Data Warehouse Architecture: Traditional vs Cloud

Outgrowing Data Warehouses 

Our data production and consumption capacity has increased exponentially in the last several years. In fact, the amount of data created, captured, copied, and consumed in the world has grown almost 5,000% in the last decade. With data volumes rising from 1.2 trillion gigabytes to 59 trillion gigabytes in a single decade, on-premise legacy data warehouses are pressed for performance (Source: Mint). Moreover, the data generated was mostly structured and followed certain rules. But today, we are working with a mixed bag of structured, semi-structured, and unstructured data. The unstructured data itself amounts to 90% of the data volume. For this, the traditional warehouses are not prepared to transform it, resulting in data inconsistencies, redundancies, missing values, and semantic mismatches. This poses a huge problem for data analysts that rely on correct and clean data to make decisions. Fortunately, there is an alternative: the cloud.  

This article will differentiate traditional data warehouses from cloud warehouses, address the advantages of cloud over legacy systems (capacity, interoperability, scaling, cost, and performance), and hear about the real successes enterprises experienced when they migrated their data storage to the cloud.  

The Differences Between Traditional Data Warehouses and Cloud Warehouses 

Traditional on-premises data warehouses typically follow a 3-tier model. The user interface sits in the top layer while the middle layer contains a server for online analytical processing (OLAP). The bottom layer has the database server. The warehouse serves as a central entity and aggregates data from multiple sources that usually send the same type of data. For different types of data, a summarized structure is created before it is sent to the user layer for analysis. For transforming data into a format for analysis, 3rd party extract, transform, and load (ETL) tools are used, and the process is monitored by IT teams. With a variety of formats, multiple tools must be used that makes the work tiresome, time-consuming, and costly.   

On the other hand, a cloud warehouse is a database stored as a managed service in a public cloud for scalable business intelligence. Cloud warehouses were built to address the needs of modern organizations. A major difference is due to the separation of compute and storage in the cloud that makes the warehouse dynamic. Moreover, with storage, traditional warehouses followed a star schema which was costly, especially with high volumes and wider varieties. And unlike warehouses, cloud architecture includes a shared space to access in parallel and thus, delivered improvements in the scale and performance. Sharing of resources between different users means enterprises only must pay for their utilization rather than the whole infrastructure.  

Traditional Warehouse vs Cloud Warehouse 

When it comes to data storage, most people are concerned with five key aspects: capacity, interoperability, scaling, cost, and performance. Let’s see how traditional on-premises warehouses compare to cloud storage in these areas.  

Below are some additional benefits of cloud data warehouses worth mentioning: 

• Parallel processing reduces the time required to manage data 
• Dynamic allocation of computing resources reduce cost and improve performance 
• Cost of administration is limited with cloud service providers managing backend systems 
• Cloud acts as a failsafe system as disaster recovery is assured   
• Dynamic pricing plans make it affordable even for small team operations

Cloud Data Warehouse Success Stories 

Traditional data warehouses mostly favor industries that have well-organized systems of data. However, a cloud data warehouse promises the same results regardless of the industry served. Here are a few cloud warehouse success stories from different industries:   

Major Cloud Data Warehouse Providers  

Cloud data warehouse solutions provided by major players come with a wide range of capabilities for management, administration, scalability, analytics, data streaming, backup, architecture, and parallel processing. These include Amazon Redshift, Google BigQuery, Oracle Exadata, Microsoft Azure, Teradata Intellicloud, and Snowflake Data Cloud. While these solutions might vary slightly from one another, capacities, and features, certain provisions are common such as: 

• Instant provisioning of servers for computing 
• High scalability of storage and compute capacities 
• Analytics for business intelligence 
• Data integration with supporting partners 
• Ingestion of steaming data  
• The columnar architecture of data warehouse 
• Massive Parallel processing 
• Data backup and recovery

Cloud Data Warehouse Migration with Apexon   

With the current demands of data management rising, organizations cannot rely on traditional data warehouses without losing an edge. Cloud data warehouses are an excellent alternative. The data earlier sat in silos and was difficult to manage but with cloud integrations, processes were both simplified and made effective.    

Empowering data warehouse operations with cloud technologies have proven advantages including increased performance, reduced price, simplified processes, and exceptional capabilities. Cloud warehouses have delivered the promises of excellence to many companies already. With business growth sitting on top of the list of priorities of any organization, it would be no surprise if all companies slowly begin to move to the cloud. Will you be the next to join the league?  

To learn more about how to enhance your organization’s digital capabilities and migrate to cloud infrastructure, check out Apexon’s Data Engineering Services or get in touch directly using the form below.

Also read: Transforming Retail with Apexon: Mastering Salesforce Data Cloud for Unmatched Customer Experiences