When used with big data, traditional data modeling techniques – which were created for more organized and predictable data environments – can result in inefficiencies, scalability concerns, and performance issues.
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 in order 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.
Velocity
The speed at which data is generated and needs to be processed is another major challenge. Real-time or near-real-time data processing is often required to derive actionable insights promptly. Traditional data models, which are designed for slower, batch processing, often fail to keep up with the rapid influx of data, leading to bottlenecks and delays.
Variety
Big data comes in various formats, from structured data in databases to unstructured data such as text, images, and videos. Integrating and analyzing these diverse data types requires flexible models that accommodate different formats and structures. Traditional models, which are typically rigid and schema-dependent, struggle to adapt to this variety.
Advanced data modeling techniques, such as dimensional modeling, data vault, and star schema design, are specifically developed to address these limitations. With these approaches, organizations can overcome the limitations of traditional models, ensuring their big data applications are robust, scalable, and efficient.
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Top 3 Big Data Modelling Approaches
1. Dimensional Modeling
Dimensional modeling is a design concept used to structure data warehouses for efficient retrieval and analysis. It is primarily utilized in business intelligence and data warehousing contexts to make data more accessible and understandable for end-users. This model organizes data into fact and dimension tables, facilitating easy and fast querying.
KEY COMPONENTS
Facts: These are central tables in a dimensional model containing quantitative data for analysis, such as sales revenue, quantities sold, or transaction counts.
Dimensions: These tables hold descriptive attributes related to facts, such as time, geography, product details, or customer information.
Measures: Measures are the numeric data in fact tables that are analyzed, like total sales amount or number of units sold.
Dimensional modeling simplifies the query process as it organizes data in a way that is intuitive for reporting tools, leading to faster query performance. The structure of dimensional models is straightforward, making it easier for business users to understand the data relationships and derive insights without needing in-depth technical knowledge.
2. Data Vault Modeling
Data vault modeling is a database modeling method designed to provide long-term historical storage of data from multiple operational systems. It is highly scalable and adaptable to changing business needs, making it suitable for big data environments.
KEY CONCEPTS
Hubs: Represent core business entities (e.g., customers, products) and contain unique identifiers.
Links: Capture relationships between hubs (e.g., sales transactions linking customers to products).
Satellites: Store descriptive data and track changes over time (e.g., customer address changes).
The modular nature of the data vault allows the easy addition of new data sources and adapts to changing business requirements. It supports the integration of data from multiple sources by providing a consistent and stable data model.
You can check more info about: Data Modeling Techniques.
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