What is a data lake?

Data lakes arose to help organizations manage the flood of big data generated by internet-connected apps and services in the late 2000s and early 2010s. Unlike traditional databases and data warehouses, data lakes don’t enforce strict schemas and today’s data lakes use affordable, scalable cloud storage—making them ideal for large amounts of diverse data.

Data lakes are now a core components of many organizations’ data architectures. They’re used as low-cost, general-purpose storage; archives for old or unused data; holding areas for incoming data; or to store the massive unstructured datasets necessary for data science, machine learning (ML), artificial intelligence (AI) and big data analytics workloads.

Despite evolving data needs and emerging architectures (such as data lakehouses) the low-cost flexibility of data lakes continues to prove advantageous for enterprises generating value from large data volumes. By 2030, the global market for data lakes is expected to reach USD 45.8 billion, growing at a CAGR of 23.9% from 2024.¹

Enterprise data volumes are starting to feel as limitless as the stars in the night sky—they’re vast, unbounded and seemingly never-ending.

Data arrives from Internet of Things (IoT) sensors, social media feeds, enterprise applications and countless other sources. Without a cost-effective, scalable place to store it all, organizations risk a strategic misstep: leaving petabytes of data unknown and unused.

This data could hold the insights needed to unlock new revenue streams, drive real operational efficiency or deliver hyper-personalized customer experiences. It could also be central to ensuring AI investments are effective and profitable: 72% of CEOs go so far as to say that proprietary data is key to unlocking the value of generative AI (gen AI).²

But realizing the value of this data depends on more than just a place to put it. It also requires easy access for collaborative use. According to a 2025 study from the IBM Institute for Business Value, 82% of chief data officers surveyed consider data wasted if employees can’t access it for decision-making.³

As centralized repositories, data lakes can significantly improve accessibility for previously siloed data. They typically offer self-service data access, empowering non-technical users to access and analyze trusted datasets from across the business, elevating collaboration and accelerating innovation.

For a long time, organizations relied on relational databases (developed in the 1970s) and data warehouses (developed in the 1980s) to manage their data. These solutions are still important parts of many organizations’ IT ecosystems, but they were designed primarily for structured datasets.

With the growth of the internet—and especially the arrival of social media and streaming media—organizations found themselves dealing with vast amounts of unstructured data, such as free-form text and images. Data warehouses and relational databases were ill-equipped to handle this influx of real-time data due to their strict schemas and comparatively expensive storage costs.

In 2011, James Dixon, then the chief technology officer at Pentaho, coined the term “data lake.” Dixon saw the lake as an alternative to the data warehouse. Whereas warehouses provide processed data for targeted business use cases, Dixon imagined a data lake as a large body of data housed in its natural format. Users could draw the data they needed from this lake and use it as they pleased.

Many of the first data lakes were built on the Hadoop Distributed File System (HDFS), an open source framework and one of the major components of Apache Hadoop. These early data lakes were hosted on-premises, but this quickly became an issue as the volume of data continued to surge. Cloud computing offered a solution: moving data lakes to more scalable cloud-based object storage services.

Data lakes are still evolving today. Many data lake solutions now offer features beyond cheap, scalable storage, such as data security and governance tools, data catalogs and metadata management.

Data lakes are also core components of data lakehouses, a relatively new data management solution that combines the low-cost storage of a lake and the high-performance analytics capabilities of a warehouse.

A typical data lake architecture is organized into several layers, each supporting a stage of the data lifecycle.

• Ingestion layer
• Storage layer
• Data catalog and metadata layer
• Processing and analytics layer
• Security and governance layer
• Access layer

The ingestion layer connects the central data lake storage to various data sources, such as databases, apps, Internet of Things (IoT) devices and sensors. Most data lakes use an extract, load, transform (ELT) (rather than an extract, transform, load (ETL)) process in this layer. They ingest data in its original state from various data pipelines, but do not transform it until needed. This approach—applying a schema only when data is accessed—is called “schema-on-read.”

While early data lakes were built on Apache Hadoop, the core of a modern data lake is a cloud object storage service, which can be deployed across on-premises, private cloud and public cloud environments. Common options include Amazon Simple Storage Service (Amazon S3), Microsoft Azure Blob Storage, Google Cloud Storage and IBM Cloud Object Storage.

Cloud object storage enables organizations to store different kinds of raw data all in the same data store. It is also generally more scalable and cost-effective than on-premises storage. Cloud storage providers allow organizations to spin up large storage clusters (servers that work as a unified system) on demand, requiring payment only for storage used.

The data catalog and metadata layer makes it possible for users to find and understand data within the data lake. Data catalogs act as a detailed inventory of data. They use metadata (such as author, creation data and file size) and data management tools to help users easily discover, understand, manage, curate and access data.

Without this layer, data lakes can deteriorate into data swamps, messy mires where good data is inaccessible because it lacks metadata, structure and governance. Data swamps are effectively data “dumping grounds.”

Because storage and compute are separate in a data lake architecture, data processing and analysis are performed through integration with compute engines. At this layer, data lakes support a wide range of tools. Common examples include big data processing engines such as Apache Spark and Hive; machine learning and deep learning frameworks such as TensorFlow; and analytics libraries such as Pandas.

Above all, data lake storage must be secure, especially when it contains personal or sensitive information about employees and customers. Security and governance layers include capabilities such as integrated data governance solutions, encryption, and access controls through identity and access management (IAM). These solutions help to protect against unauthorized access and support effective data management across the other layers.

These capabilities also help organizations meet regulatory requirements under data privacy laws such as the General Data Protection Regulation (GDPR) and the US Health Insurance Portability and Accountability Act (HIPAA).

