Quick Summary
Not sure what to choose between cloud data warehouse vs cloud data lake? This guide breaks down the key differences, use cases, and benefits of each to help you make the right decision.
Introduction
We generate approximately 402.74 million terabytes of data every day, and by 2025, the total amount of data generated is expected to reach 181 zettabytes (Source). With this much data to handle, businesses are now struggling to find the right data storage solutions.
Traditional storage solutions can help to an extent, but they cannot be scaled due to their physical nature. But, with cloud computing, businesses can now store unlimited amounts of data on the cloud without the worry of physical storage limitations. When using the cloud to store their data, businesses often consider two common options: Cloud Data Warehouse vs Cloud Data Lake.
Read more as we explore the key differences between these two and help you understand which one will best suit your business needs.
What Is a Cloud Data Warehouse?
A Cloud Data Warehouse is a managed database service offered by public cloud service providers like AWS, Azure, and Google Cloud Platform. It is designed to store and manage structured data, such as sales figures, customer records, and product details, and is optimized for analytical processing.
Popular cloud data warehouse solutions include Amazon Redshift, Google BigQuery, and Snowflake.
Let us now understand how a cloud data warehouse works, with a real-world example:
Imagine you run an eCommerce store, and every day, the store generates loads of structured data, like order records, customer details, payments, website visits, and even product inventory updates. Now, you don’t want this data to be scattered everywhere, and you need answers for:
- What were the total sales last month?
- Which products were added to carts but never purchased?
- How many repeat customers did we get in this quarter?
Here, a cloud data warehouse like Amazon Redshift or Google BigQuery can help by gathering all the structured data and organizing it neatly into rows and columns so you can run queries easily and fast. Further, with the help of BI tools, data analysts can use this data to build dashboards, generate performance reports, and provide valuable insights that can help answer your questions better.
What Is a Cloud Data Lake?
A Cloud Data Lake is also a managed database service, like a cloud data warehouse. But, instead of just structured data, this storage solution can store and manage large volumes of raw, unstructured, or semi-structured data(like logs, social media data, videos, images, sensor data, etc). Also, a data lake stores data in its original format, eliminating the need for data cleaning and organizing.
Let us go back to the online store example. Besides sales and customer data, you might also collect:
- Chat transcripts from customer support
- Social media comments
- App usage logs
- Product demo videos
You may not want to analyze this data now, but it is too valuable to lose. So, you upload all of this data into your Cloud Data Lake, and later, your data team can pick out what they need, process it, and use it for data analytics or machine learning purposes.
Popular cloud data lake platforms include Amazon S3, Azure Data Lake Storage, and Google Cloud Storage.
Cloud Data Warehouse vs Cloud Data Lake: 5 Key Points of Difference
Here is a brief comparison of the key differences between a cloud data warehouse and a cloud data lake.
| Feature | Cloud Data Warehouse
| Cloud Data Lake
|
|---|
| Data Type
| Primarily structured (tables, rows, columns)
| Structured, semi-structured, and unstructured
|
| Data Processing
| Requires pre-processing (ETL: Extract, Transform, Load)
| Stores raw data, processed when needed (ELT: Extract, Load, Transform)
|
| Performance
| Optimized for fast queries, but less flexible
| More flexible, but can be slower
|
| Cost
| More expensive due to optimizations
| More cost-effective for storing large volumes of raw data |
| Use Cases
| Reporting, business intelligence, and analytics
| Big data analysis, machine learning, and data science
|
When Should You Use a Cloud Data Warehouse?
A Cloud Data Warehouse is best suited for scenarios where fast, structured analysis is critical. Here are some instances for when you should use it:
- Business Intelligence and Reporting: If your business requires reporting, dashboards, and real-time business intelligence (BI), a Cloud Data Warehouse is built for that. It can help you with quick querying and faster insights.
- Financial and Customer Data: If you are working with financial data or data of customer transactions that need to be organized and accessible for quick analysis, a Cloud Data Warehouse should be your choice. The structured nature of a cloud data warehouse makes it easy to track the key metrics.
- Historical Data: If you need to track performance over time or analyze historical trends, a cloud data warehouse can store years of historical data and run complex queries efficiently.
When Should You Use a Cloud Data Lake?
A Cloud Data Lake is an excellent choice when you need to store a variety of data types, especially when you do not know exactly how you will use all of it right away. Here are some situations where a Data Lake is the right choice:
- Big Data: If your organization deals with large volumes of unstructured data (like social media posts, sensor data, or images), a cloud data lake is the right solution. It is flexible and lets you store data in many formats.
- Machine Learning: For machine learning projects, you often need large volumes of raw data to train your models. A Cloud Data Lake can provide this data without the need to clean or preprocess it upfront. Once stored, you can later process it as needed.
- Data Exploration: If you don’t know exactly how you want to analyze the data yet, or if you want to keep all of it for future analysis, a Cloud Data Lake allows you to store everything and figure out how to process and use it later.
Conclusion
Choosing between Cloud Data Warehouse vs Cloud Data Lake depends on what kind of data you work with and what your goals are with it. Use a cloud data warehouse if your team works with structured data and needs fast and reliable insights. A cloud data lake will suit you better if you collect large volumes of raw or mixed-format data and want to keep everything for future use.
Many companies use both. A data lake can hold all types of data, while the warehouse handles analytics on the most relevant parts. This balance helps you manage costs while still delivering performance.
However, setting up either system from scratch isn’t always easy. It takes planning, experience, and time.
That’s why many businesses opt for cloud managed services. An IT service provider specializing in these services can bring along the right team of experts who can help with everything from planning and setup to implementation, integration, and ongoing maintenance.
