Business communication with Madhavi.pptx

Project on business communication

1. PySpark Optimization Madhavi T

2. Big Data Huge volume of data and it is more complex. Traditional database systems can’t store large volumes of data that data is called big data Not only it is a big data it’s a more complex data like unstructured data(audio, video, graphics). To process this unstructured data we need more complex algorithms like Hadoop.

3. History of Hadoop - 2003: Inspired by Google File System and MapReduce - 2005: Created by Doug Cutting and Mike Cafarella - 2006: Became a subproject of Apache Lucene - 2008: Adopted and developed by Yahoo! - 2011+: Expanded ecosystem with Hive, Pig, HBase, Spark

4. Hadoop Introduction Hadoop is a Frame work for Big Data. It performing analytics over Big Data. It is a powerful open-source framework designed to store and process massive amounts of data across clusters of inexpensive, commodity hardware. It’s the backbone of many big data solutions, enabling organizations to handle data that’s too large or complex for traditional systems.

5. Core Concepts of Hadoop HDFS (Hadoop Distributed File System) : Breaks large files into blocks and distributes them across multiple machines, ensuring fault tolerance and scalability. MapReduce : A programming model for processing large datasets in parallel. It splits tasks into smaller chunks (Map), processes them, and then combines the results (Reduce). YARN (Yet Another Resource Negotiator) : Manages resources and schedules jobs across the cluster.

6. Components of Hadoop

7. Hadoop Architecture and History An Overview of Hadoop's Evolution and Core Components

8. Hadoop Architecture

9. HDFS Architecture - NameNode : Manages metadata and namespace - DataNodes : Store actual data blocks - Master-slave architecture

10. HDFS

11. MapReduce Architecture - JobTracker : Coordinates jobs and tasks - TaskTrackers : Execute tasks - Disk-based intermediate data storage

12. YARN Architecture - ResourceManager : Allocates cluster resources - NodeManager : Manages node-level tasks - ApplicationMaster : Manages application lifecycle

13. Hadoop Workflow Example 1. Data split and stored across DataNodes 2. Job submitted to ResourceManager 3. Tasks distributed to NodeManagers 4. Results aggregated and returned

14. Challenges in Hadoop Challenges and limitations with Hadoop systems: Slow performance Limited scalability Complex workflows Batch Processing Disk based Processing . Lack of API’s

15. Limitations of Hadoop MapReduce Why Hadoop needed improvement: - Disk-based processing is slow - Not suitable for real-time analytics - Difficult to manage multi-stage workflows

16. Apache Spark: History and Advantages Understanding the evolution and benefits over Hadoop

17. Birth of Apache Spark Spark was developed at UC Berkeley in 2009: - Open-sourced in 2010 - Became Apache project in 2013 - Designed to overcome Hadoop's limitations

18. Spark vs Hadoop Comparison Key differences: - Spark uses in-memory computation - Faster and more efficient - Rich APIs and interactive shells - Unified platform for batch, streaming, ML, and graph

19. Unified Analytics Engine Spark supports multiple workloads: - Batch processing - Real-time streaming - Machine learning (MLlib) - Graph processing (GraphX)

20. Conclusion Apache Spark revolutionized big data processing: - Faster and more flexible than Hadoop - Unified platform for diverse analytics - Widely adopted in industry

21. Apache Spark Architecture and APIs Understanding Spark's Design and Programming Interfaces

22. Introduction to Apache Spark - Open-source unified analytics engine - Developed at UC Berkeley - Supports batch, streaming, ML, and graph processing - Known for speed and scalability

23. Spark Architecture Overview - Driver Program - Cluster Manager - Executors - Tasks and Jobs - RDDs and DAG Scheduler

24. Core Components of Spark - Driver: Coordinates execution and maintains SparkContext - Executors: Run tasks and store data - Cluster Manager: Allocates resources across applications

25. RDDs and DAG Execution - RDD: Resilient Distributed Dataset - Immutable distributed collection of objects - DAG: Directed Acyclic Graph for execution planning - Enables fault tolerance and optimization

