Data Integration

Diving Into Cloud Data Lakes


3 min read

Image of a lake with mountains in the background overlayed with a graphic of a map and various data icons

As data lakes increasingly make their move to the cloud, it’s easier than ever to set up, maintain and scale storage to meet your modern analytics needs. But with so many platforms available, it can be hard to determine which is right for your business. Here’s a quick rundown of some of the leading solutions.

Data lakes are a leap forward in terms of storage and scalability. They can store raw data without having to structure it, and they can process every type of data including documents, images, video and audio. All of which are needed for today’s advanced analytics, machine learning, BI and data science initiatives.

While on-premise solutions like Hadoop paved the way for data lakes, cloud platform providers have only improved the benefits and reach. Massive scalability is a given. You can also avoid the high upfront costs of setting up and maintaining a lake, and focus instead on getting the most value out of your data.

Who are the Leading Vendors?

There are many cloud data lake options to choose from, ranging from traditional vendor platforms to newer data lakehouse offerings. At their core, these solutions share common elements: data ingestion, storage, processing, governance and analytics services. They offer a unique set of functions and capabilities too. Here’s a sampling:

Amazon Web Services

Provides flexible and cost-effective data lakes. Amazon S3 lets you seamlessly scale storage from gigabytes to petabytes of data, while paying only for what you use. It includes a service that automates the setup and creation of lakes in S3. Also on offer is an open-source-tools-based service, which automates batch and streaming data processing.


Combines data warehouses and data lakes into a lakehouse architecture, which is designed to handle all your data, analytics and AI use cases. Built on an open data foundation, it can manage all data types and it applies a common security and governance approach across all data and cloud platforms.

Google Cloud Platform

Provides its own data lake for storing large volumes of diverse data. Google offers a general-purpose storage service that includes a low-cost option, and an open-source-tools-based service that processes and analyzes cloud-scale datasets. It includes serverless data processing as well as data exploration and visualization features.

Microsoft Azure

Offers scalable data storage and performs all types of processing and analytics, across multiple platforms and programming languages. Components include an open-source-tools-based managed service and data lake analytics. As well, Microsoft’s data lakehouse architecture brings together data lake and data warehouse constructs.


Combines the scalability and low-cost storage of a data lake with the governance and performance of a warehouse. The architecture requires no management, and it features separate compute, storage and cloud services that can scale and change independently. Available in AWS, Azure and Google Cloud, Snowflake lets you load a diverse array of data in its native format, without having to transform it.

Automated Data Lake Pipelines

Regardless of cloud platform, another must-have is automated data integration. Qlik can capture and land real-time datasets into the data lake from any source including legacy mainframes, enterprise applications and data warehouses. It also standardizes, merges and refines the information.

The result? You benefit from robust pipelines that streamline the delivery of your data, so it’s always ready for your modern analytics needs.

For a side-by-side look at all the major vendors and their offerings, download the comparison guide here.

As data lakes increasingly make their move to the cloud, it’s easier than ever to set up, maintain and scale storage to meet your modern analytics needs.

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