Data is ubiquitous, and especially in businesses. However, the data is usually not all available together in one system, but is distributed across a complex system and application landscape. To enable comprehensive analyses, the data must be reliably collected, processed, transformed and integrated with each other. That’s where data engineering comes in.
The foundation for data analytics, BI, machine learning, and AI
What is Data Engineering?
Data engineering is the discipline that deals with the development, construction and maintenance of robust and scalable data pipelines. It’s about extracting data from different sources (ETL – Extract, Transform, Load), cleaning it, validating it, and storing it in a format that’s easily accessible to data scientists, analysts, and other users.
The role of the data engineer is responsible for the complete implementation of the data path from extraction from the source system to the provision of an efficient integrated data model that then meets the requirements of the users.
Goals of Data Engineering
As the name suggests, “Data Engineering” belongs to the field of engineering. It depends on a variety of aspects that an implementation must meet:
- Reliable data: Data engineering ensures that your data is accurate, complete, and consistent.
- Scalability: Robust data pipelines can easily handle growing amounts of data.
- Efficiency: Automated processes save time and resources.
Diverse challenges
To master all the aspects of data engineering, there is a wide range of technologies, tools and process models available both technically and conceptually.
You have to choose between classic graphical tools such as Talend, Informatica and between more recent approaches such as dbt.
Ultimately, it is crucial to have a goal vision in mind and to find the necessary pieces of the puzzle and put them together correctly.
Typical processing stages
In data engineering, a multi-layered architecture has now also become established, e.g. “Bronze”, “Silver”, “Gold”. In each layer, the usefulness and thus the value of the data increases through the following processing steps:
1. Extraction
In a first step, the data must be extracted from the source system or provided by a direct connection.
This raw data corresponds to the “Bronze Layer” in the terminology above.
2. Transformation
As soon as the data from different systems is available, it must be processed. The aim is to simplify the data to the essential information, to maintain sufficient quality, and, if necessary, to standardize the different terminology of the various source systems.
The result of this work is often referred to as a “silver layer”.
3. Integration
Finally, the data must be integrated into a common model. There are different approaches to this, such as the fully integrated model of a DWH or rather loosely coupled models in a data mesh. As a rule, only this level meets the qualitative requirements for BI and reporting.
This last layer is often referred to as the “gold layer”.
How dimajix helps your business
As an expert in the field of big data, dimajix supports you in developing a uniform solution strategy to master the challenges of digital transformation
Concept and architecture
Together, we define the optimal data architecture for your company, whether it is a classic data warehouse, a modern data lake or a decentralized data mesh architecture.
Consulting and optimization
We analyze your existing data infrastructures and identify potential for improvement. We also help you at short notice with the analysis and elimination of specific performance problems.
Technology expertise
Benefit from our many years of versatile experience in many areas for the selection of technology. Among other things:
- Hadoop Ecosystem, including Hive, Spark, Kafka, HBase etc
- Trino & Starburst
- dbt
- Azure SQL / SQL Server / Postgres
- Prometheus, Grafana, Graylog
- and much more…
Implementation
We actively support you in realizing your vision in the field of platform and data engineering. This includes support in setting up a suitable infrastructure as well as the implementation of concrete data processing pipelines.
