As businesses collect more and more “raw” data—such as computer logs or video files that have not yet been processed, cleaned, or analyzed for use—they need reliable and effective ways to manage that data until they’re ready to work with it. There’s also an increasing amount of “unstructured” data (ie, data that doesn’t fit neatly into a fixed schema or table-based format) in the world.
Two options for addressing large, raw and unstructured data storage demands are data fabric and data lake solutions. Whether your organization uses one or both of these approaches depends on what you want to do with your data and who will need to access it in your organization.
To help inform your decision-making, here’s a closer look at the differences between the two, as well as how they relate to another common solution for storing data—a data warehouse.
But first, let’s look at why data storage has become so important in the age of AI.
AI and the Explosion of Raw and Unstructured Data
Artificial intelligence and —particularly large language models (LLMs) and other generative AI tools—is reshaping the data landscape. AI both creates and consumes massive volumes of information, driving sharp increases in the amount of raw and unstructured data organizations must manage, often forcing them to rethink their IT infrastructure to opt for things that are more AI-ready.
More raw data
AI training pipelines ingest vast datasets—application logs, IoT sensor feeds, transaction histories, and more—often in their original form. Businesses are retaining this raw data longer because new AI models can extract fresh value from it over time.
More unstructured data
AI thrives on unstructured sources like text, images, audio, and video. This fuels a surge in the collection, storage, and analysis of everything from call center recordings and scanned documents to AI-generated images and videos.
Longer data lifecycles
Every AI output—from generated text to model embeddings—is itself new data, extending storage requirements and expanding opportunities for reprocessing and enrichment.
This rapid growth is pushing traditional storage and analytics architectures to their limits, making it essential to evaluate whether a data fabric, data lake, or data warehouse (or a combination) can best support your AI ambitions.
What Is a Data Fabric?
A data fabric is a type of data architecture in which data is provisioned through an integrated access layer available across an organization’s IT infrastructure. It provides a unified, real-time view of data, enabling businesses to integrate their data management processes with their data from various sources, including hybrid cloud environments, web applications, and edge devices.
While the data fabric approach makes data less siloed and available to more users, it can also help the business to maintain appropriate data access and governance restrictions—thereby enhancing data security and ensuring compliance with relevant regulatory requirements.
Features of Data Fabric
As a unified data management platform, a data fabric solution features services and technologies that enable processes such as data integration, governance, cataloging, discovery, orchestration, and more.
The architectural elements of data fabric include, but are not limited to, a data transport layer for moving data across the fabric and advanced algorithms for data analysis. Other components include application programming interfaces (APIs) and software development kits for making data and insights available to front-end users through tools they use to work with data—like those for business intelligence, reporting, and visualization.
Advantages and Disadvantages of Using Data Fabric
Data fabric can bring together massive amounts of complex, diverse data from multiple sources, including data lakes and data warehouses. A data fabric isn’t just for collecting and storing data, however. Data fabric also provides machine learning and analytics capabilities for transforming and processing data fast and at scale.
As Gartner explains, data fabric applies continuous analytics “over existing, discoverable and inferenced metadata assets” and “identifies and connects data from disparate applications to discover unique, business-relevant relationships between the available data points.”
A downside to using data fabric: While this approach is meant to help organizations gain a complete view of their data and use their data more effectively, implementing and maintaining a data fabric solution that is secure and integrates with all relevant data sources and platforms is a complex undertaking. It requires specialized skills and expertise—and thus a healthy IT budget.
What Is a Data Lake?
A data lake is a centralized data storage environment capable of holding massive amounts of data (e.g., petabytes) in its raw form for eventual use in data processing. Data lakes can contain semi-structured, structured, and unstructured data from relational and non-relational databases, business applications, and other sources like internet of things (IoT) sensors.
Some data found in a data lake might already be processed and contained in a “sandbox” (testing environment) for use in special projects. But a data lake solution is primarily used as a repository for raw data.
