Forem: Syed Safdar Hussain

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Syed Safdar Hussain — Wed, 24 Sep 2025 15:58:13 +0000

Syed Safdar Hussain

Sep 20 '25

Maximizing ROI with AI: Cut Development Time by 40% and Accelerate Go-to-Market

#ai #development #productivity #casestudy

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2 min read

Migrating Oracle Fusion Cloud Data to Azure Fabric: A Practical Guide

Syed Safdar Hussain — Sat, 20 Sep 2025 19:48:54 +0000

Migrating Oracle Fusion Cloud Data to Azure Fabric: A Practical Guide

Migrating tables from Oracle Fusion Cloud to Microsoft Azure Fabric is a common data integration task. While using a direct HTTP connector might seem intuitive, it's not the recommended or most practical approach for bulk data transfer.

This guide will explain why and provide a robust, scalable solution using Oracle's recommended tools and Azure's data integration services.

Why Not a Direct HTTP Connector?

A direct HTTP connector approach, likely leveraging REST APIs, is not ideal for migrating entire tables from Oracle Fusion Cloud for a few key reasons:

Volume Limitations:

Oracle Fusion Cloud's REST APIs are primarily designed for transactional operations and are not optimized for bulk data extraction. They often limit the number of records retrieved per request (e.g., 500 rows), making it inefficient to extract large tables.
Throttling and Performance:

Making numerous API calls to fetch large datasets can lead to performance degradation and potential throttling by the Oracle Fusion Cloud service.
Complexity:

You would need to build a custom solution to handle pagination, error handling, data transformation, and scheduling. This can be time-consuming and difficult to maintain.

The Recommended Solution: A Two-Step Process

A more reliable and scalable method involves a two-step process:

Extracting data from Oracle Fusion Cloud to a staging area
Ingesting that data into Azure Fabric

Step 1: Extract Data from Oracle Fusion Cloud with BICC

Oracle's recommended tool for bulk data extraction from Fusion Cloud is the Oracle BI Cloud Connector (BICC).

BICC is specifically designed to extract large volumes of data from Oracle Fusion Cloud applications and prepare it for external use.

How it works:

Configure BICC
- Within Oracle Fusion Cloud, configure BICC to extract data from Public View Objects (PVOs).
- PVOs are predefined, flattened views of the application's data, making them easier to work with.
Choose a Storage Destination
- BICC delivers extracted data as CSV files.
- The most common choice is Oracle Cloud Infrastructure (OCI) Object Storage.
Schedule the Extraction
- Set up regular extracts (daily, weekly, etc.) to keep Azure Fabric up-to-date.
- Supports incremental extracts, pulling only new or changed data after the initial full load.

Step 2: Ingest Data into Azure Fabric using Azure Data Factory

Once the data is available in OCI Object Storage, you can use Azure Data Factory (ADF), a key component of Azure Fabric, to ingest it.

Workflow:

Create a Linked Service in ADF
- Connect ADF to OCI Object Storage by providing credentials and connection details.
Create a Dataset
- Define a dataset in ADF that points to the CSV files.
- Wildcards can be used to process multiple files.
Build a Pipeline
- Use the Copy Data activity in a pipeline.

Source: Dataset pointing to the CSV files in OCI.
Sink: Your Azure Fabric destination (Lakehouse or Data Warehouse).

Schedule the Pipeline
- Automate execution after BICC extraction completes, creating a seamless migration workflow.

Alternative Extraction Method: Oracle BI Publisher

For some use cases, Oracle BI Publisher can be used instead of BICC, especially when:

You need more control over the output format.
You require complex data transformations before extraction.

In this workflow:

A data model defines what to extract.
A report generates CSV files.
Azure Data Factory ingests them, similar to the BICC approach.

👉 However, for straightforward bulk extraction of tables, BICC is generally more efficient.

Visualizing the Workflow


mermaid
graph TD;
    A[Oracle Fusion Cloud] -->|1. Extract with BICC| B(OCI Object Storage - CSV Files);
    B -->|2. Ingest with Azure Data Factory| C(Azure Fabric - Lakehouse/Warehouse);

Migrating Oracle Fusion Cloud Data to Azure Fabric: A Practical Guide

Syed Safdar Hussain — Sat, 20 Sep 2025 19:48:54 +0000

Migrating Oracle Fusion Cloud Data to Azure Fabric: A Practical Guide

This guide will explain why and provide a robust, scalable solution using Oracle's recommended tools and Azure's data integration services.

