• Extensive experience in development and troubleshooting on Hadoop technologies like HDFS, MapReduce, Spark, Hive, Sqoop, etc. • Architectured and Implemented Database migration, End-to-End Datalake projects • Experience with Cloud and Hybrid implementation of large-scale data projects • Good in both Azure & Amazon Web Services (AWS) • Expertise in Data Engineering and Cloud migration activities • Developed data dictionaries, business glossaries, ERD models, established data quality processes and KPIs • Collaboration with stakeholders to define data requirements and data models • Capable of processing large sets of structured, semi-structured and unstructured data and supporting systems application architecture • Designed and Developed optimized and standardized solutions for Data Integration (from upstream systems), Data Cleansing, Data Transformation and Data Delivery to downstream systems. • Developed and Maintained Spark Batch and Streaming applications • Experience in Agile development process using scrum & kanban methodology ❇️ AWS x3. Azure x3. 𝐀𝐑𝐄𝐀𝐒 𝐎𝐅 𝐄𝐗𝐏𝐄𝐑𝐓𝐈𝐒𝐄 ★ Big Data Ecosystem: Hadoop, Hive, Sqoop, Spark (Core & Streaming), Oozie, Kafka, Nifi ★ Cloud: Azure, AWS ★ Languages: Python, Scala, SQL, Java, C#, Shell Scripting ★ Log Analytics: ElasticSearch, Logstash, Kibana ★ Database: Oracle, SQL Server, MySQL, Teradata ★ Data Platform: Snowflake, Databricks, Dataiku ★ DevOps: Docker, Jenkins, Git, Maven ★ API Framework: Python Flask, FastAPI ★ Methodologies: Agile - Scrum, Kanban ★ Domain: Healthcare, Banking, Insurance, Retail Positions: Data Platform Architect at Presight.ai (2022 - Present), Lead Data Engineer/Solutions Architect at Change Healthcare (2021 - 2022), Technology Architect - Cloud & Big Data at Infosys (2021 - 2021), Data Engineering Technical Lead at Accenture (2019 - 2021), Associate at Cognizant Technology Solutions (2015 - 2019), IT Analyst at Tata Consultancy Services (2011 - 2015) Skills: Data Engineering, Artificial Intelligence (AI), Large Language Models (LLM), Optical Character Recognition (OCR), Data Analytics, Data Migration, Extract, Transform, Load (ETL), Business Intelligence (BI), Database Design, Archimate, Predictive Modeling, Data Modeling, Data Warehousing, Elasticsearch, Elastic Stack (ELK), Python (Programming Language), Solution Architecture, Azure Data Factory, Azure Databricks, Azure Data Lake, Azure Functions, AWS Lambda, AWS Glue, Amazon Relational Database Service (RDS), Project Implementation, Service-Oriented Architecture (SOA), Technical Architecture, Apache Spark, Amazon Web Services (AWS), Microsoft Azure, Software Engineering, Software Development Life Cycle (SDLC), Agile Methodolgy, Big Data, Scala, SQL, Hadoop, Python, MapReduce, Cloudera, Git, Java, JIRA, ElasticSearch, Kibana, Jenkins, REST API, Linux, REST APIs, Hive Recent Posts: #Presight is proud to announce a landmark USD $190 million partnership with the city of Astana in Kazakhstan to implement an #AI-powered smart city project. This six-year initiative will transform urban infrastructure, optimize traffic management, and modernize public services through advanced AI and IoT solutions. Building on our expanding presence in Kazakhstan, this collaboration follows our partnerships with the Ministry of Digital Development, SAMRUK-KAZYNA, and Astana Hub. Additionally, we’re excited to announce the expansion of our Astana office, further reinforcing our commitment to Kazakhstan and Central Asia. 