What Is a Data Lake?

Nowadays, we’re swimming in data, from apps, IoT devices, marketing dashboards, social sentiment, support logs, and so much more. Because teams have more data than ever, the challenge has become storing it in a way that keeps it usable, no matter the format or volume. That’s where a data lake comes in.

A data lake is a centralized repository for raw data of all types: structured, semi-structured, and unstructured. It’s designed to store everything as-is, giving data engineers, analysts, and scientists the flexibility to explore, transform, and analyze information when they’re ready.

Unlike traditional systems that require clean, pre-modeled data, a data lake supports exploration without forcing structure too soon. This kind of architecture is especially useful for analysts, data scientists, and IT teams that need to explore large volumes of data or feed models with historical trends. But it’s also increasingly valuable to operational teams like finance, marketing, or supply chain who want a complete picture of what’s happening, without waiting for clean data to arrive in a dashboard.

In this guide, we’ll cover what a data lake is, how it compares to other types of data storage, when it makes sense to use one, and how to build and manage it effectively.

What is a data lake?

A data lake is a centralized system that stores large volumes of raw data in its native format. Unlike traditional databases or warehouses, it doesn’t require a predefined structure. That means structured tables, unstructured documents, log files, images, videos, and real-time streams can all live in the same place, ready for analysis when needed.

This approach is known as schema-on-read, meaning you decide how to structure and query the data only when it’s time to use it. It’s especially useful when you don’t know all the ways your data might be needed in the future—or when the formats change frequently.

At its core, a data lake unifies fragmented data sources into one scalable environment. That flexibility supports a range of use cases, from machine learning and predictive analytics to self-service dashboards and ad hoc exploration.

Common data types stored in a data lake include:

• Tables from relational databases
• Semi-structured files (e.g., JSON, XML)
• Unstructured data (e.g., emails, PDFs, video, audio)
• Real-time and event-driven streams
• Sensor or machine data (IoT)

Data lakes are often a critical part of modern data architecture, acting as a foundation for storing and exploring data before it’s transformed or refined for downstream tools.

Data lake vs data warehouse vs data lakehouse

While data lakes offer flexibility, they’re not the only option for managing large-scale data. To choose the right architecture, you should understand how data lakes compare to traditional data warehouses and emerging hybrid models known as data lakehouses.

A data warehouse stores structured data that's already been cleaned and modeled. It uses a schema-on-write approach, meaning the data must conform to a predefined structure before it’s loaded. Warehouses are optimized for reporting, dashboards, and business intelligence tools that rely on fast, reliable queries.

A data lakehouse blends the flexibility of a data lake with some of the performance and governance features of a warehouse. It allows raw and structured data to live side by side and supports both exploration and high-performance analytics.

Here’s a quick comparison:

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If your team primarily works with clean, structured data for scheduled reports, a warehouse might be enough. But if you’re exploring real-time inputs, AI models, or cross-functional data that changes often, a data lake or lakehouse may offer the flexibility you need.

Why use a data lake?

A data lake is how teams store and use raw data of all types without having to define structure upfront. Here are four reasons why that matters:

1. Eliminates data silos

Instead of separating customer, finance, and operations data across systems, a data lake brings everything together in one place. This centralization makes it easier to combine sources and support analysis across teams.

2. Supports self-service and data exploration

Because data lakes store raw and semi-structured data, analysts can run custom queries, apply filters, or transform it on the fly. With AI-powered analytics, non-technical teams can also explore trends without relying on IT or data engineers..

3. Enables advanced analytics and AI

A data lake gives data scientists the historical depth and variety of data needed to train predictive models or support ML workflows. As Gartner notes, AI strategies depend on having high-quality, accessible data at scale.

4. Builds a data-driven culture

When people can access more of the data they need—in a flexible format—they’re more likely to use it. That leads to faster decisions, better questions, and stronger outcomes. The result isn’t just better analytics; it’s a more connected way of working.

For teams moving toward AI analytics adoption or expanding how data is used across departments, a data lake is a practical and scalable foundation.

Data lake architecture: Tools and technology

A data lake doesn’t follow a single architecture. Instead, it relies on a stack of tools to store, organize, process, and protect data. While every setup looks a little different, most data lakes rely on core components like the ones below.

Storage and compute engines

Data lakes typically use scalable cloud storage to hold raw data in its native format. Compute engines are then used to query and process that data for transformation, modeling, or analysis.

Data ingestion and connectors

To bring data into the lake, teams use connectors that support both batch and real-time ingestion. These tools move structured and unstructured data from a wide variety of sources. ETL pipelines help automate this process.

Processing and transformation

Raw data rarely arrives ready for use. Cleaning, joining, or enriching it’s often necessary. This transformation can be done using SQL, scripting languages, or no-code ETL tools, which make it easier for more people to prepare data for analysis.

