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𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗧𝗲𝗿𝗺𝘀 𝗬𝗼𝘂 𝗡𝗲𝗲𝗱 𝘁𝗼 𝗞𝗻𝗼𝘄! If you're into data engineering, knowing these terms will help you work with data storage, moving data around, and making sense of it all. ⚙ 𝗗𝗮𝘁𝗮 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲: An automated process that moves and prepares data. 💾 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲: An organized collection of data for easy access. 📋 𝗦𝗰𝗵𝗲𝗺𝗮: The blueprint defining a database's structure. 💡 𝗧𝗮𝗯𝗹𝗲: A structured grid containing related data points. 🏠 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲: A central hub for integrated data analysis. ⤵️ 𝗘𝗧𝗟: Extract, Transform, Load - The traditional way to extract, clean, and load data. ⤴️ 𝗘𝗟𝗧: Extract, Load, Transform - The modern approach of loading data first, then transforming it. 🏞️ 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲: Massive storage for raw, unorganized data. ⏱️ 𝗕𝗮𝘁𝗰𝗵 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Processing data in large chunks at set times. ⏱️ 𝗦𝘁𝗿𝗲𝗮𝗺 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Processing data in real-time as it arrives. 📊 𝗗𝗮𝘁𝗮 𝗠𝗮𝗿𝘁: A specific slice of a data warehouse for a particular domain. 🔍 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆: Ensuring data accuracy, consistency, and reliability. 🕸️ 𝗗𝗮𝘁𝗮 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴: Designing the logical structure and connections of data. 🌊 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲𝗵𝗼𝘂𝘀𝗲: Combines the flexibility of a data lake with a data warehouse's structure. 🎻 𝗗𝗮𝘁𝗮 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻: Coordinating and managing complex data workflows. 🔎 𝗗𝗮𝘁𝗮 𝗟𝗶𝗻𝗲𝗮𝗴𝗲: Tracing a data's origin and journey through its use. Credit - Data and AI Central Get 𝙩𝙧𝙖𝙞𝙣𝙚𝙙, get 𝙝𝙞𝙧𝙚𝙙 💁🏻♂️. Register for 𝘼𝙒𝙎 𝘾𝙡𝙤𝙪𝙙 𝘿𝙚𝙫𝙊𝙥𝙨 𝙫𝙞𝙧𝙩𝙪𝙖𝙡 training 👨🏻💻 today by submitting this 𝙚𝙖𝙨𝙮 2𝙢𝙞𝙣𝙨 𝙂𝙤𝙤𝙜𝙡𝙚 𝙛𝙤𝙧𝙢: https://lnkd.in/gsBppVnT 📲 Contact Dhruv R. 👨🏫 for more information ℹ️ CloudSpikes MultiCloud Solutions Inc. https://lnkd.in/gbTzebec 👨🏻🏫💻 𝐀𝐖𝐒 𝐂𝐥𝐨𝐮𝐝 𝐃𝐞𝐯𝐎𝐩𝐬 𝐯𝐢𝐫𝐭𝐮𝐚𝐥 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐩𝐫𝐨𝐠𝐫𝐚𝐦 💻👨🏻🏫 #dataengineering #terms LP678

𝐔𝐧𝐥𝐨𝐜𝐤𝐢𝐧𝐠 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐃𝐚𝐭𝐚-𝐂𝐞𝐧𝐭𝐫𝐢𝐜 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠. In the Big Data era, placing data at the heart of application development is key to transforming insights into action. Data-centric application engineering not only enhances decision-making and efficiency but also addresses challenges like integration complexity, scalability, and data security. 𝐊𝐞𝐲 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 𝐟𝐨𝐫 𝐒𝐮𝐜𝐜𝐞𝐬𝐬: 🔵Leverage cloud technologies for scalability and flexibility. 🔵Implement robust security measures to protect data integrity. 🔵Focus on quality assurance to ensure data accuracy. 🔵Adopt agile methodologies to improve responsiveness and collaboration. Embracing these strategies can help overcome the hurdles of Big Data, enabling businesses to harness its full potential. Let's discuss how we can drive innovation by focusing on data-centric solutions! #pronixinc #datacentricengineering #bigdatasolutions #innovationwithdata #cloudscalability #datasecurity #qualityassurance #agilemethodologies #techtransformation #datadrivendecisions #unlockinginnovation

