What is a data lake?
A data lake stores diverse, raw data in a single, flexible repository. It lets you explore, analyze, and uncover deep insights, powering better decisions.
A data lake stores diverse, raw data in a single, flexible repository. It lets you explore, analyze, and uncover deep insights, powering better decisions.
Unlike traditional databases or data warehouses, which require data to be cleaned, organized, and structured, data lakes welcome information exactly as it arrives, allowing you to store lots of data quickly and at scale. Imagine it as a vast, digital storage room where every kind of data – emails, images, logs, videos, sensor data – can rest side by side, ready to be fished back out whenever the need arises. This flexibility is what makes data lakes particularly appealing for modern businesses dealing with vast volumes of diverse and rapidly changing data.
A data lake is a centralized data repository, optimized to rapidly ingest large volumes of raw data in its original format (structured, semi-structured, or unstructured) without reformatting so that it can be accessed, processed, and analyzed as needed.
AI and data operate as an inseparable team. AI thrives on vast amounts of data – the more information it has, the smarter and more effective it becomes. But huge datasets alone aren’t very useful unless you have AI tools capable of uncovering meaningful insights.
That’s where data lakes come in. They’re uniquely suited to storing enormous quantities of diverse data, from sensor readings to customer interactions, all in their raw, original forms. By providing AI systems with immediate and flexible access to rich, real-time data, data lakes act like fuel tanks, powering the ability for AI and machine learning to analyze, learn, and guide strategic decisions.
In short, data lakes enable AI to perform at its best, and AI unlocks the full value hidden in your data lake – creating a powerful combination that can move your business forward at incredible speed.
A data lake relies on a simple yet powerful architectural approach, designed to handle immense data volumes and accommodate diverse data types. Here are some of its key components:
Data lakes need efficient ways to bring in data. This is done through a robust ingestion layer equipped with tools that handle everything from batch uploads to real-time streaming. Whether data flows in sporadically from IoT devices or steadily from transaction logs, the ingestion layer ensures the lake can continuously absorb new information without missing a beat.
This is the foundation of a data lake, typically built upon a scalable, cloud-based platform. Think of this as the "bottomless pit" of your data lake, capable of growing seamlessly as your data needs expand. It's designed to store enormous amounts of data efficiently, without the constant concern of space running out or storage becoming prohibitively expensive.
Raw data stored in a lake becomes truly valuable when it’s transformed into actionable insights. Processing engines handle tasks like cleaning, structuring, and analyzing data. From unified statistical engines for large-scale data processing to simple database querying tools, this layer enables users to process data precisely how they need it, quickly and efficiently.
A data lake’s enormous storage capacity is of limited utility without information such as its origin, format, and relationships to other datasets. In other words: metadata. Effective metadata management acts like an intelligent digital librarian, meticulously tracking each datapoint’s origin, format, location, and relationships to other datasets. This crucial layer helps users to quickly zero in on the specific data they need.
Finally, an access and analytics layer serves as the front end, making data usable and accessible to business users and analysts alike. This layer includes advanced analytics tools, visualization dashboards, locally stored AI learning databases and AI-driven interfaces, ensuring insights can be rapidly extracted and understood without deep technical expertise.
Below are just a few of the benefits of a data lake solution for your business:
Data lakes remove the need for complicated data prep, redundant systems, and extra software. This cuts storage and operating costs. It also frees up your teams from repetitive low value tasks.
Improved data storage lets you retain information from customer engagements at every touchpoint. This lets you deliver highly personalized experiences, leading to happier customers and greater loyalty.
By providing one centralized source of truth, data lakes help teams collaborate more smoothly. With fewer silos and barriers, your departments can more easily share valuable intel and collaborate.
Tracking, security, and governance get a boost when you bring all your data together into a centralized place. This makes regulatory compliance easier, reduces risks, and gives you greater peace of mind.
Team leaders have access to a clearer and more accurate view of the business. This perspective leads to more effective operational strategies, smarter investments, and heightened visibility overall.
