WEDATA&AI

WEDATA&AI

Services et conseil en informatique

Unlock the full power of your DATA to fuel your AI with WEDATA&AI. Your DATA. Your AI.

À propos

Société de Conseil et Services en informatique spécialisée dans l'Architecture, Management et Gouvernance DATA & AI.

Secteur
Services et conseil en informatique
Taille de l’entreprise
1 employé
Siège social
Paris
Type
Travailleur indépendant
Fondée en
2023
Domaines
DATA, AI, DATA ARCHITECTURE, DATA MANAGEMENT, DATA GOVERNANCE, MASTER DATA MANAGEMENT, MARKET DATA et DATA INTEGRATION

Lieux

Nouvelles

  • Voir la page d’organisation pour WEDATA&AI, visuel

    12  abonnés

    What is RAG? And how to build your own RAG with your own DATA?

    Voir le profil de Deepak Bhardwaj, visuel

    28K LinkedIn | Top Voice - Data Architecture | Top 1% | GenAI & MLOps Leader | Data Mesh & Cloud Expert | Mentor & Content Creator | Enterprise Architect | Azure, GCP, AWS Specialist

    What is RAG? ⎯⎯⎯⎯⎯⎯⎯⎯⎯   Don’t forget to: ♻️ 𝘙𝘦𝘱𝘰𝘴𝘵 if you found this post interesting and helpful!   💡 𝘍𝘰𝘭𝘭𝘰𝘸 me for more insights and 𝘵𝘪𝘱𝘴 on 𝐃𝐚𝐭𝐚 𝐚𝐧𝐝 𝐀𝐈.   ⎯⎯⎯⎯⎯⎯⎯⎯⎯ Are you curious about the latest AI technologies, such as information retrieval and generation? Let’s discuss Retrieval-Augmented Generation (RAG), a game-changing approach that transforms and optimises how we interact with large language models (LLMs). 🔍 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐑𝐀𝐆? Imagine accessing vast amounts of data in real-time, enhancing it with the latest insights, and generating responses that are not just accurate but contextually rich. That’s RAG in action. 🔘 𝐇𝐨𝐰 𝐃𝐨𝐞𝐬 𝐈𝐭 𝐖𝐨𝐫𝐤? ↳ 𝘋𝘰𝘤𝘶𝘮𝘦𝘯𝘵 𝘗𝘳𝘰𝘤𝘦𝘴𝘴𝘪𝘯𝘨: Start by breaking down massive datasets, be it PDFs, videos, or CSV files, into manageable chunks. ↳ 𝘌𝘮𝘣𝘦𝘥𝘥𝘪𝘯𝘨: These chunks are then transformed into embeddings – vectors capturing the essence of the data. ↳ 𝘙𝘦𝘵𝘳𝘪𝘦𝘷𝘢𝘭: When a user query arrives, the RAG application converts the user query into embedding and fetches relevant data from a vector database based on similarities, ensuring that the information is context-specific. ↳ 𝘈𝘶𝘨𝘮𝘦𝘯𝘵𝘦𝘥 𝘘𝘶𝘦𝘳𝘺: The user’s query is enriched with the retrieved data, adding depth to the context. ↳ 𝘎𝘦𝘯𝘦𝘳𝘢𝘵𝘪𝘰𝘯: Finally, an LLM generates a response that’s not only relevant but enhanced by precise data from your knowledge sources. 🔘 𝐖𝐡𝐲 𝐈𝐭 𝐌𝐚𝐭𝐭𝐞𝐫𝐬: ↳ RAG bridges the gap between static data and dynamic user needs, making it a powerful tool for businesses and developers aiming for more innovative, faster, and more accurate solutions. Ready to explore how RAG can revolutionise your AI-driven applications? Cheers!   Deepak Bhardwaj #AI #MachineLearning #DataScience #RAG #LLM #Innovation #TechTrends #AIinBusiness #DeepLearning #NLP

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  • WEDATA&AI a republié ceci

    Voir le profil de Deepak Bhardwaj, visuel

    28K LinkedIn | Top Voice - Data Architecture | Top 1% | GenAI & MLOps Leader | Data Mesh & Cloud Expert | Mentor & Content Creator | Enterprise Architect | Azure, GCP, AWS Specialist