Governance and security

Controlling access, applying permissions, encrypting sensitive information, and tracking data changes are all part of a healthy governance strategy. These tools also help prevent the lake from turning into a data swamp.

Discovery and data culture

Metadata, clear ownership, and data documentation help people find and use data confidently. Encouraging collaboration and improving data literacy are key to making the most of a data lake.

How to build a data lake

Building a data lake doesn’t require starting from scratch—but it does take planning. The goal is to bring in raw data from many sources and make it usable without forcing structure too early. While every implementation looks different, most follow a few key steps.

1. Start with ingestion

Bring in raw data from every source that matters—spreadsheets, databases, APIs, applications, or event streams. Choose connectors and ingestion tools that match your data volume and update frequency.

2. Add context, not complexity

Structure comes later, but metadata comes early. Tag data by owner, source, format, and purpose so it’s easier to find, manage, and secure down the line.

3. Transform with usability in mind

Clean and prepare the data using SQL or ETL tools. Think about how people will explore and use it, not just how to model it.

4. Connect to real work

Link the refined data to the tools teams rely on: dashboards, apps, workflows, or AI models. A lake only adds value when the data is in motion.

Data lake use cases

Because a data lake can store any type of raw data, the ways teams can use it are just as varied. Here are a few common use cases that show how different teams turn data lakes into everyday tools.

AI and machine learning

Data scientists use data lakes to store large, varied data sets that power training and model development. Having access to raw historical data makes it easier to experiment, refine algorithms, and improve machine learning accuracy over time. This setup also supports predictive analytics, classification, recommendation systems, and more.

Business intelligence and ad hoc analysis

Data analysts and BI teams use lakes to explore unstructured or fast-changing data that hasn’t yet been cleaned for reporting. A data lake supports deeper insight generation, prototyping, and flexible decision-making. It also helps teams connect the dots between departments.

Customer experience

Marketing and digital teams combine web analytics, transaction history, and engagement data to build more personalized customer journeys. With access to real-time signals and unstructured inputs through a data lake, it’s easier to improve targeting, messaging, and retention.

Sales and revenue operations

RevOps teams use data lakes to bring together pipeline data, account activity, and third-party signals and store it all in one place. It helps identify trends, remove blockers, and support revenue forecasting with more context.

Finance and IT

From usage logs to vendor spend to audit trails, finance and IT teams benefit from being able to store and query granular data without overloading their core systems.

Manufacturing and IoT

Data lakes are especially useful for storing sensor data from machines, facilities, or vehicles. You can use the data for monitoring, automated reporting, and predictive maintenance.

Data lake challenges and how to solve them 

A data lake can open the door to more flexible, scalable analytics, but it also introduces new challenges. The key is to plan for those issues early and design systems to evolve as your needs or data volume change. Below are common challenges data lakes face and the actions you can take to ensure they operate successfully.

Challenge: Too many small files

When data is ingested in small batches or real-time streams, it can create thousands of tiny files. A large volume of small files can slow down performance and increase storage costs.

Solution:

Use file compaction processes or choose formats that support block-level storage and compression. Batch small updates where possible before writing to storage.

Challenge: Poor performance or bottlenecks

Without structure, it can be harder to optimize queries or manage transformations efficiently, especially when working with large volumes of data.

Solution:

Organize data using partitioning, caching, and indexing based on how people actually access it. Monitor workloads and adjust as usage patterns evolve.

Challenge: Inefficient partitioning

Partitioning improves performance, but poor choices—like using fields with too many unique values—can create more problems than they solve.

Solution: 

Start with basic partitions by date or team function. Review performance regularly and adjust strategies based on real usage.

Challenge: Weak governance or compliance

Without clear rules, a data lake can become disorganized—or worse, noncompliant with privacy regulations.

Solution: 

Create clear access rules, tag sensitive data, and set up audit trails. The right governance model should match your use case, maturity, and regulatory requirements. Data governance tools provide the framework to help maintain structure without slowing down access.

Challenge: Risk of a data swamp

When data lacks context, ownership, or consistency, a data lake can lose its value.

Solution:

Prioritize metadata, data stewardship, and internal documentation. A lake isn’t just for storage; it should be designed for people to find and use data confidently.

Don’t just store data—put it to work

A data lake makes it possible to store and work with all your data, without forcing it into a predefined structure. That flexibility are part of how analysts, engineers, and decision-makers can explore what’s possible, whether that means training machine learning models, combining siloed data, or developing custom analytics tools.

But building a useful data lake takes more than just cloud storage. It requires governance, transformation tools, and a focus on usability. When those pieces are in place, a data lake becomes more than a repository—it becomes a resource.

Domo helps teams connect, prepare, and explore their data, no matter where it lives. With Magic ETL, self-service dashboards, and built-in governance features, it’s easier to put your data lake to work.

Ready to make your data more usable across your business? Talk to our team to see how Domo fits into your data architecture.