Top 6 Popular Data Warehouse Tools: Feature, Pricing, Benefits

Data warehousing is a non-negotiable for businesses wanting to leverage insights and make informed decisions. A warehouse helps manage and analyze large volumes of marketing and sales data from different sources, giving marketing departments insights into customer behavior and marketing performance.

But making the right choice can be daunting, as there are seemingly endless data warehouse options on the market today. To help simplify this process, we’ve compiled a list of the top warehousing tools. This article explores the features, pricing, and pros and cons of each warehouse and suggests one solution for how to get all the benefits of a marketing data warehouse without dealing with any of the drawbacks

What Is a Data Warehouse?

To understand the function of a data warehouse, we first need to clarify the purpose of one.

In short, data warehouses store and manage vast amounts of data, helping keep that data safe while making it easy to access and organize. A data warehouse serves as a single source of truth that marketing and sales departments use to align teams, build and optimize their strategies, and inform their decisions.

Here are some factors to consider when selecting a data warehouse provider:

Cloud vs. on-premise tools: Cloud solutions are often more cost-effective and require less IT overhead, while on-premise warehouses offer more control and security.
Different use cases: Data warehouses help facilitate analysis, reporting, and business intelligence. Select a tool that is tailored to the specific needs of your company, the data type and the volume you wish to store.
Integrations: Look for a data warehouse tool that integrates with other systems and applications of your MarTech stack. This will ensure that the data can be easily accessed and transferred, making the process more efficient.
Scalability: Your data warehouse needs the flexibility to scale as your business grows. Seek tools with automatic scaling, replication, and backups to ensure smooth operation. To make the right decision, consider how your needs will grow in the upcoming year or two.
Pricing: The cost of a data warehousing solution will depend on the features and usage, so compare different options before deciding.

Top Data Warehouse Tools

The list considers usability, scalability, cost, and integration capabilities of the most popular data warehouse tools. Evaluate carefully and decide which best fits your requirements.

1. Google BigQuery

Google BigQuery is a cloud-based data warehousing solution. — *An example of how to run scripts in Google BigQuery*

As a powerful cloud-based data warehouse, Google BigQuery is an ideal tool for organizations of all sizes to store large amounts of data and easily access it with fast query performance.

Features

The BigQuery serverless architecture eliminates the need for businesses to manage infrastructure. Thus, data engineers and analysts focus on what matters the most: data analysis and insights.

BigQuery has a built-in machine learning capability, allowing you to integrate it with Cloud ML and TensorFlow for powerful AI models. It also can execute queries on petabytes of data in seconds for real-time analytics. BigQuery supports geospatial analytics, which helps users to see the bigger picture of marketing events and make more accurate predictions.

The warehouse integrates seamlessly with other Google Cloud Platform services and popular data visualization and analysis tools, such as Tableau, Power BI, or Looker Studio.

BigQuery supports standard SQL queries, Java, Python, C#, Go, Node.js, PHP, and Ruby client libraries.

Pricing

Pricing for Google BigQuery is based on storage and query costs. Storage is differentiated as active or long-term, with the latter being data stored in partitions that have not been modified in more than 90 days.

The cost for active Google BigQuery storage is $0.020 per GB/month, and long-term storage is $0.010 per GB/month, with the first 10 GB/month being free for both types.

BigQuery offers two pricing models for queries: on-demand and flat-rate. On-demand pricing for Google BigQuery is $5 per TB, with 1 TB free monthly. Monthly flat-rate pricing is billed at $10,000 per 500 slots.