Why Not a Direct HTTP Connector?

A direct HTTP connector approach, likely leveraging REST APIs, is not ideal for migrating entire tables from Oracle Fusion Cloud for a few key reasons:

Volume Limitations:

Oracle Fusion Cloud's REST APIs are primarily designed for transactional operations and are not optimized for bulk data extraction. They often limit the number of records retrieved per request (e.g., 500 rows), making it inefficient to extract large tables.
Throttling and Performance:

Making numerous API calls to fetch large datasets can lead to performance degradation and potential throttling by the Oracle Fusion Cloud service.
Complexity:

You would need to build a custom solution to handle pagination, error handling, data transformation, and scheduling. This can be time-consuming and difficult to maintain.

The Recommended Solution: A Two-Step Process

A more reliable and scalable method involves a two-step process:

Extracting data from Oracle Fusion Cloud to a staging area
Ingesting that data into Azure Fabric

Step 1: Extract Data from Oracle Fusion Cloud with BICC

Oracle's recommended tool for bulk data extraction from Fusion Cloud is the Oracle BI Cloud Connector (BICC).

BICC is specifically designed to extract large volumes of data from Oracle Fusion Cloud applications and prepare it for external use.

How it works:

Configure BICC
- Within Oracle Fusion Cloud, configure BICC to extract data from Public View Objects (PVOs).
- PVOs are predefined, flattened views of the application's data, making them easier to work with.
Choose a Storage Destination
- BICC delivers extracted data as CSV files.
- The most common choice is Oracle Cloud Infrastructure (OCI) Object Storage.
Schedule the Extraction
- Set up regular extracts (daily, weekly, etc.) to keep Azure Fabric up-to-date.
- Supports incremental extracts, pulling only new or changed data after the initial full load.

Step 2: Ingest Data into Azure Fabric using Azure Data Factory

Once the data is available in OCI Object Storage, you can use Azure Data Factory (ADF), a key component of Azure Fabric, to ingest it.

Workflow:

Create a Linked Service in ADF
- Connect ADF to OCI Object Storage by providing credentials and connection details.
Create a Dataset
- Define a dataset in ADF that points to the CSV files.
- Wildcards can be used to process multiple files.
Build a Pipeline
- Use the Copy Data activity in a pipeline.

Source: Dataset pointing to the CSV files in OCI.
Sink: Your Azure Fabric destination (Lakehouse or Data Warehouse).

Schedule the Pipeline
- Automate execution after BICC extraction completes, creating a seamless migration workflow.

Alternative Extraction Method: Oracle BI Publisher

For some use cases, Oracle BI Publisher can be used instead of BICC, especially when:

You need more control over the output format.
You require complex data transformations before extraction.

In this workflow:

A data model defines what to extract.
A report generates CSV files.
Azure Data Factory ingests them, similar to the BICC approach.

👉 However, for straightforward bulk extraction of tables, BICC is generally more efficient.

Visualizing the Workflow


mermaid
graph TD;
    A[Oracle Fusion Cloud] -->|1. Extract with BICC| B(OCI Object Storage - CSV Files);
    B -->|2. Ingest with Azure Data Factory| C(Azure Fabric - Lakehouse/Warehouse);

Maximizing ROI with AI: Cut Development Time by 40% and Accelerate Go-to-Market

Syed Safdar Hussain — Sat, 20 Sep 2025 18:55:12 +0000

We recently experienced a paradigm shift in our development lifecycle by leveraging AI. What traditionally took weeks was accomplished in mere hours. This is a look at the incredible return on investment from AI-powered development, transforming not just our timelines, but our entire workflow and business strategy.

AI’s Impact on Web App Development

The results are clear: AI reduces repetitive coding tasks, speeds up prototyping, and automates testing. This directly translates into measurable savings.

Small to Mid-Sized Web Apps: ~25–40% reduction in project effort/cost
Enterprise/Complex Apps (with compliance, integrations, security): ~15–25% reduction

The Old Way vs. The New Way

Traditional development requires significant time for coding, testing, and deployment. AI doesn’t replace developers but boosts them—automating the repetitive and freeing them to focus on architecture, logic, and quality.

