You may have seen Raconteur’s infographic in 2019 of “A Day in Data,” shown above. Some highlights include:
- 500 million (1,000,000) tweets sent every day
- 294 billion (1,000,000,000) emails sent every day
- 4 petabytes (1,000,000,000,000,000 bytes) of data created by Facebook every day
- 463 exabytes (1,000,000,000,000,000,000 bytes) of data predicted to be created every day by 2025
For context, the laptop this article is being written on has 500 gigabytes of storage. One gigabyte is only 1 billion bytes. Data on the magnitude of petabytes and exabytes is insane. But that was our reality in 2019, and now it's 2023. With so much data, the question is always, how can we store it, and use it optimally, without wasting time, resources, and working hours.
Data lakes and data warehouses are two widely used methods for storing and managing big data. While they share the common goal of providing a centralized repository for storing and accessing data, there are several key differences between the two that make them suited for different purposes. Understanding the differences is crucial for organizations that want to make informed decisions about which type of repository is best for their specific needs and requirements.
The key differences between data lakes and data warehouses lie in the data.
- Data lakes are large pools of raw, unstructured data that are stored in its original format until it is needed.
- Data warehouses, on the other hand, are repositories for structured and sometimes semi-structured data that has been specifically prepared for reporting and analysis.
These differences in data structure and purpose are just some of the key factors that differentiate data lakes and data warehouses, and that make them suitable for different types of data management and analysis tasks.
In this post, we will explore the key differences between data lakes and data warehouses, and discuss the advantages and disadvantages of each approach, as well as industry-specific use cases for each. By the end of this document, you will have a better understanding of the differences between data lakes and data warehouses, and you will be able to make more informed decisions about which type of repository is best for your organization. If you’re interested in databases as well, you can read our article about the differences between databases and data warehouses.
Data differences: data lakes vs. data warehouses
One of the main differences between data lakes and data warehouses is the way they store and manage data. A data lake is a large, centralized repository that allows you to store all your structured and unstructured data at any scale. Data is stored in its raw format and can be queried, analyzed, and transformed using a variety of tools and technologies. Data lakes are designed to be highly scalable, flexible, and cost-effective, and are often used for big data and data science projects.
A data warehouse, on the other hand, is a system designed to store and manage large amounts of structured data that has been extracted from various sources. Data in a data warehouse is typically stored in a highly-organized, relational format and can be accessed and analyzed using SQL-based tools. Data warehouses are designed to support business intelligence and analytics applications, and are optimized for fast query and analysis performance.
In terms of data structure, data in a data lake is typically stored in its raw format and is not organized in a specific way. Data can be stored in a variety of file formats. In contrast, data in a data warehouse is typically organized into a set of tables, with a strict schema defined for each table and data is organized in a tabular format. Data transformation and cleaning happens before loading into a data warehouse.
Schemas in data warehouses vs. data lakes
A schema is a blueprint that defines the structure of the data in a database or data storage system. Schemas are used to ensure data consistency and integrity, and to make it easier to query and analyze data. In a data warehouse and data lake, schemas work differently in terms of how they are applied and enforced.
In a data warehouse, schemas are typically defined and enforced at the time data is loaded into the system. Data is extracted from various sources, cleaned and transformed to fit the schema, and then loaded into the data warehouse. Once the data is loaded, it is stored in a highly-organized, relational format, and the schema is enforced by the system. This means that the data must conform to the schema, and any data that does not conform will be rejected.
The schema in a data warehouse is typically defined using a relational data modeling technique, such as entity-relationship modeling. This creates a series of tables with predefined columns and relationships between them, which makes it easy to query and analyze the data. The schema also helps to ensure data consistency and integrity by enforcing constraints and rules on the data.
In a data lake, schemas are often not defined or enforced until the data is queried or analyzed. Data lakes do not enforce any constraints on the data, as the goal is to store as much data as possible, in its raw format, for later use. Instead of predefined schema, data lake uses schema-on-read, where the schema is applied when the data is read and analyzed, rather than when it is stored. This allows users to work with the data in its raw format, and to apply different schemas to the same data for different purposes. This also allows for more flexibility and scalability, as new data can be added to the data lake without requiring changes to the schema.
To summarize, a data lake is where you store raw, unstructured data for future processing and analysis, whereas a data warehouse is a place where the cleaned, transformed data is stored and made available for efficient querying and analysis by business users and BI tools.
ETL vs. ELT
Given the differences between data lakes and data warehouses, users have also started adapting other parts of the data pipeline, namely ETL into ELT in some cases. ELT (Extract, Load, Transform) is a variation of the ETL (Extract, Transform, Load) process, where the data is first loaded into the target system (such as a data lake or data warehouse) in its raw format and then transformed, as opposed to transforming the data before loading it into the target system.