Features of Data Lakes
Data lake structures vary, depending on which technologies are used in their architecture (e.g., Hadoop Distributed File System (HDFS), Apache Spark, NoSQL database). But generally, they include the various data sources that “flow” into the lake; the repository of raw data that is the lake itself; and the tools needed to transform the data so it can be transferred to another environment, like a data warehouse, where it can then be used in other applications.
A data lake typically includes, among other features, metadata, data asset catalogs, permission management, data life cycle management, and quality management.
Advantages and Disadvantages of Data Lakes
Data lakes make it faster and easier for data scientists, business analysts, and other data specialists to access and explore all of an organization’s diverse data sources, from log files to financial reports. (You can download and retain any data you want in a data lake.) Data lakes also help to support data-heavy processes, like predictive analytics and machine learning.
While data lakes are a cost-effective solution for storing large raw data sets, there are some downsides. First, users must have specialized skills to work with them effectively. Data quality can also be an issue. Without good data governance, a data lake can quickly turn into a murky data swamp. Also, as the data lake expands, it can take longer to query the data within it, especially if the data has not been well-managed.
Security is another concern with data lakes. Without proper controls and strong access management practices, sensitive data is at risk of being compromised.
How a Data Lake Works
To extract business intelligence insights from the raw data sources in a data lake, the data must be processed (aka transformed) using advanced analytics tools and then transferred to another environment, such as a data warehouse or a data mart. Users of applications, such as data visualization tools, and business systems like databases, can then work with that data.
Here’s a high-level overview of this process:
Data Mart vs. Data Lake
While data marts and data lakes are both systems for storing data, data marts are more akin to data warehouses because they hold processed data. Data marts are databases that hold a limited amount of well-structured data to serve the needs of a specific business function, like the finance department.
Data Lake Implementation: Cloud or on-Premises
You can implement a data lake in two ways: in the cloud or on-premises.
With a cloud data lake, you’ll work with a provider—AWS, Microsoft Azure, and Google are examples—that hosts your data lake on its platform and handles all the details like managing security and backing up your data. You can access your cloud data lake via the internet, and you’ll likely pay the provider for services via a subscription-based model. Many companies choose to set up cloud data lakes because they’re less labor-intensive and allow the business to focus more on working with its data.
On-premises data lakes, meanwhile, are a heavier lift, as companies need to buy and implement the hardware and software to set up and maintain them. They also need to invest in hiring specialists, like data engineers, to manage the data lake, and ensure it’s secure and performing optimally. Space and power needs are also major considerations with on-premises data lakes. In short, this approach is often very resource-intensive—which is why many organizations today head straight for the cloud when they need to create a data lake.
What Is a Data Warehouse?
A data warehouse is a repository for integrating and storing structured data, like spreadsheet data, pulled from multiple data sources across an organization. Data warehouses and the processed, refined data they hold are vital for everyday data decision-making in many modern businesses—and for giving many people across the organization access to data analytics.
Features of a Data Warehouse
Data warehouses can receive structured data from multiple sources, including transactional systems like those for customer relationship management (CRM) and enterprise resource planning (ERP). The database within the data warehouse is relational, meaning that the data is structured and stored in tables consisting of columns and rows.
Data warehouses ingest information through the Extract, Transform, Load (ETL) data integration process. They’re also optimized to perform high-speed structured query language (SQL) queries so that organizations can derive timely BI from structured data stores.
Advantages and Disadvantages of Data Warehouses
Among the key benefits of using a data warehouse is the ability to consolidate structured data from multiple disparate sources, perform analytical queries from relational databases, and use a dedicated storage solution to conduct faster, more cost-effective queries and reporting.
Data lakes and data warehouses complement each other and often sit together in an organization’s data infrastructure, including in the cloud. With a data lake, the business can experiment with data and pull insights from it before transforming it so it can be moved to a data warehouse and used directly by the business.
