If you’ve been exploring other aspects of the data analytics pipeline, you’ll most likely encounter data warehouses at some point. These tools are essential for storing and managing large amounts of data in a structured and accessible manner.
With the rapid growth of big data, there has also been an increase in interest in data warehouse tools to store all this data for downstream analysis.
In this article, we will explore seven of the best data warehouse tools that you should consider using in 2024.
Table of contents:
- What is a data warehouse?
- Why use data warehouses?
- Who uses data warehouses?
- Snowflake
- Amazon S3
- Google BigQuery
- Databricks
- Amazon Redshift
- Oracle Autonomous Data Warehouse
- PostgreSQL
- Next steps
Let’s explore this list further below.
1. What is a data warehouse?
A data warehouse is a central repository that stores large amounts of data from various sources in a structured and organized manner. It allows for efficient querying, analysis, and reporting of this data.
Types of data warehouses
Data warehouses can be split into several types based on the type of data stored.
Here are some examples:
- Enterprise data warehouse (EDW): Stores all enterprise data in one central location
- Operational data store (ODS): Stores real-time data that is frequently accessed and updated
- Online analytical processing (OLAP): Used for complex analytical queries on large datasets
- Data mart: A subset of a data warehouse that is focused on a specific department or area within an organization
Now, you must be wondering—what is the purpose of having them over databases or Excel flat files?
I’ll explain more below.
2. Why use data warehouses?
Data warehouses are used for a variety of purposes, but the primary reason is to store and organize data in a central location. This allows for faster and more efficient analysis of large datasets.
Other benefits include:
- Improved data quality: Data warehouses often have processes in place to ensure data integrity and consistency
- Historical data storage: Data warehouses can store large amounts of historical data, allowing for trend analysis and forecasting
- Data accessibility: Data warehouses make it easier to access and query data from various sources in one location
3. Who uses data warehouses?
Data warehouses are used by organizations of all sizes and industries to store and manage their large datasets. Most data professionals will be interacting with data warehouses but for different purposes.
Some examples of data professionals that use them are:
- Data analyst: Query data warehouses and analyze the data for insights.
- Data engineer: Build and maintain the infrastructure for data warehouses.
- Business intelligence analyst: Use data warehouses to create reports and visualizations for business stakeholders.
- Analytics engineer: Create and optimize data pipelines to load data into the warehouse.
Companies tend to use data warehouses to store large amounts of data from multiple sources, loaded in from sources that contain customer data, sales information, and financial records, for example.
In fact, many companies have also chosen to explore other forms of the data warehouse, such as the data lake and the data lakehouse.
4. Snowflake
Snowflake is a cloud-based data warehouse platform that offers a fully managed environment with automatic scaling and concurrency. It’s known for its ease of use, security, and speed.
Some key features of Snowflake include:
- Multi-cluster architecture: Allows for scalability and separation of compute and storage layers
- Virtual warehouses: Can be created on-demand to handle different workloads in parallel
- Data sharing: Allows for the secure sharing of data between organizations
It uses a cloud-centric approach that ensures seamless scalability and concurrency.
With a unique architecture that segregates storage and computing, Snowflake offers a pay-for-what-you-use pricing model, ensuring cost-effective resource management.
Snowflake is also known to be a common tool used in the modern data stack, integrating well with popular data tools such as dbt, Tableau, and Looker.
5. Amazon S3
Amazon S3 is a highly-scalable, object-based storage service provided by Amazon Web Services (AWS). It’s often used as a data warehouse for storing large amounts of data in its native format, making it incredibly flexible..
Some key features of Amazon S3 include:
- Scalability: Can store any amount of data and handle millions of requests per second
- AWS integrations: A rich ecosystem of integrated services for data processing and analytics
- Cost-effective: Pay-for-what-you-use pricing model
It is a robust and versatile data warehousing solution designed for scalability and durability.
It excels in providing a secure, high-performance backbone for storing and retrieving any amount of data.
Amazon S3 is best suited for organizations that already use Amazon in their tech stack, such as AWS EC2 or Amazon EMR.
6. Google BigQuery
Google BigQuery is a serverless, highly-scalable data warehouse offered by Google Cloud. It’s known for its speed and ease of use, making it an excellent option for organizations that require real-time insights from their data.
Some key features of Google BigQuery include:
- Fast querying capabilities: Can process petabytes of data in seconds.
- Automatic scaling: No need to manage computing resources, as it is done automatically.
- Machine learning capabilities: Can perform advanced analytics and machine learning on the data stored in BigQuery.
As a cloud-native solution, Google BigQuery offers a seamless integration with other Google Cloud services on the Google Cloud Platform and third-party tools.
This means that data transfers from Google Analytics and Google Ads are much more efficient, thus enabling a holistic view of data across various marketing channels.
BigQuery is also a known tool in most implementations of the modern data stack too.