🚀 Introducing FastMRZ: Open-Source MRZ Extraction for Python Follow Sivakumar Mahalingam for more update Are you working with passport scanning, ID verification, or automated data extraction? FastMRZ is an open-source Python package that makes MRZ (Machine Readable Zone) extraction seamless, efficient, and developer-friendly. ✅ Supports Multiple Input Formats: • 📸 Images • 🔢 Base64 Strings • 📝 MRZ Strings • 📊 NumPy Arrays 💡 Why FastMRZ? • Fast and accurate MRZ detection • Easy integration into existing pipelines • Open-source and community-driven #OpenSource #Python #MRZ #OCR #ComputerVision #IdentityVerification #MachineLearning Check it out on GitHub: We are proud to see the #UAE and #US reinforce their strategic partnership on #artificialintelligence, as highlighted in yesterday's joint statement by President His Highness Sheikh Mohamed bin Zayed Al Nahyan and President Joe Biden. At G42, we are honored to be part of this transformative journey along with our partners Microsoft, NVIDIA, OpenAI, Cerebras Systems, among others. With #AI becoming increasingly embedded into all facets of our daily lives, the establishment of internationally recognized frameworks for responsible, safe, secure and equitable AI, remains a top priority, while we collectively continue to further innovation. Our strategic partnership with Microsoft perfectly exemplifies such vision, setting the foundation for the development and deployment of cutting-edge AI solutions that are both powerful and responsible. Read the Official Statement here: https://lnkd.in/e7Y-YdFC Excited to have been part of the UAE AI Summer Camp 6.0, an initiative from the Minister of State for Artificial Intelligence, Digital Economy and Remote Work Applications Office (UAE) to drive AI learning for the UAE's youth and align with the ambitious UAE AI Strategy 2031, where Martin Yates, our Senior Government Technology Advisor at Presight, led an insightful session entitled 'Smart Cities and the Future: How AI is Shaping the Lives of Today’s Youth’. He took the students on a journey through: 🚀 What makes a city smart, with real-world examples of smart city projects globally? 💡 How AI is revolutionizing urban planning, traffic flow, and public safety—transforming everyday city life with smart solutions like AI-powered transportation and waste management. 👩‍🎓 The impact of AI on youth, exploring how tech is opening up new doors in education and careers, while smart city infrastructure is reshaping the way young people live and engage with their communities. 🔮 Practical advice on how students can gear up for careers in AI and smart city development. This session was all about equipping the nation’s youth with the knowledge and tools to navigate and thrive in the evolving world of AI and smart cities, empowering them to be the leaders of tomorrow. #Presight #UAEAICamp #SmartCities #FutureLeaders #UAE2031 #AIforGood Excited to share the successful conclusion of Presight's AI Enablement Workshop in Addis Ababa, attended by Ethiopia’s Deputy Prime Minister Temesgen Tiruneh and several high-level Ethiopian government officials. This three-day event, in partnership with Open Innovation AI, was designed to equip high-level government leaders with knowledge and insight into generative AI. The workshop covered big data analytics and other important technologies that could be deployed to support government decision-making, policy, planning, and other aspects of public sector work. It also highlighted the potential of generative AI to enhance public services and increase government efficiency. Ethiopia’s Deputy