Here is a breakdown of these related – yet different – concepts:
Data lakes are repositories for storing large amounts of big data without the need to structure, organize, or otherwise extensively manage it. In order to optimize the functionality of a data lake, many users also leverage data warehouses and data lakehouses.
Data warehouses function more like structured libraries, meticulously organizing processed and structured data for rapid retrieval and clear, predictable analysis. Before it enters a warehouse, data is cleaned, organized, and formatted. This allows for quick response times for reports, queries, and recurring business-critical activities.
Data lakehouses blend the attributes of data lakes and data warehouses. By storing both raw and structured data together, lakehouses offer a highly flexible approach. They accommodate exploratory data analysis and real-time queries. And at the same time, deliver structured, reliable insights for routine business tasks.
| Feature | Data lake | Data warehouse | Data lakehouse |
| Primary data type | Raw and unfiltered (unstructured, semi-structured, structured) | Structured and processed | Both raw and structured |
| Purpose | Flexible storage and rapid access for analysis | Business reporting, structured analytics | Unified analytics and reporting |
| Data processing | Schema-on-read (structured during analysis) | Schema-on-write (structured before storage) | Hybrid schema approach (flexible yet structured) |
| Performance | Ideal for real-time and exploratory analytics | Optimized for predictable, recurring queries | Combines real-time flexibility with warehouse efficiency |
| Cost profile | Typically lower cost due to raw storage | Higher cost due to structured storage and processing requirements | Balanced cost efficiency and analytics readiness |
A secure and well-managed data lake gives your organization the confidence to freely explore data-driven innovations without fear of unintended exposure, risk, or regulatory complications.
Clear governance identifies data ownership, permitted uses, and accountability. An effective framework fosters team understanding, compliant data usage, builds trust, and promotes confident adoption across your business.
Data lakes use multiple reinforcing security layers, including role-based access control, encryption at rest and in transit, and continuous monitoring for anomalies – ensuring your data stays safe from unauthorized access or threats.
Transparency and detailed audit trails are essential, especially in regulated sectors like healthcare or finance. Transparent auditing supports data provenance, quick error correction, and regulatory readiness.
The best data lakes embed privacy and compliance from the start, using data anonymization, consent protocols, and automated checks – ensuring alignment with regulatory standards and internal policies at every step.
As with any other significant change to longstanding practices and procedures, moving to a data lake architecture can be a bit challenging. A well-tailored transition plan will help you achieve a smooth migration.
In 2024, the total global data volume was 149 zettabytes. That would be the equivalent of about 15 trillion hours of HD video – or watching YouTube non-stop for 1.7 billion years! The world’s data volume is predicted to double by 2028, and presumably so on after that. And if your business is like most, your data storage needs are quickly doubling and tripling as well.
Looking ahead, expect AI to become even more deeply woven into the fabric of data lakes. AI won't just be using the data; it will likely help manage the lake itself, by automatically organizing data, suggesting quality improvements, or optimizing storage. The "lakehouse" concept will likely mature and become more common, offering a more standardized best-of-both-worlds approach. We'll see more sophisticated AI, including generative AI, being trained and run on the specific data held within a company's data lake to create highly detailed and deep data analyses. As AI makes more critical decisions, there will be a growing emphasis on "explainable AI" – tools and techniques to understand why an AI model reached a particular conclusion based on the lake's data. Also, expect continued efforts towards data democratization, to make these powerful tools accessible to more people within an organization, further embedding data-driven and AI-powered decision-making into everyday business operations.
Just a few years ago, “more storage” was the cry that could be heard from every business. Today, it’s not just bigger storage that is needed – but smarter, more agile data management solutions. Data lakes eliminate many traditional barriers to meaningful AI-powered analytics – and are positioned to handle the growing flood of raw data being created from one end of your business to the other.
See how Infor’s data lake tools can capture raw big data from across your enterprise – and power your next-generation analytics and AI solutions.