Data Lake vs Data Warehouse: A Detailed Comparison

There is more data available to us than ever. Storing this data is important — but deciding on the right type of data storage solution is not so clear.

This article explores two primary types of big data storage: data lakes and data warehouses. We’ll examine the benefits of each, then discuss the key differences between a data lake and a data warehouse, so you can decide on the best approach for your business.

Data is the new oil In today’s data-driven world, data-powered insights can give organizations a significant competitive advantage. To fully harness the power of data, many companies are implementing data lakes and data warehouses. But what exactly are these data storage solutions, and what are the key differences between data lakes and data warehouses? Let’s explore.

What is a Data Lake?

A data lake is a centralized repository that stores large amounts of structured semi-structured and unstructured data in its raw, original format. The key benefit of a data lake is that it allows you to store all your data in one place, without having to define any schema or structure upfront.

Here are some key characteristics of data lakes:

Stores all types of data (structured, semi-structured, and unstructured) from disparate sources. This includes IoT sensor data, social media posts, images, videos, audio files, and more.
Schema-on-read architecture, The schema is defined when the data is being read/analyzed instead of at data ingestion This provides flexibility,
Scalable storage at low cost. Data lakes leverage object storage systems like Azure Data Lake Storage for limitless storage capacity.
Supports multiple analytics engines for exploration and discovery. From SQL queries to machine learning, you can analyze the data in different ways.

Data lakes are great for storing raw data for exploratory analytics and data science projects. Common use cases include smart cities, fraud detection, predictive maintenance, customer 360 analysis, and healthcare analytics.

What is a Data Warehouse?

A data warehouse is a centralized repository for structured, filtered data that has already been processed for a specific purpose. Data from transactional systems, applications, and other sources is cleansed, aggregated, and loaded into a data warehouse so business users can analyze it using BI tools.

Here are some key aspects of data warehouses:

Stores refined, structured data optimized for analysis and reporting.
Schema-on-write architecture. The schema is defined at data ingestion time based on business requirements.
Support analytics using SQL queries and business intelligence tools like Power BI, Tableau.
Ideal for predefined, standardized reports and dashboards.

While data lakes house raw data, data warehouses contain processed, refined data ready for analysis by business users. Common use cases include sales reports, marketing analytics, financial reporting, and operational analytics.

Key Differences Between Data Lakes and Data Warehouses

While both store large amounts of data, data lakes and data warehouses are quite different in terms of architecture, use cases, and typical users.

Parameter	Data Lake	Data Warehouse
Data Types	Structured, semi-structured, and unstructured data	Structured and filtered data
Schema	Schema-on-read	Schema-on-write
Processing	Minimal data processing at ingestion	Extensive data processing using ETL/ELT
Query Capabilities	Queries using big data engines like Spark, Hive, SQL	SQL-based querying and analysis
Users	Data engineers, data scientists	Business analysts, business users
Use Cases	Exploratory analytics, data science apps	Standard reports, dashboards, BI apps
Storage Format	Files in various formats like JSON, CSV, Parquet	Relational database with tabular format
Latency	High latency due to on-demand processing	Low latency as data is indexed and optimized
Cost	Lower cost, leverages object storage	Higher cost for performance

Data lakes are like raw material repositories where you collect and store all kinds of data in its original format. This data is then used to power analytics and machine learning models.
Data warehouses contain structured, cleansed data that is optimized for fast SQL queries and analytics. Data warehouses support standard BI use cases.

When to Use a Data Lake vs a Data Warehouse

Based on their distinct capabilities, here are some guidelines on when to use a data lake versus a data warehouse:

Use a data lake when:

You want to store all your raw data in its original fidelity, without having to structure it upfront.
You need to build machine learning models using diverse, unstructured data.
You want to analyze semi-structured data like JSON, XML, Avro.
You need to perform exploratory analytics on heterogeneous data.
You want to store data first and decide how to use it later.

Use a data warehouse when:

You need to create dashboards, reports and BI apps with SQL queries.
Your business users need to analyze clean, structured data using BI tools.
You want to analyze historical trends for business insights.
You want to define the data model upfront based on analysis requirements.
Query performance and low latency is critical for your use case.

For many enterprises, a hybrid approach combining data lakes and data warehouses works best. You can ingest data into data lakes for flexibility, then structure, process, and pipe the data into data warehouses for business intelligence needs.

Key Considerations for Data Lakes vs Data Warehouses

Here are some other factors to consider when evaluating data lakes versus data warehouses:

Data governance – Data lakes can turn into data swamps without proper governance. Define data cataloging, metadata, security policies and access controls to ensure high quality data.
Schema flexibility – Data warehouses require rigid schemas defined upfront. Data lakes offer more flexibility for storing data first and evolving schema later.
Performance and scaling – Data warehouse systems are optimized for fast analytical queries. But data lakes can scale storage and compute independently based on need.
Time to value – Data warehouses require significant upfront effort for ETL processes. Data lakes allow faster innovation cycles.
Cost – Data warehouses can have higher infrastructure and ETL processing costs. Data lakes provide cheap storage along with on-demand compute.

Examples of Data Lakes and Data Warehouses

Here are some examples of data lakes and data warehouses:

Data Lakes

Azure Data Lake Storage – Azure’s limitless cloud storage for big data analytics workloads
Amazon S3 – Scalable object storage that can serve as a data lake
Databricks Delta Lake – Provides data lake capabilities like ACID transactions on top of cloud storage

Data Warehouses

Azure Synapse Analytics – Fully managed cloud data warehouse with integral Spark analytics
Snowflake – Cloud-native elastic data warehousing solution
Amazon Redshift – Fast, scalable cloud data warehouse service
Google BigQuery – Serverless enterprise data warehouse

When to Use Both Together

For large enterprises, it often makes sense to implement both data lakes and data warehouses to get the best of both worlds. Here are some examples:

Use Azure Data Lake Storage and Azure Synapse Analytics to build a modern cloud analytics platform
Stream raw data into AWS S3 data lake and build Amazon Redshift data warehouse on top, taking advantage of both storage and analytic capabilities
Load data into Snowflake for centralized data warehousing, while storing raw data in an S3 data lake for data science experiments.

