What is the best way to use dimensional modeling for data aggregation in ETL?

Dimensional modeling is a technique to organize and structure the data in a efficient way in data warehouse. In dimensional modeling, at a high level, there are two different types of data descriptive data and numerical data. Descriptive data provide a context to the numeric data and without descriptive data, most of the time, numeric data does not make sense. Numeric data are those on which aggregation function can be applied if needed

The essence of dimensional modelling is to provide valuable insights from the available data and make fruitful decisions. An example is the data warehouse with healthcare providers data. where Provider info is your fact table and it has dimensions like contract period, network, demographics, incentives, member associated and many more. Here using Star schema we can connect provider with all the dimensions and provide insights on how many members being served, which provider has max incentives earned for given period, provider ratings etc. Hope this is helpful.

Dimensional modelling is a data modelling technique, where one breaks data up into facts & dimensions to reduce the data redundancy, save the storage & for a better query performance & analysis within DWH. Dimension: A dimension is a structure that categorizes facts & measures in order to enable users to answer business questions. Dimensions are typically likened to nouns. Fact: A fact table consists of the measurements/metrics/facts of a business process. It consists of numerical data of actual transactions like Salary etc. Summary Fact: A fact table might contain either detail-level facts or aggregated facts. A fact table that contains aggregated facts are often called summary Fact.

Dimensional modeling used in data warehousing to organize and structure data for easy and efficient querying and analysis. It involves designing a database schema that focuses on capturing and representing business measures (facts) and the contexts in which they occur (dimensions). Fact Table: This is the central table in the dimensional model where the facts are stored. Each row in the fact table represents a single measurement event or transaction, and each column represents a different measure or attribute of the event. Dimension Tables: Each dimension table typically corresponds to a single dimension and contains the attributes related to that dimension. Dimension tables are joined to the fact table to provide context to the facts.

1. Check business requirement and process 2. Start design DIM and FACT table 3. Check aggregation levels required for analysis and reporting 4. Implement incremental load 5. Establish data quality check 6. Improve performance 7. Validate

At the early stage of my career as a Data Analyst, I had no idea of what dimensional Modelling was but I was familiar with the challenges of not having one. Dimension modeling is a way of organizing data in a database that makes it easy to analyze and understand. It involves separating data into two types of tables: facts and dimensions. Facts are numerical values that measure something, such as sales, revenue, or quantity. Dimensions are descriptive attributes that provide context for the facts, such as product, customer, date, or location.

The key idea behind dimensional modeling is to create a data structure that is easy to understand and navigate, while providing high performance for analytical queries. The star schema, with a central fact table surrounded by dimension tables, even offers a visual representation. This alignment also facilitates effective communication between IT and business stakeholders.

Dimensional modeling is a data modeling technique used in data warehousing and business intelligence to facilitate efficient storage and improve query performance. Mainly, it consists of Fact and Dimension tables. Fact table: This table stores numerical facts such as sales details of a company. Dimension table: It stores descriptive attributes for data in the fact tables upon which analysis can be performed. For example, sales details in the fact table can be analyzed by region, product, etc., which are different dimensions upon which sales details can be analyzed. Dimensions are often called Slowly Changing Dimensions as values don’t change frequently.

Dimensional modeling is a data modeling technique used in data warehousing to organize and structure data for optimal querying and analysis. It involves designing data models composed of facts (measurable business metrics) and dimensions (descriptive attributes) that facilitate efficient data aggregation and analysis.

Dimensional models are scalable and can accommodate large volumes of data and complex analytical queries. This scalability is crucial for data warehouse ETL processes, which often involve processing and loading large amounts of data from multiple source systems.These models are optimized for query performance, as they are designed to support analytical queries commonly performed in business intelligence and reporting. By denormalizing data and structuring it around facts and dimensions, dimensional models facilitate faster query execution, leading to improved performance for reporting and analysis.

