Within togaether, we are strong believers of the data Lakehouse concept. Data Lakehouse utilizes the standard Data Warehousing approach, and combines it with all the advantages of Data Lake. Using a Data Lakehouse, you can store/backup your raw data in a governed way in the bronze layer. Clean/enrich, perform Master Data Management and create history in the silver layer and apply business logic in the Golden layer.
Because of this setup, the data Lakehouse concept closes the GAP between business and IT: once the Silver layer is properly filled, data can be explored using SQL commands and new insights can become visible using dashboarding. This approach is for us a great differentiator compared to architectural designs we implemented in the past. Before this concept, we where always in favor of setting up a Data Warehouse acting as the beating heart of a data platform.
Choosing for a Data Warehouse means that a lot of the decisions concerning your data are taken before loading your data into the DWH, decreasing the flexibility of all the available data points.
Last couple of years, we see an increase of possibilities when it comes to ingest, transform and visualize data, which means we can even bring more value to our customers because of all these technological possibilities.
While we fully applaud this evolution, we’d like to underscore in this blog that ingesting, transforming, and visualizing your data doesn’t cover all aspects necessary to become a data-driven company. We observe that an increasing number of companies, once their data is ingested into a platform, often overlook a concept that we believe has become somewhat neglected: the need to model the data when aiming to make it available across the entire organization.
This is exactly the strength of the Data Warehouse: storing your data in such a way the data is presented in the most optimal way, bot from technical as organizational perspective. This is what we call data modeling.
Data modeling plays a crucial role within the field of data engineering for several reasons, each contributing to the overall efficiency, reliability, and adaptability of data systems. Here are some key reasons why we think that data modeling is important:
- Data modeling provides a visual representation of the data structure, including entities, attributes, and relationships. This serves as a blueprint for designing databases that are not only efficient in terms of storage but also aligned with the specific needs and requirements of the business.
- By defining relationships between different data entities, data modeling helps maintain data integrity. This ensures that data is accurate and consistent across the entire system, reducing the risk of errors and discrepancies that could compromise the reliability of analytical results.
- A well-defined data model accelerates the development process. Developers can more easily understand the structure of the data they are working with, leading to faster implementation of data solutions. This efficiency is critical in the fast-paced environment of data engineering where rapid development cycles are often necessary.
- Data modeling serves as a common language that bridges the communication gap between different teams involved in the data pipeline, such as data engineers, data scientists, analysts, and business stakeholders. This shared understanding ensures that everyone is on the same page, fostering collaboration and preventing misunderstandings that can arise from differing interpretations of data structures.
The data landscape is constantly evolving, with changes in technology, business requirements, and data sources. Data modeling allows for the anticipation of potential changes and the incorporation of flexibility into the data architecture. This forward-thinking approach ensures that systems can adapt to new requirements without significant rework, future-proofing the data infrastructure.
At togaether, we fully understand the importance and the need of data modelling when creating data platforms. We have +15 years of experience in concepts like Dimensional Modelling and Data Vault, making us not only a reliable partner when it comes to designing the right architecture, making decisions regarding the correct technology, but also making sure your data model is capable of helping your organization to become data-driven!