Prime Minister Temesgen Tiruneh said, “Investing in AI education and training is essential to build a workforce capable of developing AI solutions and realizing the full potential of this #technology. Workshops such as these are a positive step towards achieving this.” Our COO, Dr. Adel Alsharji, said, “We look forward to further initiatives as part of the program to continue to contribute to the digital transformation of government for the benefit of the citizens of Ethiopia.” Read the full press release: 🔗 https://lnkd.in/dEqfpgVf #Presight #DigitalTransformation #GenerativeAI #UAE #Ethiopia Excited to see the launch of Presight Connect, a UAE-hosted AI assistant that promises to revolutionize business operations! Seamless integration with databases and top SaaS apps means real-time insights and smarter decision-making. It's incredible to see AI augmenting human capabilities for enhanced productivity. This is a game-changer for data-driven organizations! #AI #GPT #BusinessInsights #DataDriven #Innovation 𝐆𝐢𝐭𝐇𝐮𝐛 𝐑𝐞𝐩𝐨𝐬𝐢𝐭𝐨𝐫𝐢𝐞𝐬 𝐭𝐨 𝐄𝐱𝐜𝐞𝐥 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 🚀 In the era of big data, Data Engineering has emerged as a cornerstone for businesses aiming to harness the power of their data. As companies generate vast amounts of information, the need for skilled data engineers to design, build, and manage scalable data infrastructure has never been greater. This demand is reflected in the job market, where opportunities for data engineers proliferate, offering lucrative salaries and career growth. Beyond job prospects, the industry relies on data engineers to ensure data is reliable, accessible, and actionable, driving informed decision-making and innovation across sectors. ➡️ 𝐀𝐰𝐞𝐬𝐨𝐦𝐞 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠: Tools, frameworks, and libraries. https://lnkd.in/d3zRe3zu ➡️ 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐇𝐚𝐧𝐝𝐛𝐨𝐨𝐤: Comprehensive resources. https://lnkd.in/dqEv-c2c ➡️ 𝐓𝐡𝐞 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐂𝐨𝐨𝐤𝐛𝐨𝐨𝐤: Articles and tutorials. https://lnkd.in/dGk5xrTE ➡️ 𝐏𝐲𝐬𝐩𝐚𝐫𝐤 𝐄𝐱𝐚𝐦𝐩𝐥𝐞 𝐏𝐫𝐨𝐣𝐞𝐜𝐭: Best practices for PySpark. https://lnkd.in/dNwefKeu ➡️ 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞: Hands-on projects. https://lnkd.in/dsCdTpxA ➡️ 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐖𝐢𝐤𝐢: Community-driven wiki. https://lnkd.in/dQJm32w4 ➡️ 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐑𝐨𝐚𝐝𝐦𝐚𝐩: Step-by-step guide. https://lnkd.in/dDiFhUnd ➡️ 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐇𝐨𝐰𝐓𝐨: Beginner-friendly resources. https://lnkd.in/dUXeMxQw ➡️ 𝐀𝐰𝐞𝐬𝐨𝐦𝐞 𝐎𝐩𝐞𝐧 𝐒𝐨𝐮𝐫𝐜𝐞 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠: Open-source tools. https://lnkd.in/d3UFYc4k ➡️ 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐙𝐨𝐨𝐦𝐜𝐚𝐦𝐩: Hands-on course. https://lnkd.in/dpBhR3ys #DataEngineering #BigData #DataEngineer #Hadoop #Git #GitHub #Python #Spark #Azure #AWS #SQL #InterivewPreparation #Interview #CareerDevelopment 🔍 𝐀𝐈 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐒𝐢𝐦𝐩𝐥𝐢𝐟𝐢𝐞𝐝: A Quick Guide for Engineers and Architects Understanding AI algorithms can feel like learning a new language. Here's a brief, simplified guide to some of the most important algorithms you should know.  📊 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 1. 𝑳𝒐𝒈𝒊𝒔𝒕𝒊𝒄 𝑹𝒆𝒈𝒓𝒆𝒔𝒔𝒊𝒐𝒏: Perfect for predicting yes/no outcomes. 2. 𝑳𝒊𝒏𝒆𝒂𝒓 𝑹𝒆𝒈𝒓𝒆𝒔𝒔𝒊𝒐𝒏: Uses past data to predict future outcomes. 3. 𝑵𝒂𝒊𝒗𝒆 𝑩𝒂𝒚𝒆𝒔: Predicts results based on prior probabilities. 4. 