What is ETL Data Modeling: Working, Techniques, Benefits and Best Practices
Extract, Transform, and Load (ETL) is a crucial process in data management that ensures data is migrated efficiently from disparate sources to a centralized repository. The process involves extracting, transforming, and loading data between operational systems.
However, to make the most out of the process, you must have a clear roadmap for every step involved. That's where data modeling comes into play.
In this article, we will discuss ETL data modeling, and you will learn what it is, how it works, its benefits, and best practices in detail.
Understanding ETL Data Modeling
ETL data modeling can be broken down into two terms: ETL and data modeling. ETL is a process for centralizing data, and data modeling is the theoretical representation of various data figures, objects, and rules.
Combined, the theoretical representation of the ETL process for storing data in a data warehouse can be defined as ETL data modeling. Data modeling analyzes the structure of data objects and their mutual relations, while ETL applies the rules, inspects them for abnormalities, and loads them into the data warehouse.
Overall, it defines a relationship between the types of data used and how it is grouped and organized in a visual format to make it actionable and easy to understand.
How Does ETL Data Modeling Work?
The first step in ETL data modeling is identifying data sources, which can include APIs, spreadsheets, databases, etc. This involves knowing where data resides, what formats it's in, and any limitations or constraints associated with every data source.
After identifying the sources, you need to perform data extraction. This involves considering freshness requirements, extraction frequency, and extraction methods, which can vary depending on source systems.
The next step is data transformation, which converts the extracted data into a standard format. This step of data modeling for ETL requires the most care and attention to detail, depending on the data source. Some tasks of data transformation are data cleansing, validation, normalization, aggregation, and enrichment.
Lastly, data loading is done in ETL data modeling. Loading is inserting extracted and transformed data into the centralized repository. This step involves considering how to design the target schema, the structure of tables, indexing strategies, and the relationship between data.
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What are ETL Data Modeling Techniques?
Here are some of the key and most common techniques of ETL data modeling to give you a better idea of how it works:
Dimensional Modeling
Dimensional modeling is a data warehousing technique that organizes data into facts and dimensions. Here, facts are numerical measures that represent business events, such as sales, orders, or clicks. Dimensions are detailed attributes that provide context to facts, such as location, date, time, or customer. This modeling technique uses a star or snowflake schema for storing facts and dimensions in separate tables linked by foreign keys.
Data Vault Modeling
Data vault modeling focuses on capturing the changes and history of data over time. It includes three types of tables: hubs, links, and satellites. Hubs store business entities' keys or unique identifiers such as services, customers, or accounts. Links store relationships and associations between entities like orders, transactions, or documents. Finally, satellites store attributes and details of entities and their relationships, including names, addresses, prices, or statuses. This technique is ideal for incremental loading, parallel processing, and data auditing.
Anchor Modeling
Anchor data modeling combines aspects of dimensional and data vault modeling. It allows you to use four types of tables: anchors, attributes, ties, and knots. Anchors store the keys and identities of business entities. Attributes store changes and values. Ties store the associations and relationships. Lastly, knots store distinct values of entity attributes. This is a graph-based data modeling technique ideal for handling complex data relationships.
Benefits of ETL Data Modeling
If you want to create data management strategies efficiently, you have to combine ETL and data modeling. Some of the benefits of this are as follows:
Enhances Data Quality
Data modeling allows you to organize and structure data strategies logically, enabling more accessibility and clear understanding. Practices like defining relationships between data and establishing standardized formats allow you to identify and rectify inconsistencies in data, leading to better data quality.
Increases Operational Efficiency
Data modeling of the ETL process streamlines the extract, transform, and loading process by reducing unpredictability and manual efforts. You can improve operational efficiency and productivity by understanding the strategies and optimizing data workflows to automate repetitive tasks.
Promotes Scalability
A good ETL data model allows you to easily accommodate more data volumes, integrate new data sources, and support changes in business logic without too much effort. With these capabilities, you can efficiently maintain performance levels, manage expanding data ecosystems, and sustain good performance over time.
Improves Awareness
The data modeling ETL process improves overall awareness of the data stack, strategies, data sources, and security measures. This makes it easy for all data professionals to identify the nature of data and practices implemented across organizations. This practice makes almost all processes easy, for instance, creating robust data governance policies and being compliant with regulatory requirements.
Best Practices for ETL Data Modeling
Below are some of the key best practices for implementing data modeling in the ETL process:
Materialization
Materialization is the process of pre-aggregating data for improving query performance and response times. Pre-aggregating the data reduces the number of tables that need to be processed and expands their width by increasing the number of columns. This best practice makes it easier for you to write queries to address the challenge of simple joins.
Grain
The grain in an ETL data model is a very small piece of data in an entire dataset. For instance, in a table named products, the grain can be a single product. To implement it, you need to understand how detailed your data should be and build data models accordingly. Usually, the smallest grain serves as the conventional data modeling unit.
Data Partitioning
Data partitioning is the division of stored database objects like indexes and tables into different parts with separate physical storage parameters. In this process, the data is distributed across partitions by some rule, for instance, dividing by key, where the key is the year. Data partitioning is most recommended when building predictive models when the data model requires sufficient storage capacity.
Document the ETL Data Model
While creating a data model for the ETL process, you should maintain detailed documentation of the model using entity-relationship diagrams, transformation rules, and attribute definitions. This will help you improve source-to-target mapping, error handling procedures, and other important ETL processes, facilitating a deeper understanding, maintenance, and troubleshooting.
Test And Improve Continuously
ETL data modeling is not a rigid process; it is a continuous journey where you have to keep improving to perform efficient data management. To improve, you have to conduct detailed testing of the ETL process and ensure data is extracted, transformed, and loaded according to your strategy. Some of the common testing approaches include unit testing, integration testing, and end-to-end testing.
Streamline Data Modeling for ETL Using Airbyte
Data modeling for ETL brings many challenges. However, the most common challenge is the manual effort of building a data model using custom code or APIs. To deal with this challenge, you can use SaaS tools like Airbyte.
Airbyte is an ELT platform that provides the largest library of 350+ pre-built connectors for synchronizing disparate data sources such as BigQuery, Snowflake, or Redshift to a destination of your choice. The good thing about this tool is that you can automate the processes involved in data integration and replication within minutes, which otherwise takes days with custom coding. This eliminates the need for manual scripting, saving you valuable time and resources.
If you need customization, the new PyAirbyte library of Airbyte offers all the connectors of Airbyte within a library and makes them available as code. This allows you to make data pipelines according to your choice using Python programming.
Key features of Airbyte include:
- Custom Connectors: If you don't find a specific pre-build connector in an extensive Airbyte library, the platform allows you to build a custom one. Using Airbyte’s connector development kit, you can build custom connectors within a few clicks.
- Scheduling And Monitoring: Airbyte offers robust features for scheduling and monitoring data pipeline workflows. You can schedule tasks according to your requirements and monitor them with features like connection logging, airbyte data dog, and open telemetry integration.
- Change Data Capture (CDC): Using the CDC feature of Airbyte, you can automatically track incremental updates and changes in your data sources.
Conclusion
Data modeling is a crucial part of ETL. It gives your data stack an understandable and actionable structure. To make the most of ETL modeling, you should consider the best practices mentioned above, including materialization, data partitioning, documenting, and testing.
However, building efficient data modeling for data replication can be challenging. To overcome this issue, you can use Airbyte. Its automated approach, intuitive user interface, and robust orchestration capabilities streamline the task of data migration.
Join the community of over 15,000 developers today and sign up for Airbyte!
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