What is a data lake?

What is a data lake?

A data lake is a type of repository that stores data in its natural (or raw) format. Also called “data pools,” data lakes are a feature of object storage, a cloud-based storage system designed to handle large amounts of structured and unstructured data.

Data lakes’ non-hierarchical structure makes them a flexible and scalable option compared to more traditional, file-based storage systems. However, organizing and retrieving data from data lakes can be both slow and costly, due to their organizational design and complex data egress pricing.

How do data lakes store data?

To understand how data lakes store data, it is important to first understand how object storage works. Unlike traditional file-based storage, in which data is stored in a hierarchy of folders and files, object storage collects individual data (or objects) in the same location and tags them with customizable metadata.

This metadata — the information used to identify a file (e.g. name, type, size, or unique identifiers) — helps users or applications locate and retrieve data without needing to follow a specific path from folder to folder. Because data lakes are designed to contain vast amounts of data, the metadata assigned to each object can be highly detailed, which helps speed up retrieval.

To illustrate the difference between hierarchical and non-hierarchical data storage, imagine that Bob wants to store thousands of vinyl records. With a hierarchical storage system, he could sort records into large bins (or folders) categorized by music genre. This would allow him to quickly locate any album, but he might run out of space in a bin if he acquires more records in that genre. This method is similar to file-based storage, in which data must be organized and stored in a specific location.

By contrast, a non-hierarchical storage system would allow Bob to place all of his records in a room (or data lake), in any order he wanted. Each record would be tagged with a label that displayed its genre. This method would slow down the process of identifying a single record, but would allow Bob to add many more records to his collection without needing to store them in a specific bin. This method is similar to object storage, in which larger amounts of data can be stored in the same location.

For an in-depth explanation of this process, read What is object storage?

What is data lake architecture?

Data lake architecture refers to the processes and tools used to store, transform, access, and secure data within a data lake. While this architecture may be located in the cloud or on-premises, it often shares several of the following components:

• Data sources: The original format of the data, whether structured (i.e. data that fits into a tabular structure, like SQL databases), semi-structured (i.e. data that may not easily fit into a tabular structure, like HTML files), or unstructured (e.g. videos, audio files, and images)
• Data extraction: Extract, load, transform (ELT) is the multi-step process of moving data from its original source to the raw zone of the data lake, then altering it to become more usable
• Data ingestion and storage: The method by which data is added to a data lake — either real-time ingestion (adding data as it is acquired) or batch ingestion (adding groups of data at regular intervals). No matter the method of ingestion, all data is initially stored in the raw store data section; in other words, it is added to a data lake in its original, raw format
• Data persistence and cataloging: The process of adding metadata to raw data so that it can be more easily accessed and retrieved
• Data processing: Different transformations of raw data — including data cleansing (removing inaccuracies or inconsistencies), data normalization (reformatting data so that it all exists in the same form), data enrichment (adding context or necessary information), and data structuring (transforming semi-structured or unstructured data into structured data)
• Data lineage: The process of tracking data from its original, raw format to its transformed state
• Data security and governance: Different methods of ensuring data security and access control, data lineage, data quality, and data analysis and auditing

Data lake use cases

Data lakes can be used for a wide range of purposes, including data analytics and exploration, Internet of Things (IoT) management, personalized consumer experiences, advanced machine learning, and much more. Data lakes are also helpful for training artificial intelligence (AI) models, which often need very large datasets to produce effective outputs.

For example, imagine that a travel company wants to offer tailored, automated travel recommendations to their clientele. With a data lake, they can ingest a large amount of customer data related to common travel patterns, popular destinations, length of stay, type of accommodations, and activities. Then, they can use that data to train an AI model to develop more advanced recommendations and, ideally, ensure better customer satisfaction as well.

What are the benefits of data lakes?

• Flexibility: By design, data lakes can store data in any format, without requiring file compression or reformatting
• Scalability: Data lakes can handle almost unlimited quantities of data, making them a more popular choice for organizations that needs to process and store large (and growing) amounts of data
• Searchability: Data lakes allow for straightforward data retrieval via highly customizable and detailed metadata
• Simplicity: All data is stored within the same data lake, rather than complex hierarchical configurations

What are the limitations of data lakes?

• Reliability issues: Data lakes may become data swamps when too much data is added to a repository without effective categorization and transformation — rendering the data lake unreliable and difficult to use
• Slow performance: Although data lakes are designed to operate at a massive scale, too much data (or ineffective query engines) can affect query times and overall performance
• Data egress fees: Data egress (or data transfer) is the process of retrieving data from an organization’s cloud storage provider. Often, cloud providers charge for these transfers, and fees may skyrocket based on the amount of data an organization needs to move

Data lakes vs. data warehouses

Data lakes are large repositories of structured and unstructured data. Their main advantage is their ability to operate cost-effectively at a large scale but their size and the complexity of their categorization systems may make them inefficient compared to other types of data processing and storage.

Like data lakes, data warehouses are also large repositories of data. Unlike data lakes, they only store structured data, and use traditional file hierarchies to organize, store, and retrieve data. This architecture enables faster data retrieval and performance, although it can be exponentially more expensive to scale than a data lake.

Some cloud vendors offer a hybrid approach called data lakehouses, which combine the core functionalities and benefits of data lakes and warehouses. Rather than keeping structured and unstructured data siloed in separate systems, organizations can use data lakehouses to process and store all types of data, with the organizational capabilities and high performance of a data warehouse and the cost-effective scalability of a data lake. This approach also allows organizations to ensure greater data integrity and reliability via automated data governance and compliance tools.

Does Cloudflare support data lakes?

Cloudflare R2 is a no-egress fee object storage solution that allows organizations to develop their own data lakes. Backed by Cloudflare’s global network, R2 helps ensure optimum data durability and reliability by replicating objects multiple times, so that they remain easily accessible and highly resistant to regional failure and data loss.

Learn more about R2. and how a connectivity cloud lowers egress fees when moving data between clouds.