Dimensional modeling offers several benefits for data warehouse ETL processes: -Simplified Querying: Dimensional models are designed to optimize querying and analysis, making it easier for users to retrieve and aggregate data for reporting and decision-making. -Improved Performance: By organizing data into star or snowflake schema structures, dimensional modeling minimizes join complexity and improves query performance, resulting in faster data retrieval. -Flexibility and Scalability: Dimensional models are flexible and scalable, allowing for easy integration of new data sources and changes to existing schemas as business requirements evolve.

Dimensional Modeling categorizes facts and measures in order to enable users to answer business questions. It helps to improve performance on reporting as most of the aggregation happens at the ETL layer. It also helps to have less joins (in ideal world, one fact table joins with other dimensional tables to get the descriptive values) to have a better performance. With dimensional modeling drilling up, down and across is possible, hence it give flexibility to end user to view the data at different levels.

Primary thing being dividing your schema into dim and fact table on basis of which you can descide the exact schema that you’ll be needing, per business iase case pf course , thereby giving you a sufficiently funnelled idea for using the join condition ..! And of course ensuring the materalised views and indexes to be put as an add on ..! Cheers for the thought though 🤟🫡

Dimensional modeling is used in data warehouse ETL not only for simplifying complex data and optimizing query performance but also for efficiently supporting data aggregation, enabling faster and more scalable summarization and analysis of business information.

To aggregate data from a Dimensional Modeling in ETL tools, in my opinion the ETL process should use the database to execute the aggregation as the ETL server require huge servers resources to aggregate big volumes from the Dimensional tables. The databases specially columnar or delta lake platforms are better prepared to aggregate the data.

Como falei acima, a modelagem dimensional é frequentemente utilizada para ETL de data warehouses por várias razões: - Facilita a compreensão dos dados - Suporta consultas analíticas complexas - Flexibilidade na análise - Desempenho aprimorado - Agregações pré-calculadas - Integração com ferramentas de BI - Escalabilidade Então no geral, a modelagem dimensional é uma abordagem eficaz para ETL de data warehouses., permitindo assim uma análise eficiente, flexível e intuitiva dos dados, ela bem feita vai atender todas as necessidades de usuários e analistas.

Dimension modeling is a straightforward and powerful method for structuring data in a data warehouse. It aligns with how organizations commonly understand and express data entities, making it accessible for business analysts and non-experts to analyze and comprehend. Even in low-code ETL environments, employing dimension modeling simplifies feed development for non-technical stakeholders. While some may criticize dimension modeling for resulting in queries with numerous joins, an experienced modeling team can address this by offering simplified views for commonly used abstractions.

Designing a dimensional model for data warehouse ETL is simple in writing but harder for a beginner to implement. My approach when learning this was to take a business process through these steps. Starting from the simplest using Adventureworks OLTP database. I setup some business process and questions. I also setup some assumptions to make it easier to execute. In practice, there will be several back and forth between the project team and the stakeholders The steps. - identify the business process. - determine the grain or the level of detail of the fact table. - identify the facts or the measures that you want to store in the fact table. - identify the dimensions or the attributes that you want to store in the dimension tables.

Understand the business requirements and objectives that the data warehouse will support. This includes identifying the key performance indicators (KPIs), business processes, and analytical needs of the organization. Design the fact tables that will store the measurable metrics or facts. Each fact table represents a specific business process or subject area and contains numeric measures at the defined grain. Ensure that each fact table has appropriate foreign key relationships to the associated dimension tables.

- Understand business needs. - Define data granularity. - Choose star schema for simplicity or snowflake for complexity. - Use conformed dimensions; manage updates with SCD. - Identify fact measures. - Select dimensions based on business needs. - Improve query speed with indexing and partitioning.

