Top 6 Data Warehouses and Best Picks for a Modern Data Stack
Data analysis has become an essential component of business in the Information Age. For the better part of the last two decades, businesses have been investing in data collection resources, and they now have access to massive amounts of data across multiple platforms. Today, the difficulty is not gathering data but determining what to do with it. This is where a data warehouse can help.
As organizations strive to make the best use of their data, data warehouse solutions are becoming increasingly important. However, choosing the best data warehouse for your needs can be difficult, as numerous options are available.
Continue reading to learn more about best practices for data warehousing and how to find the best tool for your company’s needs.
What is a data warehouse?
A data warehouse is a more sophisticated and structured database. Yes, it stores your data and provides context, history, analysis, organization, and possibly AI parsing.
These additional features make data warehouses an efficient way to store large amounts of data. And by vast, we mean data pools that exceed terabytes in size. Businesses collect petabytes of data from their teams and customers’ apps, communications, and services.
For many businesses, this data is currently going to waste. The immense value it can provide is overshadowed by its tremendous size, rendering it unusable. A data warehouse can help solve this challenge and support big data analytics efforts at your company.
When should you use a data warehouse instead of a database?
There’s nothing inherently wrong with databases. However, a database is too simple for most businesses to be useful for business intelligence, mainly when a company pulls data from multiple sources. A plain text editor can be used as an integrated development environment (IDE) as a purpose-built IDE can.
Data warehouses are designed for the modern age. They can collect data from various sources, such as internal databases, third-party apps, and services, customer support systems, diagnostics, etc. This data is saved and kept safe (like in a database) and is also structured, organized, and analyzed in helpful ways.
In short, a database is valuable when you only need a place to store your data. A data warehouse is a way to go when you need to store large amounts of data from various sources and work with that data.
What makes cloud data warehouses the best choice?
Once you’ve determined that a data warehouse will add value to your business, you must determine which type of data warehouse is best. Traditional data warehouses and cloud data warehouses are the two main types.
Traditional data warehouses
Traditional data warehouses are typically built by investing in physical computing hardware (think rooms filled with blinking lights and server racks) and IT personnel. These data warehouses have become the industry standard for a reason: they keep your data on-site and can make you feel more secure.
This traditional data warehouse structure, however, has drawbacks. Server rooms consume space. And as your business expands, investing in new servers and keeping existing equipment up to date can become costly.
Cloud data warehouses
Like so many other things, data warehouses have begun to migrate to the cloud. Customers can access data storage solutions from large corporations such as Google and Amazon through the internet. These data warehouses have several advantages, including keeping your data up to date in real-time.
Selecting a real-time, cloud-based data warehouse allows you to begin managing your company’s data immediately. You’re ready to go with just a few clicks. Furthermore, cloud data warehouses scale with you, allowing you to quickly scale your business and your data storage and management simultaneously.
The 6 best data warehouses
Are you ready to invest in a solution but unsure which data warehouse to use? These are the top six data warehouse platforms on the market and some of the key advantages of each.
It’s one of the most modern data warehouses, and one of its main selling points is its flexibility.
Snowflake is cloud-agnostic, which means it can be deployed on any cloud platform, including AWS, Azure, and Google Cloud. That’s great news for many businesses! You can begin using Snowflake immediately after manually transferring your data or using an ELT tool like Weld. It allows for nearly limitless data storage, sources, and concurrent users.
Snowflake is one of our top data warehouse recommendations, with BigQuery as the only viable alternative. The separation of storage and computing simplifies capacity management and ensures quick response times for all warehouse workloads.
2. Google BigQuery
Google BigQuery is Google’s data warehouse offering. It is similar to most of Google’s other software products: It’s entirely cloud-based, free (up to 10GB), and extremely simple to use.
Apart from integrating with the rest of Google’s services, one of BigQuery’s main selling points is its analytic capabilities. Google’s ability to work with large amounts of data cannot be overstated, and BigQuery is no exception. It includes features such as predictions, insights, and intelligence, making it a scalable and long-term solution.
Because of this, BigQuery is an excellent warehouse for those constructing a Modern Data Stack. And, if you’re looking for a plug-and-play solution for a Modern Data Stack that integrates with Google BigQuery, Weld could be the answer.
3. Amazon Redshift
Amazon Redshift was one of the first cloud data warehouses to launch in 2012, and it has played an essential role in developing the data warehousing industry. Amazon, like Google, will not be left behind in any digital sector. And there are few better solutions available for businesses. Amazon Redshift can support exabytes (one billion gigabytes) of data, allowing for nearly limitless data storage.
However, they have lagged in development and have only recently begun to make efforts to separate computing and storage — a feature that Snowflake and BigQuery already have. Redshift is an AWS product, a popular cloud platform among large enterprises. However, because it is a more technical platform, it necessitates a team capable of integrating and managing your Redshift data warehouse.
4. Azure Synapse Analytics
Azure Synapse Analytics, formerly Microsoft Azure SQL Data Warehouse, is Microsoft’s data warehouse solution. Because of its simple integration with Microsoft SQL server, this cloud data warehouse is ideal for organizations looking for an easy on-ramp into cloud data warehouse solutions.
Dynamic Data Masking (DDM), which adds a layer of security by masking sensitive data from non-privileged users, is one of its key differentiators. Regarding product features, Azure Synapse Analytics provides:
- A unified analytics platform.
- A choice of the query language.
- End-to-end data monitoring in addition to enterprise data warehousing.
One thing to remember is that Azure Synapse Analytics is an excellent data warehousing solution if you already use the Microsoft suite of business tools. It does not, however, integrate as well with third-party tools as other data warehousing solutions.
5. IBM Db2 Warehouse
The Db2 warehouse on the cloud is IBM’s answer to the modern cloud data warehouse. It is well-known for its dependability, transaction control, and high availability. It also uses IBM’s Netezza technology, which provides users with advanced data lookup capabilities.
It’s an excellent choice for businesses that want to integrate with other IBM tools and Oracle products. IBM Db2 is designed for enterprise use, like SAP Data Warehouse or Oracle Autonomous Data Warehouse. As a result of its high price point and limited usability features, we do not recommend Db2 for small businesses just to get started with cloud data warehousing.
Firebolt is another major player in data warehousing and is popular among both Data Engineers and Data Analysts. The primary focus of Firebolt is speed, and their order-of-magnitude performance distinguishes them from the competition.
Firebolt, designed for everyday use, can handle semi-structured data or datasets that fall between fully structured and unstructured. Firebolt claims to be designed for data lake scale volumes, and its decoupled storage and compute architecture allows for easy scalability.