Data Platform Architectures & Design Patterns: A Comparative Analysis

Hello, data aficionados! Welcome back to our DATA LEAGUE series 'From Inception to Insights.' Today, we're uncovering the different types of data platform architectures and design patterns available in the world of data analytics. Ready to take flight?

Data Platform Architecture and Design Pattern, while related, serve different purposes in the realm of data management.?

Data Platform Architecture is about the overall infrastructure where data and data models exist. It's an eclectic mix of old and new data, managed on traditional and modern data platforms, whether on-premises or in the cloud, with diverse tool types from many providers. It's characterized by its large number and diversity of data persistence platforms, as well as its broad range of data structures, types, and containers. It sets standards across data systems, acting as a vision or a pattern for how data systems interact.?

On the other hand, a Design Pattern is more about the relationships across multiple data sets and their platforms. It's about creating a representation of the enterprise's data in the form of a model. This model entails the business concepts, how they are related to each other, and it defines rules, default values, and naming conventions.

In short, while Data Platform Architecture is about the infrastructure and the platforms where data resides, a Data Pattern is about the relationships and rules that govern the data across these platforms. Both are crucial for effective data management and serve complementary roles in the data landscape

Firstly, let's explore the Data Platform Architectures:

Lambda Architecture

Lambda Architecture is a data-processing architecture designed to handle massive quantities of data by taking advantage of both batch and stream-processing methods. It consists of three layers: batch processing, speed (or real-time) processing, and a serving layer for responding to queries.

• A batch layer (cold path) stores all of the incoming data in its raw form and performs batch processing on the data. The result of this processing is stored as a batch view. The batch layer processes big data sets in intervals to create batch views that will be stored by the serving layer. The data in this layer is immutable. Immutability and receiving data in append-only format is what makes the Lambda architecture fault tolerant and prevents data loss.?
• A speed layer (hot path) analyzes data in real time. This layer is designed for low latency, at the expense of accuracy. The speed layer focuses on filling the gaps left by the batch layer. This layer uses complex incremental algorithms and computation.
• A Serving layer queues batch views that have been prepared by the batch layer and then indexes them. The serving layer’s goal is to make the data query-able in a very short period of time. The server layer stores the output and merges the batch layer output with the speed layer output with an incremental updates based on the most recent data.

A drawback to the lambda architecture is its complexity. Processing logic appears in two different places — the cold and hot paths — using different frameworks. This leads to duplicate computation logic and the complexity of managing the architecture for both paths. A data set modeled with Lambda architecture is difficult to migrate or reorganize.

The good part about this architecture is that if the batch layer fails, the speed layer will process the recent data while you re-run the batch layer.?

Use Cases

• User queries are required to be served on an ad-hoc basis using immutable data storage.
• Quick responses are required, and the system should handle various updates in new data streams.
• None of the stored records shall be erased, and it should allow the addition of updates and new data to the database.

Kappa Architecture?

Kappa Architecture is a variant of the Lambda Architecture. It simplifies the process by removing the batch layer and processing all data as a stream. This makes it ideal for real-time data processing and analysis.

Advantages:

1. Simplicity: Kappa architecture uses a single data processing system to handle both batch processing and stream processing workloads, which makes it simpler to set up and maintain compared to other architectures.

2. Reduced Latency: By eliminating the batch layer, it provides faster processing.

3. Scalability: Kappa architecture is scalable, making it suitable for handling large volumes of data.

4. Lower Costs: It can potentially lead to cost savings as it eliminates the need for separate batch and stream processing systems.?

Disadvantages:

1. Requires Experience: Implementing Kappa architecture requires experience in stream processing and distributed systems.

2. Less Suitable for Historical Data Analysis: Kappa architecture is ideal for scenarios where real-time insights are critical and historical data analysis is less important.

Use Cases

• When the algorithms applied to the real-time data and the historical data are identical, it is very beneficial to use the same code base to process historical and real-time data and, therefore, implement the use-case using the Kappa architecture.
• Kappa architecture can be used to develop data systems that are online learners and therefore don't need the batch layer.
• The order of the events and queries is not predetermined. Stream processing platforms can interact with the database at any time.

Data Mesh Architecture?

Data Mesh Architecture is a decentralized data architecture that organizes data by a specific business domain. It provides more ownership to the producers of a given dataset.