𝑺𝒖𝒑𝒑𝒐𝒓𝒕 𝑽𝒆𝒄𝒕𝒐𝒓 𝑴𝒂𝒄𝒉𝒊𝒏𝒆 (𝑺𝑽𝑴): Draws the clearest line to separate categories.  🧠 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐬 1. 𝑵𝒆𝒖𝒓𝒂𝒍 𝑵𝒆𝒕𝒘𝒐𝒓𝒌𝒔: Mimics the human brain by learning from examples. 2. 𝑪𝒐𝒏𝒗𝒐𝒍𝒖𝒕𝒊𝒐𝒏𝒂𝒍 𝑵𝒆𝒖𝒓𝒂𝒍 𝑵𝒆𝒕𝒘𝒐𝒓𝒌𝒔 (𝑪𝑵𝑵): Excels at recognizing patterns, such as faces. 3. 𝑹𝒆𝒄𝒖𝒓𝒓𝒆𝒏𝒕 𝑵𝒆𝒖𝒓𝒂𝒍 𝑵𝒆𝒕𝒘𝒐𝒓𝒌𝒔 (𝑹𝑵𝑵): Understands and predicts sequences, like sentences in a story. 4. 𝑨𝒖𝒕𝒐𝒆𝒏𝒄𝒐𝒅𝒆𝒓𝒔: Compresses data and then reconstructs it, often used in image processing.  📈 𝐂𝐥𝐮𝐬𝐭𝐞𝐫𝐢𝐧𝐠 𝐚𝐧𝐝 𝐃𝐢𝐦𝐞𝐧𝐬𝐢𝐨𝐧𝐚𝐥𝐢𝐭𝐲 𝐑𝐞𝐝𝐮𝐜𝐭𝐢𝐨𝐧 1. 𝑲-𝑴𝒆𝒂𝒏𝒔 𝑪𝒍𝒖𝒔𝒕𝒆𝒓𝒊𝒏𝒈: Groups similar items into clusters. 2. 𝑷𝒓𝒊𝒏𝒄𝒊𝒑𝒂𝒍 𝑪𝒐𝒎𝒑𝒐𝒏𝒆𝒏𝒕 𝑨𝒏𝒂𝒍𝒚𝒔𝒊𝒔 (𝑷𝑪𝑨): Reduces data complexity while retaining important information.  🤖 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 1. 𝑹𝒆𝒊𝒏𝒇𝒐𝒓𝒄𝒆𝒎𝒆𝒏𝒕 𝑳𝒆𝒂𝒓𝒏𝒊𝒏𝒈: Learns optimal actions through rewards and penalties, much like training a pet. 2. 𝑸-𝑳𝒆𝒂𝒓𝒏𝒊𝒏𝒈: Finds the best path or strategy in a given environment, like navigating a maze. 3. 𝑮𝒆𝒏𝒆𝒕𝒊𝒄 𝑨𝒍𝒈𝒐𝒓𝒊𝒕𝒉𝒎𝒔: Combines traits to evolve the best solution over time.  🌳 𝐄𝐧𝐬𝐞𝐦𝐛𝐥𝐞 𝐚𝐧𝐝 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐌𝐞𝐭𝐡𝐨𝐝𝐬 1. 𝑫𝒆𝒄𝒊𝒔𝒊𝒐𝒏 𝑻𝒓𝒆𝒆𝒔: Makes decisions by asking a series of yes/no questions. 2. 𝑹𝒂𝒏𝒅𝒐𝒎 𝑭𝒐𝒓𝒆𝒔𝒕𝒔: Enhances accuracy by combining multiple decision trees. 3. 𝑮𝒓𝒂𝒅𝒊𝒆𝒏𝒕 𝑩𝒐𝒐𝒔𝒕𝒊𝒏𝒈: Improves predictions by focusing on errors from previous models.  📍 𝐈𝐧𝐬𝐭𝐚𝐧𝐜𝐞-𝐁𝐚𝐬𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 1. 𝒌-𝑵𝒆𝒂𝒓𝒆𝒔𝒕 𝑵𝒆𝒊𝒈𝒉𝒃𝒐𝒓𝒔 (𝒌-𝑵𝑵): Classifies items based on the closest examples, like asking friends for advice.  🔗 𝐏𝐫𝐨𝐛𝐚𝐛𝐢𝐥𝐢𝐬𝐭𝐢𝐜 𝐌𝐨𝐝𝐞𝐥𝐬 1. 𝑩𝒂𝒚𝒆𝒔𝒊𝒂𝒏 𝑵𝒆𝒕𝒘𝒐𝒓𝒌𝒔: Predicts outcomes by considering various interdependent factors. AI algorithms may seem complex, but breaking them down into these core concepts makes them more approachable. Which algorithms do you find most useful in your work? Share your thoughts and experiences below! Let's decode AI together. #AI #MachineLearning #DataScience #NeuralNetworks #AlgorithmExplained New Model in IBM watsonx! --> We are excited to announce that an Arabic-language foundation model is now available to all our users and customers The jais-13b-chat foundation model provided by Inception, Mohamed bin Zayed University of Artificial Intelligence, and Cerebras Systems is available for our customers.. Key points to highlight: - Jais-13b-chat is a 13 billion parameter bilingual large language model for Arabic and English, based on GPT-3 architecture with SwiGLU non-linearity and ALiBi position embeddings, designed for enhanced context handling and precision in longer sequences. - The model is fine-tuned using a dataset of 4 million Arabic and 6 million English prompt-response pairs, incorporating safety features and extra guardrails through safety-oriented instructions and prompts. - It utilizes the largest curated dataset of Arabic and English instruction tuning, allowing for multi-turn conversations across diverse topics, especially relevant to the Arab world. Our strategy at IBM is Multi-Model and Multi-Lingual. We offer a rich and curated model library that gives you the choice and flexibility to choose the model that best fits your business needs, regional interests, and risk profiles from a library of proprietary, open-source, and third-party models. You can give it a try here: https://lnkd.in/g4pYRDT8 Time to retrospect :) Just curious to know Monolithic Microservice MVC Event Driven Layered Which of the above Architectural patterns do you prefer frequently? Cloud Comparison Comparison of cloud services #azure #aws #gcp #cloudservice #cloud #cloudcomputing #amazonwebservices Exploring