-Identify Business Processes: Understand the key business processes and metrics that need to be captured and analyzed in the data warehouse. -Define Dimensions and Facts: Identify the descriptive attributes (dimensions) and measurable metrics (facts) that will form the basis of the dimensional model. -Choose Schema Structure: Select an appropriate schema structure (e.g., star schema, snowflake schema) based on the complexity and relationships of the data entities. -Normalize Dimensions: Normalize dimension tables to eliminate data redundancy and improve data integrity, if necessary. -Define Hierarchies: Establish hierarchies within dimension tables to facilitate drill-down and roll-up analysis.

The key for creating dimensional models lies in the business requirements, definition of key measures and performance indicators and their use in the value driver of the business. The business requirements and KPIs help to identify the facts that take various dimensions as arguments. For example, the sales performance in a quarter require Sales fact which requires time, product and location dimensions as arguments. One of the basic assumptions behind this is the quality of data. So before the data is ingested and integrated , transformed into dimensional model elements, data quality and conformance to key business rule must be validated

Designing a dimensional model for data warehouse ETL involves: identifying business processes and requirements, defining dimensions and facts, creating a star or snowflake schema, and implementing ETL processes for data extraction, transformation, and loading while ensuring data quality and consistency.

A good thing to keep in mind is domain modelling and declarative programming. With an informative domain model that is well maintained and respected. Most of the dimensional modelling will complete itself. Even if you only have a basic understanding. IE you have a business online that sells shoes? Your domain model likely contains ‘shoes (sold to) customers (delivered from) storage locations (by interacting with) an online service’. Those in brackets will be some facts whilst those outside are dimensions. From there you choose how you’d like to aggregate or partition your objects/relationships, then build your ETLs declaratively from here. These concepts ensure your dimensional models stay relevant and pipelines are maintainable.

Determine if any dimensions have attributes that change over time. If so, implement strategies to handle slowly changing dimensions, such as Type 1 (overwrite), Type 2 (historical tracking), or Type 3 (add new attribute).

One of the crucial aspects of data modelling is to understand the current needs as well as likely future needs. To do that you will need to not just know what happens currently, what is required currently but also what is round the corner. e.g. monthly aggregations may be needed now but the company is venturing into a new market next year where daily reporting is required.

1) Identify the Business process: identify the business requirements and the business process that the data warehouse will support. 2) Determine the Data : Determine what data need to be analyzed once we identify the business process. identify the data sources and data elements. 3) Identify Dimensions: Once we finalize on Data source we now need to identify the dimensions to describe the data. 4) Find out Facts: Find out the facts. 5)Identify the level of details at which data will be stored, identify the grain (granularity ) 6) Design the Schema this includes creating fact and dimension table based on dimensions and fact identified in earlier steps.

Dimensional data modeling implementation steps: 1. Identify the source systems 2. Define staging layer to bring data from source to staging layer(raw data), this data is used for processing. In few scenarios, these are typical truncate and load. 3. After data is available in staging layer, data movement happens from staging to curation layer ( in few cases) where we append the data everyday/ depending upon data frequency. 4. The next layer is where we perform transformations to clean the data. We also define and create tables in fact and dimensional ways. In few cases, creation of aggregate tables is peformed in this layer itself. There are situations where we have one more layer known as datamart and perform aggregation on datamart.

When designing ETL for dimensional modeling, it helps to do the transformation in stages. 1. Stage all the data as is from your source system into staging tables. I call this the "raw" layer 2. Cleans and Transform and put it into another set of tables but without changing the granularity of the data. I call this is the "cleansed" layer. 3. Finally create the Fact and Dimension tables which include aggregate tables ready to be queried by Business Users. I call this is the "enhanced" layer.

1.Define primary key constraints on the dimension tables to ensure uniqueness, and define foreign key constraints on the fact tables to establish relationships with the dimension tables. 2.Load the descriptive data into dimension tables by performing data transformations and insert into dimension table 3. Extract the data from the source system and transform it to fit the granularity of the fact tables, and load it into the appropriate fact tables. 4. Implement SCD types to maintain the historical changes. 5.Schedule the ETL jobs using the tools like Airflow.Monitor the ETL process and data warehouse performance to ensure the dimensional model is functioning as expected.

