Top 8 Cloud Datawarehousing Technologies

At a time where information and insights from data are the most significant assets that any business has, implementing data warehouse solutions has been more critical than it has been at any other time in the past. So, what exactly does a data warehouse mean? In a nutshell, a Data Warehouse is a basic data set for supporting data analysis and acting as a channel across the different sets of analytical tools and data stores.

At its core, Data Warehousing solutions incorporate very versatile features that cater to varied scopes for data analysis, management, and consolidation. Not just that, you can even extrapolate crucial business data points to ensure consistency across your analytics platforms. Modern data warehouse solutions are now even coming up with inbuilt AI and ML algorithms that can tremendously help you in making key business decisions.

As of today, almost all the major data warehouse solutions are delivered through the cloud with the flexibility of adding/removing features, scale-up/down within a few seconds with just a click of a mouse.

Now let’s check which cloud data warehousing services providers are the best as of today.

Teradata Integrated Data Warehouse:

Teradata has been a market leader when comes to data warehousing and data management for more than 35 years. Teradata data warehouse is built upon the most impressive database technologies and has been serving the most leading organizations of the world.

Teradata offers a 360-degree understanding and insights of the data, that can be pulled together from a range of sources. Teradata QueryGrid provides insights into actionable big data. In addition, you can deploy Teradata on IntelliCloud which is also provided by Teradata, on-premise or public, private, or hybrid cloud setup.

SAP Data Warehouse Cloud

SAP is very popular in the world of data analysis, data development, and business analytics. The SAP data warehouse cloud is ideal for organizations that need to make more insightful business choices. This enterprise-ready data warehouse merges all the possible data sources into one single environment that helps to get more insights from your data that can help amplify crucial business decisions.

SAP’s data warehousing semantic layer helps with making analytics easier for clients with persona-driven insightful data information. It also provides instant access to application data with its pre-built adapters. Even better, SAP information warehousing is versatile, adaptable, elastic, and open, settling on it is a decent decision for organizations, all things considered.

Oracle Autonomous Warehouse

The Oracle Autonomous Data Warehouse offers organizations a simple to-utilize and available framework that scales with user activities. It was designed to provide super-fast, reliable, and elastic performance with minimal to zero administration. Oracle is a great product for novices and beginners who are trying to balance out the pros and cons of data warehouses on the cloud. It is the best choice for an end-to-end fully managed and reliable cloud service that makes using and implementing cloud services a walk in a park.

Furthermore, Oracle’s data warehouse is exceptionally flexible, highly elastic, allowing organizations to expand their computing capabilities limits as their organization’s requirements change. You just need to pay for what you use, and everything seamlessly integrates with a range of business analytics and IoT tools.

Microsoft Azure Synapse

Microsoft Azure Synapse is evolved from Microsoft Azure SQL Data warehouse offerings. Synapse is the most advanced enterprise data warehousing solution that Microsoft has come up with to date. With Microsoft’s data warehouse cloud offering, you can easily query data according to individual requirements. There’s flexibility to access both provisioned and serverless on-demand resources as well.

Also, Synapse empowers to leverage the power of AI, ML, and business intelligence as a part of the combined business intelligence solution ecosystem. Additionally, Microsoft has the most advanced privacy and security features across its data warehousing solutions.

IBM Db2 Warehouse

The IBM Db2 Warehouse provides a great relational database solution that delivers high performance and high-quality analytics to its customer.

IBM Db2 seamlessly integrates with the in-memory columnar database technology from IBM. This provides an enormous advantage for organizations requiring a high-performance database solution. Users can quickly initiate the cloud deployment on the IBM cloud. There’s also a traditional on-premise version of the Db2 data warehouse solution.

Google BigQuery

Google BigQuery is a major component of the Google cloud ecosystem. This exceptionally adaptable and serverless cloud data warehouse solution is ideal for organizations that need to minimize expenses and at the same time benefit from the power of cloud computing. If you need to make quick business crucial decisions using data analytics BigQuery has you covered.

BigQuery separates itself by its availability and accessibility. Moreover, you can proficiently run your analytics environments with a three-year TCO that is up to 34% less expensive than other cloud offerings. Integrating with AI and ML tools of Google is another key differentiator in case you’re keen on venturing into the world of AI/ML that Google BigQuery has to offer.

Snowflake

Snowflake is a very popular data warehousing solution that offers an assortment of choices for public cloud technology. With Snowflake, you can make your business more information-driven, empowering you to create stunning user experiences.

The convenient and flexible pricing model from Snowflake helps you to save on costs and pay only for resources and services that you use. Snowflake’s very robust data warehouse architecture improves dataflow and while reducing unnecessary complexities of your data model. You additionally get self-administration admittance to all the additional usefulness that you need.

Amazon Redshift

Amazon Redshift is quite undoubtedly the most well-known data warehouse solution available in the market today. The service drives the analytical initiatives of new businesses and startups and Fortune 500 organizations at the same time. The biggest, greatest brands utilizing Redshift today are Intuit, Lyft, Yelp, and surprisingly Mcdonald’s.

Probably the best thing about Redshift is that it integrates seamlessly with the data lake and AWS environment. Redshift allows technical users and business users to query and analyze the immense amount of non-structured, semi structured, and fully structured from a host of different offerings and services. Also, Redshift’s performance leverages and profits by Amazon’s extraordinary AWS framework, so you know you will get an incredible user experience. For data outside of your S3 data lake, you can utilize AWS Glue to extract and transform the data before being loaded into the data warehouse.

Concluding Remarks:

Modern cloud data warehouse systems are changing the way how we think about data as a whole. With these new systems, we are now able to get deeper insights out of data which was thought to be impossible just a few years ago. The hardware and performance of these systems are quadrupling every 5 years. The cloud data warehouse systems provide on-demand compute and cheap storage without the additional overhead of maintaining them. And not just that cloud data warehouses provide full scaling for storage, compute, and services.

It’s no doubt that cloud data warehouses have lowered the complexity and time-to-value that comes up with traditional on-premise data warehouse systems.

In this article, we have looked into the major players in the cloud data warehouse space. There is no doubt the big traditional software giants like AWS, Microsoft, IBM, and Google are leading this space and will do so for years to come. Every year they are coming up with new services in the data warehousing space that helps customers to get more insights from their data. There is no doubt that the future belongs to cloud data warehouse systems there by phasing out the traditional on-premise data warehouse setups.