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Free Download Udemy - How to Live Stream & Record Content - OBS Studio Published: 3/2025 Created by: Aran Graham MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: Beginner | Genre: eLearning | Language: English | Duration: 10 Lectures ( 50m ) | Size: 574 MB Learn the essential skills to start live streaming or record video content. What you'll learn Be able to live stream to platforms like Twitch and Youtube. Record video content offline for uploading to video viewing platforms. Optimize audio and cameras for high quality results for viewers. Optimize recording settings to ensure an excellent end result. Requirements You must have a computer, a webcam, a microphone, and an internet connection. Description In this comprehensive course, you will learn the essential skills to start streaming live to popular platforms like Twitch, while also gaining the knowledge to record content offline for uploading to various platforms such as YouTube or even creating your own online course for sites like Udemy. Whether you're interested in live streaming video games, hosting live shows, or creating educational content, this course will guide you through every step of the process.Starting with the basics, you'll be introduced to the tools and software required for streaming and recording. You'll learn how to set up your streaming software, configure your audio and video settings, and optimize your stream for the best quality. Additionally, you'll discover how to troubleshoot common issues that may arise while streaming, ensuring a smooth and professional experience.For offline recording, the course covers techniques for capturing high-quality video and audio, as well as editing tips to make your content polished and engaging.No prior experience is necessary, as this course is designed for beginners. By the end, you'll be confident in your ability to stream live or record content, creating engaging videos to share with the world. Whether you're looking to build a personal brand, launch a channel, or share your knowledge, this course provides the tools and knowledge to get you started. Who this course is for This course is for people who have never live streamed or recorded high quality video content from home. If you have already at a beginner level, but want to ensure you're fully optimizing your settings, this course may also benefit you. Homepage: https://www.udemy.com/course/how-to-stream-and-record/ [b]AusFile[/b] https://ausfile.com/zu4yq3nvczj0/kpwdw.How.to.Live.Stream..Record.Content..OBS.Studio.rar.html Rapidgator https://rg.to/file/b2a19ee487f026c956da0e12061fef4a/kpwdw.How.to.Live.Stream..Record.Content..OBS.Studio.rar.html Fikper Free Download https://fikper.com/U9YHzloq20/kpwdw.How.to.Live.Stream..Record.Content..OBS.Studio.rar.html No Password - Links are Interchangeable

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Free Download Udemy - Databricks Stream Processing With Pyspark In 15 Days Published: 2/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 746.60 MB | Duration: 1h 56m Master Spark Structured Streaming with PySpark on Databricks through a Complete End to End Real Life Project What you'll learn Concept of Real-time Stream Processing in Databricks Spark Structured Streaming APIs and Medallion Architecture Working with Different Streaming Sources and Sinks Working With Kafka Source and Integrating with Spark Windowing Aggregates using Spark Stream & Streaming Joins and Aggregation Concept of State-less and State-full Streaming Transformations Handling Memory Problems with Streaming Working with Azure Databricks Platform Real Life Final Project - Streaming application in Lakehouse Requirements Python Programming Language Description Course OverviewIn today's data-driven world, real-time stream processing is a crucial skill for software engineers, data architects, and data engineers. This course, Apache Spark and Databricks - Stream Processing in Lakehouse, is designed to equip learners with hands-on experience in real-time data streaming using Apache Spark, Databricks Cloud, and the PySpark API.Whether you're a beginner or an experienced professional, this course will provide you with the practical knowledge and skills needed to build real-time data processing pipelines on Databricks, utilizing Apache Spark Structured Streaming for high-performance data processing.With a live coding approach, you'll gain deep insights into streaming architecture, message queues, event-driven applications, and real-world data processing scenarios.Why Learn Real-Time Stream Processing?Real-time stream processing is becoming a critical technology for businesses handling vast amounts of data generated by IoT devices, financial transactions, social