Big Data Distributed Processing Layer | Batch vs Stream processing | Traditional vs Big Data
🔴Welcome to our Big Data Architecture course playlist, where we'll be exploring the fascinating world of Big Data and its architecture layers. In this playlist, you'll find a series of videos that cover everything from the basics of Big Data to advanced topics like data governance, security, and scalability.
👌we'll dive into the architecture layers of Big Data, including data storage, processing, and analysis, and explore the various IT tools and technologies that are commonly used in each layer.
we'll cover important topics like data lakes, data warehousing, and data visualization, and provide
📋 You'll learn about popular Big Data tools like Hadoop, Spark, Flink , Kafka, Airflow, Kibana, Elasticsearch, Hbase, Hive, Pig, Mapreduce, Storm, and NoSQL databases, and how to select the appropriate tools for your specific business requirements. We'll also cover the different types of Big Data Architecture, including batch processing, stream processing, and real-time processing, and how to design a system that can handle large volumes of data with high velocity and variety.
👌 By the end of this playlist, you'll have a solid understanding of Big Data Architecture and its various layers and IT tools. You'll be equipped with the knowledge and skills needed to design and implement a complete Big Data system that meets the needs of your organization.
📌The Content in this Course Playlist :
Introduction to The Ultimate Big Data Architecture Course: • The Ultimate Big Data Architecture Course
Lambda, Kappa and Microservices Big Data Architectures | Comparison between big data architectures : • Lambda, Kappa and Microservices Big Data A...
Data Ingestion & Data Source | Big Data Architecture Layers: • Data Ingestion & Data Sources | Apache Kaf...
Big Data storage layer | The Ultimate Course of Big Data Architectures | Mastering Big Data 2023 : • Big Data storage layer | The Ultimate Cour...
Big Data Distributed Processing Tools & Technologies | The Ultimate Big Data Architecture Course: • Big Data Distributed Processing Tools & Te...
Big Data Distributed Processing Layer | The Ultimate Big data Architecture Course | Masting Big Data: • Big Data Distributed Processing Layer | Ba...
Big Data Query Layer | Data Querying vs Data Processing | Hive & Pig | Big Data Architecture Layers: • Big Data Query Layer | Data Querying vs Da...
Big Data Worflow Management & Scheduling | Airflow vs Oozie | The Big Data Architecture course : • Big Data Worflow Management & Scheduling |...
Advanced QOS Big Data Architecture layers| Big data Security | Data Management | Data Monitoring : • Advanced QOS Big Data Architecture layers|...
Customizing Big Data Architecture : • Video
Handson Big Data Architecture : Design a complete Big Data Architecture of Analytics Platform : • Handson Big Data Architecture : Design a c...
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We will be discussing how Big Data processing is different from normal data processing, and the tools and techniques used for Big Data processing.
---- what is Big Data processing?
Big Data processing refers to the ability to process large and complex data sets that are too big to be handled by traditional data processing systems. Big Data processing involves the use of distributed systems that can scale to handle large volumes of data.
#bigdata vs #data #processing
One of the main differences between Big Data processing and traditional data processing is the scale of the data being processed.
Another difference between Big Data processing and traditional data processing is the processing model. In traditional data processing, we usually use tools like Pandas or write Python scripts to process the data. In contrast, Big Data processing requires distributed processing systems that can handle the volume and complexity of Big Data.
Big Data processing are two types: batch processing and stream processing. Batch processing involves processing large volumes of data at regular intervals, while stream processing involves processing data as it arrives in real-time.
Some of the most famous tools for Big Data processing are Apache Spark, Apache Flink, and Apache Storm. Apache Spark is a fast and general-purpose distributed computing system that is used for Big Data processing. Apache Flink is a real-time processing framework that is used for both batch and stream processing.
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