Pros

Fast query performance
The serverless structure paves the way for handling large datasets, making it a scalable solution
Ability to run multiple queries in parallel without any additional setup
Seamless integration with other Google Cloud Platform services, as well as popular data visualization and analysis tools
No need for businesses to manage infrastructure

Cons

Trying to utilize several SQL dialects can be confusing
Data expiration features limitations
A lack of support for updates and deletions
The pay-as-you-go pricing model can become pricey for enterprise companies with heavy data processing needs
A steeper learning curve

2. Amazon Redshift

Amazon Redshift is a fully-managed cloud-based data warehouse. — *Home view in the Amazon Redshift console*

Amazon Redshift is a fully-managed data warehousing service that provides fast query performance and automatic scaling. It's suitable for high-speed data analytics, allowing marketing departments to process petabytes of data in seconds.

Features

The Redshift warehouse supports both row- and column-oriented data storage. With its columnar storage, Redshift can process queries quickly and efficiently, enabling businesses to gain insights in near real-time.

Redshift supports automatic concurrency scaling for executing hundreds of concurrent queries without additional overhead. Teams can optimize their data warehouse performance and reduce operational costs.

The warehouse supports standard SQL queries, while its simple and intuitive interface requires no specialized knowledge or expertise.

Pricing

Amazon Redshift has two pricing structures. The first option starts at $0.25 per hour. But the price can increase depending on how many computers are in a cluster.

The second is a managed store pricing structure, starting at $0.024 per GB of data per month. The price varies between regions and doesn’t include the cost of storing backups.

Pros

High-speed query performance
Support for automatic concurrency scaling
Ability to scale clusters or switch between node types
Advanced analytics features, including machine learning and predictive analytics

Cons

No multi-cloud server options
Not a serverless infrastructure
Unpredictability in query response times: query performance can vary depending on the complexity of the query and the size of the data being processed
Limited query flexibility in handling simple queries

3. PostgreSQL

PostgreSQL is an open-source relational database management system. — *Creating a new database in PostgreSQL*

PostgreSQL is an open-source data warehouse tool that enables users to store and query large amounts of data. It has built-in features such as Multi-Version Concurrency Control (MVCC), allowing users to optimize the performance of their databases.

Features

PostgreSQL is one of the most popular databases designed to process large volumes of data and complex data types and procedures.

PostgreSQL supports SQL and JSON querying, enabling developers to integrate it with their applications. You can also utilize the PostGIS extension, a geospatial data library that allows you to offer location-based business solutions.

PostgreSQL supports advanced SQL features, including subqueries, window functions, and common table expressions, making it a powerful tool for data analysis. On the flip side, PostgreSQL warehouse management requires a lot of IT resources and technical expertise.

Pricing

PostgreSQL is open-source and free to use. However, if you want security patches, maintenance updates, and technical support, you may have to pay for them.

Pros

Open-source and free to use
Easily add more functionality through custom extensions
Supports both SQL and JSON querying
Optimized database performance with MVCC

Cons

Running it locally can be challenging
No automatic scaling capabilities
Requires manual setup and maintenance
A steeper learning curve compared to other data warehouse solutions

4. Microsoft Azure

Microsoft Azure is a comprehensive cloud computing platform. — *An example of a shared data analytics dashboard*

Microsoft Azure is a cloud-based relational database that offers fast query performance and automatic scaling. It employs a node-based system and uses massively parallel processing (MPP), allowing users to extract and visualize business insights much faster.

Features

Azure SQL Data Warehouse is designed with a massively parallel processing architecture, allowing businesses to scale computing and storage resources as their needs grow.

Azure SQL is compatible with hundreds of MS Azure resources, such as Power BI, Azure Active Directory, Azure Machine Learning, and others. It enables you to scale up or down without downtime and run multiple queries in parallel without additional setup.

Azure SQL Data Warehouse supports advanced analytics features, including machine learning and predictive analytics. It allows marketing teams to go deep with their data and derive valuable insights that otherwise would be left unnoticed.

Pricing

Azure SQL pricing starts at $0.52 per V-core/hour, and storage cost is $0.115 per GB/hour with a minimum of 5GB and a maximum of 4TB. Additional charges for backup storage are $0.20 per GB/month.

