Fawad Butt’s Post

This is the most common response from my clients: "I didn’t know I had it for free." 🤯 This is what you are missing! Oracle Autonomous Database is not only a managed service, it provides great tooling for the full suite of capabilities! Yes all included! For example 👉 Data Studio Platform with AI Capabilities 🤖 👉 Data Transform for building your Data Pipelines 👉 Data Catalog for Governance 👉 Data Analysis and Data Insights for your business models and discovery insights 👉Data Sharing to improve collaboration And much more! I haven't talked about Machine Learning, Relational to Graph, Select AI for querying data with Natural Language.... Did you knew all of them? #DataEngineering #Data #innovation

📊Business representatives often overlook data preparation in #MachineLearning. However, engineers know well that data has a predominant impact on the end model’s accuracy and reliability, as per the Garbage-in-Garbage-out, or simply (GIGO) rule. 👉In our most recent blog post, an expert at Intelliarts delves into data preparation steps, indicating the best strategies and common challenges. He also provides tools and technologies commonly utilized for data preparation. We strive to help every business make the most out of their data and #ML development. Read the post available at the following link: https://lnkd.in/edKJMKSD #DigitalTransformation #Innovation #MachineLearning #DataScience

💡 The World of Data Pipelines: A Simple 5-Step Guide Data pipelines aren’t just about moving data—they’re the backbone of modern systems, powering analytics and machine learning. Many think of them as basic ETL (Extract, Transform, Load), but they’re so much more. Here’s a simple breakdown: 5 Steps of a Data Pipeline 📌 1. Collect What happens? Data is gathered from different sources—apps, devices, or streams. Why it matters: It ensures you have all the raw data for further steps. 📌 2. Ingest What happens? Data is loaded and organized into queues or buffers. Why it matters: It sets the stage for smooth storage and processing. 📌 3. Store What happens? Data is saved in warehouses, lakes, or databases. Why it matters: Proper storage keeps data secure and scalable. 📌 4. Compute What happens? Data is cleaned, aggregated, and transformed. Why it matters: This step makes raw data ready for insights. 📌 5. Consume What happens? Processed data drives reports, dashboards, and ML models. Why it matters: This is where data turns into actionable value. Why Data Pipelines Matter - Efficiency: Saves time and cuts costs. - Reliability: Ensures data is always available. - Scalability: Grows with your business. - Insights: Delivers smarter decisions, faster. 💡 Pro Tip: Review your current data processes—strong pipelines fill critical gaps. What’s your take on data pipelines? Let’s chat! ♻️ Share 👍 React 💬 Comment Special thanks to the makers of this GIF for simplifying the concept of Data Pipelines. #DataPipelines #BigData #DataEngineering #MachineLearning #DataAnalytics #ETL #DataScience #DataDriven #AI #DataTransformation #BusinessIntelligence #CloudComputing #DataStorage #TechInnovation

Exciting updates from Fabric! - Enhanced data integration and governance - Improved security and access control - New features for data exploration and visualization - Performance optimizations Reach out to us if you want to use Fabric to address your data issues. Learn how our in-house metadata-driven frameworks can enhance data ingestion, cleansing, quality, and curation. Read the full update to learn more about the latest advancements in Fabric! https://lnkd.in/gpgSvFnj  #DataEngineering #AI #FabCon #Fabric #Metadata #DataIntegration #Security #DataVisualization

𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 𝗧𝗶𝗺𝗲 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝗟𝗶𝗻𝗲𝗮𝗿 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 → Training Time Complexity: O(n * p) → Prediction Time Complexity: O(p) 🟢 Linear regression scales well with large datasets, where n is the number of data points and p is the number of features. 𝗟𝗼𝗴𝗶𝘀𝘁𝗶𝗰 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 → Training Time Complexity: O(n * p * i) → Prediction Time Complexity: O(p) 🟢 Involves iterative updates (i = iterations), making it slower than linear regression but efficient for binary classification. 𝗞-𝗡𝗲𝗮𝗿𝗲𝘀𝘁 𝗡𝗲𝗶𝗴𝗵𝗯𝗼𝗿𝘀 (𝗞-𝗡𝗡) → Training Time Complexity: O(1) → Prediction Time Complexity: O(n * p) 🟠 Training is instant, but prediction time grows with dataset size n, as it calculates the distance to every point. 𝗦𝘂𝗽𝗽𝗼𝗿𝘁 𝗩𝗲𝗰𝘁𝗼𝗿 𝗠𝗮𝗰𝗵𝗶𝗻𝗲𝘀 (𝗦𝗩𝗠) → Training Time Complexity: O(n^2 * p) (or O(n^3) for kernels) → Prediction Time Complexity: O(s * p) (s = support vectors) 🔴 Computationally heavy for training, especially with kernels, but effective in high-dimensional spaces. 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗧𝗿𝗲𝗲𝘀 → Training Time Complexity: O(n * p * log(n)) → Prediction Time Complexity: O(log(n)) 🟢 Efficient for both training and prediction, well-suited for non-linear data. 𝗥𝗮𝗻𝗱𝗼𝗺 𝗙𝗼𝗿𝗲𝘀𝘁 → Training Time Complexity: O(k * n * p * log(n)) (k = number of trees) → Prediction Time Complexity: O(k * log(n)) 🟠 Scales better than decision trees by reducing overfitting, but increases complexity with added trees. 𝗡𝗮𝗶𝘃𝗲 𝗕𝗮𝘆𝗲𝘀 → Training Time Complexity: O(n * p) → Prediction Time Complexity: O(p) 🟢 Simple and very fast, both in training and prediction, making it ideal for high-dimensional datasets. 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 → Training Time Complexity: O(i * n * p) (i = iterations) → Prediction Time Complexity: O(p) 🔴 Highly dependent on the architecture and number of layers, with significant training times. → Note: Time complexities provide a general guideline; actual performance can vary based on data, implementation, and hardware. --- 400+ 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: https://lnkd.in/gv9yvfdd 📘 𝗣𝗿𝗲𝗺𝗶𝘂𝗺 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 : https://lnkd.in/gPrWQ8is 📙 𝗣𝘆𝘁𝗵𝗼𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗟𝗶𝗯𝗿𝗮𝗿𝘆: https://lnkd.in/gHSDtsmA 📗 45+ 𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝘀 𝗕𝗼𝗼𝗸𝘀 𝗘𝘃𝗲𝗿𝘆 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 𝗡𝗲𝗲𝗱𝘀: https://lnkd.in/ghBXQfPc --- Join What's app channel for jobs updates: https://lnkd.in/gu8_ERtK 📸: @datainterview.com

😰Beware of the Cartesian Effect 🫨 In 2019, I had the incredible opportunity to train with Dan Linstedt, the founder of the Data Vault 2.0 model. This revolutionary framework is the only other model outside of a traditional relational database, designed to provide agility and scalability. Its architecture offers an innovative approach to handling complex data environments. One critical challenge in data management, especially with AI and larger datasets, is the Cartesian Effect. 🧐 So, what is the Cartesian Effect? This occurs when multiple datasets are joined together in ways that unintentionally multiply the number of rows, often due to a missing or poorly defined relationship between the datasets. The result is an exponential explosion of data💥🌋, which can overwhelm systems and create downstream issues. 🤔 Why does it matter? With the rise of AI and increasingly complex datasets, the Cartesian Effect can have serious consequences for businesses if left unchecked, unnoticed, or unknown. 🔑Key Impacts: 🛑 System Performance Degradation: The unintended increase in data volume can lead to slower query performance and system outages. ⛔️ Storage Overload💸: Massive, duplicated datasets consume significant storage resources, driving up costs unnecessarily. 🚫 Inaccurate Reporting‼️🚨‼️: Incorrect joins can distort analytics, leading to flawed business intelligence and decision-making. ⏱️ Operational Delays: Processing these inflated datasets can result in longer ETL (Extract, Transform, Load) times, disrupting workflows. As organizations increasingly leverage AI and integrate vast datasets, it's vital to monitor for this issue and design systems that mitigate its effects. The Data Vault 2.0 model offers a great starting point for ensuring scalability and resilience in modern data environments. #DataManagement #AI #DataVault #DataEngineering #BigData #DataScience #Analytics #DatabaseDesign #DataArchitecture #CartesianEffect #BusinessIntelligence #RelationalDatabases #TechInnovation