The chart below shows the productivity shift with AI-assisted workflows.

Example: Person-Hours & Cost Savings

📌 Consider a typical project estimate:

Traditional: 1,000 person-hours
With AI: 600–750 hours

That’s a 25–40% saving in effort.

💰 In cost terms:

On a $150k project, this translates to $20k–$50k in savings.

How AI Changes the Game

The efficiency gains come from AI's ability to handle major development tasks almost instantly.

Instant Infrastructure

The AI generated the entire backend foundation, including database models and API endpoints, in minutes.This eliminated a major development bottleneck from day one.

Rapid Prototyping

Functional UI components were built on-demand.This allowed for immediate validation of the user experience, turning concepts into clickable reality almost instantly.

Automated Complexity

Complex business logic, like automated invoicing, was handled by the AI.The developer provided the rules, and the AI generated precise, error-free code.

The AI-Powered Workflow

This new process shifts the developer's role from a manual coder to a strategic architect.The focus moves from writing every line to directing the AI, testing outputs, and integrating the final pieces, dramatically increasing efficiency.

The workflow is simple:

Define Goal: The architect provides high-level requirements.
AI Generation: The AI writes boilerplate code, components, and logic.
Review & Refine: The architect tests, debugs, and integrates the AI-generated parts.
Deploy: The final solution is delivered at unprecedented speed.

The Bottom-Line Business Impact

The impact on the business is tangible, affecting everything from project costs to the sales cycle.

Project Cost Reduction

Instead of >90% cuts, realistic AI-powered projects show 15–40% savings, depending on complexity.

Accelerated Sales Cycle

With the ability to build functional demos in days, not months, the sales cycle is drastically shortened.This chart compares the time from initial proposal to client demo, highlighting a key competitive advantage.

This success story demonstrates the profound impact of AI in modern software development.

This is not hype—it’s the new reality of development. AI doesn’t just accelerate timelines, it redefines the economics of building software.

Agentic AI: Powering the Future of Energy Innovation

Syed Safdar Hussain — Tue, 05 Aug 2025 13:15:23 +0000

The energy sector stands at a pivotal moment—grappling with the dual challenges of meeting rising global demand while accelerating the shift to sustainable practices.

Agentic AI — autonomous, decision-making artificial intelligence that learns, adapts, and acts with minimal human intervention is emerging as a transformative force. Unlike traditional AI, which follows predefined rules, agentic AI dynamically interacts with its environment to:

Optimize operations
Reduce costs
Drive sustainability

🔑 For energy industry executives, understanding its real-world impact is critical to remaining competitive in a rapidly evolving landscape.

🔌 Transforming Smart Grids for Resilience and Efficiency

Modern power grids are complex networks of distributed energy sources from solar farms to EV charging stations.

Agentic AI enables:

Real-time, autonomous decision-making
Detection of outages, redirection of power flows
Load balancing between renewable and conventional sources

Benefits:

Downtime reduced by up to 30%
Operational costs cut by 15–20%

📍 Case in Point:
Duke Energy uses AI-powered smart meters to forecast demand and reduce blackout risks, saving millions in operational expenses.

By integrating with IoT sensors, agentic AI also strengthens grid resilience against:

Extreme weather
Cyber threats

Executive Takeaway:

Boost reliability, cut maintenance costs, and enhance customer satisfaction are key market differentiators.

☀️ Optimizing Renewable Energy Integration

Renewables like solar and wind are unpredictable. Agentic AI solves this via:

Intelligent forecasting using weather, grid load, and historical data
Smart storage management to decide when to store/release battery energy

⚡ BP leverages AI to optimize solar and wind operations, reducing methane leaks and saving $1.6B over 5 years.

Efficiency Gains:

Up to 25% improvement in storage operations
Reduced energy waste
Better ROI on renewables
Accelerated net-zero goals

🛠️ Driving Predictive Maintenance and Cost Savings

Unexpected equipment failures are a top cost driver. Agentic AI enables predictive maintenance by analyzing live sensor data.