Message Queues A message queue is a crucial component in distributed computing systems, acting as an intermediary buffer that temporarily holds and manages messages exchanged between applications. This setup enables asynchronous communication, allowing systems to interact without real-time connections. Messages are stored in the queue until retrieved and processed by the intended recipients, ensuring reliable and decoupled communication. Key Features of Message Queues: Concurrency Control: Manages multiple messages simultaneously. Guaranteed Message Delivery: Ensures messages reach their recipients. Message Prioritization: Allows prioritization of messages. Message Transformation: Modifies messages as they pass through. Communication Patterns: Supports point-to-point and publish/subscribe communication. Components of a Message Queue: Message: Data unit exchanged between applications. Producer: Sends messages to the queue. Queue/Topic: Stores messages (queues follow FIFO; topics allow multiple subscriptions). Consumer: Retrieves and processes messages. Broker/Message Broker: Manages communication between producers and consumers. Acknowledgment Mechanism: Confirms successful message processing. Subscription: Defines which messages consumers receive. Benefits of Using Message Queues: Asynchronous Communication: Enhances system responsiveness. Decoupling Components: Improves modularity and flexibility. Scalability and Load Balancing: Distributes workload across instances. Reliability and Fault Tolerance: Ensures data integrity even with temporary failures. Real-time Processing: Supports real-time data processing. Ordering and Sequencing: Maintains specific order for message processing. Characteristics of Message Queues: High Throughput: Handles large volumes of messages efficiently. Low Latency: Minimizes transmission delay. Scalability: Adjusts resources based on demand. High Availability: Ensures continuous operation through redundancy. Global Data Replication: Synchronizes messages across locations. Ordering Guarantees: Maintains message delivery order. Permanent Storage: Provides reliable data persistence.

IBM is one of the largest corporate networks known to mankind. 🌐 With the health of that network riding on their back, the CIO Network Engineering team needs continuous, reliable, and transparent operational data that teams around the world can use in real-time. When their existing DataOps solution wasn't cutting the mustard, they turned to our smart data pipeline platform for rescue! 🦸 “We use StreamSets because it’s the only technology that handles volume at scale.” – Stephan Barabasi, big data, cloud architect, and data scientist. What's next? Plans to scale StreamSets beyond the CIO Network Engineering team. 🚀 https://bit.ly/49j4aLa #DataPipelines #DataOps #CIO

Spectrum of Consistency Models in Distributed Systems In the realm of distributed systems, understanding consistency models is paramount. These models define how data is accessed and updated across multiple nodes, influencing system reliability, performance, and scalability. Let's delve into the spectrum of consistency models: 1. Strong Consistency: This model ensures that all reads and writes are immediately reflected across all nodes. It offers a high level of consistency but can lead to increased latency and reduced availability due to synchronization requirements. 2. Eventual Consistency: Here, updates are propagated asynchronously, allowing nodes to diverge temporarily before eventually converging to a consistent state. It prioritizes availability and performance over strict consistency, making it suitable for systems with low update contention. 3. Causal Consistency: This model preserves causal relationships between events, ensuring that events causally related are observed in the correct order across nodes. It strikes a balance between strong and eventual consistency, offering better performance while maintaining causal ordering guarantees. 4. Weak Consistency: In contrast to strong consistency, weak consistency allows for greater divergence among nodes, leading to potentially stale reads or inconsistencies. However, it improves system responsiveness and scalability by relaxing synchronization constraints. 5. Read-your-writes Consistency:This model guarantees that a node will always see its own writes in subsequent read operations. It's commonly used in systems where users expect immediate visibility of their updates, enhancing user experience without sacrificing overall system consistency. 6. Monotonic Consistency:Monotonic consistency ensures that if a process reads the latest value of a data item, it will never see an earlier value in subsequent reads. This provides a monotonic progression of data views, aiding in maintaining logical orderings. Understanding these consistency models is crucial for designing distributed systems that align with specific requirements around consistency, availability, and partition tolerance (CAP theorem). By selecting the appropriate consistency model, organizations can achieve a balance between data integrity and system performance, ultimately delivering robust and scalable solutions. #DistributedSystems #ConsistencyModels #DataManagement #SystemDesign #TechTrends

Oracle Secures Patent for ML-Powered Data Centre Outage Diagnostic System Oracle has been granted a patent for a machine learning (ML) system designed to detect data centre outages and generate alerts, potentially revolutionizing downtime management and saving businesses millions in operational costs. The system processes real-time data from servers, networking hardware, power devices, and environmental sensors to identify potential outage sources. The Uptime Institute’s Annual Outage Analysis 2023 reports that more than two-thirds of data centre outages cost businesses over £78,120 ($100,000) per incident, with 25% costing over £781,200 ($1 million). Oracle's solution leverages ML models to interpret vast data, ensuring rapid identification of outage sources. For example, if a rack power source fails, the ML model detects the issue and triggers an alert, enabling swift remediation. This automation is crucial as data centres grow in size and complexity, making traditional monitoring methods inadequate. Oracle continues to integrate ML across its services, enhancing products like SQL databases and introducing spatial ML algorithms for Python to improve model quality and prediction accuracy. Adv. Jaspreet Singh Piyush Yadav Advocate Apoorva Sharma Urvashi Sharma Aditya Singh Aditi Sharma Kartik Mogha Priyanjal Jain RITIKA TAPARIA Praveen Yadav Shreya Sanghavi Sk Badsha SK SOHEL #Oracle #DataCentre #MachineLearning #ML #OutageDetection #DataCentreManagement #TechInnovation #OperationalEfficiency #DowntimeManagement #RealTimeData #DataCentreOutages #MLModels #TechPatent #Automation #ServiceReliability #UptimeInstitute #OperationalCosts #TechNews #PowerDevices #NetworkingHardware #EnvironmentalSensors #RapidIdentification #SQLDatabases #SpatialML #PythonAlgorithms #TechGiants #BusinessResiliency #CloudServices #AI #TechTrends #DataCentreSolutions https://lnkd.in/gE9asxtx

