When creating a new data warehousing environment, there is much to consider. One of the most important elements to be aware of is the fact table. The fact table is one of the central tables within a data warehouse that uses a star schema. There are three main types of fact tables, each of which will store a different set of information to be analyzed and used as part of an overall business intelligence strategy.
When setting up a data warehouse, you will need to establish the fact tables used for organizing and accessing the data. In general, there are three main types of fact tables that most organizations will have set up:
Transactional – A transactional fact table is going to have one line for each piece of data associated with a transaction. This will usually have the most details, or grains, which means it will have the largest number of dimensions.
Periodic snapshots – A periodic snapshot fact table is where data is stored that is associated with a snapshot of an environment at a specific moment in time. This allows teams to access information that was originally part of the transactional fact table at a precise point in history.
Accumulating snapshots – An accumulating snapshot is used to track activity associated with a business process where there are set start and endpoints. An example of this could be producing a product where snapshots of the related data will be taken at each key point from the beginning of production through to when it is sold.
Each of these fact sheets has a clear type of data that is incorporated into them. When planning out the data integration for your company, it is important not only to ensure data is directed to the right fact table but also that all relevant data is accounted for. While some data sources are obvious, many others can go overlooked if not planned for in an overall enterprise data warehouse strategy. Failing to bring all data together in one place will make data mining and other activities more difficult in the future.
When creating a new data warehouse, most companies today choose to do it on the cloud. There are many data warehousing services (DWaaS) that offer cloud-based managed solutions for organizations of all types. While this type of technology will operate the same, whether on-prem or on the cloud, it is important to ensure that the fact tables and other aspects of the environment are properly configured.
When creating a fact table, you need to declare a grain, which describes exactly what a given record on the table represents. Based on what the grain is, establishing dimensions of the table is also important. An example of grain on a transactional fact table would be each item on an invoice: the customer name, address, delivery date, products or services provided, amount paid, and any other related data should each be represented by a grain on the table.
In the examples given, the dimensions of each grain would be established in a way to properly accommodate the data. The dimensions of a grain used to represent a name need to be sufficient to contain the longest potential names that would be included, for example. Once the fact table is properly established, data integration can begin.
Creating and maintaining a good data warehouse requires a great deal of work as well as a level of expertise that many organizations simply do not have. Trianz offers experienced data warehouse consulting services to help you plan, create, and support your entire data warehousing environment.
We have extensive experience helping companies across nearly every industry and are authorized consultants for every major cloud data warehousing services provider. We will work closely with you to determine your exact needs, set up the required fact table, and ensure everything is configured properly so you can begin using your data warehouse right away.
Contact Us Today
Connecting more people to data has become imperative for organizations worldwide. In Top Trends in Data & Analytics for 2022, Gartner stated, “Connections between diverse and distributed data and people create truly impactful insight and innovation. These connections are critical to assisting humans and machines in making quicker, more accurate, trustworthy, and contextualized decisions while considering an increasing number of factors, stakeholders, and data sources.”Explore
Since the dawn of business, users have looked for three main components when it comes to data: Search | Secure| Share. Now let's talk about the evolution of data over the years. It's a story in itself if one pays attention. Back then, applications were created to handle a set of processes/tasks. These processes/tasks, when grouped logically, became a sub-function, a set of sub-functions constituted a function, and a set of functions made up an enterprise. Phase 1 – Data-AwareExplore
Practitioners in the data realm have gone through various acronyms over the years. It all started with "Decision Support Systems" followed by "Data Warehouse", "Data Marts", "Data Lakes", "Data Fabric", and "Data Mesh", amongst storage formats of RDBMS, MPP, Big Data, Blob, Parquet, Iceberg, etc., and data collection, consolidation, and consumption patterns that have evolved with technology.Explore
Enterprises have, over time, invested in a variety of tools, technologies, and methodologies to solve the critical problem of managing enterprise data assets, be it data catalogs, security policies associated with data access, or encryption/decryption of data (in motion and at rest) or identification of PII, PHI, PCI data. As technology has evolved, so have the tools and methodologies to implement the same. However, the issue continues to persist. There are a variety of reasons for the same:Explore
Finding Hidden Patterns and Correlations Innovative technologies such as artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) are transforming the way we approach data analytics. AI, ML and NLP are categorized under the umbrella term of “cognitive analytics,” which is an approach that leverages human-like computer intelligence to identify hidden patterns and correlations in data.Explore
What Is an SQL Query Engine? SQL query engine architecture was designed to allow users to query a variety of data sources within a single query. While early SQL-based query engines such as Apache Hive allowed analysts to cut through the clutter of analytical data, they found running SQL analytics on multi-petabyte data warehouses to be a time-intensive process that was difficult to visualize and hard to scale.Explore