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Cloud Data Warehouse 101

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 reside 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 need. Here are the key things that this post covers:

  1. What is a Data Warehouse?
  2. What is Data Warehousing?
  3. The Early Days of Data Warehousing
  4. Data Warehousing and the Dawn of the Information Superhighway
  5. Data Warehousing and the Advent of Cloud Technology
  6. Benefits of Cloud Data Warehouse
  7. Challenges of Cloud Data Warehouse
  8. Top Five Cloud Data Warehouse Services of 2019

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 organisation’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 allows analysts to glean useful insights into the organisation’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.

The Early Days of Data Warehousing

Business Intelligence has been around since analysts realised the benefits of using an organisation’s historical data as a research asset. From the 1960s new methods for managing and analysing 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 organisations, 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, organisations 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 integration 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.

Benefits of Cloud Data Warehouse

Previously, if an organisation 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 significant capital outlay. Cloud-based data warehouses minimise 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 unauthorised 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 platform 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:

  • Google BigQuery
  • Snowflake
  • Amazon Redshift
  • Microsoft Azure SQL Data Warehouse
  • Oracle Autonomous Data Warehouse

What are your thoughts and opinions about cloud data warehouse? Let us know in the comments.

This entry was published on Oct 17, 2019 / Samuel02. Posted in Data Analysis & Modeling. Bookmark the Permalink or E-mail it to a friend.
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