Many organizations of all sizes are looking to become data-driven because data-driven decisions are smarter business decisions. Cloud computing is a particularly useful technology for organizations that want to consolidate all their data in one place and perform large-scale BI analysis on that data with a positive ROI.
What are the specific use cases for cloud-based data warehouses? Are traditional processes such as ETL still relevant in the cloud? Are data warehouses based in the cloud as secure as their on-premise counterparts? In this post, you’ll get the answers to these questions and you’ll find out some examples of cloud-based data warehouse services.
Data-Driven Decisions and Cloud Computing
Large-scale BI analysis was formerly restricted to enterprises with huge budgets because implementing systems that could gather information from all possible data sources on-premise was prohibitively expensive.
However, the cloud has changed everything and made data-driven decision-making more accessible than ever.
A cloud data warehouse is a centralized repository of data that uses cloud-based computing resources instead of traditional on-premise infrastructures. The benefits of the cloud for data warehousing are many, and they include:
- Cost: it’s much cheaper to set up and maintain a cloud-based data warehouse as a service instead of on-site.
- Scalability: cloud-based data warehouses are effortlessly scalable to huge volumes of data by quickly provisioning new computing or storage resources. On the flip side, organizations can easily scale down as required, meaning no wasted computing resources are left unused.
- Convenience: by offering the data warehouse as a service, it’s much more convenient to administer, provision, and monitor the data warehouse.
Cloud Data Warehouse Use Cases
You’ve already learned about the infrastructural advantages of cloud-based data warehouses over on-premise systems, but the question remains—why use a cloud-based data warehouse? What specific needs can such systems meet?
- Cloud-based data warehouses allow you to focus on obtaining value from your data rather than operational concerns such as maintaining infrastructures.
- Cloud-based ETL tools enable you to integrate a huge variety of data sources based on ready-made “connectors” to these sources.
- Cloud-based data warehouses are ideal for organizations that need high-reliability analytics While reliability in on-premise systems is a function of the quality of hardware you use and the proficiency of your data engineering staff, most cloud providers replicate your data across multiple clusters of computing resources to ensure guaranteed high reliability.
- Cloud-based architectures use massively parallel processing and columnar storage for fast and efficient query processing and responses, making data warehouses ideal for ad hoc analysis.
- Streaming ingestion of data allows immediate querying of data as it’s collected, which supports real-time analysis.
Cloud Data Warehouse Issues
Some of the main issues and concerns with data warehouses are addressed below:
Security is a huge challenge faced by cloud providers and the organizations they pitch their data warehouse services to since the data is not physically under the user’s control.
However, such concerns aren’t that much of an issue—data warehouse providers invest heavily in security technology, with entire departments dedicated to protecting data. It’s fair to assume that since the business model of cloud data warehouse providers relies on security, they are likely to better protect your data than you can.
Even with a data warehouse in the cloud, you still need to find a way to get data from disparate sources and integrate it into the cloud platform for analysis. ETL (Extract, Transform, Load) tools have been released that can perform the same data integration functions they do for traditional warehouses—extracting data from sources, shaping the data for analysis, and moving to the cloud.
You can also stream data in real-time for rapid analysis, or you can use ELT, which is a variation on ETL that extracts raw data and moves it to the data warehouse without applying any changes to the data.
Latency issues can be a problem in the cloud—a data warehouse server based in New York would take longer to deliver results to a computer located in a far away state than if the data warehouse was on-premise in that location. However, cloud data warehouse providers will offer you a service that includes multiple locations for redundancy and improved performance.
The difference in speed between on-premise and cloud systems is likely to be negligible for most users.
Cloud Data Warehouse Examples
Three of the most popular cloud-based data warehouse services are:
- AWS Redshift: Redshift is a petabyte-scale data warehouse service that requires you to provision resources and manage those resources similar to how you would on-premise. Several ETL tools can help you load and transform data into Redshift.
- Microsoft Azure SQL Data Warehouse: Microsoft’s cloud-based offering is similar to Redshift in that it is fully managed. You can also scale compute and storage independently in the Azure SQL Data Warehouse.
- Google BigQuery: BigQuery is a serverless data warehouse service that doesn’t offer the same control and customization as AWS Redshift or Microsoft’s Azure data warehouse. The serverless design hides all resource provisioning and management from the user, meaning you simply pay for the computing resources that your analytic queries consume.
The cloud provides the ideal platform for hosting a data warehouse system, and an increasing number of companies of all sizes are looking to leverage the cloud infrastructure to become data-driven.
Cloud data warehouses provide a cheap, easily scalable, and maintainable method of consolidating all organizational data for use with BI tools.
Cloud-based data warehouses have excellent security, and traditional data integration processes such as ETL are possible either by hand-coding or by using any of several ETL tools that integrate with cloud-based data warehouses.
Streaming data ingestion is also possible in the cloud for companies that need to analyze their data as and when they gather it.
In general, cloud-based data warehouse services are either fully managed, meaning you provision resources and manage clusters of computing resources, or serverless, in which such operational concerns are abstracted away from you and you simply pay for the resources you consume.