the Intricate Architecture of Snowflake Data Warehousing #snowflake #snowflakedatacloud #dataengineering #architectureanddesign #systemarchitecture #clouddatawarehouse Streamlining Your Data Flow: The Importance of a Robust Data Pipeline Architecture #data #pipeline #architecture #dataflow #dataengineering #datapipeline Just released! #FastAPI 0.93.0 🚀 This adds support for lifespan handlers 🎉 This is instead of separated startup and shutdown event handlers 🤓 Hint: this is where you load your ML models or setup your DB connection pools, etc. 😉 New docs: https://lnkd.in/eH-_mJin 🎯 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 𝐋𝐚𝐲𝐞𝐫𝐬 𝐢𝐧 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬 𝐋𝐚𝐤𝐞𝐡𝐨𝐮𝐬𝐞 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 A 𝑳𝒂𝒌𝒆𝒉𝒐𝒖𝒔𝒆 is a new data platform architecture paradigm that combines the best features of data lakes and data warehouses.   ✳️ 𝐁𝐫𝐨𝐧𝐳𝐞 𝐋𝐚𝐲𝐞𝐫 (𝐫𝐚𝐰 𝐝𝐚𝐭𝐚)   🔹 Source data converted & loaded as delta format  🔹 Data will be appended to delta tables  🔹 Table structures in this layer correspond to source system table structures "as-is,"  🔹 Bronze tables will have additional metadata columns that capture the load date/time, process ID, etc.   🔹 The focus in this layer is quick Change Data Capture (CDC)  🔹 This also focuses to provide historical archive of source (cold storage), data lineage, auditability  🔹 Bronze can be used for reloading source data if required without rereading it  🔹 All Historical data will be managed here with audit columns    ✳️ 𝐒𝐢𝐥𝐯𝐞𝐫 𝐋𝐚𝐲𝐞𝐫  (𝐜𝐥𝐞𝐚𝐧𝐬𝐞𝐝 𝐚𝐧𝐝 𝐜𝐨𝐧𝐟𝐨𝐫𝐦𝐞𝐝 𝐝𝐚𝐭𝐚)  🔹 Uses Delta Lake tables (with SQL table names)  🔹 Preserves grain of original data (no aggregation)  🔹 Silver layer can provide an "Enterprise view" of all its key business entities, concepts and transactions  🔹 Eliminates duplicate records  🔹 Production schema enforced  🔹 Data quality checks passed  🔹 Corrupt data quarantined  🔹 Data stored to support production workloads  🔹 Optimized for long-term retention and ad-hoc queries  🔹 Validate data quality and schema  🔹 Enrich and transform data  🔹 Optimize data layout and storage for downstream queries  🔹 Provide single source of truth for analytics    ✳️ 𝐆𝐨𝐥𝐝 𝐥𝐚𝐲𝐞𝐫 (𝐜𝐮𝐫𝐚𝐭𝐞𝐝 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬-𝐥𝐞𝐯𝐞𝐥 𝐭𝐚𝐛𝐥𝐞𝐬)  🔹 Validated and business-level tables  🔹 Lakehouse is typically organized in consumption-ready "project-specific" databases  🔹 The Gold layer is for reporting and uses more de-normalized and read-optimized data models with fewer joins  🔹 The final layer of data transformations and data quality rules are applied here  🔹 Final presentation layer of projects are business data wise models  🔹 We see a lot of Kimball style star schema-based data models or Inmon style Data marts fit in this Gold Layer    🚀 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬 𝐨𝐟 𝐦𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐥𝐚𝐲𝐞𝐫𝐬  🔹 Simple data model  🔹 Easy to understand and implement  🔹 Enables incremental ETL  🔹 Can recreate your tables from raw data at any time  🔹 ACID transactions  🔹 Time travel 𝑭𝒐𝒍𝒍𝒐𝒘 𝒕𝒐 𝒍𝒆𝒂𝒓𝒏 𝒎𝒐𝒓𝒆 #DataEngineering 𝒄𝒐𝒏𝒕𝒆𝒏𝒕 📕 #databricks #lakehouse  #spark  #sql  #sparksql  #azuredatabricks  #pyspark I’m happy to share that I’ve obtained a new certification: Hands On Essentials - Data Warehouse from Snowflake! View my verified achievement from Snowflake. I’m happy to share that I’ve obtained a new certification: Databricks Lakehouse Fundamentals from Databricks! 