Compare Phi-3 SLM vs ChatGPT LLM outputs
Driving the news:
Microsoft announced Phi-3 small language model (SLM) this week tuned with just 3.8 billion parameters with a size of 2.3 GB. A stark coincidence when Apple announced that they are also working on SLM’s to run on Apple devices.
Watershed moment:
The rise of SLM’s will mark a significant shift in terms of adopting Generative AI. SLM’s could bring the costs down, run offline and save enormous amount of power and computing costs.
Upon comparing the responses from Phi-3 and ChatGPT to the given prompt, I observed no significant differences in the core ideas proposed by both.
Prompt :
You are a data architect. You are instructed to build a framework to catch ETL processing errors. design a data architecture using Informatica as your ETL tool.
Phi-3 response:
To design a data architecture framework using Informatica for catching ETL processing errors, we can follow these steps:
1. Data Modeling:
— Define the source and target data models, including the data types, relationships, and constraints.
— Identify the key data entities, attributes, and their relationships.
— Create a data dictionary to document the data model and its metadata.2. Informatica ETL Design:
— Create an Informatica ETL project and define the ETL process flow.
— Design the ETL process using Informatica’s ETL components, such as Data Pump, Data Quality, and Data Integration.
— Map the source data to the target data model, ensuring data integrity and consistency.
— Implement data validation rules and constraints to catch errors during the ETL process.3. Error Handling and Monitoring:
— Implement error handling mechanisms using Informatica’s error handling features, such as error logging, error notifications, and error handling rules.
— Set up a monitoring system to track the ETL process performance, error rates, and data quality metrics.
— Create a dashboard to visualize the ETL process performance and error metrics.4. Data Quality and Validation:
— Use Informatica’s Data Quality component to perform data cleansing, standardization, and validation.
— Implement data quality rules and constraints to catch errors during the ETL process.
— Generate data quality reports to identify and resolve data quality issues.5. Error Reporting and Notification:
— Set up an error reporting system to capture and store ETL processing errors.
— Implement an alerting mechanism to notify the relevant stakeholders when errors occur.
— Create an error log report to analyze and investigate the root cause of the errors.6. Data Governance:
— Implement data governance policies and procedures to ensure data quality, security, and compliance.
— Define roles and responsibilities for data stewards, data owners, and data users.
— Establish data access controls and audit trails to monitor data usage and changes.7. Testing and Validation:
— Perform unit testing, integration testing, and end-to-end testing to validate the ETL process and error handling mechanisms.
— Conduct data quality assessments and validation tests to ensure data accuracy and consistency.8. Documentation and Maintenance:
— Document the data architecture, ETL process, and error handling mechanisms.
— Maintain and update the data model, ETL process, and error handling mechanisms as the data environment evolves.By following this framework, you can build a robust data architecture using Informatica to catch ETL processing errors and ensure data quality and integrity.
ChatGPT response:
To design a framework for catching ETL (Extract, Transform, Load) processing errors using Informatica as the ETL tool, we’ll need to establish a robust architecture that ensures data quality and error handling throughout the ETL pipeline. Here’s a high-level overview of the architecture:
1. **Source Systems**:
— Identify and connect to various source systems (databases, files, APIs, etc.) from which data needs to be extracted.
— Ensure proper permissions and access controls are in place for extracting data securely.2. **Informatica PowerCenter**:
— Utilize Informatica PowerCenter as the primary ETL tool for extracting, transforming, and loading data.
— Leverage PowerCenter’s features for designing ETL workflows, transformations, and mappings.3. **ETL Processes**:
— Develop ETL processes within Informatica to extract data from source systems, apply necessary transformations, and load it into the target systems.
— Implement error handling mechanisms within Informatica workflows to catch errors at different stages of the ETL process.4. **Error Logging and Monitoring**:
— Set up logging mechanisms to capture detailed information about errors encountered during ETL processing.
— Configure Informatica to log errors to appropriate destinations such as log files, databases, or centralized logging systems.
— Implement alerting mechanisms to notify stakeholders (e.g., data engineers, data stewards) about critical errors in real-time.5. **Data Quality Checks**:
— Integrate data quality checks within the ETL workflows to ensure the accuracy, completeness, and consistency of the data.
— Implement data profiling tasks to analyze the quality of incoming data and identify potential issues early in the process.6. **Error Handling Strategies**:
— Define error handling strategies based on the type and severity of errors encountered during ETL processing.
— Classify errors into categories such as data validation errors, transformation errors, connectivity errors, etc.
— Implement retry mechanisms for transient errors and fallback strategies for critical errors that cannot be resolved automatically.7. **Exception Handling**:
— Implement exception handling logic to manage unexpected scenarios and edge cases.
— Design workflows to gracefully handle exceptions, perform corrective actions if possible, and escalate unresolved issues for manual intervention.8. **Data Lineage and Impact Analysis**:
— Establish data lineage tracking to trace the flow of data from source to target systems through various transformations.
— Implement impact analysis capabilities to assess the downstream effects of data errors or changes in the ETL processes.9. **Metadata Management**:
— Utilize Informatica’s metadata management capabilities to catalog and govern metadata related to ETL processes, data sources, transformations, and mappings.
— Maintain metadata repositories for documenting data lineage, data quality rules, and ETL process definitions.10. **Performance Optimization**:
— Fine-tune ETL processes and workflows for optimal performance and scalability.
— Monitor resource utilization, identify performance bottlenecks, and optimize ETL jobs accordingly.11. **Continuous Monitoring and Improvement**:
— Establish a framework for continuous monitoring of ETL processes, error rates, data quality metrics, and performance indicators.
— Iterate on the architecture based on insights gathered from monitoring data and feedback from stakeholders to continuously improve the reliability and efficiency of the ETL framework.By implementing the above architecture, you can build a robust framework for catching ETL processing errors effectively while ensuring data integrity, quality, and reliability throughout the data integration process.
I’ve installed the offline version of Phi-3 from Ollama in my mac and used it to summarize the Phi-3 hugging chat output. It’s remarkable that it can run on a laptop in offline mode without the need for API keys or complex coding. The output was outstanding.
References :
Introducing Phi-3: Redefining what’s possible with SLMs | Microsoft Azure Blog
