Modern data warehouses simplify consolidating information for easy access and analysis.
However, not all data warehouses are made the same.
You’ll need to determine your needs and the best-fitting data warehouse software’s features and functionalities to address your requirements.
Fortunately, we got you covered.
In this guide, we’ll look into five critical factors and features to look out for when comparing and finding the right data warehouse for you.
Data warehouse: A quick overview
A data warehouse is a system that aggregates your information from multiple sources into one central location for quick and easy access, management, and analysis.
Data warehouses generally store massive volumes of historical information that data engineers and business analysts can query for Business Intelligence (BI) purposes.
Instead of having separate access to your individual data sources, data warehouses funnel all your information into one place. This includes relational and operational databases, transactional systems, and even various types of company data-savvy investors use.
This makes accessing and using data across your business seamless, allowing you to gain a holistic view of your customer, company, and other data efficiently.
A data warehouse puts your data in one place, simplifying analyzing related data from multiple sources. This helps you make better data predictions and, in turn, business decisions.
6 Critical data warehouse factors and features to consider
Data warehousing allows you to answer tough analytical questions and extract relevant insights that might not be possible with standard data analytics tools alone.
Consider these factors when comparing potential data warehouses that best address your company and user needs.
1. Data storage scaling capabilities
Most data warehouses generally let you store massive volumes of data without expensive overhead costs.
If your main purpose for the data warehouse is analytics, you’re not likely to need more storage than what the system already offers.
However, it’s critical to consider how a specific data warehouse scales your data storage during high-demand situations.
For instance, Amazon Redshift requires you to manually add more nodes (the basic data warehousing structures that execute queries and store data) if you need more computing power and storage.
On the other hand, other data warehouses provide auto-scale functions that remove and add clusters of nodes dynamically when necessary.
For example, in a battle between Redshift vs. Athena, Redshift provides a more scalable option since it can handle larger datasets and huge amounts of transactions.
Anticipate the storage capacity your business needs and opt for a data warehouse that can scale with your requirements.
2. Data types
Ensure your data warehouse can support the data types you want to store for your business. This includes:
- Structured data. This is the type of data you can quantify and organize into columns and rows, such as customer contacts and sales records.
- Semi-structured data. This refers to a mix of structured and unstructured data. For instance, emails have unstructured content, but you can quantify certain email aspects, such as the sender, the date the email was sent and opened, etc.
Multimedia assets such as images stored in your Content Management Software (CMS) are also considered unstructured. However, you can quantify data such as the device type, geotags, photo size, and the time the photo was taken.
- Unstructured data. This data is often challenging to manage and analyze. Common examples of unstructured data include written content such as blog posts, videos, PDFs, and audio files.
While most data warehouses can support structured and semi-structured data management, you’re better off using data lakes for unstructured data.
Also, if you’re dealing with more semi-structured data, opt for data warehouses with the best infrastructure to support queries and storage for handling semi-structured data seamlessly.
3. Maintenance requirements
Your data warehouse should help your team and data engineers focus on building and maintaining your products.
This can streamline your workflows and help your team drive results faster instead of spending most of their time and energy on Extract, Transform, and Load (ETL) pipelines, managing data flows from your digital marketing channels, and other daily data warehouse management tasks.
Compare your top data warehouse choices and assess which ones can optimize your maintenance processes.
For example, some data warehouses offer self-optimizing features, so you won’t need to do it manually.
However, if your data engineers and analysts want more flexibility and better control over your data warehouse’s cost and performance, you might want to stick to manual maintenance.
Essentially, you’ll need to determine your maintenance requirements and assess whether to choose a data warehouse with manual or automated maintenance features.
4. Scaling for performance
Generally, a data warehouse’s performance refers to how fast your queries can run and how you can maintain that speed during high demand.
Like storage, the performance increases as you scale up the nodes within your data warehouse.
While most modern data warehouses generally offer similar performance in terms of speed, the key differentiator is how much control you want over that speed.
Opt for a data warehouse that can scale with your needs.
For instance, some data warehouses allow you to remove or add nodes for faster queries. You can do this manually with better control and flexibility, or you can choose a data warehouse that automates the process.
Simplify your implementation by choosing a data warehouse that works within your current applications’ ecosystem.
This eliminates the need for your data engineers to establish multiple custom ETL pipelines for seamless data flows since you already have the necessary infrastructure in place.
While your engineers might still need to write custom ETL to pull data from specific data sources such as dependable product management tools to your warehouse, opting for a data warehouse that works well within your existing system can save your team boatloads of time and energy.
A data warehouse’s pricing can vary depending on storage capacity, run time, warehouse size, and queries.
For example, some data warehouses require you to pay per hour based on per bytes or nodes scanned.
Other data warehouses offer a per-query and flat-rate model, compute time and storage.
Like how you would purchase articles for their quality and cost-efficiency for your content marketing efforts, avoid choosing the cheapest option and select a data warehouse that fits your budget and needs instead.
Choose the best data warehouse for you
Finding the right data warehouse takes research and in-depth comparison of your top choices to narrow down your options.
Know what your business and team need and choose a best-fitting data warehouse that can address those requirements while offering cost-efficiency.
Start your search with the data warehouse factors and features in this post.