🎯 𝐃𝐚𝐭𝐚 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞, 𝐃𝐚𝐭𝐚 𝐋𝐚𝐤𝐞, 𝐃𝐞𝐥𝐭𝐚 𝐋𝐚𝐤𝐞 & 𝐃𝐚𝐭𝐚 𝐋𝐚𝐤𝐞𝐡𝐨𝐮𝐬𝐞 𝑫𝒂𝒕𝒂 𝑾𝒂𝒓𝒆𝒉𝒐𝒖𝒔𝒆  🔸A data warehouse is a unified data repository for storing large amounts of information from multiple sources within an organization.  🔸A data warehouse represents a single source of “data truth” in an organization and serves as a core reporting and business analytics component.   𝑫𝒂𝒕𝒂 𝑳𝒂𝒌𝒆  🔸A Data Lake is storage layer or centralized repository for all structured, semi-structured and unstructured data at any scale.  🔸Data lakes are flexible, durable, and cost-effective and enable organizations to gain advanced insight from unstructured data, unlike data warehouses that struggle with data in this format.   𝑫𝒆𝒍𝒕𝒂 𝑳𝒂𝒌𝒆  🔸Delta Lake integrates batch and streaming data processing, scalable metadata management, time travel and ACID transactions on top of Data Lake.   𝑫𝒂𝒕𝒂 𝑳𝒂𝒌𝒆𝒉𝒐𝒖𝒔𝒆  🔸A data lakehouse is a new, open data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data.  🔸A data lakehouse has additional Data Governance compared to a data lake.   𝑭𝒐𝒍𝒍𝒐𝒘 𝒕𝒐 𝒍𝒆𝒂𝒓𝒏 𝒎𝒐𝒓𝒆 #DataEngineering 𝒄𝒐𝒏𝒕𝒆𝒏𝒕 📕   #datawarehouse #datalake #deltalake #datalakehouse Free to earn #AWS Cloud Practitioner certificate 🎯 𝐄𝐃𝐖, 𝐎𝐃𝐒, 𝐃𝐚𝐭𝐚 𝐌𝐚𝐫𝐭 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐃𝐚𝐭𝐚 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞 (𝐄𝐃𝐖) ✔It is a centralized warehouse ✔It provides decision support services across the enterprise.  ✔EDWs are usually a collection of databases that offer a unified approach for organizing data and classifying data according to subject 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐃𝐚𝐭𝐚 𝐒𝐭𝐨𝐫𝐞 (𝐎𝐃𝐒) ✔It is a central database used for operational reporting as a data source for the enterprise data warehouse ✔It is a complementary element to an EDW and is also used for decision making ✔In ODS, the data present in the Data warehouse keeps on updating in real-time. Hence, it is widely preferred for routine activities like storing records of the Employees, as new Employees keep on adding 𝐃𝐚𝐭𝐚 𝐌𝐚𝐫𝐭 ✔A data mart is considered a subset of a data warehouse and is usually oriented to a specific team or business line, such as finance or sales ✔It is subject-oriented, making specific data available to a defined group of users ✔The availability of specific data ensures that they do not need to waste time searching through an entire data warehouse. So data of different departments can be stored in data marts #data #warehouse #database #edw #datamart #ods I’m happy to share I passed the AWS Certified Solutions Architect Professional. One more AWS certified. Many thanks to JAYANT JHA for encouraging me. A special thanks to Stéphane Maarek for a great course! Also look into my article on How I cleared AWS Certified Solutions Architect Professional exam #aws #awscsap #awscertification #awscloud #awscertified #selflearning #selfdevelopment #gratitude Sharing for greater reach Aman found a way to get attention by marketing his resume through food delivery bagged him an internship at Digital Gurukul Metaversity. A similar method was previously executed in San Francisco #resume #marketing #internship #digital 5 simple tips to code better #Azure Event Hub - Micro Course #azureeventhub #eventhub #microcourse #tutorial Sharing for greater reach Have a look to get a gist of AWS services #aws #awscloud #amazoncloud #awstraining #cloud #amazon Please give a read and let me know your thoughts!! #aws #awscertification #awssolutionsarchitect #awscertifications #cloudcertification #cloudjourney View my verified achievement from Microsoft.