    Data Mesh Architecture Data is the new oil, but refining it into actionable insights requires more than storage. Enter Data Mesh Architecture—a game-changer for modern enterprises. Gone are the days of centralised data warehouses where all data flows into a single system. Today, it's all about decentralisation and domain-driven design. This shift allows teams to independently manage, own, and optimise their data products while ensuring governance and security. ⎯⎯⎯⎯⎯⎯⎯⎯⎯ Don’t forget to: ♻️ Repost if you found this post interesting and helpful! 💡 Follow me for more insights and tips on Data and AI. ⎯⎯⎯⎯⎯⎯⎯⎯⎯ Why does Data Mesh stand out? Let’s break it down: 🔘 Data Domains: Each domain owns its data and is responsible for delivering high-quality data products—a significant shift from traditional centralised models. 🔘 Data Products: Data within each domain is organised into Bronze, Silver, and Gold layers, representing different stages of refinement—from raw data to fully processed insights ready for analysis. 🔘 Self-Service Data Infrastructure: Empower teams with the tools and infrastructure to easily create, share, and consume data without heavy reliance on central IT. 🔘 Data Pipelines & Contracts: Seamless data flow and clear contracts ensure consistency and reliability across all domains. 🔘 Data Governance: Despite decentralisation, robust policies, quality measures, and access management systems keep data secure, consistent, and trustworthy. 🔘 Data Consumers: From Business Intelligence tools to apps and business users, everyone can easily access and utilise data, driving real value. 💡 Ready to embrace the future of data management? Data Mesh isn’t just a trend—it’s the evolution your organisation needs to turn data into a true competitive advantage. ⚠️ Caution: Data Mesh is not for everyone. It must be carefully evaluated to determine whether it aligns with your organisation’s goals and capabilities. Cheers! Deepak #DataMesh #DataArchitecture #DataStrategy #DataGovernance #BigData #DataProducts #BusinessIntelligence #DigitalTransformation #AI

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  • WEDATA&AI a republié ceci

    Voir le profil de Deepak Bhardwaj, visuel

    28K LinkedIn | Top Voice - Data Architecture | Top 1% | GenAI & MLOps Leader | Data Mesh & Cloud Expert | Mentor & Content Creator | Enterprise Architect | Azure, GCP, AWS Specialist

    Building a Robust Data Pipeline: The Medallion Approach ⎯⎯⎯⎯⎯⎯⎯⎯⎯   Don’t forget to: ♻️ Repost if you found this post interesting and helpful!   💡 Follow me for more insights and tips on Data and AI.   ⎯⎯⎯⎯⎯⎯⎯⎯⎯ Let’s discuss how the Medallion Data Architecture can streamline your data workflow and take it to the next level. 𝘏𝘦𝘳𝘦’𝘴 𝘩𝘰𝘸 𝘵𝘩𝘦 𝘭𝘢𝘺𝘦𝘳𝘴 𝘸𝘰𝘳𝘬: 🥉 𝐁𝐫𝐨𝐧𝐳𝐞 𝐋𝐚𝐲𝐞𝐫: This is where you start with the essentials—raw, unprocessed data. It’s the hub for flexible, semi-structured data management with no strict schema enforcement. Perfect for early-stage data that needs further refining. 🥈 𝐒𝐢𝐥𝐯𝐞𝐫 𝐋𝐚𝐲𝐞𝐫: Your data gets a makeover, becoming validated, clean, and enriched. With enforced schemas and the ability to evolve, the data is prepped and aligned for analytical use. 🥇 𝐆𝐨𝐥𝐝 𝐋𝐚𝐲𝐞𝐫: The crown jewel of your data pipeline—business-ready, performance-optimised data. This layer aggregates and anonymises data, making it trusted and suitable for end-users. 𝘞𝘩𝘺 𝘢𝘥𝘰𝘱𝘵 𝘵𝘩𝘦 𝘔𝘦𝘥𝘢𝘭𝘭𝘪𝘰𝘯 𝘔𝘰𝘥𝘦𝘭? 𝐒𝐭𝐫𝐞𝐚𝐦𝐥𝐢𝐧𝐞𝐝 𝐏𝐫𝐨𝐜𝐞𝐬𝐬: This architecture ensures data flows smoothly from ingestion to utilisation, minimising bottlenecks. 𝐃𝐚𝐭𝐚 𝐐𝐮𝐚𝐥𝐢𝐭𝐲: Each stage improves data quality, making it reliable for decision-making. 𝐀𝐝𝐚𝐩𝐭𝐚𝐛𝐥𝐞 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤: The flexible approach adapts to various business needs, supporting growth and innovation. Implementing this framework means transforming how you manage and leverage data. Let’s discuss your experiences with different data architectures. What challenges and benefits have you encountered? Are you curious to know more? Share your insights and questions below! Cheers!   Deepak Bhardwaj #MedallionDataArchitecture #DataPipeline #DataTransformation #DataQuality #DataStrategy #BusinessIntelligence #DataManagement #TechTalk #Innovation #DataAnalytics

