May 16, 2024

Data lake vs. data warehouse: Two distinct options for flexible, scalable data storage

Data warehouses and data lakes both offer more flexible, scalable, and agile methods for storing data from multiple sources and in various structures. Unlike traditional databases, these advanced storage options can ingest nearly any data. In a data warehouse, data is transformed into a new schema, while a data lake stores the raw data and only applies schema when queried.

They’re primarily used for handling large volumes of data for analytical processing, including batch processing and complex data analytics. Data lakes and warehouses tend to come up when data, development, database, and IT teams need an option with better:

  • Scalability for efficient storage, management, and query of massive and quickly growing data volumes
  • Flexibility to store diverse data types generated from modern applications and technologies (sensors, social networks, media, etc.)
  • Analytics to maximize the value of increasing data volumes through powerful measurement, query, machine learning, and other capabilities
  • Cost efficiency such as through cloud-based storage and processing resources, pay-as-you-go pricing models, and open-source technologies
  • Agility to act first and respond to the market more effectively while making stronger and more reliable data-driven decisions 

Benefits like these make data lakes and data warehouses increasingly appealing. To successfully adopt and deploy them, teams must prepare for changes and challenges like data governance and integration complexities. Going from a structured traditional database to one that supports multiple structures and unstructured data will require additional controls to keep data integrity intact. 

Integrating data from disparate sources into a cohesive data lake or warehouse architecture can be complex and time-consuming. Teams must develop robust data integration pipelines and workflows to ingest, transform, and load data efficiently while minimizing errors and data inconsistencies. 

They may also need to upskill – lakes and warehouses require data engineering, data science, and cloud computing skills that might not have been needed for traditional database environments. With knowledgeable experts across database, data, development, and IT teams, plus the right tools, these advanced data storage technologies can meet their full potential. 

Traditional database vs lake and warehouse

Traditional databases are foundational to most businesses and stand out for their ability to strictly enforce data integrity and consistency. However, over time the business reporting workloads associated with applications require additional, different technologies. The application database will not be able to handle the growing, evolving load by itself. A need arises for a separate data store to house historical and other data useful for business intelligence but not critical to application availability or functionality. 

Most user-facing business applications are architected, designed, and built for Online Transaction Processing (OLTP) workloads. The databases that support this type of workload are optimized to maximize integrity, consistency, and user-interactivity for specific workflows or tasks. They exist primarily to support real-time operation and availability of their target applications. 

By contrast, analytics and reporting workloads need to support exploratory work by data analysts trying to gain insights into the transactions happening between the application and the OLTP database. This requires the data store to be optimized for more arbitrary queries and less predictable workflows. This type of optimization is referred to as Online Analytical Processing (OLAP).

Traditional databases are geared toward real-time data transactions that happen within applications and ideally hold only the data necessary for the application experience. OLAP databases are geared toward the analysis of historical data beyond what is stored in the OLTP database, trading retrieval speed for depth, detail, and completeness. 

Beyond a relatively small scale, one database can’t effectively support both OLTP and OLAP workloads. They eventually interfere with each other and create performance and usability problems for all stakeholders. This limitation ultimately led to the creation of specialized database engines for large-scale OLAP (analytical) workloads — what we now know as data lakes and data warehouses.

That solves the issue of protecting application database performance while enabling deeper historical analysis without stepping on toes, so to speak. But it opened a new challenge: how to get day-to-day workflow-related data from the OLTP database into the OLAP database for the business analysts to, well, analyze.

The separation between OLTP-optimized databases and OLAP-optimized databases created a new challenge of how to get the day-to-day workflow-related data from the OLTP database into the OLAP database for the business analysts to, well, analyze. One of two processes are typically followed:

  • Batch updates, in which data is sent periodically in large chunks
  • Streaming updates, in which data is consistently replicated in as close to real-time as possible

The logical applications for each approach will become clear as we explore the applications of data lakes and data warehouses. 

Understanding the concepts of data lakes and data warehouses helps you stay on top of the evolving data landscape and identify the best choices for your organization. Explore the distinctions between these two approaches, when they make the most sense, and how to integrate them with DevOps workflows and CI/CD pipelines.