Is Small Data the Solution When Big Data Isn't an Option? In the realm of data analytics, the buzz around "big data" often overshadows the potential of "small data" solutions. While big data aims at handling massive datasets, small data can be a limitation but also an opportunity and offers surprising advantages for predictive modeling. Small data emphasizes quality over quantity. Despite having fewer data points, small data approaches can still yield robust prediction models. Here's why leveraging small data matters: - Quality over Quantity: With small data, the focus is on selecting the most relevant data points, the most recent ones, enhancing model accuracy. Each data point becomes critical for insightful analysis. - Precision and Adaptability: Models developed with small data can be simpler and easier to interpret. When built correctly, they can adapt more readily to new scenarios, making them highly practical for real-world applications. - Cost-Efficiency and Accessibility: Small data solutions are more cost-effective. They reduce experimental time and cost. Sensorization can be limited. They also require less storage and computational resources. This accessibility makes them suitable for all organizations regardless of their size. In a world dominated by discussions of big data, embracing small data can be transformative. Even with limited data, effective predictive modeling is achievable without the need for vast datasets. If you're navigating data constraints and seeking practical solutions for predictive analytics, consider exploring the power of small data. Quality and ingenuity often play a significant role in deriving meaningful insights from data. #NeurEco #Adagos #AI #Software #AISoftware #Data #SmallData #BigData #ArtificialIntelligence #IA

Exciting updates from Fabric! - Enhanced data integration and governance - Improved security and access control - New features for data exploration and visualization - Performance optimizations Reach out to us if you want to use Fabric to address your data issue. Learn how our in-house metadata-driven frameworks can enhance data ingestion, cleansing, quality, and curation. Read the full update to learn more about the latest advancements in Fabric! https://lnkd.in/gpgSvFnj  #DataEngineering #AI #FabCon #Fabric #Metadata #DataIntegration #Security #DataVisualization

All this talk about semantic layers makes it sound like they’re some new revolutionary discovery. They’re not. Well-architected data platforms have had semantic layers as a necessary component since the 1980s. Dimensional models are a semantic layer. Data marts are a semantic layer. Measures and metrics are a semantic layer. The only difference is, the fact that LLMs *need* a semantic layer in order to write remotely accurate SQL is exposing that most home-grown data platforms are, frankly, poorly architected. The fastest, most efficient data teams have always separated the semantic layer from the BI tool to retain flexibility (e.g., to move to a new BI tool or train an AI model). Now everyone else finally has a more tangible reason to catch up. #dataengineering #dataarchitecture

Data that is not consumed or queried is simply inert. Day 12/30 of our data engineering journey. Serving data is the last stage of the data engineering lifecycle. It involves getting value from the data. Getting value from data means different things to different users. It is the most exciting part of the lifecycle where the magic happens. This is where ML engineers apply the most advanced techniques. Let's look at some popular uses of data: 1) Business Intelligence (BI): BI turns raw data into useful insights through reporting, data mining, and analytics. It helps businesses make smart decisions. 2) Operational Analytics: This type of analytics focuses on real-time data to improve everyday business operations, making processes smoother and more efficient. 3) Embedded Analytics: These analytics are built into business applications, providing users with contextual data insights to improve decision-making right within their workflow. 4) Machine Learning (ML): ML uses advanced algorithms to predict outcomes and find patterns in data, helping with tasks like recommendations and detecting fraud. The data serving stage is where all our hard work pays off. Its where we turn data into actionable insights that help businesses make better decisions, streamline operations, and innovate. Whether its BI, operational analytics, embedded analytics, or ML, each use case shows how powerful data can be when used effectively. #dataengineering #30DaysofDataEngineering #FundamentalsOfDataEngineering