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.
5. 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.
6. 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.
- 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.