In the context of data lakes, ELT is often used as the process for moving and transforming data. The data is extracted from the source systems, loaded into the data lake in its raw format, and then transformed and cleaned using big data processing tools like Apache Hadoop, Apache Spark and Apache Hive, for further analysis. This allows organizations to take advantage of the scalability and cost-effectiveness of data lakes, while still being able to perform the data transformations needed for analysis.
In the context of data warehouses, ELT is less common and is often used when the data warehouse is built on top of a data lake. In this case, data is first loaded into the data lake in its raw format, and then transformed and loaded into the data warehouse for reporting and analytics. This allows organizations to take advantage of the scalability and cost-effectiveness of data lakes, while still being able to perform the data transformations needed for reporting and analytics on top of the data warehouse.
It is worth noting that the ELT approach is not suitable for all use cases, as it may come with some data quality trade-offs and security risks and it is important to evaluate the specific requirements of each use case before deciding whether ELT is the right approach.
Data science pipelines in Einblick
Once you have your data storage solution or solutions in place, it will be important to start utilizing all of the data you have. From exploratory data analysis to predictive and prescriptive analytics to various machine learning models, there are many different ways to glean insights and ultimately make data-driven decisions. With Einblick, you can connect to wherever your data lives, transform and clean your data as needed, and start extracting interesting insights instantly.
Einblick’s canvas-based approach to data science gives you the experience of working in a Python notebook on an interactive, highly visual canvas. By removing the limitations of a linear notebook, Einblick frees you of repetitive coding tasks, operationalizes code, allows you to collaborate and share results easily, and lets you branch into different thought patterns in a way that matches your mental model.
Einblick enables you to:
- Quickly switch between SQL, Python, and no-code cells so you choose the best tool for each task. No more switching between apps and platforms.
- Access cells like AutoML and key driver analysis, kickstarting your data science results via our progressive computation engine. Get pipelines on terabytes of data in seconds.
- Create colored data zones, bookmarks, and annotations to keep your canvas organized. Now you can go back to your work, and still have a clear sense of what you did.
- Collaborate with teammates and stakeholders in live mode, with live dashboards, and more.
If you have an existing project, you can simply import your notebook, and start reaping the benefits of Einblick’s unique workspace for free.
You can also check out this canvas, which explores book data, below. We review the content of the canvas in-depth in our post about exploratory data analysis (EDA) in Python:
Use-cases: data lakes vs. data warehouses
Beyond the data structure, there tend to be different costs associated with data lakes versus data warehouses, and therefore slightly different use-cases. Data lakes provide a place to store raw, unstructured data and are often used for big data and data science projects. Data lakes provide a flexible, cost-effective, and scalable storage solution, and allow users to store and process data in its raw format. Data lakes also provide a more cost-effective storage option for large amounts of data, but may not provide the same level of robust security, governance, and compliance that a data warehouse can offer.
Data warehouses are more suitable for structured, predefined and historical data, with a focus on quick querying, reporting and analysis, Business intelligence and data modeling use cases. While data lake is more of a raw data storage, a data warehouse is more of a modeled and curated data storage, with more robust security and governance in place. Both data lakes and data warehouses can be highly beneficial and effective for your team or business.
The benefits of data lakes
- Scalability: Data lakes can handle large amounts of data and can easily scale to accommodate the growing data needs of an organization. Data lakes tend to be built on distributed systems, which allows them to easily scale out and handle large data volumes.
- Cost-Effectiveness: Data lakes provide a cost-effective solution for storing large amounts of data. Data lakes often use commodity hardware and open-source software, which can greatly reduce the cost of storing and processing data.
- Flexibility: Data lakes can handle a wide range of data types, including structured, semi-structured, and unstructured data, which makes them suitable for a variety of use cases. Data lakes also support the schema-on-read approach, which allows users to apply different schemas to the same data for different purposes, which provides more flexibility in data analysis.
- Real-time Processing: Data lakes can provide near real-time processing, as it is possible to process data as it is ingested into the data lake. This makes data lakes well-suited for use cases where real-time data is needed, such as fraud detection, and IoT data analysis.
- Accessibility: Data lakes make it easy for data scientists, engineers, and analysts to access and process data, as the data is stored in its raw format and can be queried using SQL-based tools, and big data processing
The benefits of data warehouses
- Data Quality: Data warehouses are designed to ensure data quality, accuracy and consistency. Data is extracted, cleaned, transformed and loaded into the data warehouse using a predefined schema, which helps to ensure that the data conforms to certain standards and requirements.
- Performance: Data warehouses are optimized for fast query and analysis performance. Data is stored in a highly-organized, relational format, and is indexed and pre-aggregated to support reporting and analytics. This makes data warehouses well-suited for use cases where performance is critical, such as business intelligence and reporting.