The key difference between a data warehouse and lake

Before we discuss each data store in detail, it’s helpful to focus on the core difference between the two. Both types offer a more flexible and scalable model for data storage than traditional relational databases. They can take in data from an unlimited number of sources, enhance scalability, and offer other benefits over traditional databases. 

Yet, they handle data structuring and processing differently. Data warehouses clean up and reformat incoming data to fit a consistent structure, while data lakes take in raw data in any format, regardless of structure. 

Choose a data warehouse for structured, processed data optimized for fast, complex querying and reporting. Choose a data lake to store vast amounts of diverse, raw data for flexible, scalable analytics and science.

Let’s go deeper. 

What is a data warehouse?

A data warehouse is a centralized repository that stores data integrated from multiple sources, consolidating it into a single, coherent framework. They’re most commonly used to support business intelligence: analytics, reporting, and data mining. Data in a data warehouse is typically structured in a way that makes it as easy and efficient to retrieve information as possible

This structure often involves organizing data into tables and using schemas to facilitate querying and analysis. The most commonly used design patterns for data warehouse schema are star, snowflake, and galaxy. Each provide different benefits depending on the data concerned and the analytical goals of the business:

  • Star: Simplifies data organization by featuring a central fact table surrounded by dimension tables, offering simplicity, fast query performance, and flexibility for adding new dimensions or measures.
  • Snowflake: Normalizes dimension tables into multiple levels of hierarchy, enhancing data integrity and storage efficiency but introducing additional complexity in query processing.
  • Galaxy: Extends the star schema by interconnecting multiple fact tables through shared dimension tables, enabling comprehensive analysis, cross-domain insights, and advanced analytics across diverse business processes or domains.

Data warehouses are optimized for read access, meaning they're tuned to perform well for complex queries on large volumes of data (OLAP) rather than for transaction processing (OLTP).

They pull in data from different source databases using a process called Extract, Transform, Load (ETL) or, more common in modern settings, Extract, Load, Transform, (ELT). ETL and ELT don’t just transfer the data — it also summarizes it, which makes processing faster and more efficient. 

Because data warehouses use a fixed structure, they need careful planning before you set them up. This setup makes sure that the incoming data is ready for quick access and analysis, helping to speed up decision-making. Additionally, some modern data warehouses are designed to handle streaming changes in near real-time, allowing organizations to ingest and process (ETL/ELT) data continuously for up-to-date analytics insights.

Organizations that need to make informed business decisions based on comprehensive insights from their historical data across various sources often turn to a data warehouse solution. These solutions are typically subject-oriented or focused on a specific part of the business. Examples include:

  • Logistics teams focusing on supply chain optimization
  • Compliance teams focusing on easier audits and management
  • Sales teams focusing on customer insights
  • Marketing teams focusing on audience segmentation and targeting
  • Customer service teams focusing on call center and satisfaction analysis

Once data is loaded into the warehouse, it remains stable and unchanged, providing a consistent historical view. This not only enables reliable trend analysis but can lighten the load on transactional databases by shifting data out and into the warehouse. 

Types of data warehouses

Before discussing the overall benefits of data warehouses, there are a few distinct types to consider. Because data warehouses can be various sizes and used to solve problems at different levels in an organization, varying terms are used to describe them.

The term "data warehouse" usually describes an enterprise-level solution. However, sometimes, people use it to distinguish a more strategic system from tactical solutions.

An enterprise data warehouse (EDW) serves as the whole business’s main warehouse, which could then be parsed out into more manageable, tactical data warehouses. The EDW serves as a foundational data source as well as a resource to answer exceptionally interconnected data questions. 

A cloud data warehouse is simply technology hosted in the cloud, such as:

  • Snowflake
  • Amazon Redshift
  • Google BigQuery

What about the smaller, more tactical iterations? A data mart is a subset of a larger data warehouse that is typically oriented toward a specific business line, department, or team within an organization. While a data warehouse serves as a centralized repository for storing and managing vast amounts of data from various sources across the organization, a data mart focuses on a particular subject area or departmental need.