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  • WEDATA&AI a republié ceci

    Voir le profil de Gina Acosta Gutiérrez, visuel

    Daily Posts and Resources on Data Science, Data Engineering, and AI 📚 | Mentor | Google WTM Ambassador

    Understanding Key Data Terms: A Simple Guide 👇 Today, I’m sharing an infographic that breaks down some important data terms. 🔹 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲: Think of it as a big storage space for all kinds of data, whether it's organized or not. It's like a giant pool where everything goes. 🔹 𝗗𝗮𝘁𝗮 𝗠𝗮𝗿𝘁: This is a smaller, more focused version of a data warehouse. It's tailored for specific business needs or departments. 🔹 𝗗𝗮𝘁𝗮 𝗠𝗲𝘀𝗵: Imagine treating data like a product. Different teams manage their own data, making it easier to handle and use. 🔹 𝗗𝗮𝘁𝗮 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲: This involves moving data from one place to another, ensuring it’s ready for analysis. It includes processes like transforming and loading data, making it accessible for analytics and reporting. 🔹 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲: A structured place to keep large amounts of data. It's organized and ready for analysis. 🔹 𝗗𝗮𝘁𝗮 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆: This is about keeping an eye on your data's health. It ensures everything is accurate and reliable. 🔹 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆: It's all about making sure your data is good. You want it to be correct and useful. 👉 Follow Gina Acosta Gutiérrez for more! #python #data #ai #artificialintelligence #programming #coding #tech

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  • WEDATA&AI a republié ceci

    Voir le profil de Raj GROVER, visuel

    Director - Data and Digital Strategy and Transformation | New Technologies | New Product | Corporate Training

    Roles typically found in #DataArchitecture and #EnterpriseArchitecture teams. These teams often include several specialized positions to cover various aspects of their complex responsibilities. Data Architecture Team: 1. Lead Data Architect 2. Data Modelers 3. Database Administrators (DBAs) 4. #DataIntegration Specialists 5. #DataGovernance Specialists 6. #DataSecurity Experts Enterprise Architecture Team: 1. Chief Enterprise Architect 2. Domain Architects 3. #SolutionArchitects 4. #BusinessArchitects 5. #TechnologyArchitects 6. #InformationArchitects 7. EA Tool Specialists Both teams often collaborate closely and may share some roles or responsibilities. The exact composition can vary depending on the organization's size, industry, and specific needs. To receive detailed information on the roles above and how they interact and contribute to their teams, make sure you don’t miss out on our Premium Content. You can either DM or email at info@transformpartner.com for the details to subscribe. Transform Partner stands as your strategic ally in navigating the complexities of data and digital transformation. Our consultancy is equipped with industry-specific expertise, tailored solutions, and a commitment to fostering #innovation. We provide strategic planning, actionable #insights, and a detailed roadmap tailored to your unique business needs. Let’s join forces to propel your transformation initiatives towards success with agility and precision. Image Source: RedHat #TransformPartner – Your #DigitalTransformation Consultancy

