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What is a Data Lake?

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What's a data lake?

Data is everywhere and every organization uses data to make operational and business decisions. So, it’s important that an organization’s data is managed well. This requires models capable of structuring and storing holistic data as well as providing rich analytics as needed.

A data lake stores, manages, and secures voluminous amounts of raw or unprocessed data in one place. The data is generally in multiple different formats and obtained from various sources. Data lakes are a flexible and scalable way to manage and store this raw data. For the enterprise user, it provides easy access to several data formats in a central datastore using a single query and virtual query tools.

A data lake is a central repository that ingests and stores petabyte volumes of structured, semi-structured and unstructured data from many diverse sources. It comes with secure storage for any type of data - regardless of source, format or size - for later analysis.

A data lake is configured on a cluster of scalable hardware, deployed over a cloud or on-premises and stores the incoming data in long-term data containers. It is used to quickly ingest data and prepare it later. Identifiers and metadata tags are used to instantly retrieve data on demand.

Data lakes support the data needs of advanced predictive analytical applications and aid organizational reporting especially when it requires handling a variety of formats.

Data Lake vs Data Warehouse vs Data Fabric

Data lakes are often confused with data warehouses but both possess unique characteristics that make each suitable for different uses.

A data warehouse, while similar to data lakes in its most basic purpose (working as data storage repositories) is different in other ways. While it can handle unstructured data, a data warehouse most often works with processed and structured data usually sourced from relational databases. Also, its schema is predefined while data is being prepared for analysis. Complexities arise in data warehouses when modification is required after analysis, making it favorable only for predefined business needs.

Data warehouses follow schema-on-write while data lake architecture is based on schema-on-read. This means that in a data lake structure is applied only while reading the data not while writing it.

A data fabric, is a layer of interconnected data that is preprocessed and is delivered to big data stores and decision-making AI engines on demand. A data fabric compliments data lakes as it can prepare secure and trusted data. Data lakes, in turn, can provide data fabrics with operational intelligence.

Data Lake Challenges

While a data lake offers agility and petabytes of raw data storage, it lacks in some areas regarding governance, security, and complexity. Because data lakes don’t turn away data, it may ingest inaccurate or duplicate data. It lacks metadata management and data governance which may lead to the formation of data swamps. One of its major concerns is security as frameworks for data lakes, such as Hadoop are open-sourced, making sensitive data exposure threats and compromised security. Data lakes, however, are becoming crucial data management tools, with the advancements in research destined to take over such challenges.


Data Lakes are crucial for many organizations despite the challenges. Its popularity is increasing due to the booming surge in IoT. IoT devices generate and use petabytes of sensory data to extract actionable insights.

Learn more about Macrometa’s Global Data Mesh that allows enterprises to store and serve any kind of data from data lakes, warehouses or other sources. and explore ready-to-go industry solutions that accelerate insights.

Related reading:

Unleash the Power of Real-Time Insights with the Global Data Mesh

Say Yes To The Data Mesh: Explore Multi-Model Data Collections And Stores At The Edge

What is the Difference between Operational Data and Transactional Data?


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