Data marts are often designed to address the specific analytical requirements of a particular business unit or user group. They contain a subset of data from the larger data warehouse, tailored to meet the reporting and analysis needs of specific stakeholders. By organizing data into smaller, more focused subsets, data marts can provide faster query response times and more targeted insights for decision-making within a particular area of the organization.

Benefits of data warehouses

By integrating data from disparate sources into a consistent structure via dedicated data transfer and transformation processes, data warehouses unite data in a way that makes it more valuable than just the sum of its parts. This aggregation allows teams to go deeper and find richer business intelligence insights without sacrificing quality, efficiency, or performance. 

Choosing a data warehouse can improve:

  • Business intelligence, by efficiently and accurately combining data for stronger, deeper, and more interconnected analysis and reporting
  • Data quality and consistency, since ETL/ELT processes help clean data, remove duplications, and standardize formats
  • Historical data storage and scalability with more efficient methods, to better support trend analysis, forecasting, and comparisons
  • Query performance, since they’re specifically designed to handle complex queries and large volumes of data efficiently
  • Operational database functionality, ensuring that the system’s performance for day-to-day operations isn’t affected by large-scale analytics processes
  • Data security and compliance by separating critical data from transactional environments, leveraging robust security features, and transforming data as needed to handle sensitive information appropriately

These benefits help organizations streamline development, analytics, and data management processes while enabling stronger and more meaningful insights to drive strategic business, product roadmap, and operational decisions.

Data warehouse use cases

Relative to traditional OLTP databases, which tend to store transaction and engagement data, data warehouses are a place to store data for processing, analytics, and reporting. Instead of continuous updates like standard databases, data warehouses are updated at distinct intervals. Because the data undergoes ETL/ELT as it makes its way to the warehouse, you can think of the data within a data warehouse not as raw information but as a summarized version of the original data. 

A database sits within the active pipeline and data streams, so any large queries or changes to data can slow down database performance as its resources are used elsewhere. That’s why many organizations choose to instead process and query large amounts of data using data warehouses, which are separated from those live processes and won’t interfere with development, data collection, or other aspects of database performance and availability. 

Data warehouses provide the depth, breadth, and efficiency to answer a business’s most structured and complex data questions with this prepared and curated data. Due to their interconnected nature and streamlined analytical capabilities, data warehouses are commonly used for:

  • Business intelligence
  • Performance management
  • Customer relationship management (CRM)
  • Risk management
  • Financial management
  • Supply chain and logistics
  • Compliance and reporting

On the other hand, data lakes are optimal for storing vast amounts of raw, unstructured data in their native format. They offer flexibility for diverse analytical needs that require access to data in its most unprocessed form without predefined schemas.

What is a data lake?

A data lake is a centralized storage repository that holds a vast amount of raw data(structured, semi-structured, and unstructured) in its native format until it is needed. Unlike data warehouses, which transform and process data into consistent schema before storing it, data lakes store data in various raw forms. That can be anything from standard tables to more complex assets like videos, images, graphs, documents, and more.

The flexibility of data lakes lies in their schema-on-read capability, where the data structure and requirements are not defined until the data is ready to be used. This approach allows organizations to adapt to changes in data types or analytical needs without spending time first defining structures, schemas, and transformations. Since data lakes use native format data, they are better suited to supporting data science, comprehensive analytics, and machine learning projects.

In fact, machine learning (ML) and artificial intelligence (AI) are some of the fastest-growing applications of data lakes. These projects require access to tremendously large troves of diverse data. To achieve the speed and accuracy AI tools require, they need to be able to easily and efficiently explore all kinds of data from multiple sources and in sometimes completely different formats. 

The amount and variety of data in a data lake means that the underlying structure of how it stores that data is a critical decision and can be tuned for specific use cases. The most straightforward is a centralized data lake in which all data is collected and stored in a single, centralized repository accessible to multiple departments. 

