Data modeling in Databricks: maximum efficiency in data analysis

Data modeling in Databricks

Rittal, one of the world’s leading manufacturers of industrial enclosures, operates one of the world’s most modern enclosure production facilities in Haiger, Hesse, and is currently embarking on the path to the Smart Factory. The “ONCITE Digital Production System” collects all plant, product and production data. Subsequently, they are harmonized, analyzed and made transparent. This creates a virtual image of manufacturing to optimize processes to the maximum. In Haiger, 18 terabytes of data are thereby created every day. Knowing which ones are really important is a challenging task.

On the one hand, data has become the currency of the 21st century; on the other hand, the challenges of processing, harmonizing and analyzing this data correctly are increasing in sync with the growing volume of this data. Because: In today’s data-driven world, the effective use of data is the decisive factor in gaining valuable insights. Without the proper tools and techniques for data modeling and analysis, it becomes difficult to make efficient use of the data. A most popular platform that helps companies with data modeling and analysis is Databricks. In In this blog post we have already introduced Databricks to you, today we would like to give you a detailed insight into data modeling in Databricks and how Databricks helps maximizing the efficiency of data analysis.

What is Databricks?

Databricks is a cloud-based data platform built on Apache Spark and designed to facilitate the management and analysis of Big Data. Databricks provides an integrated development environment (IDE) and tools for collaboration and automation of data-related tasks, including data engineering, data science, and machine learning. The platform can also be used with all major cloud providers such as AWS, Microsoft Azure or Google Cloud Platform. With Databricks, users can create data pipelines, train models, or even perform complex data analysis. One of the most important features of Databricks is data modeling, which allows data to be structured and organized to facilitate its analysis and evaluation.

Why is data modeling important?

Data modeling plays a crucial role in the efficient analysis of data and is important to…

… map data in a structured and organized way. This in turn facilitates efficient storage, management and search for information in a database. A well-designed data model can help avoid redundancies and inconsistencies in the data.

… define integrity rules that ensure that data is stored in a consistent and valid form. By specifying relationships between data objects, data integrity rules, such as foreign key relationships, can be implemented to ensure data consistency.

… facilitate the performance of data analyses and queries. Data can be retrieved and analyzed in a structured way using query languages such as SQL. A data model that accurately represents the relationships between data objects enables complex queries and analysis.

… facilitate communication between the various stakeholders in a project, including database developers, database administrators, and business experts. A data model provides a common language to talk about the structure and behavior of data. By visualizing the data model, all stakeholders can gain a common understanding of the data and its relationships.

… support the design process of a database. Data models help identify the required data objects, define attributes, and specify relationships between data objects. A well-designed data model lays the foundation for an efficient database structure and enables effective implementation.

Data modeling in Databricks

In Databricks there are different approaches to data modeling, depending on the requirements of the project. We will take a closer look at some of them below:

  1. Databases and tables:

Databricks offers the possibility to create databases and tables to organize data logically. Databases can be considered as containers for related tables, while tables contain structured data. By using databases and tables, users can categorize data, which facilitates data retrieval and analysis.

  1. Delta Lake:

Delta Lake is an advanced data management layer based on Apache Parquet. It provides ACID (Atomicity, Consistency, Isolation, Durability) transactions, versioning, and upserts for Big Data workloads. With Delta Lakes, data engineers can set up robust data pipelines and efficiently track data changes.

  1. Schema Evolution:

In Databricks, schema mutations can be easily managed. The schema of a table can be extended or changed without affecting existing data. This in turn enables easy adaptation to changing business requirements and data structures.

  1. Data Skew Handling:

Databricks provides mechanisms to handle data skew, a condition where certain data partitions have disproportionately large sizes. By applying techniques such as data shuffling and data repartitioning, data skew can be efficiently reduced to improve data processing speed.

  1. Performance Optimization:

Databricks offers several features to optimize data processing performance. For example, the use of caching can reduce latency, while the use of Z-order indexing and clustering techniques can significantly improve query performance.


Data modeling is a critical step in performing efficient data analysis. Databricks provides powerful data modeling tools and techniques that help organizations optimize their data assets, accelerate data analysis, and gain valuable insights. By carefully designing data architecture and applying best practices, organizations can use the full power of Databricks to successfully drive their data-driven initiatives.