Data Lake vs Data Warehouse: Comparing Data Storage Solutions

Aditi Prakash
May 11, 2023
15 min read

Data lakes and data warehouses are well-known big data storage solutions. They are used to store an organization’s data and can be accessed by data scientists for analysis and business intelligence (BI).

A data lake is a storage system for massive datasets of all types. The data stored can be transformed to match multiple use cases, including analysis and simple data processing.

A data warehouse is built to hold processed data for querying and in-depth data analytics.

In this article, we will delve into the key differences between a data lake vs data warehouse. We will also explain hybrid approaches for data processing.

Data Lakes

A data lake allows organizations to store unformatted, raw data. It can handle large amounts of structured, unstructured, and semi-structured data, including text, images, video, and audio.

A data lake provides a single source of truth for all data types, enabling analysts to perform ad-hoc queries and extract insights in real-time. This is crucial for analysts to harness big data technologies and perform predictive analytics, data exploration, and more.

They store data in its native format using ELT (Extract, Load, Transform) data integration. Data is loaded into storage first and then transformed as needed.

This provides more flexibility, makes it easier to incorporate new external data sources, and enables schema changes to match evolving business requirements.

Cloud data lakes are typically built using a distributed file system, like Hadoop or Amazon S3, and can be scaled horizontally. They support OLAP (online analytical processing) and OLTP (Online Transaction Processing) workloads. 

Data lake

Key features

  • Schema-on-read approach: In a data lake, schema is applied when the data is read or queried rather than during ingestion. This provides a high degree of flexibility since the schema can be adapted to fit the needs of specific analytic use cases.
  • Storage of raw, unprocessed data: A lake centralizes raw data from various internal and external data sources, including IoT devices, social media, log files, and other sources.
  • Support for various data: Since data lakes don’t require predefined schemas, they can support many data structures, including structured, semi-structured, and unstructured data.
  • Scalability and cost-effectiveness: Data lakes are highly scalable. They use a distributed architecture to enable horizontal scaling. This also makes them more affordable to install and maintain than a traditional data warehouse. 

Popular data lake solutions

  1. Amazon S3: It is a cloud object storage service often used as the foundation for data lakes on the AWS platform. It provides low-cost storage for data of all types and sizes.
  2. Microsoft Azure Data Lake Storage: It is a cloud-based solution that provides high-performance, scalable storage. It integrates with other Azure services and makes it easy to deploy data lake solutions on the Azure platform.
  3. Google Cloud Storage: It is a data lake solution that provides multiple storage classes and security features. It provides a scalable, cost-effective way to store and analyze large data volumes.
  4. Hadoop Distributed File System (HDFS): HDFS is commonly used as the primary storage system for data lakes. It can store large datasets across multiple servers in a Hadoop cluster. Hadoop can also support specific data warehouse scenarios.
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Data Warehouses

A data warehouse is a central repository used to store, manage, and analyze high volumes of structured data.  

Businesses use ETL or ELT data integration to collect data from multiple sources, including databases, transactional systems, and applications, transform it and load it into a data warehouse. The processed data is normalized to ensure data consistency and accuracy.

Data warehouses use a fixed, predefined schema similar to traditional relational databases. While both systems organize data into tables, warehouses use columnar storage, whereas relational databases use row-based storage.

A columnar data structure is ideal for analysis and reporting.

Processed data enables fast query response times, support for complex queries, and advanced analytics workloads. Organizations can use a data mart, a subset of a data warehouse for a specific business unit, to further boost performance.

Modern data teams use cloud data warehouses as a cost-effective solution to transform and  analyze large datasets and form conclusions that can help improve business operations.

A data warehouse deployed on the cloud can be integrated with BI tools and data visualization software, making it easy to generate reports and gain insights. 

Key features

  • Schema-on-write approach: In the schema-on-write approach, data is transformed into a structured format before storing it in a warehouse.
  • Optimized for large-scale data storage and retrieval: The relational architecture of a data warehouse makes it easier to store and retrieve high data volumes.
  • Data integration: Data stored in a warehouse is sometimes transformed before storage using ETL data integration pipelines. The ELT approach can also be used, meaning data is loaded into the warehouse and then transformed inside it.
  • Support for complex queries and aggregations: They support complex queries and aggregations required for data analysis. This can include techniques to perform multidimensional analysis.