It is also possible to use a decentralized data structure. In a distributed data lake, data remains in separate storage systems (perhaps in different physical locations or clouds) but is managed under a unified architecture. However, distributing data can run into compliance and other data-sharing issues across organizational, geographical, or other lines. A federated data lake protects data sovereignty by linking together multiple data lakes that are managed independently but are connected through a system that allows for data sharing and access as needed.

Benefits of using a data lake

Making such broad connections between data types, data lakes allow teams to grow and manage data without many of the traditional challenges. Choosing a data lake can improve:

  • Cost-effectiveness, since data lakes use low-cost storage solutions to accommodate raw data with an “everything, just in case” approach rather than “just what the team needs today”
  • Scalability, by linking data sets instead of needing to stand up new environments or utilize ETL/ELT
  • Flexibility, since they accommodate data of any flavor, structured or not
  • Machine learning, by enabling quick access to larger troves of raw data with full context and detail
  • Data access and processing, since it processes schema on read for quicker ingestion and storage
  • Data discovery and quality, since data is raw and can be analyzed in its entirety, which can also enhance quality and governance
  • Agility, since organizations aren’t bound by schemas or limited by the cost of growth – they can easily tap new data streams into the data lake without big investment or disruption

Data lakes might also see data copied to a data warehouse or traditional database after it’s been queried and cleaned up. This way, it’s readily available in that specific format but also accessible in complete, raw form if needed later. 

Data lake use cases

Data lakes store and manage vast amounts of raw data from diverse sources, supporting a wide range of analytical and operational uses. Their flexibility to handle any data type at any scale and their capacity to integrate new analytics technologies make them particularly well-suited for modern data-driven enterprises.

Data lakes are commonly used for:

  • Data consolidation
  • Big data processing
  • AI, machine learning, and data science
  • Real-time analytics from high-volume data streams
  • Customer profiling and segmentation
  • Log storage and analysis
  • Compliance and auditing

Unlike data warehouses that are only updated periodically, many data lakes are continuously updated – that means they provide real-time data access, discoverability, and analytics. 

What is a data lakehouse?

Pioneered by Databricks (a Liquibase technology partner), a data lakehouse combines the best features of data lakes and data warehouses. It maintains the vast storage and flexibility of a data lake while adding the data management and schema control typically seen in data warehouses. 

This architecture supports both machine learning workflows and BI tools directly on the low-cost storage used for data lakes. Learn more about data lakehouses and how to maximize their value

DevOps for data warehouses and data lakes

Understanding data lakes and data warehouses helps teams plan for the future. Bringing these new data stores into the pipeline effectively and efficiently, though, is its own challenge. Teams often face integration complexities such as schema evolution management in data lakes or ensuring data quality and consistency during the ETL/ELT process for data warehouses.

Due to the unique approach of data handling and requirements compared to traditional relational database management systems, teams adopting data lakes and warehouses must think about:

  • Complex configuration and integration
  • Data governance and compliance
  • Skills gaps
  • Change management

Issues such as these are why DevOps practices such as Continuous Integration and Continuous Deployment (CI/CD) are useful for data pipelines. CI/CD ensures that changes and updates to data pipelines are automatically tested, integrated, and deployed to production. This facilitates consistent and reliable data processing and delivery in whichever scaled analytics environment you use, be it a data warehouse, data lake, or data lakehouse. 

Liquibase, the leading database DevOps platform, enables analytics teams to leverage practices such as CI/CD around their data lakes and data warehouses to provide the speed, governance, and observability needed for efficient and trustworthy data operations. 

With Liquibase, they can automate common tasks and processes to ensure that changes to data structures in data lakes and data warehouses are handled with precision. This reduces risks and enables faster rollouts of new features and capabilities. Automating data pipeline change management enables organizations to adapt more quickly to market changes and business needs, enhancing their ability to make data-driven decisions with confidence.

Explore Liquibase’s supported data stores (60+), including Databricks, AWS Redshift, Google BigQuery, Snowflake, and other popular data lakes and data warehouses.   

Learn more about data pipeline change management or discover how Liquibase works

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