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AI Data Infrastructure Value Chain Data Ingestion Source Identification: Identify structured, semi-structured, and unstructured data sources such as databases, APIs, and IoT devices. ETL/ELT Pipelines: Automate Extract, Transform, Load (ETL) or ELT processes to move data from various sources into a unified platform. Real-Time vs. Batch Processing: Decide between real-time data ingestion (streaming) and batch processing based on latency requirements. Data Storage and Management Scalable Data Lakes/Warehouses: Use scalable cloud-based data lakes (e.g., AWS S3, Azure Data Lake) and warehouses (e.g., Snowflake, BigQuery) for storing raw and processed data. Data Indexing: Implement robust indexing (e.g., Elasticsearch, FAISS) for efficient querying, especially for unstructured data like documents or images. Metadata Management: Store metadata to track data lineage, improve discoverability, and support governance. Data Preprocessing Data Cleaning: Automate data cleaning for missing values, outliers, and inconsistencies using AI/ML algorithms. Normalization/Standardization: Normalize data formats and structures to ensure consistency across the pipeline. Data Enrichment: Integrate external data (e.g., third-party APIs) to enrich the dataset, adding valuable contextual information. Data Annotation and Labeling Human-in-the-Loop: Employ manual annotations where necessary to refine accuracy, focusing on complex or ambiguous cases. Synthetic Data Generation: Generate synthetic data to fill gaps or balance datasets when labeled data is scarce. Model Development and Training Modeling Platforms: Utilize platforms like TensorFlow, PyTorch, or MindsDB for developing and training machine learning models. Compute Resources: Leverage cloud computing (AWS, Azure, Google Cloud) or specialized hardware (GPUs, TPUs) for scalable model training. Model Deployment and Serving Deployment Strategies: Implement continuous integration/continuous deployment (CI/CD) pipelines for AI models using tools like Kubernetes, Docker, or managed services like AWS SageMaker. Monitoring and Logging: Monitor model performance in production, track key metrics (latency, accuracy, etc.), and log for troubleshooting and improvement. AI Governance and Compliance Data Governance: Ensure compliance with data privacy regulations focusing on sensitive data handling and anonymization techniques. Model Governance: Implement model interpretability, fairness checks, and accountability for responsible AI usage. Audit Trails: Maintain logs and audit trails for data access and model decisions, ensuring traceability and transparency. AI-Powered Insights and Actionable Analytics Data Visualization: Use tools like Tableau, PowerBI, or Looker for interactive dashboards and reports to deliver actionable insights. Predictive Analytics: Integrate AI models for predicting trends, anomalies, or decision-making outcomes. Automated Decision-Making: Enable AI-driven automation for decision-making processes

Data Engineering in 2024: Pioneering the Future of Data Quantum Leaps, AI Synergy... As we approach the end of 2024, data engineering evolves rapidly, shaping how organizations leverage their data assets. Here's a concise overview of current trends and future directions: 𝗖𝘂𝗿𝗿𝗲𝗻𝘁 𝗟𝗮𝗻𝗱𝘀𝗰𝗮𝗽𝗲 1. Advanced Data Quality and Observability   - 85% of Fortune 500 companies now use AI-driven data quality tools   - "Quality-as-code" practices are becoming standard   - Causal inference techniques are enhancing anomaly detection 2. Microservices and Event-Driven Architectures   - 78% of organizations use event streaming for critical operations   - Data contracts are widely used to manage inter-service dependencies   - Specialized data mesh platforms are emerging 3. Cloud-Native and Multi-Cloud Strategies   - 92% of enterprises employ multi-cloud strategies   - Cloud-agnostic data tools market has grown 200% since 2022   - "Cloud-agnostic data fabrics" provide consistent governance across clouds 𝗖𝘂𝘁𝘁𝗶𝗻𝗴-𝗘𝗱𝗴𝗲 𝗧𝗿𝗲𝗻𝗱𝘀 1. AI-Augmented Data Engineering   - 70% of data engineering tasks are now AI-assisted   - Large language models generate and optimize ETL code   - "AIOps for data" platforms predict and prevent pipeline failures 2. Quantum-Ready Data Infrastructure   - 15% of Fortune 100 companies have initiated quantum-ready projects   - Investment in