Artificial Intelligence in Big Data

AI is only as powerful as the data it learns from. But raw data alone isn’t enough—it needs to be collected, processed, structured, and analyzed before it can drive meaningful AI applications.  How does data transform into AI-driven insights? Here’s the data journey that powers modern AI and analytics:  1. 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗲 𝗗𝗮𝘁𝗮 – AI models need diverse inputs: structured data (databases, spreadsheets) and unstructured data (text, images, audio, IoT streams). The challenge is managing high-volume, high-velocity data efficiently.  2. 𝗦𝘁𝗼𝗿𝗲 𝗗𝗮𝘁𝗮 – AI thrives on accessibility. Whether on AWS, Azure, PostgreSQL, MySQL, or Amazon S3, scalable storage ensures real-time access to training and inference data.  3. 𝗘𝗧𝗟 (𝗘𝘅𝘁𝗿𝗮𝗰𝘁, 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺, 𝗟𝗼𝗮𝗱) – Dirty data leads to bad AI decisions. Data engineers build ETL pipelines that clean, integrate, and optimize datasets before feeding them into AI and machine learning models.  4. 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗲 𝗗𝗮𝘁𝗮 – Data lakes and warehouses such as Snowflake, BigQuery, and Redshift prepare and stage data, making it easier for AI to recognize patterns and generate predictions.  5. 𝗗𝗮𝘁𝗮 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 – AI doesn’t work in silos. Well-structured dimension tables, fact tables, and Elasticube models help establish relationships between data points, enhancing model accuracy.  6. 𝗔𝗜-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 – The final step is turning data into intelligent, real-time business decisions with BI dashboards, NLP, machine learning, and augmented analytics.  AI without the right data strategy is like a high-performance engine without fuel. A well-structured data pipeline enhances model performance, ensures accuracy, and drives automation at scale.  How are you optimizing your data pipeline for AI? What challenges do you face when integrating AI into your business? Let’s discuss.  

AI for Data Engineering isn't to replace data engineers, it's to have touch of AI with their creative solutions! Data engineers can enhance productivity by leveraging AI tools through the following tasks: - Generate data pipeline templates to streamline project initiation. - Automate code review and optimization for improved efficiency. - Identify potential performance bottlenecks proactively. - Enhance data quality validation to ensure accuracy. - Implement AI-driven data governance for compliance. - Automate documentation generation for better collaboration. - Optimize infrastructure with AI insights for scalability and performance. Additionally, there’s a growing demand for real-time analytics powered by AI.  With the advent of big data and streaming technologies, data engineers must adapt to facilitate quick data processing and real-time insights. Implementing AI algorithms enables data engineers to work more effectively with real-time data, aligning their skills with the needs of businesses that require immediate insights for decision-making. Here are few resources to explore and keep the data engineering community growing: Books: - "AI Engineering: Building Applications with Foundation Models " by Chip Huyen   - "Data Engineering: A Hands-On Approach to Big Data and AI" by Andrew Brust   - "Designing Data-Intensive Applications" by Martin Kleppmann   - “Building LLMs for Production” by Louis-François Bouchard and Louie Peters   - "Fundamentals of Data Engineering": Plan and Build Robust Data Systems by Joe Reis and Matthew Housley Online Learning Platforms: Coursera   - Machine Learning Engineering for Production (MLOps) Specialization   - Deep Learning Specialization   - Data Engineering Professional Certificate DataCamp   - Data Engineering for Everyone   - Building Data Engineering Pipelines   - Introduction to PySpark Udacity   - Data Engineering Nanodegree   - Machine Learning Engineer Nanodegree   - AI Programming with Python Nanodegree Technology Providers:   - Databricks Documentation and Blog   - Amazon Web Services (AWS) Machine Learning Blog   - Google Cloud AI/ML Documentation   - Microsoft Azure AI Blog - NVIDIA AI courses Some of the amazing and my favourite folks to follow: Cassie Kozyrkov, Steve Nouri, Alex Wang, Greg Coquillo! Who is your goto person for staying updated with AI innovations? Data Engineers aren’t building pipelines, they’re building data stores that users can rely on to be more productive while using AI systems!! #Data #Engineering #ArtificialIntelligence #Innovation

𝗔𝗜 𝗮𝗻𝗱 𝗗𝗮𝘁𝗮: 𝗔 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗣𝗮𝗿𝘁𝗻𝗲𝗿𝘀𝗵𝗶𝗽 88% of organizations could improve their data usage. 79% need AI for critical tasks to stay ahead. Find out why. 👇 These findings from TechTarget's ESG highlight a clear trend: AI and data are essential for competitive business operations. The first step in this data trend involves understanding the role of modern data platforms. Why invest in modern data platforms? They serve as the foundation where we build and scale AI applications.  Without them... Even the most advanced AI technologies would struggle to manage and process the vast amounts of data generated today. Moving forward, let's talk about external data. We live in a world where data flows from every corner, from social media buzz to comprehensive industry databases. What role does it play in enhancing AI? Feeding AI with external data unlocks deep insights. It sharpens AI's accuracy, providing precise predictions and richer market intelligence. The result? Deep insights that can transform business strategies. For example: - An e-commerce platform can analyze social media for consumer trends. - A clothing retailer could track fashion trends to adjust its inventory regionally, minimizing overstock and boosting sales. - A public health organization could follow social media and health alerts to predict disease outbreaks. - An insurance company can use data from connected vehicles to set personalized insurance rates. Looking ahead, the role of AI and external data is only set to grow. We're moving towards even more integrated systems where AI can provide strategic as well as operational insights. How are you leveraging external data with AI? #webscraping #artificialintelligence #innovation #data For more insights into where AI is heading next, follow me.

❄️ Building AI Apps on a Solid Data Foundation with Iceberg 🚀 A strong data foundation is critical for building scalable, efficient, and reliable AI applications. Here’s a breakdown of a robust architecture for AI-driven workflows: 🔄 Data Capture: Debezium Server captures real-time change data from MySQL to ensure up-to-date information. Kafka streams this data seamlessly for further processing. ⚙️ Data Processing: Spark Streaming applies real-time Fraud Detection Logic, ensuring security and reliability. The processed data is funneled into a Data Science Bucket for advanced analytics and model development. 📦 Data Storage: Redis handles high-speed storage for real-time user data. PostgreSQL stores user probabilities and predictions for long-term analysis. 🔍 Advanced Querying: Tools like Trino provide efficient querying on the processed data, enabling seamless insights for decision-making. 💡 Why It Matters: This architecture ensures data consistency, supports real-time analytics, and provides a scalable foundation for AI applications. 👉 What tools or frameworks do you use for your AI data workflows? Let’s discuss in the comments! #DataArchitecture #AIApps #BigData #SparkStreaming #Kafka #MachineLearning