How a Data Warehouse Works
Data warehouses take in information from various sources through the Extract, Transform, and Load (ETL) data integration process. Sources of data for a data warehouse can include transactional systems and relational databases—and data lakes, as well. Businesses use the processed data in data warehouses to perform high-speed SQL queries for generating business intelligence (BI), data visualizations, and reporting.
Data Warehouse Examples
Types of data warehouses include the following:
- Enterprise data warehouse (EDW), which centralizes an organization’s data and makes it accessible to everyone in the business who needs it for analytics and reporting. An EDW can include one or more databases.
- Operational data store (ODS), which integrates data from multiple sources. An ODS is used primarily for querying transactional data, which is often refreshed in real time.
Another data warehouse type, mentioned earlier in this article, is a data mart. It’s considered a subset of a data warehouse. Data marts are smaller (under 100GB) and designed for a particular business unit to use, like the marketing department. In contrast, a data warehouse can serve an entire organization and exceed more than 1TB in size.
Database vs. Data Warehouse: What’s the Difference?
While a data warehouse is, technically, a database, it exists so that organizations can perform analytics on the data within it.
A database isn’t designed for analytics; it stores data from one source and is used to process simple queries.
Data Warehouse vs. Data Lake: How Data Is Stored
Data is stored in a data warehouse via the ETL process mentioned earlier. Data is extracted from various sources, it’s transformed (cleaned, converted, and reformatted to make it usable), and then, it’s loaded into the data warehouse where it’s stored hierarchically in files and folders.
Data lakes, which have a flat architecture, can receive raw data from various internal and external data sources, including social media and mobile apps, intelligent sensors, websites, and more. Data lakes store this data as files or object storage. The latter describes data stored in discrete units (objects) that have unique identifiers or keys, which allows them to be found no matter where they’re stored on a distributed system.
Want more? Read: Pure Storage and Snowflake Hybrid Cloud Solution
Data Warehouse vs. Data Lake: How Data Is Accessed
Data in data lakes can be accessed using open source frameworks like Apache Hadoop and Apache Spark, and other tools and frameworks provided by commercial vendors that are designed for processing and analyzing large data sets.
As for data warehouses, users can typically access the data within them using BI tools, dashboards, and applications. Direct SQL access is also widely used for connecting directly to data in the warehouse and to run queries.
How Data Lakes and Warehouses Work Together
Data lakes and data warehouses complement each other and often sit together in an organization’s data infrastructure, including in the cloud. With a data lake, the business can experiment with data and pull insights from it before transforming it so it can be moved into purpose-built systems—like a data warehouse—and used more directly by the organization.
Can a Data Lake Replace a Data Warehouse?
The short answer is no. Data warehouses and the processed, refined data they contain play a critical role in supporting everyday data decision-making for the business—and making analytics accessible to many people across the organization.
Data lakes are essentially testing grounds for data scientists, business analysts, and data developers who want to experiment with available data in any format and explore its potential for delivering insights to the business. Data lakes also support data-intensive processes like training artificial intelligence (AI) models.
How to Choose: Data Fabric vs. Data Lake vs. Data Warehouse
An organization can find value in using all three of these solutions for storing big data and, ultimately, making it usable to the business. They are different solutions, though, in that:
- Data lakes store raw data
- Data warehouses store processed and refined data
- Data fabric helps businesses manage all their data, regardless of where it’s stored—including data lakes and data warehouses
How do you know which approach is best for your organization’s current data needs? The following information can help.
When to Choose a Data Lake Solution
If your business is collecting a tremendous amount of data in various formats from many diverse sources, and you don’t need to access or query that data immediately, consider channeling it into a data lake. It’s a more cost-efficient option than trying to process and store that information in a data warehouse.
A data lake is also a logical option if you aren’t yet sure what to do with all the raw data you’re collecting, you just want to experiment with it, or you need to accumulate a lot of information to run data-heavy processes like artificial intelligence (AI).
When a Data Warehouse Is What You Need
If your organization wants quick and easy access to reliable, quality data for business decision-making, a data warehouse stands out as a go-to data storage solution. You can consolidate processed data, including historical data, from many sources in a data warehouse, and then use predefined questions to extract quick insights from that data.
