Cloud Data Warehouse 101

on Data Warehouse • September 6th, 2021 • Write for Hevo

With the advent of modern-day cloud infrastructure, many business-critical applications like databases, ERPs, Marketing applications have all moved to the cloud. With this, most of the business-critical data now resides in the cloud. Now that all the business data resides on the cloud, companies need a data warehouse that can seamlessly store the data from all the different cloud-based applications. Enter – Cloud Data Warehouse.  

This post aims to help you understand what is a cloud data warehouse, its evolution, and its need. Here are the key things that this post covers:

Table of Contents

What is a Data Warehouse? 

A data warehouse is a repository of the current and historical information that has been collected. The data warehouse is an information system that forms the core of an organization’s business intelligence infrastructure. It is a Relational Database Management System (RDBMS) that allows for SQ-like queries to be run on the information it contains. 

Unlike a database, a data warehouse is optimized to run analytical queries on large data sets. A database is more often used as a transaction processing system. You can read more about the need for a data warehouse here.

Querying the vast data troves present in the warehouse is taxing. This is due to the complex structure of most data warehouse table structures (multiple joins and aggregates) and the sheer amount of data stored. This requires significant computing resources to perform efficiently.  

Queries performed on data warehouses allow analysts to glean useful insights into the organization’s operations. These insights provide guidance to leadership within the company, helping them to make better decisions in improving company performance. This function is best indicated by an alternate name for Data Warehouses: Decision Support Systems.

What is Data Warehousing?

Data warehousing is the combination of various processes and methods used for collecting and storing vast amounts of data for the purpose of query and analysis, in order to generate information and insights for business intelligence. 

Getting the data from the business transaction systems to the analytical systems (known as data migration) involves the ETL Process

The ETL process is used to Extract data from the source systems, Transform the data into a usable, queryable form and then Load said data to the destination database: the data warehouse. This may also involve extracting and combining different data sets from a variety of disparate sources into a singular cohesive form. This process is referred to as data integration

Before we dive into understanding what is a Cloud Data Warehouse, it is important to understand the history and origin of Data Warehouses. 

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The Early Days of Data Warehousing

Business Intelligence has been around since analysts realized the benefits of using an organization’s historical data as a research asset. From the 1960s new methods for managing and analyzing vast amounts of data were continuously being developed. As computing systems became more affordable and more powerful, and the amount of data generated grew exponentially, data warehousing would evolve as a tool of business intelligence.

For over half a century the discipline of data science and business intelligence grew and matured into its own discipline and industry. And, as the methods and paradigms improved, so did the technology that would form the infrastructure for data warehousing.

While the concept of data warehousing was initially provided by American computer scientist, Bill Inmon, it can be said that Data Warehousing officially began in the late 1980s with the formation of the Business Data Warehouse. At this time the internet was a large, private computer network that, while spanning the continental United States, was only accessible by government and military organizations, renowned academic institutions, and large corporations. 

These entities would communicate via dedicated phone lines over the existing telecom infrastructure, migrating data to expensive onsite data warehouse servers. During this period bandwidth was extremely expensive and had to be carefully managed. This led to the practice of migrating data during non-work hours, otherwise known as the batch window. However, the internet was soon to “go public”, and that shift would lead to significant improvements.

Data Warehousing and the Dawn of the Information Superhighway

The internet began as a military project developed to enable persistent communication between diverse military divisions and the military’s Central Command and Control. However, upon the inclusion of academic institutions and large corporations, it was evident that it had potential far beyond its initial military applications.

When the internet became accessible to the public in the early-to-mid 90’s it led to a surge in the expansion and evolution of its infrastructure. The increased demand meant that bandwidth would become cheaper and vastly improve in speed and capacity. As a result of this, organizations that performed data migrations were no longer restricted to run the ETL process during the batch window and so systems could be regularly updated throughout the day.

Several new data integrations and migration processes were developed to take advantage of the increased capacity, such as Message-Oriented Movement and Data Replication. With Message-Orient Movement, data is packaged as messages and these messages are sent when triggered by specific events. Meanwhile, Data Replication involved a data source frequently sending copies of data to the destination data warehouse, providing near-real-time updates. 

Data Warehousing and the Advent of Cloud Technology

At this point in the internet’s evolution, we are experiencing a wave of new Cloud Technologies. Cloud technology is basically on-demand computer system resources that are available over the internet. Clusters of servers are integrated to provide services like data storage and computing power without the user needing to be concerned about details like which server to access or any other network details. 

Traditional Data Warehousing vs. Cloud Data Warehousing

Traditional Data Warehouse is also an on-premise Data Warehouse that is located or installed at the company’s office. Companies need to purchase hardware such as servers by themselves. The installation requires human resources and much time. The organization requires a separate staff to manage and update the Traditional Data Warehouse. Scaling the Warehouse takes time as new hardware needs to be shipped to the destination and then installation.

