If you’ve spent even a short time in an enterprise, you may have encountered the need to collect data from disparate analysis and insight sources effectively.
These data analytics have intensely affected many organizations’ revenue generation and cost containment. But you shouldn’t be surprised by the amount of data generated and analyzed as its number and types explode.
This explosion pushes data-driven companies to use reliable, scalable, and secure solutions to analyze and manage data. The systems’ requirements surpass the capabilities of the traditional database, and that’s where cloud technology comes in.
And with the advancing modern-day cloud technology, many critical business applications like enterprise resource planning (ERP), databases, and marketing tools have migrated to the cloud. While the business data resides in the cloud, companies need a solution that seamlessly stores all the data from different cloud-based apps. The solution is the cloud data warehouse.
This article will help you understand a cloud data warehouse and list a few of the best. And in conclusion, explain how to select the best one for your organization.
A Brief History of Cloud Data Cloud Data Warehouses
As with any technical domain, you must understand why it exists to understand it truly. This convention applies to understanding the operation model of the cloud data warehouse.
According to Education Ecosystem, data warehouses came first in the 1980s and were purposed to help data flow from operation systems into decision support systems (DSSs). The early versions required a vast amount of redundancy, and many organizations had to have multiple DSSs environments to serve several users. DSSs environments use the same data. However, the gathering, cleaning, and integration were often replicated.
As the data warehouses increased efficiency, they evolved from information-supporting traditional business intelligence (BI) platforms into broad analytics architectures that support various applications like performance management and performance analytics.
Over the years, explosive progress has been made in delivering incremental value to enterprises with the latest data-driven warehouses (EWD) that provide real-time data access and machine learning insights. However, that’s beyond the scope of this post.
What is a Cloud Data Warehouse
If you want to embrace intelligence in business infrastructure, the data warehouse is your architecture’s core. Unlike ordinary databases, data warehouses are designed to offer optimum analytic queries on massive data sets. Databases are often transaction processing systems.
A cloud data warehouse entails a database available as a managed service in a public cloud and is optimizable for scalable BI and analytics. You can also view it as a collection of current and past information.
While many cloud data warehouses are available, each will offer its flavor of services. But there are some common factors that you’d expect to be present across all these platforms: data storage and management, automatic software upgrades, and flexible capacity management that seamlessly expands or contracts your data footprints.
- Massively parallel processing (MPP) – This feature is found in cloud data warehouses supporting big data projects to avail high-performance queries when dealing with large data volumes. MPP comprises multiple servers running in parallel to distribute processing, input, and output loads.
- Columnar data store – This feature exhibits economic flexibility when handling analytics. Columnar data stores process data in columns instead of rows, making it faster when aggregating queries like in reporting.
Cloud data warehouses showcase their need to be in every modern business for their analytics and business insights that improve operations and enhance customer services giving your business a competitive advantage. Here are the benefits of using cloud data warehouses.
- Faster insights – Cloud data warehouses are the means to powerful computing capabilities and provide real-time based analytics from the data gathered across multiple sources, unlike the traditional on-premise solutions, allowing your business to access better insights faster.
- Scalability – Cloud data warehouses offer close to unlimited storage for your business as the storage needs evolve. Unlike on-premise solutions that need new hardware when expanding your storage, cloud data warehouses provide more space at a fraction of the cost.
- Overhead – If you opt to use on-premises solutions, you will need to have server hardware (which is expensive) and employee folks to oversee, conduct manual upgrades, and troubleshoot the system. On the other hand, cloud data warehouses do not need physical hardware, thus significantly lowering the cost.
Cloud Data Warehouse Vendors
Now that you know the deal with cloud data warehouses, you can pick the right one for your needs. While these listed here aren’t ranked in any particular order, we started with those with the best technical expertise.
Developed by Google, BigQuery is a fully managed serverless data warehouse that is automatically scalable to match your storage and computing needs. Like other Google products, it offers powerful analytic capabilities besides being cost-effective. It is also reliable and offers several business intelligence tools that you can use to gather insights and make accurate predictions. BigQuery suits complex aggregations across massive data sets following its column-based storage.
Google is keen not to let you manage your warehouse infrastructure, and thus Big Query hides the underlying hardware, nodes, database, and configuration details. And if you’d like to get started quickly, you need to create an account with the Google Cloud Platform (GCP), load a table and run a query.
You can also use BigQuery’s columnar and ANSI SQL databases to analyze petabytes of data at a fast speed. Its capabilities extend enough to accommodate spatial analysis using SQL and BigQuery GIS. Also, you can quickly create and run machine learning (ML) models on semi or large-scale structured data using simple SQL and BigQuery ML. Also, enjoy a real-time interactive dashboard using the BigQuery BI engine.
To completely leverage BigQuery data analytics capabilities, you must be well-versed in SQL, just like with other data warehouses. It is also cost-effective. But the price depends on code quality (you pay for processing speed and storage), so you must optimize your queries to counter high costs when pulling data.