    • Enterprise Architecture Role
  • WEDATA&AI a republié ceci

    Voir le profil de Deepak Bhardwaj, visuel

    28K LinkedIn | Top Voice - Data Architecture | Top 1% | GenAI & MLOps Leader | Data Mesh & Cloud Expert | Mentor & Content Creator | Enterprise Architect | Azure, GCP, AWS Specialist

    Unlock the Power of Data Mesh Architecture! Data is the new oil, but refining it into actionable insights requires more than storage. Enter Data Mesh Architecture—a game-changer for modern enterprises. Gone are the days of centralised data warehouses where all data flows into a single system. Today, it's all about decentralisation and domain-driven design. This shift allows teams to independently manage, own, and optimise their data products while ensuring governance and security. ⎯⎯⎯⎯⎯⎯⎯⎯⎯   Don’t forget to: ♻️ Repost if you found this post interesting and helpful!   💡 Follow me for more insights and tips on Data and AI.   ⎯⎯⎯⎯⎯⎯⎯⎯⎯  Why does Data Mesh stand out? Let’s break it down: 🔘 Data Domains: Each domain owns its data and is responsible for delivering high-quality data products—a significant shift from traditional centralised models. 🔘 Data Products: Data within each domain is organised into Bronze, Silver, and Gold layers, representing different stages of refinement—from raw data to fully processed insights ready for analysis. 🔘 Self-Service Data Infrastructure: Empower teams with the tools and infrastructure to easily create, share, and consume data without heavy reliance on central IT. 🔘 Data Pipelines & Contracts: Seamless data flow and clear contracts ensure consistency and reliability across all domains. 🔘 Data Governance: Despite decentralisation, robust policies, quality measures, and access management systems keep data secure, consistent, and trustworthy. 🔘 Data Consumers: From Business Intelligence tools to apps and business users, everyone can easily access and utilise data, driving real value. 💡 Ready to embrace the future of data management? Data Mesh isn’t just a trend—it’s the evolution your organisation needs to turn data into a true competitive advantage. ⚠️ Caution: Data Mesh is not for everyone. It must be carefully evaluated to determine whether it aligns with your organisation’s goals and capabilities. Cheers!   Deepak #DataMesh #DataArchitecture #DataStrategy #DataGovernance #BigData #DataProducts #BusinessIntelligence #DigitalTransformation #AI

    • Data Mesh Architecture
  • WEDATA&AI a republié ceci

    Voir le profil de Deepak Bhardwaj, visuel

    28K LinkedIn | Top Voice - Data Architecture | Top 1% | GenAI & MLOps Leader | Data Mesh & Cloud Expert | Mentor & Content Creator | Enterprise Architect | Azure, GCP, AWS Specialist

    Here is a quick recap of what we discussed last week. ↳ Kafka Architecture ↳ Kafka Resiliency Strategies ↳ Kafka 3.8.0 Release ↳ Must-know concepts in event-driven architectures ↳ Data monetisation strategies ↳ Data migration strategies Which post was most valuable and exciting to you? What type of content would you like to see more? #Kafka #Data #DataMonetisation #DataStrategy #DataMigration #EventDrivenArchitecture

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  • Voir la page d’organisation pour WEDATA&AI, visuel

    12  abonnés

    How to monetize your DATA?

    Voir le profil de Deepak Bhardwaj, visuel

    28K LinkedIn | Top Voice - Data Architecture | Top 1% | GenAI & MLOps Leader | Data Mesh & Cloud Expert | Mentor & Content Creator | Enterprise Architect | Azure, GCP, AWS Specialist

    How can you monetise data? Here are the top strategies to monetise data. ↳ Data as a Service ↳ Data Licensing ↳ Data Subscription ↳ Data Marketplace ↳ Data Enrichment Services ↳ Advertisement and Partnership ↳ Analytics and Insights ↳ Custom Solutions ↳ What else? Share your insights! ♻️ Repost if you found this post interesting and helpful!   💡 Follow me for more insights and tips on Data and AI. Cheers!   Deepak #Data #DataMonetisation #DataStrategy #BusinessGrowth #DaaS #DataAnalytics

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  • Voir la page d’organisation pour WEDATA&AI, visuel

    12  abonnés

    How to manage your own DATA Pipeline Architecture efficently?