media platforms, e-commerce websites, and more. Companies need instant insights and decisions, and Apache Spark Structured Streaming is the best tool for handling large-scale streaming data efficiently.With the rise of Lakehouse Architecture and platforms like Databricks, enterprises are moving towards unified data analytics where structured and unstructured data can be processed in real time. This course ensures that you stay ahead in the industry by mastering streaming technologies and building scalable, fault-tolerant stream processing applications.What you'll learn?This course takes an example-driven approach to teach real-time stream processing. Here's what you'll learn:Foundations of Stream Processing- Introduction to real-time stream processing and its use cases- Understanding batch vs. streaming data processing- Overview of Apache Spark Structured Streaming- Core components of Databricks Cloud and Lakehouse ArchitectureGetting Started with Apache Spark & Databricks- Setting up a Databricks workspace for real-time streaming- Understanding Databricks Runtime and optimized Spark execution- Managing data with Delta Lake and Databricks File System (DBFS)Building Real-Time Streaming Pipelines with PySpark- Introduction to PySpark API for streaming- Working with Kafka, Event Hubs, and Azure Storage for data ingestion- Implementing real-time data transformations and aggregations- Writing streaming data to Delta Lake and other storage formats- Handling late-arriving data and watermarking- Optimizing Streaming Performance on Databricks- Tuning Spark Structured Streaming applications for low latency- Implementing checkpointing and stateful processing- Understanding fault tolerance and recovery strategies- Using Databricks Job Clusters for real-time workloadsIntegrating Stream Processing with Databricks Ecosystem- Using Databricks SQL for real-time analytics- Connecting Power BI, Tableau, and other visualization tools- Automating real-time data pipelines with Databricks Workflows- Deploying streaming applications with Databricks JobsCapstone Project - End-to-End Real-Time Streaming Application- Design a real-time data processing pipeline from scratch- Implement data ingestion from Kafka or Event Hubs- Process streaming data using PySpark transformations- Store and analyze real-time insights using Delta Lake & Databricks SQL- Deploy your solution using Databricks Workflows & CI/CD PipelinesWho Should Take This Course?This course is perfect for:- Software Engineers who want to develop scalable, real-time applications.- Data Engineers & Architects who design and build enterprise-level streaming pipelines.- Machine Learning Engineers looking to process real-time data for AI/ML models.- Big Data Professionals who work with streaming frameworks like Kafka, Flink, or Spark.- Managers & Solution Architects who oversee real-time data implementations.Why Choose This Course?This course is designed with a practical, hands-on approach, ensuring you not only learn the concepts but also implement them in real-world scenarios.- Live Coding Sessions - Learn by doing, with step-by-step implementations.- Real-World Use Cases - Apply your knowledge to industry-relevant examples.- Optimized for Databricks - Best practices for deploying streaming applications on Azure Databricks.- Capstone Project - Get hands-on experience building an end-to-end streaming pipeline.Technology Stack & EnvironmentThis course is built using the latest technologies:- Apache Spark 3.5 - The most powerful version for structured streaming.- Databricks Runtime 14.1 - Optimized Spark performance on the cloud.- Azure Databricks - Scalable, serverless data analytics.- Delta Lake - Reliable storage for structured streaming.- Kafka & Event Hubs - Real-time messaging and event-driven architecture.- CI/CD Pipelines - Deploying real-time applications efficiently.Enroll Now & Start Your Journey in Real-Time Data Streaming!By the end of this course, you will be confident in building, deploying, and managing real-time streaming applications using Apache Spark Structured Streaming on Databricks Cloud.Take the next step in your career and master real-time stream processing today. 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Kathryn Kaye - Reflected in a Flowing Stream (2017) Artist: Kathryn Kaye Album: Reflected in a Flowing Stream Year Of Release: 2017 Genre: New Age Quality: Mp3 Bitrate: 320 kbps Total Time: 00:54:51 Total Size: 132 MB https://rapidu.net/5819638371/ https://pobierz.to/ff21f36ca3d18beb http://catshare.net/QSHwWqZ5Yhpyvh71 http://lunaticfiles.com/fn2aacytod2m http://kingfile.pl/download/foaolQ3a http://www.fileshark.pl/pobierz/16699214/gtv10 http://sharehost.eu/file/YFrw6lxzbteNmzcANVXP0A==

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