Saturday, January 18, 2025

Understanding Cloud Data Lakes: Their Role and Benefits Explained

Many companies are shifting toward data lakes to handle their growing information needs. These large storage systems let organizations collect and keep both structured and unstructured data. After gathering this data, companies can pass it on for processing in data warehouses, databases, enterprise applications, or to data scientists and AI tools.

With the sheer volume of data involved, more businesses are turning to the cloud for their data lake solutions.

So, what exactly is a data lake? It’s a place where raw data is stored. Once the data is in the lake, it usually moves on for processing or into a database or application. A data lake is essentially a central hub for all kinds of data—whether it comes from supply chains, customer interactions, marketing campaigns, inventory systems, or machinery sensors. You can find structured, unstructured, and semi-structured data here. Companies can use metadata tags to locate their assets, but generally, the expectation is that this data will flow into specialized applications or be analyzed by data science teams.

Amazon Web Services (AWS) defines a data lake as a “centralized repository that allows you to store all your structured and unstructured data at any scale,” emphasizing that you can keep your data in its raw form without needing to structure it first. This is different from a data warehouse, where data is organized in databases accessible to users and applications.

When it comes to cloud data lakes, their key features include scalability and ease of management. They operate on object storage, which means the capacity is practically limitless, relying mainly on a company’s data storage budget for constraints. Businesses can scale their data lakes up or down, adjusting capacity and costs based on their needs. The cloud provider handles adding capacity, maintenance, redundancy, and security, freeing up data science teams from that workload.

“Managed data lake services from cloud hyperscalers allow data engineering teams to focus on business analytics,” notes Srivatsa Nori, a data expert at PA Consulting. “This takes the time-consuming maintenance tasks off their hands.” Cloud providers also bring reliability and up-to-date technology, making these services increasingly popular.

Aside from performance, hyperscalers offer advanced access controls, auditing, and simplified billing. Although data lakes and data warehouses traditionally served separate functions, they are increasingly converging, sometimes operating on a single platform, now referred to as “data lakehouses.” Nori says there’s a role for both in modern data architecture, with cloud infrastructures providing an ideal space to integrate them.

There are clear benefits to using cloud data lakes—such as scalability, flexibility, and easy management. Organizations can avoid hefty upfront capital expenses and lengthy construction timelines typically associated with setting up data centers and procuring hardware.

However, businesses should also think about potential drawbacks, like losing some control, especially over costs. The cloud’s flexibility can lead to unexpected expenses if data usage exceeds estimates. Along with that, organizations need to consider any egress and bandwidth costs when transferring data downstream to databases and applications.

Security and data sovereignty can be significant concerns for some organizations. Regulations may dictate where data can be stored, and since unprocessed data can be sensitive, firms must ensure their cloud providers offer the necessary geographical limitations for data storage. While performance issues aren’t often a concern in data lake projects—most heavy processing happens later—data warehouse speed is essential for efficient operations.

For businesses building cloud data lakes, Microsoft provides Azure Data Lake Storage (ADLS) along with Azure Synapse and Azure Purview for analytics and governance. AWS offers AWS Lake Formation for building data lakes on S3, integrating with analytics and machine learning tools like Athena and SageMaker. Meanwhile, Google pairs Google Cloud Storage with open-source tools like BigQuery and VertexAI, and has also introduced BigLake for unifying storage across various platforms and combining data lakes with warehouses.