quantum-resistant encryption has grown 300% since 2022   - Quantum machine learning is being explored for complex data analysis 3. Edge Computing and Real-Time Analytics   - 65% of enterprises process some data at the edge   - "Edge data mesh" architectures enable distributed processing   - 5G and satellite internet facilitate real-time data streaming from remote locations 𝗥𝗲𝗴𝗶𝗼𝗻𝗮𝗹 𝗩𝗮𝗿𝗶𝗮𝘁𝗶𝗼𝗻𝘀 - North America leads in AI-augmented data engineering adoption - Europe shows highest adoption of privacy-enhancing technologies - Asia-Pacific leads in edge computing, especially in manufacturing and smart cities - Latin America sees the fastest cloud adoption growth for data workloads 𝗙𝘂𝘁𝘂𝗿𝗲 𝗢𝘂𝘁𝗹𝗼𝗼𝗸 1. Autonomous Data Ecosystems: Expected by 2026, self-optimizing and self-healing 2. Quantum Data Analytics: Significant advantages in specific domains by 2027 3. Brain-Computer Interfaces: Experimental systems for data interaction by 2028 4. Ethical AI Governance Platforms: Widespread adoption expected by 2025 5. Exascale Data Processing: Available as a service by 2026 𝗖𝗼𝗻𝗰𝗹𝘂𝘀𝗶𝗼𝗻 Data engineering in 2024 spearheads innovation in AI, quantum readiness, edge processing, and ethical data practices. As we approach 2025, the field promises incremental gains and paradigm shifts in data handling. Organizations adept at navigating these trends will lead to our data-driven future. 👋 I'm Siddhartha Vemuganti, Data Engineering & AI/ML leader. Passionate about scalable AI futures. Repost ♻️, Follow & 🔔 for more insights on data, AI, and tech's future!

Title: Exploring the Power of Data Science: Transforming Data into Insights 1.Introduction: In today’s digital era, data has become one of the most valuable assets across industries. Every click, transaction, or online interaction generates data, and the volume is growing exponentially. However, raw data itself has little value unless it is properly analyzed and interpreted. 2.Key Components of Data Science: Data Collection & Cleaning: The first step involves gathering data from various sources, whether structured (databases, spreadsheets) or unstructured ( text, images). Cleaning the data is crucial to ensure that the analysis is based on high-quality, accurate information. Data Analysis: - Once the data is prepared, analytical techniques such as **descriptive statistics, correlation analysis, and data mining** are used to uncover patterns, relationships, and trends within the data. Machine Learning & Predictive Modeling: - With the rise of artificial intelligence, machine learning has become an integral part of Data Science. Supervised and unsupervised learning algorithmsare applied to build models that can make predictions or classify data. Predictive modeling is used in various fields, from healthcare (predicting patient outcomes) to finance (forecasting stock prices). Data Visualization: - Communicating findings effectively is key to Data Science. Tools like Tableau, Power BI, and Python libraries ( Matplotlib, Seaborn) help create visual representations of data, making complex insights easier to understand for stakeholders. 3.Applications of Data Science: Data Science has applications in nearly every industry: Healthcare:Enhancing patient care by analyzing medical records to predict diseases and optimize treatments. Finance: Detecting fraudulent transactions and assessing risks for investments. 4.Challenges in Data Science: Despite its potential, Data Science faces several challenges. One of the biggest hurdles is managing the sheer volume of data, especially with the rise of big data. Additionally, ensuring data privacy and security has become a major concern, particularly with new regulations like GDPR. Furthermore, developing models that are both accurate and unbiased requires careful tuning and ongoing monitoring. 5.The Future of Data Science: Looking ahead, Data Science is expected to grow even further with advancements in technologies such as deep learning, artificial intelligence, and cloud computing. 6.Conclusion: Data Science is the key to unlocking the potential of data. Its ability to turn raw data into actionable insights has transformed industries and will continue to play a vital role in shaping the future. Whether you are an organization looking to make smarter decisions or an individual aiming to pursue a career in this exciting field, understanding the power of Data Science is crucial in today’s data-driven world. #snsinstitutions #snsdesignthinkers #designthinking