- Data Security and Governance: Data warehouses provide robust security controls and can be used to restrict access to sensitive data, and also enforce data lineage, data cataloging, and data lineage policies. This helps to ensure compliance with data privacy and security regulations.
- Easier for Business Users: Data warehouses provide a centralized, easy-to-use repository of structured data, which can be accessed and analyzed using SQL-based tools, such as BI tools, and OLAP cubes. This makes it easy for business users, analysts, and other non-technical users to access and analyze data, which supports business decision making.
- Single source of truth: Data warehouses provide a single source of truth for data, which is the most accurate and up-to-date version of data available. This can be beneficial for reporting and analytics use cases, where a single version of truth is needed for consistency and accuracy in the reports.
Based on your team and your needs, there may be a clear choice between data lakes and data warehouses, or perhaps a hybrid solution is the best. To help you in your decision-making, B\ we’ve provided some illustrative examples of when to use a data lake versus a data warehouse below. Note that there is no one-size, perfect solution. Your choice will depend on your organization’s or team’s priorities, personnel, skill sets, and resource allocation.
Industry-specific data lake use cases
- Healthcare: In the healthcare industry, data lakes can be used to store and analyze large amounts of patient data, such as electronic health records (EHRs), imaging data, and clinical trial data. For example, a data lake can be used to store and process EHRs, and then use machine learning algorithms to identify patients at high risk of certain conditions or diseases. Data lakes can also be used to store and process imaging data, such as X-rays and CT scans, and then use image analysis algorithms to detect abnormalities.
- Retail: In the retail industry, data lakes can be used to store and analyze large amounts of customer data, such as purchase history, browsing history, and demographic data. For example, a data lake can be used to store and process customer purchase history, and then use machine learning algorithms to predict which products customers are likely to purchase in the future. Retailers can also use data lakes to store and process web analytics data, and then use data visualization tools to gain insights about customer behavior on their website.
- Finance: In the finance industry, data lakes can be used to store and analyze large amounts of financial data, such as trading data, market data, and risk management data. For example, a data lake can be used to store and process trading data, and then use machine learning algorithms to predict which trades are likely to be profitable. Financial institutions can also use data lakes to store and process market data, and then use data visualization tools to gain insights about market trends and patterns.
Industry-specific data warehouse use cases
- Banking: In the banking industry, data warehouses can be used to store and analyze large amounts of financial data, such as customer transactions, credit scores, and loan applications. Data warehouses provide a centralized, highly-organized repository of structured data that can be accessed and analyzed using SQL-based tools, such as BI tools and OLAP cubes. This allows banks to perform data analysis and gain insights to support business decision making and compliance with regulations. Additionally, Data warehouse allows the bank to provide a single version of truth, which enables the bank to make real-time, data-driven decisions.
- Manufacturing: In the manufacturing industry, data warehouses can be used to store and analyze data from factory floor sensors, machine learning models, and production data, in order to monitor and optimize factory operations. Data warehouses provide an easy and fast way of querying and visualizing data, which helps the production managers and engineers to get real-time insights about the production process, such as machine downtime, production efficiency and inventory levels. This can help the manufacturing companies to identify patterns and trends that can be used to improve the production process.
- Telecommunications: In the telecommunications industry, data warehouses can be used to store and analyze large amounts of customer data, such as call data records, internet usage data, and demographic data. Data warehouses can be used to analyze customer usage patterns, network performance and service quality, which allows telecommunications companies to improve network operations and customer service. Data warehouses can also be used to perform customer segmentation and to identify the most profitable customers, which helps the telecommunications companies to prioritize their marketing efforts.
- Data lakes and data warehouses are two popular data storage solutions. They can be used independently or together.
- A key difference between data lakes and data warehouses is the kinds of data that they can store. Data lakes can store unstructured, raw data (as well as structured and semi-structured data), whereas data warehouses can only store structured or semi-structured data.
- Data warehouses enforce schemas as data is ingested, whereas data lakes utilize a schema-on-read system, where the schema is enforced when the data is used.
- Some benefits of data lakes are accessibility, scalability, and flexibility.
- Some benefits of data warehouses are enforced data quality, higher levels of data governance and security, and ease-of-use for business users.
Einblick is an AI-native data science platform that provides data teams with an agile workflow to swiftly explore data, build predictive models, and deploy data apps. Founded in 2020, Einblick was developed based on six years of research at MIT and Brown University. Einblick is funded by Amplify Partners, Flybridge, Samsung Next, Dell Technologies Capital, and Intel Capital. For more information, please visit www.einblick.ai and follow us on LinkedIn and Twitter.