Enter the data warehouse, data lake, and emerging data lakehouse. Each of these technologies promises to solve the complex puzzle of big data management, but each comes with its own set of strengths and limitations.
The pressure is on. Your choice of architecture for storing and processing data will shape your organization’s data strategy for years to come. Pick wrong and you could face performance bottlenecks, spiraling costs, or worse – a data swamp that becomes more a liability than an asset.
But what if you didn’t have to choose? What if there was a way to harness the strengths of each approach while mitigating its weaknesses?
This guide cuts through the jargon to help you understand the differences between the data warehouse vs. data lake vs. data lakehouse. By the end, you’ll have the insights you need to confidently chart your organization’s course through the evolving data management landscape.
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What is a data warehouse?
A data warehouse is a central repository that consolidates large volumes of structured (and sometimes semi-structured) data from multiple sources such as operational databases, cloud applications, and external data feeds to support complex reporting and analysis.
Businesses use data warehouses for a variety of business intelligence and data management activities, including:
- Performance reporting: Generating reports, dashboards, and visualizations to help understand and track performance
- Trend analysis: Analyzing historical data to build predictive models and forecast future outcomes to optimize resource allocation and streamline processes
- Compliance reporting: Providing a reliable audit trail of accurate and consistent historical data to ensure compliance with legal and regulatory requirements
Thanks to their highly structured nature, data warehouses help businesses standardize and consolidate data from many sources. This makes them ideal for organizations that need to establish a single source of truth with high-quality, consistent, and accessible data to support decision-making and maintain compliance.
Characteristics of a data warehouse
Data warehouses are managed solutions in which the storage, compute, and metadata layers are fully integrated into a unified system. This is one way a data warehouse varies vs. data lakes, in which separate vendors may provide different layers of the system.
In a data warehouse, data from disparate sources is consolidated and standardized through extract, transform, and load (ETL) processes. Data is transformed based on the predefined schema of the data warehouse (schema-on-write), ensuring that it meets the structure required for fast and efficient querying.
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Possible layers of a data warehouse |
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| Layer | Description |
| Presentation | Contains interfaces and tools that allow end-users to query, analyze, and visualize the data. Popular external BI platforms include Tableau, Power BI, and Looker. |
| Compute | Houses the systems and engines that process complex analytical queries, often optimized for OLAP workloads. In modern cloud-based warehouses, compute and storage are decoupled to allow for scalable performance. |
| Metadata | Metadata, or data about the data, is stored to enable data discovery, governance, and query performance optimization. It ensures that data lineage, quality, and compliance are maintained. |
| Data storage | Data is stored in schemas, tables, and partitions designed for optimized querying. Schema designs typically follow star or snowflake models to support analytical queries. |
| Data ingestion | Extracts data from sources, transforms it into a consistent format, and loads it into the data warehouse using ETL or ELT processes. |
| Data sources | Includes operational databases, external data sources and APIs, and other raw data inputs. Data warehouses can also handle streaming data in modern architectures. |
Pros and cons of data warehouses
A data warehouse is a powerful tool for managing and analyzing large volumes of data, but like any technology, it has advantages and disadvantages.
Benefits of data warehouses
Centralized data storage: A unified view of the organization’s data facilitates analysis and reporting, improving decision-making.
Improved data quality: Cleansing, transformation, and standardization of data during the ETL process reduces inconsistencies, leading to higher-quality data.
Enhanced query performance: Because they’re optimized for retrieving and querying structured data, data warehouses enable efficient query response times and support timely SQL-based analysis.
Drawbacks of data warehouses
Inflexible data storage and processing: Because data warehouses require a predefined schema, they aren’t well-suited for unstructured or semi-structured data such as text or images.
Cost: Data warehouses tend to involve higher infrastructure and operational costs due to their specialized hardware, licensing fees, and complex data management processes.
Complexity of data integration: Integrating diverse data sources is a complex and time-consuming process that requires specialized skills and resources.
What is a data lake?
A data lake is a centralized storage system designed to hold vast amounts of raw, structured, semi-structured, and unstructured data. In contrast to data warehouses, data lakes allow you to store data in its native format (including text, images, video, log files, and sensor data) without requiring upfront transformations.