A key advantage of data lakes is that they provide access to raw, previously inaccessible data. The access layer enables users to query, explore and extract insights from the lake. Downstream users typically include data engineers and data scientists, as well as business users with less technical expertise.

This layer uses query interfaces and application programming interfaces (APIs) to connect users to data. Common examples include SQL query engines such as Presto and Spark APIs.

Data lakes help organizations get more value from their data by making it easier to share and use. More specifically, data lakes can provide:

• Flexible, easy data collection and ingestion
• Cost and resource optimization
• Scalability and performance
• Faster, more collaborative decision-making

Data lakes, warehouses and lakehouses are all distinct types of data storage solutions. But their differences complement each other, and they’re often used together in an integrated data architecture to support various use cases.

Like a data lake, a data warehouse aggregates data from disparate sources into a central store. The key difference is that data warehouses typically clean and prepare data before ingestion so that it’s immediately ready for analytics.

Warehouses are optimized for structured data and tightly integrated with analytics engines, business intelligence (BI) dashboards and data visualization tools. As such, warehouses tend to offer strong performance but at a higher cost and with less flexibility than data lakes. Organizations generally use data warehouses for specific analytics projects while relying on data lakes for large-scale, multipurpose data storage. 

A data lakehouse is a data management solution that combines the flexible, low-cost data storage of a data lake with the high-performance analytics capabilities of a warehouse. Like a data lake, a data lakehouse can store data in any format at a low cost. However, it also provides a warehouse-style analytics infrastructure on top of cloud data lake storage. 

Organizations can use lakehouses to support numerous workloads, including AI, ML, BI and real-time analytics. Lakehouses can also serve as a modernization pathway for data architectures: Organizations can slot lakehouses alongside existing lakes and warehouses without a costly rip-and-replace effort.

Organizations can use data lakes for a wide range of reasons across industries. Some of the most common include:

• All-purpose storage
• Data backups and archiving
• Advanced analytics and AI
• Data integration

For many organizations, data lakes function as all-purpose storage solutions for large volumes of data. Rather than spend time and resources transforming data for ingestion, organizations can store raw incoming data into scalable object storage—which can easily house petabytes of data in virtually any format. Users can either query data from the lake directly using analytics engines or move it to a warehouse or other data platform as needed.

Organizations may also use data lakes to store “just-in-case” data with as-yet-undefined use cases. Because object storage is relatively inexpensive and scalable, organizations don’t have to worry about overspending on data they might not need yet.

High storage capacity and relatively low storage costs make data lakes a common component of backup and disaster recovery strategies for critical data. Data lakes are also frequently used to store cold or inactive data at a lower cost. This approach is useful for archiving old data and maintaining historical records for compliance audits, regulatory inquiries or future analytics use cases.

For example, the banking and finance industry generates high-velocity transaction data from stock markets, credit cards and other financial activities. It must also retain legal documents and other records to meet regulatory and audit requirements. Data lake architectures are well-suited for storing these mixed data formats and preserving legacy and historical data for easy querying.

According to the 2025 IBM CEO Study, 61% of top-performing CEOs agree that having the most advanced generative AI tools gives an organization a competitive advantage. Data lakes play an important role in AI, ML and big data analytics workloads, including building predictive models and training generative AI systems.

These projects require access to large and diverse datasets of structured, unstructured and semi-structured data. Data lake architecture provides the cost-effective, scalable storage and integration capabilities with processing frameworks to support these needs.

According to benchmarking data from the IBM Institute for Business Value, 64% of organizations report that breaking down organizational barriers to data sharing is one of their greatest people-related challenges. Organizations can’t fully benefit from their data if it’s siloed and difficult to access.

Data lakes can help support data integration initiatives by providing a centralized repository for data from multiple sources. By consolidating diverse data in one environment, they create a strong foundation for downstream harmonization and transformation.

While data lakes offer scalability, flexibility and cost advantages, there are three main challenges organizations should consider.

• Data quality: Because data lakes do not enforce a strict schema and accept many different data types from many sources, they can struggle with data governance and data quality. Without proper management, data lakes can easily become data swamps.
  
  
• Data security: Data lakes store large volumes of diverse data from many different sources. It can be challenging to make sure all this data is not accessed, used or altered without authorization and fully complies with data privacy regulations.
  
  
• Performance: Data lakes do not have built-in processing and querying tools like many warehouses and lakehouses do. Query and analytics performance can suffer as the volume of data fed into a data lake grows, especially if data is not optimized for retrieval.

Avoiding data swamps requires strong data governance, data quality and data security practices from day one. Defining and enforcing data standards, metadata management and documentation, and access controls will help to ensure that data lakes remain organized, useful and secure.

Dinesh Nirmal, Senior Vice President of IBM Software, points out that these principles are especially critical when preparing data lakes to support generative AI:

“While the data is unstructured, it’s important to apply the same governance and security that you use for structured data. There is a huge opportunity there: Generative AI can only be successful if we give models governed, trusted data.”

You may not need a data lake unless you manage large volumes of semi-structured and unstructured data for AI, machine learning or data science. Data lakes offer cost-effective, scalable cloud storage with separate compute. Alternatively, a data lakehouse pairs that scalability with the built-in data analytics capabilities of a data warehouse.

Data lakes are not secure by default and can be prime targets for security threats because they are centralized repositories of large volumes of data (some of it sensitive information). Secure data lakes use data encryption, access controls and network protections to safeguard datasets from unauthorized access.

Yes, data lakes are well-suited for machine learning because they store the massive volumes of raw, diverse data needed for training, validating, tuning and deploying ML models. Using data processing and analytics engines (such as Apache Spark), data science teams can access and prepare raw datasets directly within the lake to build and refine their models.