-Extract Data: Extract data from various source systems using ETL tools or processes, ensuring data quality and consistency. -Transform Data: Transform and cleanse the extracted data to conform to the dimensional model structure, including mapping source data to dimension and fact tables. -Load Data: Load the transformed data into the dimensional model using bulk loading or incremental loading techniques, maintaining referential integrity and ensuring data consistency.

Implementing a dimensional model for ETL involves: Business Process Identification: Pinpoint measurable events. Grain Definition: Clarify the exact nature of fact table rows. Dimension Design: Develop detailed dimension tables for entity categorization, ensuring uniform dimensions across the warehouse. Fact Table Construction: Centralize numerical metrics, linking to dimension tables for a cohesive star schema. ETL Rigor: Cleanse and transform data, managing slowly changing dimensions for integrity. Iterative Development: Start small, deliver quick wins, and expand, aligning closely with business value. This approach enhances data accessibility and strategic decision-making. #DataWarehousing #ETL #DimensionalModeling

ETL for dimensional modelling: 1) Source the data 1:1 from source into raw layer(for dimension it can be full on incremental based in requirement, for facts based on some parameter like SalesDate) 2) Transform the data from raw layer into EDW layer where we have dim and facts in the loading order dimension->facts. No business logic just data loaded with relation between dimension and fact. 3) Apply the logic on data loaded in second layer to persist the data in Datawarehouse. 4) Provide access to the data to end users based on datamarts.

Olanrewaju Oyinbooke is spot on, exactly right. I have built so many warehouses effectively. Much easier with Oracle years ago as data links are a great way to load data avoiding ODBC mess, slowness etc. Liasing with the business to define the queries that will be required of it is paramount. BUT this is not 100% possible as those needs change swiftly and constantly, which company doesn't want more views of their data over long periods? So it is not an effective method any longer, at all. Biggest fault is how long that model takes, some places it can be a very long time; banks, the politics etc. Hence AWS S3 with Athena is fast as are so many other GCP, Azure tools too, rapid development, lean and dynamic etc.

Utilize Extract, Transform, Load (ETL) tools, such as Informatica, Talend, SSIS, or custom scripts, to extract data from source systems. ETL tools provide capabilities for connecting to various data sources and efficiently pulling data.

Before you extract the data, make sure you check for any inconsistencies/ gaps. Else after implementing the Datawarehouse, your results may be incorrect, and you will have tough time to track it down to the inconsistencies/ gaps. Always assess accuracy of data at each step of ETL. It's better to spend little more time here and plan & execute the testing well to ensure robust implementation.

Firstly identify dimension and fact tables, ensuring clear separation of descriptive attributes and measurable metrics. Aggregate fact data at the required granularity level, employing functions like summing or averaging as needed. Implement summary tables to store pre-aggregated data at different levels. Optimize join operations between dimension and fact tables. Consider OLAP cubes for multidimensional analysis and faster querying. Implement incremental loading techniques to update aggregated data incrementally, minimizing processing time. Maintain data integrity by enforcing referential integrity constraints between tables. Test and optimize ETL processes regularly to ensure optimal performance and scalability.

-Indexing: Create appropriate indexes on dimension and fact tables to optimize query performance. -Partitioning: Implement partitioning strategies to improve data load and query performance, especially for large fact tables. -Aggregation Tables: Create pre-aggregated tables to store summarized data at different levels of granularity, reducing query complexity and improving performance. -Materialized Views: Use materialized views to cache frequently accessed queries and improve query response times. -Query Optimization: Analyze query patterns and optimize SQL queries to leverage dimensional model structures effectively.

Some of the optimizing technique for dimension model. 1) Carefully design dimension with Type2 attributes. This can save lot of disk space and faster retrieval of data. 2) Incremental extract wherever possible. 3) Make use of hashbyte for comparing data in dimension for loading. 4)Proper indexes on the table. 5)Partitioning in case of fact tables.