🛢️ Shell monitors over 10,000 pieces of equipment using AI, reducing both downtime and environmental risk.

Impact:

Maintenance costs reduced by 20–30%
Asset lifespan extended
Enhanced safety and compliance

💡 Result: Reinvest savings into innovation or pass them to customers — increasing market competitiveness.

💹 Enhancing Energy Trading and Market Agility

The energy market is volatile. Agentic AI gives you an edge by:

Automating trading decisions in milliseconds
Predicting prices using weather, market, and geopolitical data
Enabling peer-to-peer energy exchanges

🔄 Circularr uses blockchain + AI to let solar-powered households sell surplus energy directly.

💼 ExxonMobil reduced exploration costs by 15% using AI-driven forecasting.

Benefits:

Better margins
Faster market response
Competitive advantage in decentralized energy ecosystems

🌍 Advancing Sustainability and Compliance

Sustainability is now a compliance issue as much as a brand imperative.

Agentic AI helps:

Optimize Carbon Capture, Usage & Storage (CCUS)
Cut emissions via intelligent energy use
Monitor environment using IoT + sensors

🌱 Repsol’s AI-driven analytics reduced operational costs by 20% while supporting renewable projects.

🔎 According to KPMG, 43% of energy executives use AI for environmental monitoring.

Executive Value:

Meet regulatory standards + build a reputation for sustainability — attracting eco-conscious investors and consumers.

⚠️ Addressing Challenges for Maximum Impact

Adopting Agentic AI isn’t plug-and-play. Key challenges include:

🧠 Talent Shortage

68% of executives struggle to find data scientists with energy expertise.

🔐 Cybersecurity

20% cite data breaches as a top concern.

Solutions:

Encrypt data and conduct regular security audits
Build strategic partnerships
Upskill your workforce with AI readiness programs
Use platforms for structured implementation

🚀 The Path Forward for Energy Executives

Agentic AI is not just tech — it’s a strategic imperative.

The global AI energy market is projected to hit $13B, with oil & gas alone spending $3B+ in 2024.

Industry leaders like Siemens, GE, and Tesla already leverage agentic AI to optimize:

Grid management
Storage systems
Energy production

✅ Executive Action Plan:

Assess your data infrastructure
Partner with experienced AI firms
Align AI investments with sustainability goals

🌟 Final Thoughts

The future of energy is:

Autonomous
Intelligent
Sustainable
Agentic AI is the key to leading it.

By: Syed Safdar Hussain
Principal Cloud & AI Solution Architect

Deepseek R1 vs V3: Performance, Features, and Beyond

Syed Safdar Hussain — Sun, 02 Feb 2025 08:45:31 +0000

Deepseek R1 and Deepseek V3 are two distinct models developed by Deepseek AI, each designed for specific use cases and with different capabilities. Below, I’ll break down the key differences between Deepseek R1 and Deepseek V3 to help you understand their unique features and applications.

1. Purpose and Use Cases

Deepseek R1

Focus: Deepseek R1 is a general-purpose large language model (LLM) designed for tasks like text generation, summarization, question answering, and multilingual support.
Use Cases:
- Content creation (blogs, articles, social media posts).
- Customer support chatbots.
- Language translation and multilingual applications.
- Educational tools and interactive learning.

Deepseek V3

Focus: Deepseek V3 is a specialized model optimized for vision-language tasks, combining natural language processing (NLP) with computer vision capabilities.
Use Cases:
- Image captioning.
- Visual question answering (VQA).
- Multimodal content generation (e.g., generating text descriptions for images).
- Applications requiring both text and image understanding.

2. Architecture and Capabilities

Deepseek R1

Architecture: Deepseek R1 is a text-only model based on a transformer architecture, optimized for efficient text processing and generation.
Capabilities:
- High-quality text generation.
- Multilingual support (works across multiple languages).
- Fine-tuning for specific tasks.
- Open-source and lightweight, making it easy to deploy.

Deepseek V3

Architecture: Deepseek V3 is a multimodal model that integrates both text and image processing using a combination of transformer-based NLP and convolutional neural networks (CNNs) or vision transformers (ViTs).
Capabilities:
- Image understanding and analysis.
- Text generation based on visual input (e.g., describing an image).
- Multimodal reasoning (combining text and image data for complex tasks).
- Fine-tuning for vision-language tasks.