Popular data warehouse solutions

  1. Amazon Redshift: Redshift is a fully-managed cloud data warehouse service by Amazon Web Services (AWS). It is highly scalable and can handle petabyte-scale data warehousing workloads. It is compatible with various business intelligence tools and supports SQL querying.
  1. Google BigQuery: BigQuery is a serverless, cloud data warehouse by Google Cloud. It enables fast, scalable querying of large datasets.
  1. Snowflake: Snowflake is a cloud data warehouse that provides on-demand scalability and performance. It also supports multiple workloads, including data engineering, data science, and data visualization.
  1. Microsoft Azure Synapse Analytics: Synapse Analytics is a cloud data warehouse and big data analysis service by Microsoft Azure. It is integrated with other Azure services and supports both SQL querying and Spark-based processing.

Data Lake vs Data Warehouse: A Detailed Comparison

Here’s a quick overview of the differences between a data lake vs data warehouse: 

Data lake vs data warehouse comparison table

Data Storage and Organization

Data lake

Data lakes have a flat architecture that stores data in its unprocessed form in a distributed file system.

Since they store massive amounts of raw data, data lakes require more storage space than a data warehouse. Data developers must also implement adequate data quality and governance measures to prevent data swamps, which contain high volumes of unorganized, irrelevant data.

Data warehouse

A data warehouse uses a relational architecture to store data in predefined tables and columns. They centralize processed data from multiple sources to support analytical use cases.

Only highly organized data is stored in a data warehouse. As a result, they make efficient use of storage capacity. Additionally, data analysts can easily mine data mart find and retrieve the data they need.

They can also directly integrate cloud warehouses with other analytics software to create an efficient data pipeline.

Data Processing and Transformation

Data lakes use a schema-on-read approach where data is loaded into storage and transformed as needed. This approach provides more flexibility and agility in storing data for processing but can make it more challenging to ensure data consistency.

Data warehouses employ a schema-on-write approach where data is transformed and structured before storage. This provides greater control over data quality but can be less flexible.

Data Types and Formats

A data lake can support any data structure, including structured, semi-structured, and unstructured data. This added flexibility enables a broader range of use cases.

Data warehouses are optimized for OLAP workloads and can only work with structured and semi-structured data. They have strict quality controls and validation processes to ensure accurate and consistent data.

Query Performance and Optimization

Data Lake

The performance of a data lake is impacted by the sheer volume and variety of data it stores. The data model can also affect query response times.

To optimize query performance, a data engineer can employ techniques like partitioning, compression, and indexing.

Data Warehouse

Data warehouses provide rapid query response times on large datasets. Since little or no data prep is needed, data analysts and business users have fast data access. 

Data teams employ techniques like dimensional modeling, compression, and partitioning to improve query performance further and gain insights faster. Data marts can also help with this.

Scalability and Cost

Data Lake

Data lakes are affordable and highly scalable. Since they use distributed file systems, they can accommodate large volumes of data through horizontal scaling. 

Most cloud data lake providers are also cost-effective since they have subscription plans, where users only pay for their needed resources.

However, data lakes require a much larger storage capacity since they store raw, unprocessed data. This could add to expenses, along with investments in data processing resources.

Data Warehouses

Storing data in traditional on-premise data warehouses can be expensive since physical hardware and software for data management must be installed. Scaling requires installing or upgrading hardware and components, which further adds to expenses.

However, a cloud data warehouse is a more affordable option. This system requires no hardware installation. Instead, scaling is done by purchasing additional vendor resources.

Use Cases and Examples

We’ve explored the common use cases for data lakes and data warehouses with examples to illustrate the best situation for each solution:

When to choose a Data Lake

1. Large-scale, unprocessed data storage

Organizations can store large-scale, raw data in a data lake. It enables real-time processing and analysis of enormous data sets in the healthcare, retail, and entertainment industries.

For example, a data lake can be used in manufacturing to centralize data from sensors, IoT devices, and other sources. By analyzing this data, manufacturers can gain insights into production efficiency and optimize supply chain management.

2. Exploration and discovery of patterns and insights

A data lake concept enables data exploration and analysis without predefined schemas or data models.

Data scientists can discover data and perform advanced analytics, including predictive modeling and machine learning. They can mine historical data to uncover patterns, detect anomalies, and gain insights into trends that drive business decisions.

For example, a data scientist can use a data lake to analyze user behavior on streaming services. This helps them personalize the user experience and provide relevant content recommendations.

3. Flexibility in data formats and types

A data lake works best for storing massive data sets in their original format. They provide a flexible data processing platform that supports various use cases.