7. Databricks
Databricks is a unified data analytics platform that combines data engineering, data science, and business analytics in one collaborative workspace. It is optimized for large-scale data processing and real-time analytics.
Some key features of Databricks include:
- Spark-based engine: Allows for fast data processing and can handle complex queries
- Unified analytics platform: Streamline workflows from ETL to model training and deployment.
- MLflow: An open-source platform to manage the complete machine learning lifecycle.
- Real-time analytics: Process streaming data in real time with structured streaming.
- Collaborative notebooks: Enable data teams to collaborate using shared Databricks notebooks and languages.
- Databricks SQL: Run SQL queries rapidly and gain insights with optimized execution.
- Data governance: Built-in robust security controls and compliance standards.
Databricks’ compatibility with multiple programming languages like Python, SQL, R, and Scala ensures its accessibility for a broad range of data professionals.
8. Amazon Redshift
Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse offered by Amazon Web Services (AWS). It’s mainly optimized for querying and analyzing structured and semi-structured data.
It can quickly analyze and report on large datasets, making it suitable for organizations that need quick insights from their data.
Some key features of Amazon Redshift include:
- Columnar storage: Organize data in a columnar format to optimize query performance.
- Massively parallel processing (MPP): Distribute queries across multiple nodes to process them in parallel, resulting in faster performance.
- Integrations with BI tools: Can be easily integrated with popular BI tools such as Tableau, Looker, and Power BI.
- Pay-for-what-you-use pricing: Only pay for the resources used to run queries.
- Automatic backups and recovery: Automatically backs up data to S3, making it easy to recover in case of an outage.
Startup companies tend to like using Redshift as they can start small and scale up as needed, adjusting their usage to match their budget and data requirements.
Its integration with AWS ecosystem services like S3, DynamoDB, and Elastic MapReduce further empowers users to blend traditional data warehousing with big data analytics for a holistic insight generation workflow.
Overall, Amazon Redshift is a good data warehouse option if you already use AWS in your data stack or engineering tech stack or if you need a highly-scalable solution for large datasets.
9. Oracle Autonomous Data Warehouse
Oracle Autonomous Data Warehouse is a cloud-based data warehouse service offered by Oracle. It is designed to automate and simplify the process of creating, securing, and managing a data warehouse.
Some key features of Oracle Autonomous Data Warehouse include:
- Autonomous operations: Uses machine learning to automatically tune performance, optimize storage, and secure data.
- High performance: Can run complex queries in parallel for faster processing.
- Elastic scalability: Can scale compute resources up or down based on workload, reducing costs for idle resources.
- Built-in machine learning algorithms: Allows for advanced data analysis and predictive modeling.
- Low-code app development platform: Users can build and deploy custom applications on the data warehouse without using much code.
Oracle Autonomous Data Warehouse is suitable for organizations that require a robust, enterprise-level data warehouse with advanced machine learning capabilities.
It’s also a good option for organizations that use Oracle databases and want a fully automated solution with high performance and scalability capabilities.
10. PostgreSQL
PostgreSQL is an open-source, relational database management system that is highly customizable and extensible. It supports both structured and unstructured data and offers a wide range of features for data warehousing.
Known for its robustness, extensibility, and SQL compliance, it has stood the test of time as a foundation for many types of data-intensive applications.
As open-source software, it benefits from vast community support and resources, making it an attractive option for organizations of various sizes.
Some key features of PostgreSQL include:
- ACID compliance: Ensures data integrity and consistency.
- Multi-version concurrency control (MVCC): Enables multiple users to access and update the same data without interference.
- Data types for structured and unstructured data: Can store JSON, XML, and other non-relational data in addition to traditional relational data.
- Extensions and plugins: Offers a wide array of extensions and plugins to extend functionality, such as data warehousing features like PostgreSQL Data Warehousing by Citus.
- High availability: Supports high availability configurations for automatic failover and data protection.
- Scalability: Can scale horizontally by adding more servers, allowing for the handling of large datasets.
Overall, PostgreSQL is a reliable and versatile choice for organizations looking for an open-source, customizable, and scalable data warehousing solution.
11. Next steps
To sum things, up here are some points to take away from this article:
- A data warehouse is a central repository that stores data from various sources.
- Data warehouses help to organize data for faster and more efficient analysis of large datasets.
- Common data warehouse tools used are: Snowflake, Amazon S3, Google BigQuery, Databricks, Amazon Redshift, Oracle, and PostgreSQL.
These tools are some of the most commonly used among data analysts and data engineers. If you’re keen on a career in data, you might want to pick up knowledge on one along the way.
So, now that you’ve learned about various data warehousing solutions, what’s next?
Here are a few steps you can take to continue your learning journey:
- Research about data analytics tools: Familiarize yourself with popular data analytics tools.
- Learn data analytics: Try out CareerFoundry’s free, 5-day data analytics short course that covers all the basics.