When companies begin to use big data, they often face significant difficulties in organizing, storing, and interpreting the vast amounts of data collected.
Applying traditional data modeling techniques to big data can lead to performance concerns, scalability issues, and inefficiencies because they were created for more organized and predictable data environments.
These problems arise from the mismatch between traditional approaches and the dynamic nature of big data, causing decisions to take longer, be more expensive, and data not be used appropriately.
The Challenges of Big Data
The three characteristics that define big data are volume, velocity, and variety. It is important to understand these aspects to deal with the specific obstacles they pose.
Volume
It’s amazing how much data is generated these days. Businesses collect data from a variety of sources, such as social media interactions, sensors, and consumer transactions. Scalable storage systems and data models that can effectively manage large datasets without compromising performance are essential to manage this vast amount of data.
Velocity
Another significant constraint is the rate at which data is created and must be analyzed. It is often necessary to process data in real time or near real time to quickly gain meaningful insights. The rapid flow of data often overwhelms traditional data models, which are built for slow, batch processing and causes bottlenecks and delays.
Variety
Big data can be found in many different forms, including unstructured data such as text, photos, and videos, as well as structured data found in databases. This requires adaptable models that can take into account different formats and structures to integrate and analyze these disparate data types. Traditional models find it difficult to accommodate this diversity because they are generally inflexible and schema-dependent.
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Top 3 Big Data Modelling Approaches
1. Dimensional Modeling
Data warehouses are organized using the design principle of dimensional modeling to facilitate effective retrieval and analysis. It is mostly used in business intelligence and data warehousing contexts to improve end users’ access to and understanding of data. This architecture makes data organization simple and quick by grouping data into fact and dimension tables.
KEY COMPONENTS
Facts: These are the main tables in the dimensional model that contain quantitative information for analysis, such as number of transactions, sales revenue, and quantity sold.
Dimensions: These tables provide fact-related descriptive features such as time, location, product specifications, and customer data.
Measures: Measurement fact tables contain numerical data that is analyzed, such as total sales amount or number of units sold.
2. Data Vault Modeling
A database modeling technique called “data vault modeling” was created to offer long-term historical data storage from several active systems. It is appropriate for big data situations since it is extremely scalable and flexible to changing business needs.
KEY CONCEPTS
Hubs: Have unique IDs and act as a representation of important corporate entities such as customers and products.
Links: Record connections between hubs, such as sales exchanges that connect goods to customers.
Satellites: Track descriptive data changes over time, such as modifications to customer addresses.
Star Schema Design
Star schema is a popular data modeling technique in data warehousing and business intelligence used to organize data to maximize query performance and simplify analysis. It is distinguished by a star-shaped primary fact table surrounded by multiple dimension tables.
You can check more info about: Data Modeling Techniques.
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