3. Performance and Efficiency

Deepseek R1

Performance: Optimized for text-based tasks, Deepseek R1 delivers fast inference speeds and high accuracy in text generation and understanding.
Efficiency: Designed to be lightweight and resource-efficient, making it suitable for deployment in environments with limited computational resources.

Deepseek V3

Performance: Excels in multimodal tasks, offering strong performance in both text and image understanding. However, it may require more computational resources due to its dual focus on text and vision.
Efficiency: While efficient for a multimodal model, Deepseek V3 is generally more resource-intensive than Deepseek R1 due to the additional complexity of processing visual data.

4. Open-Source and Accessibility

Deepseek R1

Open-Source: Yes, Deepseek R1 is open-source, allowing developers to freely use, modify, and deploy the model.
Accessibility: Easily integrated with frameworks like Ollama for local deployment and experimentation.

Deepseek V3

Open-Source: Likely open-source (depending on Deepseek AI’s release policy), but with a focus on multimodal capabilities.
Accessibility: Requires additional tools and libraries for handling image data, making it slightly more complex to set up compared to Deepseek R1.

5. Sample Code Comparison

Deepseek R1 with Ollama

Here’s an example of using Deepseek R1 for text generation with Ollama:

import ollama

# Initialize Ollama client
client = ollama.Client()

# Generate text using Deepseek R1
response = client.generate(
    model="deepseek-r1",
    prompt="Explain the benefits of renewable energy."
)

# Print the generated text
print(response['text'])

Deepseek V3 with Ollama

For Deepseek V3, you’d typically need to handle both text and image inputs. Here’s an example of generating a caption for an image:

import ollama
from PIL import Image

# Initialize Ollama client
client = ollama.Client()

# Load an image
image = Image.open("example_image.jpg")

# Generate a caption using Deepseek V3
response = client.generate(
    model="deepseek-v3",
    prompt="Describe the image.",
    image=image
)

# Print the generated caption
print(response['text'])

6. Comparison Table

Feature	Deepseek R1	Deepseek V3
Primary Focus	Text-based tasks	Multimodal (text + image) tasks
Use Cases	Text generation, summarization, multilingual support	Image captioning, visual question answering, multimodal reasoning
Architecture	Transformer-based (text-only)	Transformer + CNN/ViT (multimodal)
Efficiency	Lightweight and resource-efficient	More resource-intensive due to image processing
Open-Source	Yes	Likely yes
Accessibility	Easy to deploy with Ollama	Requires additional setup for image handling

Conclusion

Deepseek R1 is ideal for developers who need a text-focused, lightweight, and efficient LLM for tasks like content creation, customer support, and multilingual applications.
Deepseek V3 is better suited for multimodal applications that require both text and image understanding, such as image captioning, visual question answering, and multimodal content generation.

Choosing between Deepseek R1 and Deepseek V3 depends on your specific use case. If you’re working with text-only tasks, Deepseek R1 is the way to go. For projects involving both text and images, Deepseek V3 offers the necessary capabilities.

By: Syed Safdar Hussain

DeepSeek R1: A New Contender in the World of Large Language Models

Syed Safdar Hussain — Fri, 31 Jan 2025 10:47:17 +0000

🚀 DeepSeek R1: A New Contender in the World of Large Language Models

The field of artificial intelligence (AI) has seen rapid advancements, particularly in large language models (LLMs). These models, designed to understand and generate human-like text, have become indispensable in NLP, content creation, and AI-driven applications.

Among the latest entrants in this space is DeepSeek R1, a promising new LLM competing with OpenAI’s GPT-4, Google’s Gemini, and Meta’s LLaMA 2.

In this article, we’ll explore:

✅ What makes DeepSeek R1 unique

✅ How it compares to other LLMs

✅ A step-by-step guide to running DeepSeek R1 locally using Ollama

🔍 What is DeepSeek R1?

DeepSeek R1 is a state-of-the-art large language model developed by DeepSeek AI. It is designed for high-quality text generation, summarization, and Q&A capabilities while being optimized for performance and resource efficiency.