26. Spark APIs Overview - Spark Core: Basic functionality - Spark SQL: Structured data processing - Spark Streaming: Real-time data processing - MLlib: Machine learning library - GraphX: Graph computation library

27. Use Cases of Spark APIs - Spark SQL: ETL, BI, Data Warehousing - Spark Streaming: Log analysis, fraud detection - MLlib: Predictive analytics, recommendation systems - GraphX: Social network analysis, graph algorithms

28. RDD vs DataFrame vs Dataset RDD: - Low-level API - No schema - Functional programming DataFrame : - Distributed collection of data - Schema-based - Optimized execution

29. Data set: A Spark Dataset is a distributed collection of strongly-typed JVM objects that combines the advantages of RDDs (Resilient Distributed Datasets) and DataFrames .

30. Conclusion - Spark offers a robust architecture for big data - Rich APIs for diverse processing needs - RDD, DataFrame , and Dataset serve different use cases

31. Conclusion - PySpark bridges Python and Spark - Ideal for scalable cloud-based analytics - Widely supported across major cloud platforms

32. PySpark Introduction and Cloud Integration Understanding PySpark and Its Role in Cloud-Based Big Data Processing

33. What is PySpark? - PySpark is the Python API for Apache Spark - Enables Python developers to harness Spark's capabilities - Supports distributed data processing and analytics - Ideal for big data and machine learning tasks

34. PySpark Architecture - Driver Program: Runs Python code - SparkContext : Connects to Spark cluster - Cluster Manager: Allocates resources - Executors: Execute tasks and return results

35. PySpark vs Apache Spark - PySpark is a wrapper over Apache Spark - Uses Py4J to interface with JVM - Offers Pythonic syntax and integration - Same performance and scalability as Spark

36. PySpark APIs Overview - SparkContext and SparkSession - RDD and DataFrame APIs - MLlib for machine learning - Spark SQL for structured queries

37. PySpark Workflow 1. Initialize SparkSession 2. Load and transform data 3. Apply transformations and actions 4. Analyze and visualize results

38. Integrating PySpark with Cloud Platforms - AWS EMR: Managed Spark clusters - Azure HDInsight: Spark on Azure - GCP Dataproc: Spark on Google Cloud - Cloud storage integration with S3, ADLS, GCS

39. Use Cases in Cloud - Real-time data analytics - ETL pipelines - Machine learning model training - Log and event processing

40. Real-Time Use Cases of PySpark Leveraging PySpark for Scalable Real-Time Data Processing

41. Introduction to PySpark in Real-Time Applications - PySpark enables distributed real-time data processing - Ideal for streaming analytics, fraud detection, and monitoring - Integrates with cloud platforms for scalability

42. Use Case 1: Fraud Detection in Financial Transactions - Monitor transactions in real-time - Identify anomalies using ML models - Trigger alerts for suspicious activity - Reduce financial losses and improve security

43. Architecture and Workflow of Fraud Detection - Data Ingestion: Kafka or cloud streams - Processing: PySpark Streaming - ML Model: Predict fraud probability - Output: Alerts to dashboard or messaging system

44. Use Case 2: Real-Time Log Analytics for System Monitoring - Collect logs from servers and applications - Analyze patterns and detect issues - Visualize metrics on dashboards - Improve uptime and performance

45. Architecture and Workflow of Log Analytics - Data Source: Syslog, application logs - Ingestion: Kafka, cloud storage - Processing: PySpark Streaming and SQL - Visualization: BI tools or custom dashboards

46. Benefits of Using PySpark - Scalable and fault-tolerant - Supports real-time and batch processing - Integrates with ML and SQL - Compatible with cloud platforms

47. Cloud Integration for Real-Time Processing - AWS EMR, Azure HDInsight, GCP Dataproc - Use cloud-native storage and messaging - Auto-scaling and monitoring - Secure and cost-effective deployments

48. Optimization Prefer DataFrames /Datasets over RDDs Minimize Data Shuffling Effective Caching and Persistence Avoid User-Defined Functions (UDFs) when possible