Businesses use data lakes for several key purposes, including:
Flexible data storage: Data lakes support schema-on-read, meaning you can store data in its raw form from various sources (logs, social media, IoT devices) and in a structured form when needed. This flexibility makes data lakes ideal for storing diverse data types that may be used at a later time for different purposes.
Data exploration: Data scientists and analysts can explore raw data and perform ad-hoc queries without the constraints of predefined schemas, promoting discovery, experimentation, and the ability to derive new insights from diverse datasets.
Machine learning: A data lake’s ability to handle large volumes of various data types and support extensive data processing makes it a crucial component in training and deploying effective machine learning models. Storing structured data (such as labeled data) and unstructured data (such as images and video) in one place is crucial for many machine learning pipelines.
Because data lakes provide scalability and flexibility for handling large volumes of disparate data formats, they’re well-suited for organizations that need to support a wide range of analytics, machine learning, and big data processing initiatives.
Characteristics of a data lake
In contrast to a data warehouse, in a data lake:
- The storage, compute, and metadata layers are typically decoupled and may be managed by separate tools or vendors. For instance, storage could be provided by a distributed file system (Amazon S3) while compute is handled by a separate engine (Apache Spark or AWS Glue).
- Data may enter the data lake without a schema; a schema is applied when the data is processed or queried (schema-on-read), offering flexibility but also introducing challenges around data management and governance.
- Data is stored in a distributed file system or object storage.
Because they don’t require structure, data lakes offer cheaper storage than data warehouses. However, processing unstructured data can be resource-intensive, making it more expensive to retrieve and analyze in comparison to structured data.
| Layer | Description |
|---|---|
| Presentation | Interface and tools that allow end-users to query, analyze, and visualize data in the data lake. Common tools include query engines like AWS Athena, SparkSQL, and external BI platforms (Tableau, Looker). |
| Compute | Technologies like Apache Spark, Hadoop, and AWS Glue are used to clean, transform, and enrich data for analysis. This layer supports both batch and stream processing as well as real-time and machine learning workflows. |
| Metadata | Enables data discovery, lineage tracking, and governance. Tools like Apache Atlas and AWS Glue Data Catalog are often used to manage metadata in data lakes. |
| Data storage | Raw, unstructured, semi-structured, and structured data is stored in a distributed file system or object storage such as Hadoop HDFS, Amazon S3, or Azure Blob Storage. Common data formats include JSON, Parquet, ORC, and multimedia files. |
| Data ingestion | Data is ingested from batch and streaming sources using tools like Apache Nifi, Kafka, or AWS Kinesis. The raw data is processed as needed, depending on the use case. |
| Data sources | Includes operational databases, IoT devices, logs, clickstreams, sensor data, and third-party APIs. |
Data lake pros and cons
A data lake offers significant benefits for managing and analyzing large volumes of diverse data, but it also has some limitations.
Benefits of data lakes
- Scalability: Data lakes are designed to handle large volumes of data from diverse sources, with scalable cloud-based storage options like Amazon S3 and Azure Blob Storage.
- Flexibility: Traditional databases require data to be categorized, but some data doesn’t lend itself to categorization. Or a business may not want to categorize data until later. Data lakes are ideal for these scenarios.
- Advanced analytics: Because of their flexibility, data lakes are better suited for many advanced use cases such as building machine learning models, conducting predictive analytics, and applying advanced algorithms.
Drawbacks of data lakes
- Data quality and governance: The ease with which data can be ingested into a data lake is a blessing and a curse. Without proper governance, data lakes can quickly turn into disorganized data swamps in which data becomes low-quality, irrelevant, and outdated.
- Complexity of data management: Preventing a data swamp requires careful oversight. This is why managing and cataloging data in a data lake demands robust metadata management and data cataloging solutions.
- Performance challenges: If not properly optimized or managed, data lakes can have performance issues with large-scale queries or complex analytics.
What is a data lakehouse?
A data lakehouse is a hybrid architecture that combines the flexibility and scalability of a data lake with the structured data management, ACID transactional support, and high-performance querying capabilities of a data warehouse. It provides a unified platform where unstructured and structured data can coexist, enabling businesses to store, manage, and analyze all data types in a single system.
