Assette’s Data Block Categories—Transformation, Integration, Configuration, and Decorator—are essential components that facilitate data management and processing within the Assette platform. These categories define the roles and functionalities of Data Blocks, enabling users to manipulate, integrate, and present data effectively to meet various business needs.
Transformation Data Blocks #
Transformation Data Blocks are designed to manipulate and reshape data to meet specific analytical or reporting requirements. They perform operations such as filtering, sorting, aggregating, and calculating new values from existing data. By applying business logic and rules, Transformation Data Blocks convert raw data into meaningful insights that align with the user’s objectives.
Key Features:
- Data Manipulation: Modify data structures and values to suit specific purposes.
- Calculations and Derivations: Compute new metrics or indicators based on existing data.
- Data Cleansing: Remove inconsistencies and correct errors in the data set.
Use Cases:
- Calculating investment performance metrics from raw financial data.
- Aggregating client transaction data to provide summary reports.
- Normalizing data from different sources for consistency in analysis.
Integration Data Blocks #
Integration Data Blocks handle the connection and data retrieval from various external sources. They enable Assette to interface with databases, APIs, and other data repositories, importing data into the platform for further processing. Integration Data Blocks ensure that the data used within Assette is current, accurate, and comprehensive.
Key Features:
- Data Connectivity: Establish connections with external data sources.
- Data Importation: Fetch and import data into Assette’s environment.
- Real-Time Updates: Retrieve the latest data to keep analyses up-to-date.
Use Cases:
- Connecting to a CRM system to import client information.
- Fetching market data from financial APIs for real-time analysis.
- Importing portfolio data from external accounting systems.
Configuration Data Blocks #
Configuration Data Blocks allow users to define settings and parameters that influence how data is processed and presented. They act as a centralized location for managing configurations that affect multiple Data Blocks or the overall behavior of data workflows. Configuration Data Blocks enhance flexibility and reusability by enabling users to adjust parameters without modifying the underlying logic.
Key Features:
- Parameter Management: Define and manage variables used across Data Blocks.
- Global Settings: Set configurations that impact the entire data processing pipeline.
- User Preferences: Customize data presentation formats and processing rules.
Use Cases:
- Setting default currency or date formats for reports.
- Defining threshold values for risk assessments.
- Managing user-specific preferences for data visibility.
Decorator Data Blocks #
Decorator Data Blocks enhance or modify the output of other Data Blocks without changing their core functionality. Following the decorator design pattern, they allow additional features to be layered onto existing Data Blocks dynamically. This modular approach promotes code reuse and flexibility, enabling users to extend functionalities as needed.
Key Features:
- Output Enhancement: Add supplementary information or formatting to data outputs.
- Dynamic Modification: Apply changes to Data Blocks at runtime without altering original logic.
- Layered Functionality: Stack multiple decorators to build complex features incrementally.
Use Cases:
- Formatting numerical data to include currency symbols or percentage signs.
- Adding conditional highlighting to report figures based on performance criteria.
- Appending footnotes or annotations to data tables for additional context.