High Level Overview of Windows Azure - EPC Group
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The document discusses the architecture of Windows Azure SQL Database and Windows Azure Table Storage. It describes four layers - client, services, platform, and infrastructure - for SQL Database. For Table Storage, it covers key concepts like entities, partitions, queries with no fixed schema, and how tables are designed for scalability with partitions and transaction support within partitions.
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Operating System Used by Users in day-to-day life.pptx
Dive into the realm of operating systems (OS) with Pravash Chandra Das, a seasoned Digital Forensic Analyst, as your guide. 🚀 This comprehensive presentation illuminates the core concepts, types, and evolution of OS, essential for understanding modern computing landscapes.
Beginning with the foundational definition, Das clarifies the pivotal role of OS as system software orchestrating hardware resources, software applications, and user interactions. Through succinct descriptions, he delineates the diverse types of OS, from single-user, single-task environments like early MS-DOS iterations, to multi-user, multi-tasking systems exemplified by modern Linux distributions.
Crucial components like the kernel and shell are dissected, highlighting their indispensable functions in resource management and user interface interaction. Das elucidates how the kernel acts as the central nervous system, orchestrating process scheduling, memory allocation, and device management. Meanwhile, the shell serves as the gateway for user commands, bridging the gap between human input and machine execution. 💻
The narrative then shifts to a captivating exploration of prominent desktop OSs, Windows, macOS, and Linux. Windows, with its globally ubiquitous presence and user-friendly interface, emerges as a cornerstone in personal computing history. macOS, lauded for its sleek design and seamless integration with Apple's ecosystem, stands as a beacon of stability and creativity. Linux, an open-source marvel, offers unparalleled flexibility and security, revolutionizing the computing landscape. 🖥️
Moving to the realm of mobile devices, Das unravels the dominance of Android and iOS. Android's open-source ethos fosters a vibrant ecosystem of customization and innovation, while iOS boasts a seamless user experience and robust security infrastructure. Meanwhile, discontinued platforms like Symbian and Palm OS evoke nostalgia for their pioneering roles in the smartphone revolution.
The journey concludes with a reflection on the ever-evolving landscape of OS, underscored by the emergence of real-time operating systems (RTOS) and the persistent quest for innovation and efficiency. As technology continues to shape our world, understanding the foundations and evolution of operating systems remains paramount. Join Pravash Chandra Das on this illuminating journey through the heart of computing. 🌟
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Columbus Data & Analytics Wednesdays - June 2024
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Building Production Ready Search Pipelines with Spark and Milvus
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
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5th LF Energy Power Grid Model Meet-up Slides
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
A Comprehensive Guide to DeFi Development Services in 2024
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
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System Design Case Study: Building a Scalable E-Commerce Platform - Hiike
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- 3. Client Layer - Used by application to communicate directly with SQL Database. • Services Layer – Gateway between Client layer and Platform layer. • Platform Layer – Includes physical servicers and services that support the Services layer. • Infrastructure Layer – IT administration of the physical HW and OS. •
- 4. Topics to Review • Windows Azure SQL Database • Architecture Application Connectivity Scalability • • Architecture Querying • • • Windows Azure Table Storage • • • Best Practices and Considerations Cost Transactions
- 5. Application Connectivity Considerations And Best Practices • login: [login]@[server] • Idle connections • Long running transactions • DoS guard • Failover events • Throttling • Connection pooling and Retry logic • Latency introduced for updates • No cross-database dependencies
- 6. A Seamless Offering from the Private Cloud to the Public Cloud
- 7. Scalability Model For The Cloud Cloud Applications Require Scale Beyond Scale-Up Demand the Best Economics • • • • Best Price/Performance Elasticity + Pay-as-you-go
- 8. Obstacles • • • • • • Defining the Tenant Establishing Tenant’s surrogate key Elastic Scalability (Splits/Merges/Tenant Moving) Application Lifecycle Management (Dev; Test; Deploy; Upgrades) Overcoming limitations of existing tools & available features Transient nature of connectivity
- 13. No Fixed Schema
- 14. Querying
- 15. Purpose of the PartitionKey Entity Locality • Entities in the same partition will be stored together Efficient querying and cache locality • Endeavour to include partition key in all queries • Entity Group Transactions • Atomic multiple Insert/Update/Delete in same partition in a single transaction Table Scalability • • • Target throughput – 20,000 tps/partition, several thousand tps/account Windows Azure monitors the usage patterns of partitions Automatically load balance partitions • • Each partition can be served by a different storage node Scale to meet the traffic needs of your table
- 16. Partitions and Partition Ranges PARTITIONKEY (CATEGORY) ROWKEY (TITLE) TIMESTAMP MODELYEAR Bikes Super Duper Cycle … 2009 Bikes Quick Cycle 200 Deluxe … 2007 … … … … Canoes Whitewater … 2009 Canoes Flatwater … 2006 Rafts PARTITIONKEY (CATEGORY) … 14ft Super ROWKEY Tourer (TITLE) … … TIMESTAMP … 1999 MODELYEAR … Rafts Skis … … 1999 2009 … … … … Skis Tents 14ft Super Tourer Fabrikam Back Trackers … … Fabrikam Back Super Palace Trackers … … 2009 2008 … … … … Tents Super Palace … 2008 … …
Editor's Notes
- Here we can see that the Front-End layer takes incoming requests, and a given front-end server can talk to all of the partition servers it needs to in order to process the incoming requests. The partition layer consists of all of the partition servers, with a master system to perform the automatic load balancing (described below) and assignments of partitions. As shown in the figure, each partition server is assigned a set of object partitions (Blobs, Entities, Queues). The Partition Master constantly monitors the overall load on each partition sever as well the individual partitions, and uses this for load balancing. Then the lowest layer of the storage architecture is the Distributed File System layer, which stores and replicates the data, and all partition servers can access any of the DFS severs.It is important to understand that partitions are not tied to specific partition servers, since the data is stored in the DFS layer. The partition layer can therefore easily load balance and assign partitions to different partition servers, since any partition server can potentially provide access to any partition.The partition layer assigns partitions to partition severs based on each partition’s load. A given partition server may serve many partitions, and the Partition Master continuously monitors the load on all partition servers. If it sees that a partition server has too much load, the partition layer will automatically load balance some of the partitions from that partition server to a partition server with low load.