    Voir le profil de Ravit Jain, visuel
    Ravit Jain Ravit Jain est un Influencer

    Founder & Host of "The Ravit Show" | LinkedIn Top Voice | Startups Advisor | Gartner Ambassador | Evangelist | Data & AI Community Builder | Influencer Marketing B2B | Marketing & Media | (Mumbai/San Francisco)

    Have you ever wondered how to manage a Data Pipeline efficiently? This detailed visual breaks down the architecture into five essential stages: Collect, Ingest, Store, Compute, and Use. Each stage ensures a smooth and efficient data lifecycle, from gathering data to transforming it into actionable insights. Collect: Data is gathered from a variety of internal and external sources, including: -- Mobile Applications and Web Apps: Data generated from user interactions. -- Microservices: Capturing microservice interactions and transactions. -- IoT Devices: Collecting sensor data through MQTT protocols. -- Batch Data: Historical data collected in batches. Ingest: In this stage, the collected data is ingested into the system through batch jobs or streaming methods: -- Event Queue: Manages and queues incoming data streams. -- Extracting Raw Event Stream: Moving data to a data lake or warehouse. -- Tools Used: MQTT for real-time streaming, Kafka for managing data streams, and Airbyte or Gobblin for data integration. Store: The ingested data is then stored in a structured manner for efficient access and processing: -- Data Lake: Storing raw data in its native format. -- Data Warehouse: Structured storage for easy querying and analysis. -- Technologies Used: MinIO for object storage, Iceberg, and Delta Lake for managing large datasets. Compute: This stage involves processing the stored data to generate meaningful insights: -- Batch Processing: Handling large volumes of data in batches using tools like Apache Spark. -- Stream Processing: Real-time data processing with Flink and Beam. -- ML Feature Engineering: Preparing data for machine learning models. -- Caching: Using technologies like Ignite to speed up data access. Use: Finally, the processed data is utilized in various applications: -- Dashboards: Visualizing data for business insights using tools like Metabase and Superset. -- Data Science Projects: Conducting complex analyses and building predictive models using Jupyter notebooks. -- Real-Time Analytics: Providing immediate insights for decision-making. -- ML Services: Deploying machine learning models to provide AI-driven solutions. Key supporting functions such as: -- Orchestration: Managed by tools like Airflow to automate and schedule tasks. -- Data Quality: Ensuring the accuracy and reliability of data throughout the pipeline. -- Cataloging: Maintaining an organized inventory of data assets. -- Governance: Enforcing policies and ensuring compliance with frameworks like Apache Atlas. This comprehensive guide illustrates how each component fits into the overall pipeline, showcasing the integration of various tools and technologies. Check out this detailed breakdown and see how these elements can enhance your data management strategies. How are you currently handling your data pipeline architecture? Let's discuss and share best practices! #data #ai #datapipeline #dataengineering #theravitshow

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  • Voir la page d’organisation pour WEDATA&AI, visuel

    12  abonnés

    Define your own DATA Strategy, your own roadmap for Data and Analytics projects.

    Voir le profil de Piotr Czarnas, visuel

    Founder @ DQOps open-source Data Quality platform | Detect any data quality issue and watch for new issues with Data Observability

    Data Strategy is a roadmap for Data & Analytics projects. Without a roadmap, a data team will get lost in the complexity of random technologies and architectures that will not work together. Small and detached data projects can be successful without a data strategy. The problem begins when many small teams want to integrate their data products. Each team has a different technology stack and follows different procedures. If you see more than a few potential or live data products, it is wise to review the components of a data strategy and identify what is missing. I am not a big fan of running huge, corporate-wide initiatives, such as defining a "one data strategy that fits all." Instead, I prefer to look at the areas that can benefit the most from having a unified process, join forces with other teams that face the same problems, and define a common process from the ground up. Please review these areas in the cheat sheet and consider what you are missing. If your week spot is data quality, please DM me. I am curious to hear what problems you are facing. #dataquality #dataengineering #datagovernance

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