Choosing the right technology for storing and processing data is crucial for organizations that want to derive insights from their data and implement data-driven decision-making. Three popular approaches for data storage and processing are data warehouses, data lakes, and a more recent concept, data lakehouses.
Each technology has its own strengths and weaknesses and choosing the right one for your business’s needs depends on several factors, such as the type of data, the volume of data, and the use cases it will serve.
Explore the differences between data warehouses, data lakes, and data lakehouses, what each architecture does and doesn’t do well, and how to choose between them.
Understand the Technology Options
Data warehouses, data lakes, and data lakehouses are all used for storing and processing data, but they have different strengths and weaknesses depending on the use case.
Understanding each structure’s foundational purpose and key benefits can help identify the architecture best suited to your company’s needs and goals.
Data Warehouses
A data warehouse is a centralized repository for structured data that’s optimized for querying and analysis. It’s designed to support business intelligence and reporting applications that require fast access to data.
Data warehouses are typically built using a relational database management system (RDBMS), such as AWS Redshift or Azure Synapse, and follow a schema-on-write approach, meaning data is structured and organized before it’s loaded into the warehouse.
Data warehouses specialize in providing consistent and reliable data for reporting and analysis. Because the data is structured and organized, it’s easier to query and analyze, and the results are more predictable. Data warehouses also support complex queries and aggregations, making them ideal for business intelligence applications, such as dashboards and automated, repeatable reporting and analysis.
Data warehouses are not well-suited for storing unstructured data, such as free-form text or images, and they can be expensive to scale. Additionally, data warehouses require a lot of upfront planning and design, which can be time-consuming and costly.
Data Lakes
A data lake is a centralized repository for raw, unstructured data that’s optimized for storage and processing. It’s designed to support big data and machine learning applications that require large volumes of data.
Data lakes are typically built using a distributed file system, such as Hadoop or Amazon S3, and follow a schema-on-read approach, meaning data is stored in its raw form and structured as needed when it’s queried.
Data lakes can store large volumes of data in its raw form, making it easier to handle unstructured data. Data lakes are also highly scalable and cost-effective, as they can be built using commodity hardware and open-source software. They can support a wide range of data processing tools and frameworks, such as Spark and Hive, making them ideal for big data and machine learning applications.
Because data lakes store data in its raw form, querying and analysis can be difficult without first structuring it. This can make ensuring data quality, lineage, integrity, and consistency without a thoughtfully implemented architecture challenging. Data lakes can be complex to manage and require specialized skills to set up and maintain. Setup typically requires a team of both software and data engineering resources.
Data Lakehouses
This relatively new data architecture combines the best features of data lakes and data warehouses and addresses the limitations associated with traditional data warehouses and data lakes.
A data lakehouse is a centralized repository that stores data in its raw, unprocessed form, like a data lake. However, it includes features that are typically associated with data warehouses, such as schema enforcement, indexing, and query optimization.
Data lakehouses are designed to provide a single platform for storing and processing data that can handle both structured and unstructured data. This makes it easier for organizations to manage their data and derive insights from it.
Benefits include:
- Flexibility. It can handle both structured and unstructured data, making it more flexible than traditional data warehouses.
- Scalability. It can scale horizontally to handle large volumes of data.
- Cost Effectiveness. It can be more cost-effective than traditional data warehouses because it allows organizations to store data in its raw form.
Consider Use Cases and Needs
Choosing the right technology for your data storage and processing needs depends on several factors, including:
- Data type
- Data inputs volume and diversity
- Use case complexity
- Budget
Here are examples of how use cases can help determine which solution is the best fit.
Data Warehouses
Data warehouses are best suited for structured data used for reporting and analysis. If you need to run complex queries and aggregations on your data, a data warehouse is the way to go. Data warehouses are also a good option if you need consistent and reliable data supplied to business intelligence and reporting applications.
Common use cases for data warehouses include:
- Financial Reporting. Often used to store financial data, such as sales figures and revenue, and to generate financial reports
- Customer Analytics. Can be used to store customer data, such as demographics and purchase history, and to analyze customer behavior
- Supply Chain Management. Can be used to store supply chain data, such as inventory levels and shipping information, and to optimize supply chain operations
Data Lakes
Data lakes are best suited for unstructured and semi-structured data used for big data, machine learning (ML), or artificial intelligence (AI) applications.
Data lakes are a good fit for storing large volumes of data in its raw form and processing it using big data tools and frameworks. Data lakes can also handle a variety of data types and formats.
Common use cases include:
- Vendor Data Intake. In an evolving business environment where cloud-based software-as-a-service (SaaS) vendors are frequently used for installations; data stewardship has shifted from organizations to vendors. Using a data lake to accept vendor extracts and reports of your organization’s data increases format flexibility, reducing costly custom data feed development and maintenance.
- Internet of Things (IoT) Data Processing. Data lakes can be used to store and process data from IoT devices, such as sensors and cameras, and to derive insights from that data.
- Social Media Analytics. Data lakes can be used to store and analyze social media data, such as tweets and posts, and to understand customer sentiment.
- AI and ML. Data lakes can be used to store training data for ML models and to train those models using big data tools and frameworks.
Data Lakehouses
Data lakehouses are best suited for organizations that need to handle both structured and unstructured data in a single, central repository and want to avoid the complexity and cost of managing multiple data storage and processing systems. Data lakehouses allow for storing and processing a variety of data types and formats and supports reporting and analysis as well as big data and machine learning applications.
Common use cases include:
- Health Care Data Management. Store and process health care data, such as patient records and medical images, and support both reporting and analysis and machine learning applications.
- Financial Services Data Management. Store and process financial data, such as transaction records and market data, and support both reporting and analysis and big data and machine learning applications.
- Retail Data Management. Store and process retail data, such as sales data and customer data, and support both reporting and analysis and big data and machine learning applications.
Choosing the Right Solution
When choosing data storage and processing technology, consider the following factors.
Data Type and Format
Data type and format form the basis of your technology choice. For example:
- Structured data is best handled by data warehouses
- Unstructured data is best handled by data lakes
- Structured and unstructured data together is best handled by data lakehouses
Data Volume
Working with extremely large volumes of data points to a data lake or data lakehouse. Data warehouses are typically better suited for smaller data volumes.
Query Complexity
Running complex queries and aggregations on your data requires a data warehouse or data lakehouse. Data lakes may struggle with complex queries if using raw and unstructured data, but a data lakehouse with schema enforcement and query optimization features can work well for this use case.
Budget
Data warehouses can be expensive to scale, while data lakes and data lakehouses are typically more cost-effective.
Team Skillset
Data warehouses typically require specialized skills that traditionally map to BIE (business intelligence engineering) and DE (data engineering) roles, such as SQL and database design.
Data lakes and data lakehouses require big data tools and frameworks skill sets, such as Hadoop and Spark. While they require BIE and DE resources, they also rely heavily on as well as more traditional software and data engineering skillsets as well.
Integration With Existing Systems
How the technology integrates with your existing systems and tools is a significant contributor to its effectiveness. For example, if you're already using a cloud provider like AWS, a data lake built on S3 may be the most seamless option.
We’re Here to Help
To learn more about these data storage and processing options and how they can contribute to your company’s success, contact your Moss Adams professional.
Additional Resources