Article about Data science #snsinstitutions #snsdesignthinkers #designthinking Data science combines math and statistics, specialized programming, advanced analytics, artificial intelligence (AI), and machine learning with specific subject matter expertise to uncover actionable insights hidden in an organization’s data. These insights can be used to guide decision making and strategic planning. The accelerating volume of data sources, and subsequently data, has made data science is one of the fastest growing field across every industry. As a result, it is no surprise that the role of the data scientist was dubbed the “sexiest job of the 21st century” by Harvard Business Review (link resides outside of IBM). Organizations are increasingly reliant on them to interpret data and provide actionable recommendations to improve business outcomes. The data science lifecycle involves various roles, tools, and processes, which enables analysts to glean actionable insights. Typically, a data science project undergoes the following stages: Data ingestion: The lifecycle begins with the data collection--both raw structured and unstructured data from all relevant sources using a variety of methods. These methods can include manual entry, web scraping, and real-time streaming data from systems and devices. Data sources can include structured data, such as customer data, along with unstructured data like log files, video, audio, pictures, the Internet of Things (IoT), social media, and more. Data storage and data processing: Since data can have different formats and structures, companies need to consider different storage systems based on the type of data that needs to be captured. Data management teams help to set standards around data storage and structure, which facilitate workflows around analytics, machine learning and deep learning models. This stage includes cleaning data, deduplicating, transforming and combining the data using ETL (extract, transform, load) jobs or other data integration technologies. This data preparation is essential for promoting data quality before loading into a data warehouse, data lake, or other repository. Data analysis: Here, data scientists conduct an exploratory data analysis to examine biases, patterns, ranges, and distributions of values within the data. This data analytics exploration drives hypothesis generation for a/b testing. It also allows analysts to determine the data’s relevance for use within modeling efforts for predictive analytics, machine learning, and/or deep learning. Depending on a model’s accuracy, organizations can become reliant on these insights for business decision making, allowing them to drive more scalability. Communicate: Finally, insights are presented as reports and other data visualizations that make the insights—and their impact on business—easier for business analysts and other decision-makers to understand.

#Integration of #AI in #databases is revolutionizing how we manage and utilize data. With more new DB-AI tools like SuperDuperDB, Towhee, PostgresML, Zilliz or MindsDB, developers are now empowered to deploy AI capabilities directly within their databases.

🚀 Unlocking the Future with Data Science: Key Trends and Tools You Need to Know In today’s data-driven world, the ability to extract meaningful insights from vast datasets has never been more crucial. Data Science is not just about crunching numbers, it’s about solving real-world problems and transforming industries. Here are 5 trends in Data Science that are shaping the future: 1️⃣ AI and Automation: AI is no longer a buzzword, it’s transforming industries. From predictive maintenance to personalized recommendations, automation is changing the game. 💡 How are you leveraging AI in your data workflows? 2️⃣ Real-Time Data Processing: As businesses move faster, real-time analytics is key. Tools like Apache Kafka and Spark Streaming are helping companies make decisions on-the-go. Are you ready for real-time analytics? 3️⃣ The Rise of Explainable AI (XAI): With AI’s growing influence comes the need for transparency. XAI allows us to understand how models make decisions, fostering trust and wider adoption. Have you explored the impact of XAI in your field? 