BigQuery handles heavy computing operations based on its separated computing and storage layers and thus suits organizations prioritizing availability over consistency.
Coined in November 2021, Amazon Redshift was launched as a fully managed cloud data warehouse that can handle petabyte-scale data. While it was not the first cloud data warehouse, it became the first to proliferate in the market share after a large-scale adoption. Redshift uses SQL dialect based on PostgreSQL, which is well-known by many analysts globally, and its architecture resembles that of on-premise data warehouses.
On its downside, Redshift is different from other solutions in this list. Its computing and storage layers are not entirely separate. This architecture significantly impacts the performance of analytic queries if you do many write operations. Therefore, you’ll need an in-house staff to update the systems with ongoing maintenance and updates.
If you are looking for excellent row-level consistency, like that which is used in the banking sector, Redshift is a good choice. However, it may not be the best choice if your organization needs to do the write and process operations concurrently.
Snowflake cloud data warehouse is one of its kind; it is fully managed and runs on AWS, GCP, and Azure, unlike other warehouses profiled here running on their cloud. Snowflake is easy to use and is well known for its advanced ability to transform, execute speedy queries, avail high security, and automatically scale based on your demand needs.
Snowflake’s flexible code base allows you to run global data replication activities like storing data in any cloud without recoding or learning a new skill.
Snowflake accommodates data analysts of all levels since it does not use Python or R programming language. It is also well known for its secure and compressed storage for semi-structured data. Besides this, it allows you to spin multiple virtual warehouses based on your needs while parallelizing and isolating individual queries boosting their performance. You can interact with Snowflake using a web browser, the command line, analytics platforms, and other supported drivers.
Even though Snowflake is preferred for its ability to run queries that aren’t possible with other solutions, it does offer the best dashboard creations; you need to code custom functions and routines.
Snowflake is popular among middle-size companies that don’t need to perform high-volume write and process operations or require consistency across large data volumes.
Azure SQL Database
This product is a managed database-as-a-service available as a section of Microsoft Azure, the cloud computing platform. If your organization uses Microsoft’s business tools, this might be a natural selection for you.
The Azure SQL database is prominent for cloud-based hosting with an interactive user journey from creating SQL servers to configuring databases. It is also widely preferred because of its easy-to-use interface and many functionalities for manipulating data. Also, it is scalable to reduce costs and optimize performance on low usage.
On its downside, it is not designed for large loads of data. It is suited for online transaction processing (OLTP) workloads and handles large volumes of mall read-and-write processes.
This tool would be a favorite choice if your business deals with simple queries and small data loads. However, it is not the best if your business needs heavy analytics firepower.
This section of the Azure platform is geared towards analytics and combines several services like data integration, data warehousing, and huge data analytics. While it seems similar to the Azure SQL database, it is different.
Azure Synapse analytics is scalable for large data tables based on its distributed computing. It relies on the MPP (mentioned in the beginning, revisit if you did not grasp it) to quickly run high volumes of complex queries across multiple nodes. With Synapse, there’s an extra emphasis on security and privacy.
Although it’s a standard option for businesses that are already using Microsoft tools, it is difficult to integrate with products other than data warehouses from other companies. The service can occasionally get buggy as it is constantly updated.
Azure Synapse is designed for online analytical processing and thus best preferred for processing large data sets in real-time. You can consider using Azure Synapse over SQL if your warehouse data is more significant than one terabyte
While still new to the field. Firebolt claims to be a future-generation warehouse performing 182 times faster than that SQL-based systems. Firebolt is fast since it uses new data parsing and compressing techniques.
During its queries, it accesses small data ranges using indexes, unlike other data warehouses that use entire partitions and segments, freeing up your network’s bandwidth. It is scalable and can query large data sets at impressive speeds.
Although it is new to the market, it does not integrate with the entire ecosystem (which is extensive) of business platforms and intelligence tools. However, the problem is easily solved using a specific extract, transform, and load (ETL) tool for funneling data to and from the warehouse.
Firebolt’s storage and computing powers are separated, making it economic for large and small institutions. It is best for businesses that need fast analytics, although experienced in-house data analysts are required.
Choosing the Right Cloud Data Warehouse
If you need a cloud data warehouse and want a good one, consider the size of your organization and how you manage the data. If you own a small organization that manages small data sizes and with little or no human resources to handle the data analytics sector, like some e-commerce sites, you’d want to choose a data house that’s easy to use and cost-effective instead of outlooking performance.
On the other hand, if you run a large organization that needs a particular set of data needs, you are bound to face a tradeoff. The tradeoff is in detail description as per the CAP theorem that states that any distributed data guarantees security, availability, and partition tolerance (meaning protection against failure.) In most cases, every organization will need partial tolerance leaving the tradeoff between consistency and availability.
You can now check out the most reliable data integration tools.