Agree on all but dont forget parallelism on hash join cause It makes the real difference. Consider also to materialize what Is usually queried and Is not too volatile.

Take advantage of parallel processing capabilities in the ETL tool or data warehouse to distribute the load across multiple processors or nodes. This speeds up data loading operations.

Two key things are ... 1. Granularity: Optimize data dimensional modeling by selecting the appropriate level of granularity for dimensions and facts to provide meaningful insights without being too detailed, and facts contain necessary measures without redundancy. 2.Aggregating and Summarizing Data: Enhance query performance and reduce processing time by pre-aggregating and summarizing data where appropriate. Utilize summary tables or materialized views to store aggregated results of commonly used queries, reducing the need for complex calculations during runtime. However, balance the trade-off between storage overhead and query performance based on the frequency of data updates and query requirements.

Improving a layered model for information stockroom ETL includes different procedures to upgrade execution . Below are the key things for optimisation Indexing Partitioning Emerged Views Compression ETL Execution Tuning Simultaneousness and Parallelism Inquiry Optimization Denormalization Information Pressure and Encoding Memory Management

There are many ways to optimise the data in a data warehouse. Some of them are discussed below: 1. Index frequently queried columns to reduce query response time. 2. Create stored procedures for frequently used queries. 3. Create stored queries for frequently used queries that require response for the where clause. 4. Use materialized views for frequently viewed temporary tables. 5. Use CTEs instead of subqueries. 6. Scale up hardware where speed improvement depend on hardware. Scale out to improve parallelization of queries. 7. And so on. There are other

Dimensional modeling is essential for analytics and works hand-in-hand with ETL processes. Choosing the right architecture—whether it's a star schema or a snowflake schema—depends on the specific nature of your business data and the requirements of your analytics dashboard. The decision is driven by the data structure and the analytical needs. Dimensional modeling enhances the ETL process by enabling faster, more efficient data aggregation, ultimately supporting robust and insightful analytical reporting.

In-database analytics is especially potent within columnar databases, optimized for the read-intensive analytical queries characteristic of dimensional models. Combining ELT processes, which prioritize in-database transformation from normalized to dimensional models, creates a highly efficient, scalable, and high-performing environment for data management and analysis. This significantly accelerates the speed at which decision-makers can gain insights.

Il est important de comprendre comment les utilisateurs métiers vont utiliser les données : agrégats mais aussi drill down sur des donnée de détail (chercher les pistes d'amélioration), pour trouver le bon compromis

Based on my experience below are some the points to be considered to leverage dimensional modeling for data aggregation in ETL: - Understand the specific business requirements thoroughly. - Craft a well-designed dimensional model reflecting business hierarchies. - Choose appropriate aggregation levels and clear aggregation rules - Implement efficient aggregation logic and enhance performance through optimization techniques like query tuning and indexing. - Conduct rigorous testing to ensure data accuracy and reliability. - Continuously monitor and adapt to changes for sustained efficiency and effectiveness.

Also consider how the DWH will be used and work with the front-end team developing the applications / tools that use the DWH. I often see clients have a great DWH but lack the tools / front end tools to use it. They then force the team managing the transaction data to provide bespoke reports as and when required, clearly defeating the purpose of having a DWH.

One of the key questions you might face while developing a Dimensional Models is how to deal with dimension tables which consist of hierarchies. For example the location hierarchy could have city, state and country. Sometimes you want to put them all in one dimension table or sometime you may want to split them up into their individual tables. My suggestion is do both. Have a table that has all three and also have separate tables for each. This way if you have an aggregate table at country level, it will point to the country table and if you have an aggregate table at state level then you connect that to the state+country table. For more on ETL techniques you can read "The Datawarehouse ETL Toolkit" by Ralph Kimball

Get a copy of "The Data Warehouse Toolkit" by Kimball & Maggie Ross. This will help improve your dimension modeling skillsets for various industries like retail, stocks, marketing. You will also learn how to model the date dim table to its various grains.