Today, only 2% of data is being saved and used, and businesses lose over $600 billion a year because of bad data. While those numbers are expected to improve as technology improves, the fact is that most of the data that people generate are either not collected, not organized, or cannot provide value in its current state. That means that, by and large, businesses are not properly storing, using, and analyzing the data that they collect each day.
This is a huge problem, especially in today’s data-driven culture. Your data is only powerful if you can use it, and it is no good to you if you can’t properly organize and analyze it.
For many, this bad data problem comes from choosing the wrong type of data storage and running ineffective analytics as a result. Here, we break down the basics of data warehouses and discuss the important criteria that companies must keep in mind when considering a data warehouse for their business.
In broad terms, a data warehouse (DWH) is a place that stores current and historical data — from marketing, sales, and other departments — in one central repository. That data is kept for later use in:
- Daily business operations like account management
- Data analytics to create business intelligence for decision making
- Maintaining secure records of transactions
- Many other business processes that require detailed knowledge of customers and operational details
Managing Data Warehouses
While traditional operational databases are updated in real-time to maintain accurate, up-to-the-minute data, data warehouses are updated less frequently and are designed to give a historical, long-range view of data. The analytics that is ran in a data warehouse, then, is usually more of a comprehensive view of your company’s history rather than a snapshot of the current state of your business.
Further, since the data in a data warehouse is already integrated and transformed, it allows you to easily compare older, historical data and track marketing and sales trends. These historical comparisons can be used to track successes and failures and predict how to best proceed with your business ventures in order to increase profit and long-term ROI.
Specifically, end users can use the information in their data warehouses to:
- Monitor or modify marketing campaigns
- Manage and improve customer relationships
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Clean and organize company data (learn about the importance of data hygiene)
- Predict future growth, needs, and pain points
- Track, understand, and improve company performance
- Consolidate data from multiple sources.
- Create detailed data visualizations to aid business processes
In short, data warehouses are great for long-range, comprehensive analytics without negatively impacting daily operations, and this kind of analytics is always necessary if businesses expect to improve their strategies and make long-term progress.
The Data Warehouse Options
There are four primary types of data warehouse platforms and many options within each of these categories. These are:
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Traditional Database Management Systems (DBMS): These are usually row-based relational platforms. Dominant brands of this software include IBM DB2, Microsoft SQL Server, Oracle, and SAP.
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Specialized Analytics DBMS: These are usually columnar data stores designed specifically for managing and running analytics. Dominant brands of this software include Teradata, HPE Vertica, and Greenplum.
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Out-of-the-box data warehouse appliances: These typically combine software and hardware with a DBMS pre-installed. In theory, all the customer needs to do is plug it in and turn it on. Dominant brands of this solution include IBM Netezza, Oracle Exadata, and SAP Hana.
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Cloud-hosted data warehouse tools: These data warehouses give you internet-based access to large amounts of data storage. Data can be sent to and stored in the cloud for fast access, backups, and rapid editing for analytics.
Related Reading: Best Data Warehouse Tools
Cloud-Based Data Warehouses
Cloud-based data warehouses are the leading option when it comes to DWH usage. The benefits of being able to access data quickly, remotely, and with larger amounts of storage make it the best option for most companies. Because it can be accessed quickly and from any location, ETL platforms can update that information instantly using change data capture (CDC) processes.
Change data capture uses the change request sent to databases to make updates to a copy of the database in real-time. Cloud services make it possible to automate the process and make extensive analytical processes possible without hurting operations. ETL systems can even use cloud-based data warehouses to create detailed visualizations to use in business leadership decisions.
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Data Warehouses From Major Platform Providers
Major platform providers see the value in this IT infrastructure and make cloud-based data warehouse platforms available to other companies. This essentially offers data warehouses as a service without requiring any hardware. Dominant brands here include Amazon Redshift, Google BigQuery, and Microsoft Azure SQL Data Warehouse.
Each of these are good options. Making a choice for your business is all about figuring out what will work best for your company, your budget, your employees, and your overall structure. Let’s get into what you should look out for and how to choose.