Data lakehouses are ideal for organizations that need to handle large volumes of diverse data while also requiring the governance, reliability, and query optimization traditionally found in data warehouses. This architecture supports a wide range of workloads such as real-time data streaming, machine learning, and business intelligence reporting from a single platform.
By integrating the governance and transactional integrity of data warehouses with the flexibility of a data lake, data lakehouses allow organizations to reduce the complexity of managing separate systems, simplifying data architecture for both analytics and operational workloads. This approach helps organizations avoid data swamps by ensuring data is high-quality, governed, and optimized for querying.
Characteristics of a data lakehouse
Rather than possessing a definitive set of characteristics, data lakehouses exist on a spectrum between data warehouses and data lakes.
Over the years, certain cloud warehouse platforms such as Snowflake and Redshift Spectrum have incorporated data lake functionality. Similarly, data lakes such as Delta Lake and Apache Hudi have incorporated data warehouse functionality.
A true data lakehouse contains all the layers that a data lake and warehouse would, from data ingestion and storage to metadata and presentation.
Pros and cons of data lakehouses
A data lakehouse combines the best of data lakes and data warehouses. But it also comes with its own distinct set of benefits and drawbacks.
Benefits of data lakehouses:
- Unified data platform: With the combined flexibility of a data lake and the processing capabilities of a data warehouse, a data lakehouse allows for comprehensive data management.
- Flexibility: Since data lakehouses accommodate the schema-on-read model, they’re ideal for storing many different kinds of data.
- Query performance: Because parts of a data lakehouse are optimized for querying structured data, they improve query response times.
Drawbacks of data lakehouses:
- Complexity: Combining features of both data lakes and data warehouses complicates both the implementation and operation of data lakehouses.
- Performance variability: Because data lakehouses are a less mature and specialized technology than data warehouses, their query performance is not as reliable.
- Governance challenges: Just like a data lake, a data lakehouse can turn into a data swamp. Combine that with the added complexity of managing a hybrid platform and data governance, quality, and security becomes more challenging.
Data warehouse / data lake / data lakehouse innovators
The more data warehouses and data lakes evolve, the blurrier the line between these technologies becomes. Considering the growth of diverse data ingestion, processing, and storage use cases, the logical endgame is a blend of data warehouses and data lakes.
Two companies at the vanguard of this continuing innovation are Snowflake and Databricks. Intellias partners with both of them.
Snowflake
Snowflake began as a cloud data warehousing platform but quickly evolved to accommodate many functions of a data lake. In doing so, Snowflake set a new standard for flexibility and choice in data warehousing.
While Snowflake wasn’t the first data warehouse to integrate data lake functionality, its easy-to-use platform popularized many key innovations.
This includes important data warehouse innovations such as:
- Accommodating semi-structured and structured data
- Decoupling storage and compute
- Implementing a cloud-agnostic architecture
- Supporting a developer’s preferred programming languages through Snowpark
These innovations have eliminated many of the starkest differences between a modern data warehouse and data lake.
Databricks
The engineers behind Apache Spark started Databricks to build a user-friendly commercial product around Spark. At the start, Databricks was focused on big data and machine learning. But it has since evolved to incorporate many features traditionally associated with data warehouses.
In fact, Databricks was widely credited with introducing the lakehouse architecture when they released Delta Lake.
Delta Lake enhances data lakes with features like ACID transactions, schema enforcement, and indexing. This represents a major step forward for data management by making it possible to perform analytics and machine learning on data stored in a data lake with the reliability and performance of a traditional data warehouse.
Also, by advancing the open-source Apache Spark project, Databricks has helped develop Spark, which plays a critical role in the effectiveness of modern data lakes.
Main considerations for designing a cloud data platform architecture
The key to designing the ideal cloud data platform architecture is to begin with the end in mind. This means determining your initial and ongoing budget, who will be using the system, how they’ll use it, and how to keep it secure.
Users and use cases: Data scientists developing machine learning models need a much different architecture than business analysts monitoring dashboards and generating reports. Similarly, certain use cases, such as machine learning, have specific requirements that must be considered. This is why determining your end-users’ expertise, skills, and needs is a critical cloud data platform design consideration.