Advantages:

1. Scalability: Data Mesh architecture is highly scalable and has the ability to start small and grow as demand grows.

2. Improved Performance: It can lead to improved performance.

3. Reduced Complexity: Data Mesh can help to reduce the complexity of data architectures and make them more manageable.

4. Domain-centric Control of Data: Data mesh promotes distributed data across domains, self-service operations by non-IT staff, and domain-centric control of data.

5. Revenue Generation: The data-as-a-product (DaaP) approach enables domain owners to move away from “centralized data lakes,” and directly sell their domain data to other domains, thus creating new avenues for revenue generation.?

Disadvantages:

1. Increased Complexity: A data mesh network requires more devices than an end-to-end solution. This means the network will be larger, more complex, and more expensive to implement than an end-to-end solution.

2. Lack of Incentive for Data Sharing: The incentive of sharing data between owners is not there.

3. Potential for Small Mistakes: Early mistakes tend to be small mistakes and teams learn through experience how to manage increased demand for data while avoiding the political and technical pitfalls inherent in providing actionable data to business users.

Now, let's look into the Data Design Patterns:?

Medallion Architecture?

Medallion Architecture is a data design pattern used to logically organize data in a lakehouse. It aims to incrementally and progressively improve the structure and quality of data as it flows through each layer of the architecture (from Bronze ? Silver ? Gold layer tables).

Advantages:

1. Atomicity, Consistency, Isolation, and Durability: This architecture guarantees these properties as data passes through multiple layers of validations and transformations before being stored in a layout optimized for efficient analytics.

2. Single Source of Truth: It helps in building a single source of truth for enterprise data products.

3. Data Quality: The data quality improves as it moves from the Bronze (raw) to Silver (validated) to Gold (enriched) layers.

4. Efficient Analytics: The Gold layer contains data that powers analytics, machine learning, and production applications.

5. Historical Archive: The Bronze layer provides an historical archive of source data, data lineage, auditability, and reprocessing if needed without rereading the data from the source system.

6. Enterprise View: The Silver layer provides an "Enterprise view" of all its key business entities, concepts, and transactions.?

Disadvantages:

1. Complexity: It can be complex to mix appends, update, and delete in the data lake.

2. Data Governance: Improper data governance in the lake can result in data swamps instead of data lakes.

3. Handling Raw Data: Raw data (Bronze layer) is challenging to handle as it necessitates a deep understanding of the source system’s design.

It's important to note that this Medallion Architecture does not replace other dimensional modeling techniques. Schemas and tables within each layer can take on a variety of forms and degrees of normalization depending on the frequency and nature of data updates and the downstream use cases for the data.

Data Vault?

Data Vault Architecture is a data modeling design pattern used to build a data warehouse for enterprise-scale analytics. It consists of three types of entities: hubs, links, and satellites. ?

• Hubs - Each hub represents a core business concept, such as they represent Customer Id/Product Number/Vehicle identification number (VIN). Users will use a business key to get information about a Hub. The business key may have a combination of business concept ID and sequence ID, load date, and other metadata information.
• Links - Links represent the relationship between Hub entities.
• Satellites - Satellites fill the gap in answering the missing descriptive information on core business concepts. Satellites store information that belongs to Hub and relationships between them.

A few additional things to keep in mind:

• A satellite cannot have a direct connection to another satellite.
• A hub or link may have one or more satellites.

Advantages:

• Scalability: Data Vault is highly scalable, making it well-suited for large, complex environments. It allows for easy addition of new data sources and changes to the data structure without disrupting existing systems.
• Flexibility & Agility: The design is adaptable to changing business requirements. It supports Agile development, as you can start small and expand the data model iteratively. New data sources or business rules can be incorporated incrementally.
• Auditable and Traceable: Data Vault’s structure allows for a high degree of traceability, providing an audit trail of data movements and transformations. This is crucial for compliance, especially in regulated industries
• Handles Source System Changes: It is well-suited to handle changes in source systems without requiring significant re-engineering. The design allows for historical data storage without overwriting or losing past information.

Disadvantages:

• Complexity: The Data Vault model can be complex to design and implement, especially for teams unfamiliar with it. The ETL or ELT processes in Data Vault are typically more involved than in traditional models (like star schemas). Transforming and loading data into Hubs, Links, and Satellites requires more intricate data pipelines.
• Requires Expertise: Data Vault is a specialized methodology, and its implementation requires expertise in the model's design principles and best practices. If the team doesn't have experience, it could lead to inefficient or error-prone implementations

Don’t hesitate to drop your thoughts, questions, or experiences below. Your input is the compass that guides us. Thank you for reading!

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