Cloud Data Warehouse, as the name suggests is the Data Warehouse solution available on the cloud. Companies don’t have to own hardware and maintain it. All the updates, maintenance, and scalability of hardware are managed by 3rd party Cloud Data Warehouse Service providers such as Google BigQuery, Snowflake, etc. Because of the availability of data on the cloud, companies can easily integrate Cloud Data Warehouses with other SaaS (Software as a Service) platforms and tools for Business Analytics.

5 Reasons to Move to a Cloud Data Warehouse

As businesses started implementing data-driven decisions, the need to process data quickly increased. Databases are unable to provide the flexibility and computation power as per the business requirements, and Traditional Data Warehouse is not an efficient way for most companies. Cloud Data Warehouse becomes the best solution for companies to smartly make business decisions while staying in the competition. A few more reasons to move to a Cloud Data Warehouse are listed below:

  • Reducing Cost: Traditional Data Warehouses are useful to process data to generate insights, but that is not a cost-effective solution. Companies need to maintain hardware and software updates for on-premise Data Warehouses. Also, one doesn’t need to own any hardware and personally maintain it. Cloud Data Warehouse service providers provide excellent support in managing your data in Data Warehouses. It costs much less than the on-premises Data Warehouses.
  • Integrations: The processed data in Data Warehouses requires analysis to generate insights from it. More and more businesses are opting for 3rd party Business Intelligence and Analytics tools to get a deeper understanding of their data. Cloud Data Warehouses supports easy integrations with several BI tools and platforms that help data analysts effortlessly use data from Cloud Data Warehouse and use it in a better way. 
  • Scalability: The volume of data generation is increasing gradually, and so is the demand for more storage by enterprises. In Traditional Data Warehouses, companies need to pre-order new hardware for scalability, and it can also delay shipment. Cloud Data Warehouse solves this problem as a user can quickly scale and descale based on the company’s requirements. Companies can increase only the computation power based on the data processing requirements and descale it afterward.
  • Security and Backup: Cloud Data Warehouses service providers offer high-grade data security features allowing companies to store their data in a secure place. Cloud Data Warehouses follow the fault-tolerant architecture and provide data backup facilities in multiple zones of the world to avoid any data loss in any disaster.
  • Accessibility: By storing data in Cloud Data Warehouses allows companies to access data remotely. A small failure in the system can cause downtime of servers. Cloud Data Warehouses ensure the availability of data by storing a copy of data in multiple regions around the world. 

Benefits of Cloud Data Warehouse

Previously, if an organization needed data warehousing capabilities then that would require, firstly, either building and configuring an on-site server or renting servers off-site and, secondly, configuring the connections between relevant assets. Either option requires a significant capital outlay. Cloud-based data warehouses minimize these issues. 

Cloud-based Data Warehousing services are offered at varying price points that are a fraction of what the previous options would cost in terms of capital, time, and stress. Apart from ease of implementation, cloud-based data warehouse solutions also offered scalability. Previous iterations would require building capacity that took possible future growth into consideration. With cloud-based data warehouses, that question is now redundant as your package can be easily scaled to your needs, no matter how they fluctuate over time (as long as it’s within the service’s limits).

Challenges of Cloud Data Warehouse

Security is a concern for cloud-based data warehousing. This is specifically due to the fact that service providers have access to their customer’s data. While service agreements and public legislation around data privacy do exist, it must be borne in mind that it is possible that these entities could, accidentally or deliberately, alter or delete the data.

Another major security concern is the penetration of cloud systems by hackers who are constantly searching for and exploiting vulnerabilities in these systems in order to gain access to user’s personal data and data belonging to large corporations. Providers take maximum precautions in protecting user’s data. To this end, users are also offered choices in how their data is stored, such as having it encrypted in order to prevent unauthorized access.

Given the large variety of applications, businesses use today, loading all this data present in different formats into a data warehouse is a huge task for engineers. However, fully-managed data integration platforms like Hevo Data (Features and 14-day free trial) help easily mitigate this problem by providing an easy, point-and-click platform to load data to the warehouse. 

Top Five Cloud Data Warehouse Services

There are many cloud data warehouse vendors offering a wide variety of solutions. According to IT Central Station, the top 5 cloud data warehouse providers are:

Conclusion

This article provided a comprehensive guide on a Cloud Data Warehouse. It also explained the benefits and needs of a Cloud Data Warehouse in detail. It also lists the top Cloud Data Warehouse Services in the market today.

With the complexity involves in Manual Integration, businesses are leaning more towards Automated and Continous Integration. This is not only hassle-free but also easy to operate and does not require any technical proficiency. In such a case, Hevo Data is the right choice for you! It will help simplify your Data Analysis seamlessly.

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