Criteria to Keep in Mind When Choosing a Data Warehouse Platform
Choosing a data warehouse platform is an important decision. Fortunately, the major options have plenty of information available about their services. There are many use cases to help you decide which one is best for your needs. Keep this criterion in mind when choosing your data warehouse option.
1. Cloud vs. On-Premise
The first decision that one has to make when choosing a data warehouse is between cloud and on-premise data warehouse systems, and there are certainly advantages and disadvantages to both.
In a nutshell, cloud services offer:
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Scalability: With no on-premise software or hardware, it’s easy, cost-effective, simple, and flexible to scale with cloud services.
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Low entry cost: With no servers, hardware, IT work, or operational costs, cloud services cost substantially less up-front.
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Connectivity: With cloud services, it is much easier to connect to other cloud services, which means that it’s easier to digest, store and file data.
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Security: Typical cloud providers stay hyper up-to-date with security patches and protocols to keep their host of customers safe and happy.
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Availability/Reliability: If you have a strong provider, cloud solutions can have a very high uptime, which makes them reliable no matter what.
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Redundancy: Cloud systems are unique in that they provide a level of redundancy in case of emergencies. If something happens to the company, data can be restored from the cloud servers located in a safe place somewhere else.
Conversely, on-premise solutions offer:
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Speed: Cloud solutions usually rely on far-away servers, and the time that it takes for data to bounce through those servers and reach the end-user can be unacceptable for many businesses. On-premise solutions don’t have to deal with this problem; with local servers, speed and latency can be better managed, at least for businesses based in one geographical location.
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Security: On-premise warehouses allow organizations to have full control over their security and access, which is a priority for many businesses.
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Availability/Reliability: If you have a good staff and exceptional hardware, on-premise solutions can be highly available and very reliable.
Related Reading: Cloud vs. On-Premise Data Warehouse
2. Vendor Selection
Once you choose between cloud and on-premise systems, there are various vendors to choose from. For on-premise systems, some options include:
- Oracle Database
- Microsoft SQL Server
- IBM DB2
- MySQL
- PostgreSQL
While there are many fantastic options here, we recommend PostgreSQL for its proven track record of reliability, compatibility and open source roots. Of course, in any case, it is important to consider your company’s data types and your existing ecosystem, since you’ll have to choose a data warehouse that’s compatible.
Related Reading: PostgreSQL vs MySQL
For cloud systems, top solutions include:
- Amazon RedShift
- Microsoft Azure SQL Data Warehouse
- Google BigQuery
- Snowflake Computing
Here, Amazon RedShift and Google BigQuery are certainly the biggest players. That said, most recent data shows that Amazon RedShift is the highest-performing cloud solution overall.
Final Thoughts
In the big picture, it’s important to choose a data warehouse solution that will fit in with your business model, your budget, and your existing systems.
A fit with your systems is especially important. A high-quality data warehouse that forces you to change your entire ecosystem may not be worth the added cost. Fortunately, many of the available solutions work with other software options, and you can find solutions that work with any system.
If you’re looking for speed, security, and control, then an on-premise solution may be the best choice for you. Conversely, if you’re looking for low entry cost and easy scalability, you may want to go with a cloud service.
Either way, making the right decision for your company will help you run the effective analytics and BI that you will need for long-term stability and success. Regardless of which option you choose, consider the requirements of your ETL solution first. Your ETL must work with your data warehouse option. Otherwise, you miss out on many of the data mining and visualization benefits that can make your business more successful.
Integrate.io Can Help You Visualize Data
Choosing the right ETL system makes a big difference in how your data warehouse serves your organization. Integrate.io is an ETL system that connects to any data warehouse and extrapolates the data so that you can create visualizations.
Charts, graphs, and other types of data visualizations become possible when you can extract, transform, and load (ETL) your data into any system that you need.
Contact Integrate.io to schedule a demo of the system and to learn more about how Integrate.io can help you turn raw data into refined visual intelligence.
Meta: Choose the right data warehouse to get the most value from data. Learn to choose the right data warehouse and how Integrate.io puts data to use.