For example, lakes help store versatile data from social media, streaming platforms, and other sources in the entertainment industry. This data is used to gain insights into audience behavior and identify trends that decide content creation and distribution strategies.

When to choose a Data Warehouse

1. Structured data storage and analysis

A data warehouse is ideal for storing and analyzing structured or transformed data, such as data from relational database technologies, CRM, and ERP systems. Processed data is typically well-defined, with a predefined schema, making it easier to analyze.

Government, educational, and healthcare institutions use data warehouses because they have strict data governance measures, including data integrity, metadata management, and security.

2. Reporting and business intelligence (BI) applications

A warehouse unifies and harmonizes data, allowing business users to to leverage BI and reporting applications to streamline in-depth analysis. Data engineers and analysts can generate insights using SQL queries and reporting tools.

It also enables data mining, augmented analytics, and machine learning. For example, a data warehouse can have artificial intelligence to help business professionals analyze market data to identify trading opportunities and manage risk.

3. High-performance analytical queries

Data is transformed before storage to match analytical requirements, so business analysts can rapidly query large data sets to answer predetermined questions.

For example, global manufacturing companies use data warehouses to store and analyze production data to optimize their processes and improve quality control.

Hybrid Approaches: Combining Data Lakes and Data Warehouses

Hybrid approaches that leverage the strengths of both storage systems big data technologies are becoming increasingly popular among organizations.

Here are some ways that organizations can combine data lakes and data warehouses:

  • Data ingestion

A data lake is used as a data staging area to collect raw data. This stores data that can be transformed and loaded into a warehouse for further analysis. Organizations can use this method to ensure that high-quality, refined data is loaded into the warehouse.

  • Data storage

In a hybrid system, organizations can use a data lake for raw data and a data warehouse or data mart for structured data. This boosts data management.

  • Data exploration

By using a data lake for data exploration, organizations can gain insights into their business data, and identify patterns. These insights can be integrated into data warehouses for further analysis.

  • Real-time analytics

In a hybrid data architecture, organizations can use the data lake to perform real-time analysis of streaming data and use a full enterprise data warehouse for historical data. This can help organizations gain insights in real-time, which can be critical for applications such as fraud detection or predictive maintenance.

Data scientists are also turning to data lakehouses, a relatively new type of data architecture. It provides a single platform to handle analytical and transactional workloads without sacrificing performance or scalability.

Benefits of using data lakes and data warehouses

Here are five key benefits of using both a data lake and a data warehouse:

  1. Boost scalability: Organizations can use the appropriate storage repository based on its structure and use case. This ensures scalability when they need to support their growing data needs.
  2. Added flexibility: Data teams can benefit from the flexibility of the data lake for exploratory analysis while leveraging the reliability and consistency of warehouses for structured analysis.
  3. Cost optimization: Companies can store raw data in a lake at a lower cost. Then, only relevant data can be moved to an existing data warehouse. This saves money on storage and processing costs.
  4. Better data governance: Businesses can implement different governance policies for various data types. For example, sensitive data can be stored in data marts or warehouses with strict access controls, while a lake can store less sensitive data.
  5. Improved query performance: By using a lake as a staging area, organizations can ensure that only relevant data is loaded into the warehouse for further analysis. This can improve query performance and accuracy.

Examples of hybrid solutions

1. Databricks Delta Lake: It is a fully managed service that provides a scalable and reliable storage layer for big data workloads. It adds transactional capabilities to data lakes, making it easier to manage large-scale data pipelines.

2. Microsoft Azure Synapse Analytics: Azure Synapse Analytics is a cloud service that combines data warehousing and analytics. It provides an integrated experience for data ingestion, data preparation, and analytics.

3. Google BigQuery: Google BigQuery is a fully managed cloud data warehouse that supports structured and unstructured data. It is designed for high scalability and can handle big data analytics workloads.

Conclusion

Data lakes and data warehouses differ in the types of data they can handle, the schemas they use, and the use cases they support. 

Data scientists and engineers use a data lake to analyze raw data. This helps in predictive analytics and machine learning.

A data warehouse is a data structure used by analysts and business professionals, like managers, for data visualization, BI, and analytics.

Understanding the key differences between a data lake vs an operational data store or warehouse helps teams optimize their data workflows. They can create a data infrastructure that maximizes the value of their data and helps achieve business goals.

Organizations should focus on their specific data needs and analytical requirements while picking a data storage solution. In some cases, a hybrid approach may be the best way to achieve the required performance, flexibility, and scalability.

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