4️⃣ Data Ethics and Privacy: As data collection increases, so does the responsibility to handle it ethically. Complying with GDPR and ensuring data privacy should be top priorities for every data professional. How are you ensuring ethical data usage? 5️⃣ Cloud-Based Data Solutions: Platforms like Google Cloud, AWS, and Microsoft Azure are powering modern data architectures. Hybrid and multi-cloud strategies are leading the way for scalable, flexible, and secure data solutions. 💻 My Experience: In my journey as a Data Analyst, I’ve found that continuous learning is the key to staying ahead. From mastering Python, SQL, and Tableau to exploring advanced machine learning models, the learning never stops! 🛠 Tools of the Trade: Python for data manipulation and machine learning. Power BI and Tableau for beautiful visualizations. SQL for structured data management and querying. 👥 Let’s grow together! Drop a comment below with your thoughts on these trends or share how you’re navigating the fast-paced world of data science. 💡 If you found this helpful, like, repost, and let’s keep the conversation going! Together, we can unlock the full potential of data science! 𝗜𝗳 𝘆𝗼𝘂 𝗳𝗼𝘂𝗻𝗱 𝘁𝗵𝗶𝘀 𝘃𝗮𝗹𝘂𝗮𝗯𝗹𝗲, 𝗱𝗼𝗻'𝘁 𝗳𝗼𝗿𝗴𝗲𝘁 𝘁𝗼 𝗵𝗶𝘁 "𝗟𝗶𝗸𝗲" 𝗯𝘂𝘁𝘁𝗼𝗻 𝗮𝗻𝗱 𝘀𝗵𝗮𝗿𝗲 𝗶𝘁 𝘄𝗶𝘁𝗵 𝘆𝗼𝘂𝗿 𝗻𝗲𝘁𝘄𝗼𝗿𝗸. ✅️ And some of the top companies in data science: @AnalyticsVidhya @GoogleCloud Amazon Web Services (AWS) Tableau #DataScience #MachineLearning #AI #DataEthics #RealTimeAnalytics #DataEngineering #Python #CloudData Tags : Shashank Singh 🇮🇳 | Vaibhav Lambat | Shashwath Shenoy | Kratika Jain | Thodupunuri Bharath | Raghavan P | Kratika Jain | Ayan Khan

What is data science? Data science combines math and statistics, specialized programming, advanced analytics, artificial intelligence (AI) and machine learning with specific subject matter expertise to uncover actionable insights hidden in an organization’s data. These insights can be used to guide decision making and strategic planning. The accelerating volume of data sources, and subsequently data, has made data science is one of the fastest growing field across every industry. As a result, it is no surprise that the role of the data scientist was dubbed the “sexiest job of the 21st century” by Harvard Business Review (link resides outside ibm.com). Organizations are increasingly reliant on them to interpret data and provide actionable recommendations to improve business outcomes. The data science lifecycle involves various roles, tools, and processes, which enables analysts to glean actionable insights. Typically, a data science project undergoes the following stages: Data ingestion: The lifecycle begins with the data collection—both raw structured and unstructured data from all relevant sources using a variety of methods. These methods can include manual entry, web scraping, and real-time streaming data from systems and devices. Data sources can include structured data, such as customer data, along with unstructured data like log files, video, audio, pictures, the Internet of Things (IoT), social media, and more. Data storage and data processing: Since data can have different formats and structures, companies need to consider different storage systems based on the type of data that needs to be captured. Data management teams help to set standards around data storage and structure, which facilitate workflows around analytics, machine learning and deep learning models. This stage includes cleaning data, deduplicating, transforming and combining the data using ETL (extract, transform, load) jobs or other data integration technologies. This data preparation is essential for promoting data quality before loading into a data warehouse, data lake, or other repository. Data analysis: Here, data scientists conduct an exploratory data analysis to examine biases, patterns, ranges, and distributions of values within the data. This data analytics exploration drives hypothesis generation for a/b testing. It also allows analysts to determine the data’s relevance for use within modeling efforts for predictive analytics, machine learning, and/or deep learning. Depending on a model’s accuracy, organizations can become reliant on these insights for business decision making, allowing them to drive more scalability. Communicate: Finally, insights are presented as reports and other data visualizations that make the insights—and their impact on business—easier for business analysts and other decision-makers to understand.