The data lake provides the flexibility and scalability to store all raw data, while the data warehouse provides performant structured data and BI capabilities. Together they provide an end-to-end analytics solution.

Key Takeaways

Data lakes store raw, unprocessed data in its original format. Data warehouses contain structured, processed data for analysis.
Data lakes have schema-on-read. Data warehouses use schema-on-write.
Data lakes support big data analytics. Warehouses are optimized for SQL queries and BI.
For advanced analytics, you typically need both data lakes and warehouses to get the best of both paradigms.

Leveraging the strengths of both data lakes and data warehouses allows organizations to build a comprehensive data analytics foundation for deriving value from data. Carefully evaluating your specific use cases is key to determining the right data architecture.

data lake vs data warehouse

What is a data warehouse?

In contrast to the limitless realm of data lakes, data warehouses store large amounts of structured data that is filtered and organized for a specific purpose.

As with data lakes, data in a data warehouse is also collected from a variety of sources, but this typically takes the form of processed data from internal and external systems in an organization. This data consists of specific insights such as product, customer, or employee information.

With their rigid structure, the queries and analysis that can be performed using data warehouse information is fixed. Businesses have been traditionally drawn to data warehouses due to the ease of sharing department-specific data and content to guide decisions made by management teams. A well-known data warehouse is Snowflake, but there are several others including from the Big 3 cloud service providers.

Data structure & schema

Data warehouses only store structured, refined data, whereas data lakes can store any form of raw data: unstructured, structured, and semi-structured.

More specifically: In data lakes, schema refers to the organization and structure of the data stored in the lake. That means a data lake does not impose a strict schema on the data it contains. Instead, data is stored in its native format, and the schema is applied when the data is queried or analyzed. This is known as schema-on-read, which allows for more flexibility and agility in data processing, as new data can be added to the lake without requiring a pre-defined schema.

In contrast, a data warehouse typically uses a pre-defined schema to organize and structure the data, known as schema-on-write. The schema is designed to optimize query performance and ensure data consistency.

Data is typically transformed and cleaned before being loaded into the warehouse to conform to the schema. This approach provides greater control over the data and can lead to better query performance, but it can also be more rigid and less adaptable to changing data requirements. Basically, when it comes to data structure, we can sum it up like this:

A warehouse is a home for processed data.
A data lake can house any type of unfiltered data from multiple sources.

Another differentiating factor of data lakes vs. warehouses is the user. Who is using which storage?

A data warehouse can usually be set up and interpreted by a data analyst or business analyst, providing that they have an awareness and knowledge of the functions/outcomes of that specific processed data set.
Data lake solutions are more complex due to the vast quantities of unstructured data present, which requires the specialist knowledge of a data scientist or data engineer. These professionals are able to interpret and organize unprocessed data before it can be analyzed, which requires employing and/or outsourcing experts.

Data lakes are more cost-effective than data warehouses. By storing large amounts of data of any structure, they are more flexible and scalable due to the removed need for data to adhere to a fixed schema. Practically speaking, depositing huge quantities of data in one place takes away the need for filtration, which can amount to higher storage costs associated with data warehousing.

The trade-off of higher costs is the fact that structured data in a data warehouse can be analyzed more quickly and easily than data in a lake.

As you may recognize, another difference between data warehouses and data lakes is their structural disparity:

Data lakes are agile by nature, allowing data to be added and stored more easily. It also means that they’re flexible enough for data scientists and developers to configure data models and applications, and enable tools for big data analytics.
Data warehouses have a specific structure and are more difficult to alter. They typically have a ‘read only’ format which analysts can scan to garner insights from historical, clean data.

Data lakes store petabytes of information — that’s 1,000 terabytes per unit! Their sheer size and their lack of selectivity on the data stored means that they’re inherently less secure than a more compact, structured data warehouse.

In addition to this, data warehouse technology is a lot more established than the relatively new big data technologies. That is: data warehouse security is mature in comparison. Big data security measures are rapidly evolving however, so it’s likely that data lakes will eventually become more secure.

(Understand data security through the lens of cyber hygiene.)

Database vs Data Warehouse vs Data Lake | What is the Difference?

What is the difference between a data lake and a warehouse?

To help remember the difference between a data lake and a data warehouse, picture actual warehouses and lakes: Warehouses store curated goods from specific sources, whereas a lake is fed by rivers, streams and other unfiltered sources of water. The same kind of distinction applies to their data counterparts, in a general sense.

How do data lakes and data warehouses work together?

You may notice that data lakes and data warehouses complement each other in a data workflow. Ingested company data will be stored immediately into a data lake. If a specific business question comes up, a portion of the data deemed relevant is extracted from the lake, cleaned, and exported into a data warehouse.

What is a data lake?

Data lakes help manufacturers consolidate disparate warehousing data, including EDI systems, XML, and JSONs. Sales . Data scientists and sales engineers often build predictive models to help determine customer behavior and reduce overall churn. Now you know what a data lake is, why it matters, and how it’s used across a variety of organizations.

What is a data Lakehouse?

A data lakehouse is an open standards-based storage solution that is multifaceted in nature. It can address the needs of data scientists and engineers who conduct deep data analysis and processing, as well as the needs of traditional data warehouse professionals who curate and publish data for business intelligence and reporting purposes.