Data lake

When large sets of raw data need to be stored, data lakes offer organizations the ability to securely store and manage the data in them. 

Understand more about why and how data lakes help organizations across a wide range of industries with Micron, or connect with our Sales Support team to find out more.

What is a data lake?

Data lake definition: A data lake is a centralized repository that enables organizations to store vast amounts of raw data — structured, semi-structured and unstructured — at any scale. Unlike traditional databases, data lakes do not require a predefined schema, making them ideal for flexible data ingestion, exploratory analytics, and machine learning. Organizations use data lakes to store:

• Structured data (e.g., SQL databases)
• Semi-structured data (e.g., CSV files, HTML)
• Unstructured data (e.g., multimedia files, documents, social media posts)

This flexibility allows organizations to ingest raw data in its native format — whether quantitative (e.g., sensor readings, sales figures) or qualitative (e.g., survey responses, customer feedback) — and structure it later for analysis. For example, a manufacturer might collect temperature readings from factory sensors, export customer orders from an e-commerce platform, and analyze video reviews — all within the same data lake.

Think of a data lake as a vast reservoir — capable of holding many types of water (data) from different sources. You don’t need to purify the water before storing it; you can treat and use it as needed. This makes data lakes especially valuable for organizations managing diverse and evolving datasets.

As data demands grow more complex, organizations are building on this foundation with networked data lakes — federated, or interconnected, systems that link multiple data lakes across departments or platforms. These systems allow data to remain distributed while still being accessible through a unified framework, enabling cross-organizational sharing, centralized governance and scalable analytics — think of networked data lakes as a system of reservoirs connected by pipelines. These interconnected systems enhance:

• Security: Centralized governance and access controls across distributed data sources improve data protection and long-term reliability
• Scalability: Seamless expansion to accommodate growing data volumes
• Accessibility: On-demand structuring of raw data for analysis

This foundational role of the data lake as a flexible, scalable repository for diverse and evolving datasets has also led to the emergence of the data lake house — a next-generation architecture that integrates the raw storage capabilities of data lakes with the performance, governance and transactional features of data warehouses. By unifying these strengths, lake houses support real-time analytics, structured and unstructured data and enterprise-grade reliability. 

This evolution aligns with the growing demands of data-intensive industries — where solutions like Micron’s high-capacity SSDs deliver the performance, efficiency and volume needed to power AI data lakes, high-performance computing and advanced analytics workloads.

How do data lakes work?

Data lakes support both relational (tabular) and non-relational (non-tabular) data formats. They enable:

• Big data analytics
• Real-time data processing
• Machine learning applications

These capabilities help organizations uncover insights, personalize customer experiences and improve decision-making. For example, combining structured data from a data lake (e.g., phone numbers) with unstructured data in the same data lake (e.g., images) allows for deeper customer behavior analysis.

Data lakes collect raw data from many different sources — like apps, devices, websites and databases — without needing to organize it first. This means companies can store everything as-is and decide how to use it later, depending on the question they want to answer. This flexibility makes exploration and analysis easier for large amounts of information quickly.To keep things organized, data lakes use tools that label and catalog the data, so it’s easier to find and use when needed.

Networked data lakes take this a step further by connecting multiple data lakes across different teams or locations. This helps organizations share insights, work with larger datasets and support technologies like artificial intelligence (AI), which rely on massive amounts of data to make accurate predictions and improve operations.

What is the history of data lakes?

Understanding the history of data lakes involves acknowledging the history of data storage and databases as a whole.

• 1970s, first use of databases: Databases allowed organizations to collect, store and analyze data in-house. Relational databases enabled companies to analyze structured data, using structured query language (SQL).
• 1980-90s, data warehouses and data silos: Technological advancements across two decades led to more data gathering, which required storage. Data warehouses and data silos emerged, providing secure, decentralized solutions for storing fragmented data.
• 2000s, big data: Big data refers to more complex datasets that traditional data processing tools can’t analyze. Data lakes became sought-after solutions due to their ability to store large amounts of varied data.
• 2010s, data lakes emerge: The term 'data lake' was coined in 2010, likening it to a large body of water constantly being fed by streams. Data lakes quickly became universal data storage solutions for organizations generating large amounts of data.

What are the key types of data lake architectures?

Organizations can consider different types of data lake architectures: 

On-premise 

On-premise data lakes are located within an organization's infrastructure. They provide greater flexibility and control over stored data. However, they are generally less cost-effective than cloud-based data lake architecture due to higher maintenance and operational costs. 

Cloud-based 

Cloud-based data lake architectures offer flexibility and advantages for storing big data due to the lack of a required schema. 

Companies like Amazon^TM, Microsoft^TM and Google^TM offer various cloud-based data lake storage solutions for various companies.

Hybrid

Hybrid data lakes combine on-premise and cloud-based solutions, providing cost-effectiveness and control. However, managing hybrid data lakes can be more complex due to the combination of different data lake architectures.

How are data lakes used?

Data lakes have several popular uses:

Entertainment streaming platforms use networked data lakes to store personal data and information, allowing insights about customer behavior. Data lakes give organizations the ability to turn this insight into tailored streaming recommendations.

Data lakes are also used for healthcare. They can store and manage complex datasets, providing healthcare professionals a streamlined pathway to improve patient care and reduce costs. 

The financial industry benefits significantly from data lakes, especially in enhancing machine learning and artificial intelligence capabilities. Investment firms use data lakes to manage portfolios more effectively, analyze financial risks in real time and identify market trends faster.

For example, a hedge fund might use networked data lakes to ingest and analyze streaming data from global stock exchanges, enabling its AI models to detect anomalies and adjust trading strategies within milliseconds.