Unlike a data lake, you know exactly why you’re storing data in a data warehouse and what you want to get from it, and that information is at the ready for you to work with when you need it.
When Data Fabric Is the Best Choice
If you want to “democratize” your data—that is, make it available to everyone in the business instead of just data specialists like data scientists and business analysts—and gain real-time, actionable insight from that information, then implementing a data fabric architecture is likely the right move.
A data fabric solution allows you to unify your data—of all types, from all sources—and generate a consistent view of it that will be accessible to users across the business. With data fabric, you can also break down data silos and generate insights faster, opening the door to more data-driven innovation.
Free, Open Source, and Popular Data Providers
If you’re ready to explore implementing data fabric, data lake, or data warehouse solutions in your organization, here are some resources to help with your research and get you started on your journey.
Data Fabric Resources
- Apache Flink is an open source, unified stream-processing and batch-processing framework. It’s designed to run in all common cluster environments and perform computations at in-memory speed and at any scale.
- Apache Nifi is a solution for processing and distributing data. It’s designed to automate the flow of data between disparate software systems.
- Azure Data Factory is a cloud-based data integration service for creating data-driven workflows needed to orchestrate and automate data movement and transformation.
- Confluent is a full-scale data streaming platform that lets you access, store, and manage data as continuous, real-time streams, with speed and ease.
Pure Storage partnered with Confluent to offer the first-ever on-premises tiered storage solution for streaming data. Learn more about our partnership.
Data Lake Resources
- Apache Hadoop is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models.
- Azure Data Lake is a scalable data storage and analytics service that is hosted in Azure, Microsoft’s public cloud solution.
- Data Lake on AWS automatically configures the services from Amazon Web Services (AWS) needed to tag, search, share, transform, analyze, and govern specific data subsets across a business or with external users.
- Databricks provides a “lakehouse” platform that is built on open source and open standards and helps organizations to simplify their data estate so they can accelerate data and AI initiatives.
- Google Cloud Platform’s (GCP) data lake can be used to store any type of data. It’s meant to help businesses ingest, store, and analyze large volumes of diverse data securely and cost-effectively.
- Presto is an open source SQL query engine for running interactive and ad hoc queries at subsecond performance for high-volume applications.
- Snowflake offers a cross-cloud platform that’s designed to break down data silos by supporting various data types, including structured, unstructured, and semi-structured data and storage patterns.
Get details on the strategic partnership between Pure Storage and Snowflake.
Data Warehouse Resources
- Apache Druid is not a traditional data warehouse—it can power real-time analytic workloads for event-driven data. It incorporates architecture ideas from data warehouses such as column-oriented storage, as well as designs from search systems and time series databases.
- Apache Hive is a distributed, fault-tolerant data warehouse system that enables analytics at a massive scale and facilitates reading, writing, and managing petabytes of data residing in distributed storage using SQL.
- Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes using AWS-designed hardware and machine learning.
- Google BigQuery is a serverless enterprise data warehouse that can work across clouds and scales with data. It includes built-in AI, machine learning, and BI capabilities.
- Oracle Autonomous Data Warehouse is optimized for analytic workloads, including data warehouses and data lakes. This cloud-based solution can be used by data experts and non-experts for business insights.
Discover how Pure Storage solutions can help simplify how your Oracle data is stored, mobilized, and protected so you can optimize your data operations and more.
Data Fabric, Data Lake, or Data Warehouse? Or…All of the Above?
Being a modern, data-driven organization requires managing all of your data effectively—and with speed and agility. As your data ecosystem grows and evolves, the solutions you need to access and analyze data and take action on data insights will change, too.
Over time, it’s likely that you’ll end up with all three of the solutions described above—data fabric, data lake, and data warehouse—at work within your ecosystem. These solutions can coexist, and they can complement each other, helping you to make the most of your data of every type, from every source.
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