Home Others Criteria For Choosing A Data Warehouse

Criteria For Choosing A Data Warehouse


As it’s astoundingly complex and tedious to extract actionable information from the colossal wealth of available data, there are various data warehouses you can choose from, and introducing the right platform will definitely set the parameters for your company’s IT culture

For that reason, it’s crucial to make proper choices from the outset when picking a data warehouse platform. Either if you’re implementing a new data warehouse solution or developing your existing one, you have to select the best choice available.

If you are developing your existing data warehouse, you have to compare your present solution with others on the market to understand if there are more appropriate features. This choice is incredibly complicated if you are a new user and don’t have any experience to base your choice on.

Whereas there’s no universal answer, there are still better and worse choices for each scenario. To evade the insufferable pain of being stuck with an ill-fitting solution, we suggest using the following criteria for assessing data warehouse vendors and platforms.

According To Cloud Vendors, Redshift Is In The Lead

Many vendors have run comprehensive performance testing between various cloud technologies. Most of the time, AWS Redshift comes out on top. However, in some categories, BigQuery has the upper hand.

So, experts did a performance benchmark test and compared Redshift vs. BigQuery. They found out that contrary to earlier findings when properly optimized, Redshift surpasses BigQuery in 9 out of 11 use-cases. The only case where BigQuery had shown better performance was in assembly operations.

Also, after a series of examinations and comparing BigQuery and Redshift pricing, Redshift was declared an obvious winner in terms of both price and performance. It was found that Redshift is the best option for real-time query speeds on customers’ ordinary data volumes.


Many companies falsely believe that DWaaS (data warehouse as a service) can be low on your list because speed restrictions are imposed by network delays with cloud access. That leads many companies to go for on-premises deployment. Indeed, numerous issues from security to flexibility and scalability in changing node types are naturally substandard in an on-premise solution.

For most users, higher performance and availability can be reached with leading cloud data warehouse providers. Most companies are better off with a cloud vendor in terms of their data warehouse or overall analytics infrastructure needs.  

It’s a common misconception to believe that cloud solutions don’t require comprehensive in-house adjustment and administration. Anybody who’s ever dealt with data management in the cloud understands that the tasks involved are complex and ongoing. Having said that, in relation to on-premise solutions, it’s a walk in the park.


The leaders when it comes to cloud infrastructure technologies Google, Microsoft and Amazon are all usually reliable, particularly when compared to the on-premise option where far more factors in the chain depend on you. With that said, no matter how respectable the vendor is, as the AWS S3 outage showed, even the best vendors can have a rough day. So, not only do you need to look at the frequency of incidents in the past, but also at how thoroughly and quickly the vendor reacted to it.

Professional and reliable support is one of the critical criteria to consider once choosing a DWaaS platform. Today, according to some beliefs, only a few vendors genuinely offer a good enough SLA for resolving today’s on-demand support needs for data-savvy customers. 


For every company that is building on enormous growth, the infrastructure scalability in the cloud should be measured in the context of resources, costs, and simplicity. Many infrastructure cloud providers offer an easy way to scale your cluster, while others, such as Google BigQuery, scale smoothly in the background.

BigQuery’s most crucial advantage is the stable and quick resizing of a cluster up to a petabyte scale. Contrary to Redshift, there’s no need to continuously track and analyze the cluster size and growth in an attempt to optimize its size to meet the current dataset requirements. Besides, Redshift scalability allows users to enjoy a boost in performance when resources including memory and I/O capacity rise.  

Usability, Security, And Integration

When your data expands, the number of data sources grows and data logic becomes more complex. Furthermore, you’ll also want to include management functions and features such as DBA productivity tools, locking schemes, monitoring utilities, and similar security mechanisms, remote maintenance abilities, and user charge-back enhancements into your infrastructure. 

The capability to change data types and apply new tables and indexes as you please may sometimes be a lengthy procedure. By taking this into account beforehand may prevent unbearable pain in the future.

When absorbing data into your analytics architecture it’s vital to evaluate the type of methodology in order to do so. The difference between the right absorption methodology and the wrong one is generally the difference between data loss and abundant data and the difference between a well-ordered scheme and a data swamp.

Final Words

So, which DW is the best? That’s totally up to you. Each business has its unique needs and specifications that might relate better to one over the other. Anyhow, you can’t go wrong because they all pair well. Although this list is tailored to Redshift and BigQuery, the above-given criteria can be applied to other data warehouses as well.