All decision-makers out there have a difficult time when they need to choose and implement a good data warehouse for their businesses.
The responsibility is not insignificant, the options on the market are numerous, and each option has a unique set of functionalities that should be considered. Additionally, setting up a data warehouse without the proper expertise or help can take a lot of engineering hours.
Obviously, this is something that shouldn’t be done in a hurry. In the process, you need to consider a number of factors when selecting the right data warehouse solution for your business.
Take a look below and you will see what factors should be considered when picking your new data warehouse solution.
A data warehouse is a system designed to aggregate your data from multiple sources with the aim of making it easy to access and analyze. Data warehouses usually store ample amounts of historical data that can be queried by data engineers and business analysts for the purpose of business intelligence.
With a data warehouse, one doesn’t need to access any chunk of data in individual sources. Instead, you can get your warehouse management system software designed to funnel all of the data from disparate sources into one place. As soon as the data is in the warehouse, it becomes accessible and usable across the business, which is a great way to get a holistic view of the customers.
When all of the data is in one place, one can analyze related data from different sources, devise better predictions, and ultimately make better business decisions.
The database and data warehouse technologies come down to two key database types. These are relational (SQL) and non-relational (NoSQL) types. You should know that the differences between these are rooted in the way they are designed, which data types they support, and how they store them.
Knowing the exact differences between the NoSQL vs SQL databases will help you make the right decision regarding the database for your business.
SQL databases are relationally structured entities representing a real-world object most of the time. For instance, these can represent a person or shopping cart details.
On the other hand, NoSQL databases are document-structured and distributed, which means that they hold information in a folder-like hierarchy that stores the data in an unstructured format.
Now that you are familiar with the main types and fundamentals of database and data warehouse technologies, let’s go through various factors that you should consider when choosing a data warehouse solution for your business.
The vast majority of data warehouses allow users to store huge amounts of data without a lot of overhead costs. The chances are that you probably won’t need more than what they offer, especially if analytics is the primary purpose, which is the case for most businesses.
However, you should consider this. Find out how a certain warehouse you are considering scales data storage during times of demand.
For instance, Amazon Redshift will require users to manually add more nodes (the basic structures in data warehousing that store data and execute queries) when they need more storage and computing power.
On the contrary, a warehouse such as Snowflake offers an auto-scale function that adds and removes clusters of nodes dynamically as necessary.
The term performance in relation to a data warehouse refers to the speed of your queries and how you can maintain that speed during times of high demand. So, it is clear that scaling for performance and data storage are closely connected. After all, performance, just like storage, will increase as you scale up the nodes in your data warehouse.
Bear in mind that speed will not be a problem for you as almost every warehouse is as fast as the others. However, you should consider factors that deal with the amount of control you want over your speed.
You should be able to add and remove nodes for faster queries. For some warehouses, that can be done manually, but you will be able to tune it to your preferences. For others, the process will happen automatically.
There are high chances that you will need to have your engineers focused on building and maintaining your products and not worrying about ETL pipelines and daily management of your business’s data warehouse, especially if you have a small team.
If that is the case, you should look for a self-optimizing data warehouse and one good example of such a warehouse is IBM Db2.
However, by maintaining your warehouse manually, experienced data warehouse architects can have better control and flexibility to optimize it precisely for your company’s needs. Make sure to remember this if you ever want a high level of control over your warehouse’s performance and cost.
It is important to consider going for a data warehouse that is within the ecosystem of the applications you already have in use.
For instance, Azure Synapse Analytics belongs to the ecosystem of Microsoft products, Redshift is within AWS, and BigQuery is within the Google Cloud ecosystem.
Logically, this will make the implementation smoother since you already have an infrastructure in place. Fail to do so, and you will need your engineers to develop multiple customer ETL pipelines to get the data where it needs to be.
Also, you may still need to write a customer ETL to get data into your warehouse from various data sources. Just remember that you should minimize that work as much as you can.
Last but certainly not least, you should know that a platter of factors go into data warehouse pricing. This includes storage, warehouse size, run time, and queries.
For example, for Redshift, you pay per hour based on nodes or per bytes scanned. BigQuery, on the other hand, has both a flat-rate model and a per-query model. Additionally, Snowflake, IBM Db2, and Azure are all based on storage and compute time.
In the end, you should pick a data warehouse that can do what you need it to do instead of going for the cheapest option.
To recap everything, when choosing a data warehouse, consider the type of data, how dynamically you need it to scale, how fast you need your queries, whether you want manual or automatic maintenance, the cost, and the compatibility of the data warehouse with your current tech stack.
When you take all these factors into account, the chances of you not selecting a decent option are close to none.
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