Initial and ongoing costs: Data lakes have cheap storage but high ongoing processing costs relative to data warehouses. And their unstructured nature means they require costly, specialized workers to manage them. Of course, depending on your needs, the cost may be well worth it.
Data governance and security: Governance and security must be foundational elements of any cloud platform architecture, not afterthoughts. This is especially true when handling sensitive data subject to compliance requirements like the GDPR and HIPAA. An insecure data platform can become a serious liability rather than a value-generating asset.
3 key factors to consider when choosing between a data lake and a data warehouse
If you’re choosing between a data lake and a data warehouse, you can make your decision easier by considering a few key factors:
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The volume, velocity, and variety of your data
Big data’s three V’s – volume, velocity, and variety – can tell you whether a data lake or data warehouse makes sense.
Volume refers to the amount of data. All else being equal, data lakes are better suited for cost-effectively storing a large volume of data.
Velocity refers to how fast data is created and moves. For instance, streaming data moves faster than static data. Data warehouses are better suited for slower data because they’re optimized for processing structured data in batches.
Variety refers to the number of different data formats. Compared to data warehouses, data lakes are far better suited to handling datasets with a lot of variety.
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Your desired use cases
Data lakes and data warehouses are tools. And like any tool, each is suited to specific jobs.
If you need to store vast amounts of raw, diverse data for data science, machine learning, and exploratory analytics, you’ll need a data lake.
If you need high-performance querying of structured data for business intelligence and operational reporting, you’ll need a data warehouse.
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The hybrid approach
When comparing data lakes vs. data warehouses vs. data lakehouses, you don’t necessarily have to choose between a data lake and a data warehouse. By combining the functionality of both in a data lakehouse, you can build a platform that meets diverse needs within a unified architecture. Just make sure you are equipped to handle the additional complexity of implementing and maintaining a data lakehouse.
| Data Warehouse | Data Lake | Data Lakehouse | |
|---|---|---|---|
| Types of supported data | Relational data and custom built-in data formats | All formats (structured, semi-structured, unstructured) | All formats (structured, semi-structured, unstructured) |
| Data storage format | Structured data stored in tables with rows and columns | Raw data in its native format | Raw data with the option for schema enforcement; combines structured and unstructured data |
| Data processing | Predefined schema; ETL (Extract, Transform, Load) processes | Schema-on-read | Schema-on-read and schema-on-write for more flexibility; ACID compliance for reliability |
| Data accessibility | Optimized for fast query performance with a predefined schema | Flexible; can store vast amounts of diverse data | Combines fast querying of structured data with the flexibility of unstructured data |
| Storage capacity | Typically limited by cost and infrastructure constraints | High scalability; capable of handling massive volumes | Highly scalable like a data lake but with optimized query performance like a warehouse |
| Analytics capabilities | Advanced querying and reporting; optimized for BI tools | Supports big data analytics; often integrated with processing frameworks like Apache Spark | Combines advanced querying and reporting with support for big data analytics and machine learning |
| Main users | Business analysts, data scientists, and decision-makers | Data engineers, data scientists, and big data analysts | Business analysts, data scientists, and engineers, as it serves both operational and analytical use cases |
| Recommended use cases | Structured data analysis, business intelligence, reporting | Big data storage, integration of diverse data sources, and data exploration | Unified platform for real-time analytics, machine learning, and both structured and unstructured data use cases |
Data lake, data warehouse, or data lakehouse? Examples of use cases by industry
Here are a few industry-specific use cases that illustrate different scenarios:
Healthcare
Data warehouse: A data warehouse consolidates data from patient charts and makes it readily available for querying. Analysts use the warehouse to run queries and build dashboards to track patient outcomes and treatment efficacy and ensure adherence to regulatory standards, providing the organization with reliable insights for decision-making and auditing.
Data lake: A data lake stores vast amounts of unstructured data, such as medical images and wearable device outputs. Unstructured data allows healthcare providers to build machine learning models to make early diagnoses through image recognition and continuous monitoring of patient vitals.
