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.
|Traditional Data Warehouse
|Cloud-based Data Warehouse
|Can handle only a limited amount of data based on the availability of systems and resources at a time
|Can handle virtually limitless data with parallel processing and infinite scalability
|Semi-structured and unstructured data is difficult to handle with on-prem warehouses
|Tuned to handle unstructured data which is automatically transformed for usability with ‘schema-on-write’
|The interoperability of different technologies and orchestration of separate systems is challenging
|A virtual interoperable layer sits on the data source to allow easy integration of data from different systems
|Scaling up is tedious and time-consuming as both hardware and software must be reconfigured
|Instant scaling is possible on demand, both vertically and horizontally
|Scaling up requires huge investments in hardware and human resources
|On-demand scaling allows companies to make incremental investments that are affordable
|On-prem infrastructure adds additional overheads for maintenance and support
|Maintenance and support overheads are included in the cost of cloud service provider
|The high cost of maintenance (often 40% of the software cost) often outweighs the operational costs
|Both set-up and maintenance costs are reduced drastically. A survey by the Tech Republic suggests a 50% drop in IT costs with cloud implementation as confirmed by 50% of respondents
|Dedicated hardware used to meet storage and computing needs has high upfront costs
|The pay-as-you-go model drastically reduces the need for heavy initial investment
|High volume and complexities of user queries increase server load and diminish performance
|Peak workloads are split dynamically between resources for load balancing to maintain performance
|Availability depends on the quality of hardware and software as well as the skills of the IT team
|Cloud service providers like Google, AWS, and Microsoft guarantee uptime of 99.9% irrespective of conditions
Below are some additional benefits of cloud data warehouses worth mentioning:
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:
With help from a custom data reporting system, a national leader in health and life insurance had much better access to critical information on the company’s performance and more scalable data and analytics operations which provide a full view into revenue, policies, and market performance allowing executives and analysts to leverage KPIs to develop new strategies to support its growth.
This healthcare technology company was able to achieve higher service levels, increased flexibility and ease of use, as well as faster cycle times by migrating their database to cloud and implementing a scalable, cloud-based data warehouse and data analytics solution built on AWS and Snowflake.
The world’s leading travel security company migrated their legacy databases to the cloud for faster cycle times, increased efficiencies in development and testing, and reduced resource requirements.
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:
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.