The Rejuvenation of Modern Data Lakes to Support Generative AI The era of generative AI has ushered in a new wave of innovation and disruption across industries. As these models become more sophisticated and widely adopted, the demand for high-quality, diverse, and well-structured data has skyrocketed. This may have potentially reignited interest in modern data lake architectures, which offer a compelling solution for managing the vast amounts of data required to train and operate generative AI systems. Traditional data lakes were often criticized for their lack of data governance, schema enforcement, and performance optimizations. However, the advent of technologies like Delta Lake and Apache Iceberg has transformed data lakes into robust, enterprise-grade data management platforms, now commonly referred to as "lakehouses." These modern data lakes leverage cloud object storage as a centralized, scalable, and cost-effective data repository. They introduce features like ACID transactions, data versioning, schema evolution, and performance optimizations, addressing many of the historical challenges associated with data lakes. This combination of scalability, data integrity, and performance makes them well-suited for the demanding data requirements of generative AI workloads. Benefits of modern data lakes for Gen AI: ☑ Data Diversity: Generative AI models thrive on diverse, multi-modal data sources, including text, images, audio, and video. Modern data lakes can easily ingest and store these heterogeneous data types in their raw form, providing a comprehensive data repository for model training. ☑ Data Versioning and Reproducibility: The ability to version data and query historical snapshots is crucial for AI model development, testing, and deployment. Modern data lakes, with their built-in data versioning capabilities, enable reproducible experiments and auditing, ensuring transparency and trustworthiness in AI systems. ☑ Schema Flexibility: Generative AI models often require constant tweaking and refinement, leading to evolving data schemas. Modern data lakes support schema evolution, allowing seamless adaptation to changing data structures without costly data migrations. ☑ Scalability and Cost-Efficiency: Training large generative AI models requires massive datasets, often in the petabyte range. Modern data lakes, built on cloud object storage, offer virtually unlimited scalability and cost-effective storage, making them an ideal choice for managing these vast data repositories. ☑ Open Ecosystem: Many modern data lake solutions, like Delta Lake and Iceberg, are open-source and integrate seamlessly with various data processing engines and AI/ML frameworks, fostering an open and collaborative ecosystem for generative AI development. As the generative AI revolution continues, modern data lakes, are well-positioned to become the foundational focus of data platforms for this new era of AI innovation. #Data #ModernDataLake #AI #GenAI

4 Stages of Data Modernization for AI (Concluding) How each stage of next-gen data engineering supports today’s AI.   Summary: Changing needs of modern AI application forces us to re-look at how we do data engineering. Data engineering today must be re-shaped to enable knowledge creation & reasoning engines, without giving up the operational and semantic needs of traditional insight generation. From part 2 - the structure of this data engineering shift is a set of stages with each stage addressing some specific needs/gaps of AI enablement: 1.     Trusted Actionable Insights 2.     Traditional ML for qoq revenue/profitability 3.     LLM-apps, Vision-products etc. 4.     Multi-component inference, Agents & Systems Intelligence & Ops artifacts that are added to each stage of Data Engineering Each stage needs specific add-on components to enable the kind of semantic intelligence and operational effectiveness necessary for the modern array of AI apps. These add on mechanisms, when aggregated, is called Data-Intelligence-Ops. (see Graphic in attached paper). Formally, DataIntelligenceOps is an abstract set of operations meant to increase (a) semantic intelligence (b) operational intelligence & (c) governance abilities of data. It builds on top of existing investments in data-lakes, cloud-EDW, dbt automation, ELT, feature-stores etc. The main architectural artifacts are: · Semantic Intelligence Enhancements: a broad set of components for complex data products, which can be aggregated or configured through a low code IDE. · Connected DataOps: a connected DataOps architecture that “causally” ties together observability, lineage, storage/gov/sec-Ops, programmable pipelines, data contracts – to create an embedding layer for the above intelligence enhancements. Implemented as a full-featured knowledge graph that captures data platform wide meta-data. · Governance as Code enablement: Governance DAGs embeddable within pipelines allow for governance simplification, as well as policy implementations to be seamlessly executed. The effect of DataIntelligenceOps is to enhance the “intelligence” of a firm’s data, thus facilitating today’s AI apps. Parting thoughts:   1.     AI apps are rapidly increasing in complexity and capability – so, old boundaries of data engineering do not apply. 2.     The way to enable this AI led shift is to move to a modern style of data engineering that systematically adds semantic and operational value in 4 different stages of maturity. 3.     In many cases firms will choose to skip a stage to move faster and nothing prevents that. 4.     Existing building blocks such as ingestion mechanisms, pipeline tools, cloud EDW etc., remain unaffected – this is not a rip n’ replace design. 5.     Data engineering must now support knowledge enoblement, reasoning engines & qoq AI ROI. Paper - https://lnkd.in/gqG25drN