✨ Key Features of DeepSeek R1

🚀 Efficiency – Optimized for fast inference and reduced resource usage.
🌎 Multilingual Support – Supports multiple languages for global applications.
🔧 Fine-Tuning – Can be customized for specific tasks.
🛠 Open-Source Friendly – Seamless integration with open-source tools like Ollama.

⚖️ How Does DeepSeek R1 Compare to Other LLMs?

Let’s see how DeepSeek R1 stacks up against the competition:

Feature	DeepSeek R1	GPT-4 (OpenAI)	Gemini (Google)	LLaMA 2 (Meta)
Model Size	Medium	Very Large	Large	Medium to Large
Efficiency	High	Moderate	Moderate	High
Multilingual	✅ Yes	✅ Yes	✅ Yes	❌ Limited
Fine-Tuning	✅ Yes	❌ Limited	❌ Limited	✅ Yes
Open-Source	✅ Yes	❌ No	❌ No	✅ Yes
Inference Speed	⚡ Fast	🐢 Moderate	🐢 Moderate	⚡ Fast

🧠 Model Parameters and Architecture

DeepSeek's latest model, DeepSeek-R1, utilizes a Mixture-of-Experts (MoE) architecture, comprising a total of 671 billion parameters. However, due to the MoE design, only 37 billion parameters are activated during each inference pass, optimizing computational efficiency.

💸 Training Cost

The model was trained using ~2,000 Nvidia H800 GPUs, with an estimated total expenditure of $5.6 million.
This is significantly lower than the training costs associated with comparable LLMs.

⚡ Performance

DeepSeek-R1 excels in mathematical reasoning and coding tasks.
Benchmarks reveal that it matches or surpasses OpenAI’s o1 model in tests like AIME and MATH datasets.

🔒 Security Considerations

Being open-source, DeepSeek allows for transparency and custom security implementations.
Organizations should ensure secure deployment, particularly due to data compliance concerns in enterprise environments.

☁️ Deployment Options

Cloud Deployment: Available for integration into Azure, AWS, and other cloud platforms.
On-Prem Deployment: Can be hosted locally for maximum security and compliance.

🌟 Why DeepSeek Stands Out

Open-Source Flexibility – Developers and enterprises can fine-tune and customize DeepSeek to fit specific use cases without being locked into proprietary ecosystems.
Optimized for Coding – DeepSeek includes specialized training for code generation and completion, making it a strong alternative to Copilot and CodeLlama.
Enterprise-Friendly Deployment – With options for on-premises and cloud-based setups, DeepSeek ensures security and compliance for organizations working with sensitive data.

🌍 Use Cases for Deepseek R1

Deepseek R1’s versatility makes it suitable for a wide range of applications, including:

Content Creation: Generate high-quality articles, blogs, and social media posts.
Customer Support: Build AI-powered chatbots for handling customer queries.
Language Translation: Leverage its multilingual capabilities for translation tasks.
Education: Create interactive learning tools and generate educational content.

🛠 Getting Started with DeepSeek R1 Using Ollama

Ollama is a powerful framework that allows you to run large language models locally. It supports multiple models, including DeepSeek R1, making it an excellent choice for experimentation and deployment.

Step 1: Install Ollama

To install Ollama, run the following commands:

# Clone the Ollama repository
git clone https://github.com/ollama/ollama.git
cd ollama

# Install dependencies and set up Ollama
pip install -r requirements.txt

Step 2: Download Deepseek R1

Once Ollama is set up, you can download the Deepseek R1 model using the following command:

bash

ollama pull deepseek-r1

Step 3: Run Deepseek R1 Locally

After downloading the model, you can start generating text using Deepseek R1.

python

import ollama

# Initialize the Ollama client
client = ollama.Client()

# Generate text using Deepseek R1
response = client.generate(
    model="deepseek-r1",
    prompt="Explain the benefits of using Deepseek R1 over other LLMs."
)

# Print the generated text
print(response['text'])

Deepseek R1 offers several advantages over other large language models, including its efficiency, multilingual support, and fine-tuning capabilities. Unlike proprietary models like GPT-4, Deepseek R1 is open-source, giving developers more flexibility and control over their applications. Additionally, its optimized architecture ensures fast inference speeds, making it ideal for real-time applications.

Git: https://github.com/deepseek-ai/DeepSeek-R1