Data lakehouse: Since a data lakehouse integrates the strengths of both data warehouses and data lakes, healthcare organizations can store and manage both structured data (such as patient records) and unstructured data (such as medical images) in one platform. This enables real-time analytics, supports machine learning applications for diagnosis, and ensures compliance with strict healthcare requirements, all from a unified system.
Finance
Data warehouse: By centralizing and standardizing relevant data from various sources in a data warehouse, financial services providers create an accessible data repository. This helps them speed up the reporting they need to do to maintain US Securities and Exchange Commission (SEC) compliance.
Data lake: A data lake ingests and stores vast streams of transactional data, enabling financial institutions to build fraud detection machine learning algorithms. Using these algorithms, institutions can identify patterns indicative of fraudulent activity in real time.
Data lakehouse: Financial institutions use data lakehouses to handle both structured data for compliance reporting and unstructured data for fraud detection in a single environment. Transactional support and governance features help ensure data integrity and compliance while also supporting machine learning models for real-time fraud detection, allowing for faster and more effective decision-making.
Retail
Data warehouse: Retailers use data warehouses to aggregate and analyze sales and inventory data from geographically distributed stores and various sales channels. Using the aggregated data, analysts generate accurate sales reports that they can use to forecast demand and optimize inventory levels.
Data lake: A data lake captures and processes real-time data from customer service interactions, social media mentions, and online reviews. This supports sentiment analysis, which helps retailers personalize messaging and product recommendations.
Data lakehouse: Retailers leverage data lakehouses to integrate sales data, customer interactions, and social media insights. This allows them to run real-time analytics and machine learning models for sentiment analysis, personalized product recommendations, and sales forecasting all from one consolidated platform. The system offers the flexibility of a data lake with the governance and query performance of a data warehouse.
Manufacturing
Data warehouse: To facilitate reporting, manufacturers use data warehouses to consolidate production and supply chain data. This allows for a detailed analysis of production efficiency, supply chain bottlenecks, and costs, helping companies make more informed business decisions.
Data lake: A data lake stores IoT sensor data from machinery and equipment in real or near real-time. By analyzing this data, manufacturers can build predictive models that help them create predictive maintenance protocols to reduce downtime and optimize maintenance schedules.
Data lakehouse: Manufacturers use data lakehouses to combine structured production data with unstructured IoT sensor data from machinery in one system. This architecture supports predictive maintenance, real-time monitoring, and operational reporting, enabling manufacturers to improve equipment efficiency and reduce downtime while benefiting from advanced analytics and machine learning capabilities.
How Intellias can help
Case study: How we transformed data management for a global client
Challenge: A leading Ukrainian telecom operator faced high costs, long deployment cycles, and regulatory challenges with their legacy on-premises data platforms. They needed to re-engineer their Oracle DB and Hadoop data lake to improve performance, scalability, and compliance with data protection regulations.
Solution: Legacy systems were replaced with a unified cloud platform using Azure tools and classic SQL technology, ensuring secure data anonymization, reliable maintenance with an end-to-end SLA, and a future-proof architecture designed for versatile data processing.
Business impact:
- Reduced Total Cost of Ownership by 20%
- Achieved cloud elasticity with scalable resources to meet evolving business needs
- Increased agility with faster deployment of new data products
Case study: Data technology assistance for a private aviation company
Challenge: A leading private aviation company with a fleet of over 350 aircraft faced high costs due to inefficient data management and an expensive technology choice (Oracle).
Solution: The company gradually migrated to a Snowflake-based data warehouse, integrating internal and third-party systems, enhancing data analytics with Tableau, and implementing ETL processes using Airflow and DBT for improved data processing.
Business impact:
- Improved operational efficiency and scalability
- Enhanced financial planning and decision-making
- Reduced data management costs
- Avoided $24 million in excess commodity purchases
Assessing your data needs
As you can tell by now, assessing your data needs is a complex and multi-faceted process.
With over 20 years in the market, Intellias can provide the expertise and experience you require to develop and implement a data platform architecture that meets your needs. From defining your business objectives and choosing the right technology to ensuring a seamless implementation and ongoing support, our experts are ready to guide you. We’ll help you compare the benefits of a data warehouse vs. data lake vs. data lakehouse.
Contact us today to transform your data into a solid foundation for future opportunities.