🚀 Discover how to design a scalable and stable on-premise infrastructure for distributed computing, big data, artificial intelligence, and scalable software projects. Read our article on maturity layers in technology infrastructure! #ITInfrastructure #DistributedComputing #BigData #AI #ScalableSoftware #Software #OnPremise #Jofrantoba 🔍 Want to build robust and efficient systems for your business? Learn how to follow a layered approach to design an on-premise infrastructure that meets your business and IT needs! #OnPremiseInfrastructure #EnterpriseTechnology #ITEfficiency #Software #Jofrantoba 🛠️ Unlock the keys to a solid and reliable technology infrastructure in our latest article. Don't miss out on the maturity layers for a scalable and stable on-premise infrastructure in cutting-edge projects! #EnterpriseTechnology #TechnologyInfrastructure #ITEfficiency #Software #Jofrantoba https://lnkd.in/eFGBRKwU

𝗢𝗿𝗮𝗰𝗹𝗲 𝗚𝗿𝗮𝗻𝘁𝗲𝗱 𝗣𝗮𝘁𝗲𝗻𝘁 𝗳𝗼𝗿 𝗠𝗟-𝗽𝗼𝘄𝗲𝗿𝗲𝗱 𝗗𝗮𝘁𝗮 𝗖𝗲𝗻𝘁𝗿𝗲 𝗢𝘂𝘁𝗮𝗴𝗲 𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝘁𝗶𝗰 💡🔍 Oracle has been granted a patent for an innovative machine learning system designed to detect data centre outages and generate alerts. This ML-based solution aims to transform downtime management, potentially saving businesses millions and enhancing service reliability. 𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗶𝘇𝗶𝗻𝗴 𝗗𝗼𝘄𝗻𝘁𝗶𝗺𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 🛠️📉 According to the Uptime Institute's Annual Outage Analysis 2023, data centre outages cost businesses over £78,120 per incident on average, with 25% of outages exceeding £781,200 ($1 million). Oracle’s new ML system could significantly reduce these costs by offering rapid and accurate outage diagnostics. Read more at https://bit.ly/4dl4gUt #technews #datacenters #machinelearning #ai #innovation

  Tel Aviv and IBM teams question the conventional benchmarking practices, which typically involve training models from scratch with random initialization as modeling long-range dependencies in sequences has led to notable architectural advancements, with state space models (SSMs) emerging as a significant alternative to Transformers. According to the team, this method may overestimate the differences between architectures.   The researchers propose pretraining models using standard denoising objectives with downstream task data, a method they term selfpretraining (SPT). This approach significantly narrows the performance gap between Transformers and SSMs. For example, pretrained vanilla Transformers can match the performance of advanced SSMs like S4 on benchmarks such as the Long Range Arena (LRA). Specifically, SPT improved the best reported results of SSMs on the PathX-256 task by 20 points.   Key findings from the study include:   1. Transformers vs. SSMs: Properly pretrained vanilla Transformers can achieve performance comparable to S4 on LRA tasks, challenging the notion that Transformers are less capable of modeling long-range dependencies. 2. Redundancy of Structured Parameterizations: Structured parameterizations in SSMs become mostly redundant with data-driven initialization through pretraining, suggesting that simpler models can match the performance of more complex architectures. 3. Effectiveness Across Data Scales: SPT is particularly beneficial when training data is scarce, with relative gains more pronounced with smaller datasets. 4. Adaptability of Convolution Kernels: Data-driven kernels learned via SPT adapt to specific task distributions, enhancing performance on long-sequence tasks.   The study emphasizes the importance of incorporating a pretraining stage in model evaluation to ensure accurate performance estimation and simplify architecture design. This approach not only provides a fair comparison between different architectures but also highlights the efficiency of pretraining in leveraging task data.    Arxiv: https://lnkd.in/enaH3mhu