How can you use different data warehouse architectures to improve your data engineering?

1 Kimball vs Inmon

Kimball and Inmon are two of the most influential and popular data warehouse architectures, named after their creators Ralph Kimball and Bill Inmon. They differ mainly in how they approach data modeling and data integration. Kimball advocates for a bottom-up approach, where data is first organized into dimensional models (also known as star or snowflake schemas) based on business processes and then integrated into a data warehouse. Inmon advocates for a top-down approach, where data is first integrated into a normalized relational model (also known as an enterprise data model) and then derived into dimensional models for specific purposes. Both architectures have their pros and cons, depending on the complexity, scope, and purpose of your data warehouse.

2 Data Vault

Data Vault is another data warehouse architecture that aims to address some of the limitations and challenges of Kimball and Inmon, such as scalability, flexibility, and agility. Data Vault is based on a hybrid model that combines aspects of both normalized and dimensional models. It consists of three types of tables: hubs, links, and satellites. Hubs store the unique identifiers and metadata of business entities, such as customers or products. Links store the associations or relationships between business entities, such as orders or transactions. Satellites store the historical attributes or details of business entities or relationships, such as names, addresses, or prices. Data Vault enables you to capture and store all the data from various sources without losing any information or history, and also allows you to easily adapt to changes in business requirements or data sources.

3 Data Lake

Data Lake is not exactly a data warehouse architecture, but rather an alternative or complementary approach to data warehousing. Data Lake is a concept that refers to storing raw and unstructured data from various sources in a single repository, usually on a cloud platform or a distributed file system, such as Amazon S3 or Hadoop. Data Lake allows you to store any type of data, such as text, images, audio, video, or sensor data, without imposing any schema or structure on it. Data Lake enables you to explore and analyze data in its original form, using various tools and techniques, such as SQL, Python, Spark, or machine learning. Data Lake can also serve as a source for data warehouse or data mart, where you can apply transformations and validations to create structured and curated data sets.

4 Lambda vs Kappa

Lambda and Kappa are two data warehouse architectures that focus on how to handle streaming or real-time data, which is becoming more and more prevalent and important in data engineering. Streaming data is data that is continuously generated and consumed, such as web logs, social media, or IoT devices. Lambda architecture is based on the idea of having two layers or paths for processing streaming data: a batch layer and a speed layer. The batch layer is responsible for ingesting, storing, and processing data in batches, using traditional data warehouse tools and methods, such as ETL, SQL, or OLAP. The speed layer is responsible for ingesting, storing, and processing data in real-time, using streaming data tools and methods, such as Kafka, Spark Streaming, or Storm. The batch layer and the speed layer are then combined or reconciled to provide a consistent and accurate view of the data. Kappa architecture is based on the idea of having only one layer or path for processing streaming data: a stream layer. The stream layer is responsible for ingesting, storing, and processing data in real-time, using the same tools and methods as the speed layer in the Lambda architecture. The stream layer can also perform batch processing by replaying or reprocessing the data from the beginning or from a certain point in time. Kappa architecture simplifies the data pipeline and eliminates the need for maintaining and synchronizing two layers, but it also requires more robust and reliable streaming data tools and methods.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?