for querying and reporting, and the metadata layer for managing and monitoring data.