Pros

Fast query performance and the ability to run multiple queries in parallel
Compatibility with MS Azure resources
Highly scalable architecture and automatic scaling capabilities

Cons

High costs for large databases
Scaling can cause latency issues
A limited set of third-party tooling compared to other data warehousing solutions

5. IBM Db2 Warehouse

IBM Db2 is a cloud-based data warehousing platform with advanced analytics and AI capabilities. — *An example of IBM Db2 database plugins*

IBM Db2 Warehouse is a fully-managed, cloud-based data warehousing solution with a built-in machine learning tool that allows users to train and deploy ML models using SQL and Python.

Features

The platform provides an intuitive user interface or REST API for managing storage and processing power, and the elastic scaling of workloads. And like Microsoft Azure, it supports MPP capabilities, enabling users to execute hundreds of concurrent queries without additional overhead.

IBM Db2 Warehouse supports advanced analytics features, including machine learning, geospatial and predictive analytics. This, together with fast ingest and querying, helps you tap into real-time insights and optimize your strategy on the go.

Additionally, IBM Db2 Warehouse supports a wide range of data sources and provides multiple ways to load and extract data.

Pricing

IBM Db2 Warehouse offers nine pricing tiers, ranging from Flex One (the most basic tier) to Elastic Compute. Cost starts at $0.68 per instance/hour and varies depending on the number of nodes in a cluster.

Pros

Easy integration with other tools
High-speed query performance
Supports automatic concurrency scaling
Ability to scale clusters or switch between node types

Cons

Can be slow to roll out new features compared to other data warehouse tools
Higher up-front investments for reserved instances
Has a steep learning curve and can be complex to set up and use

6. Snowflake

Snowflake is a cloud-based data warehousing platform. — *An example of using SQL in Slowflake*

Snowflake is a cloud-based data warehouse solution that provides enterprise-grade scalability and security. It uses a multi-cluster shared architecture to separate storage from processing power, allowing users to scale CPU resources based on user activities.

Features

Snowflake also offers integration with PostGIS extension for spatial analysis, SQL and JSON querying capabilities, query optimizers and accelerators, data lake storage integration, and automatic scaling. It supports many external data sources, including Apache Kafka, AWS S3, and Microsoft Azure.

Snowflake separates data storage and processing, making it more flexible and cost-effective than traditional data warehousing solutions. The warehouse is optimized for handling large volumes of data and complex queries, providing fast and reliable performance.

Pricing

Snowflake pricing is based on per-second billing, with a minimum of 60 seconds. Compute costs vary according to the region, platform, and pricing tier. The average compute cost for the Standard tier is $0.00056 per second per credit, while the same is $0.0011 per credit. Storage cost is $0.115 per GB/hour with no additional charges for backup storage.

Pros

Fast query performance
Ability to run multiple queries in parallel
Compatibility with MS Azure resources
Automatic scaling capabilities

Cons

High costs for large databases
Scaling can cause latency issues
Limitations with unstructured data

Marketing Data Storage Free from Data Hassle with Improvado

No matter what data warehouse you go for, data storage setup and management, database design, data modeling, and system administration require extensive technical expertise and upfront investment. If done wrong, it can lead to data inconsistencies, errors, and security violations. Ultimately, your team will be left with reports and dashboards they can't trust, no insights, and lots of issues to resolve.

Delegate data storage management to a professional partner to get all the benefits of a marketing data warehouse without dealing with any drawbacks. By partnering up with Improvado, you cover all data needs of your marketing department, from data extraction and transformation to data storage management and report automation.

Improvado is an advanced marketing analytics solution that helps companies extract data from all their marketing and sales sources, bring it to analysis-ready format, and push it to a desired destination, whether it's a data warehouse, BI, visualization, or analytics tool. Additionally, the Improvado Professional Services team provides data warehouses with deployment and maintenance services.

The team sets up and configures the data warehouse instance of your choice. Improvado manages a warehouse on the client's end to make the process transparent. You reserve full control and ownership of your data.