Data processing with spark in r & python
3 likes709 views
The document discusses the use of Apache Spark as a general-purpose engine for big data processing in R and Python, highlighting its advantages over Hadoop and MapReduce. It covers topics such as data processing operations, RDD transformations and actions, and the use of Spark SQL and DataFrames for structured data processing. The document also provides installation instructions, tips for efficient data handling, and various resources for further learning.
1 of 28
Downloaded 23 times
Ad
Recommended
Apache Spark Fundamentals
Apache Spark FundamentalsZahra Eskandari This document provides an overview of the Apache Spark framework. It covers Spark fundamentals including the Spark execution model using Resilient Distributed Datasets (RDDs), basic Spark programming, and common Spark libraries and use cases. Key topics include how Spark improves on MapReduce by operating in-memory and supporting general graphs through its directed acyclic graph execution model. The document also reviews Spark installation and provides examples of basic Spark programs in Scala.
Big data overview
Big data overviewbeCloudReady This document provides an overview of big data concepts and related technologies. It discusses what big data is, how Apache Hadoop uses MapReduce for distributed storage and processing of large datasets. Key components of the Hadoop ecosystem are described including HDFS for storage and YARN for resource management. Apache Spark is presented as an alternative to Hadoop for its in-memory computing capabilities and support for stream processing. Spark can complement Hadoop. Elasticsearch is introduced as a NoSQL database for full text search. Apache Kafka is summarized as a system for publishing and processing streams of records. Data engineering processes of acquiring, preparing, and analyzing data are outlined for both legacy and big data systems.
SF Big Analytics 20190612: Building highly efficient data lakes using Apache ...
SF Big Analytics 20190612: Building highly efficient data lakes using Apache ...Chester Chen The document discusses the challenges and requirements for building efficient data lakes using Apache Hudi, focusing on issues like incremental data ingestion, deduplication, transactional consistency, file management, and legal requirements for data retention. It provides insights into the architecture, functionality, and benefits of Hudi, including support for snapshot isolation, upserts, and optimized querying. Additionally, it outlines the roadmap for Apache Hudi's development and its current status in open-source community support.
Spark sql
Spark sqlZahra Eskandari Spark SQL is a module for structured data processing in Spark. It provides DataFrames and the ability to execute SQL queries. Some key points:
- Spark SQL allows querying structured data using SQL, or via DataFrame/Dataset APIs for Scala, Java, Python, and R.
- It supports various data sources like Hive, Parquet, JSON, and more. Data can be loaded and queried using a unified interface.
- The SparkSession API combines SparkContext with SQL functionality and is used to create DataFrames from data sources, register databases/tables, and execute SQL queries.
Reshape Data Lake (as of 2020.07)
Reshape Data Lake (as of 2020.07)Eric Sun The document discusses the evolution and comparison of data lake technologies and frameworks like Delta Lake, Iceberg, and Hudi, highlighting their features, strengths, and use cases. It emphasizes the importance of data unification, flexibility in schema management, and the shift to a cloud-based architecture for scalability and efficiency in analytics. Overall, it underscores that while significant advancements have been made, the landscape continues to evolve with various options available for data integration and insights generation.
Spark ai summit_oct_17_2019_kimhammar_jimdowling_v6
Spark ai summit_oct_17_2019_kimhammar_jimdowling_v6Kim Hammar The document discusses the integration of end-to-end machine learning pipelines using Databricks Delta and Hopsworks Feature Store, highlighting the importance of data sourcing, feature management, and the benefits of next-gen data lakes like Delta Lake, Apache Hudi, and Apache Iceberg. It emphasizes features like ACID transactions, time-travel, and incremental ingestion that improve data reliability for feature stores. Lastly, the Hopsworks platform supports various ML frameworks and offers functionalities tailored for modern data science workflows.
SPARK ARCHITECTURE
SPARK ARCHITECTUREGauravBiswas9 Spark is a framework for large-scale data processing. It includes Spark Core which provides functionality like memory management and fault recovery. Spark also includes higher level libraries like SparkSQL for SQL queries, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for real-time data streams. The core abstraction in Spark is the Resilient Distributed Dataset (RDD) which allows parallel operations on distributed data.
Optimizing Delta/Parquet Data Lakes for Apache Spark
Optimizing Delta/Parquet Data Lakes for Apache SparkDatabricks Matthew Powers gave a talk on optimizing data lakes for Apache Spark. He discussed community goals like standardizing method signatures. He advocated for using Spark helper libraries like spark-daria and spark-fast-tests. Powers explained how to build better data lakes using techniques like partitioning data on relevant fields to skip data and speed up queries significantly. He also covered modern Scala libraries, incremental updates, compacting small files, and using Delta Lakes to more easily update partitioned data lakes over time.
Hoodie: How (And Why) We built an analytical datastore on Spark
Hoodie: How (And Why) We built an analytical datastore on SparkVinoth Chandar The document discusses Uber's development of an analytical datastore using Spark, addressing challenges with large-scale data ingestion and querying. It outlines the design choices for achieving scalability, low latency, and consistency while considering various storage formats and indexing options. Additionally, it introduces Hoodie, an open-source framework for managing and querying large datasets efficiently, and invites collaboration and inquiries on future enhancements.
Apache Spark Streaming
Apache Spark StreamingZahra Eskandari Spark Streaming allows for scalable, fault-tolerant stream processing of data ingested from sources like Kafka. It works by dividing the data streams into micro-batches, which are then processed using transformations like map, reduce, join using the Spark engine. This allows streaming aggregations, windows, and stream-batch joins to be expressed similarly to batch queries. The example shows a streaming word count application that receives text from a TCP socket, splits it into words, counts the words, and updates the result continuously.
A Thorough Comparison of Delta Lake, Iceberg and Hudi
A Thorough Comparison of Delta Lake, Iceberg and HudiDatabricks The document provides a detailed comparison of Delta Lake, Apache Iceberg, and Apache Hudi, focusing on their key features such as transaction support, data mutation, and streaming capabilities. Delta Lake offers strong integration with Spark, Iceberg excels in design abstraction for large datasets, and Hudi specializes in streaming data ingestion. The analysis covers aspects like schema evolution, performance optimization, and tooling, concluding that each technology has its unique strengths and use cases.
Managing ADLS gen2 using Apache Spark
Managing ADLS gen2 using Apache SparkDatabricks The document presents the 'Octopufs' management toolkit developed by Procter & Gamble for efficient data operations in Azure, including file copy, ACL management, and delta operations. It outlines use cases, design approaches, setup requirements, and key functionalities such as distributed file handling and multithreaded operations. The toolkit is open-sourced, available on GitHub, and emphasizes optimal performance and minimal disruption during data management tasks.
Apache Spark 101
Apache Spark 101Abdullah Çetin ÇAVDAR This document provides an overview of Apache Spark, including its goal of providing a fast and general engine for large-scale data processing. It discusses Spark's programming model, components like RDDs and DAGs, and how to initialize and deploy Spark on a cluster. Key aspects covered include RDDs as the fundamental data structure in Spark, transformations and actions, and storage levels for caching data in memory or disk.
Spark SQL
Spark SQLJoud Khattab The document summarizes Spark SQL, which is a Spark module for structured data processing. It introduces key concepts like RDDs, DataFrames, and interacting with data sources. The architecture of Spark SQL is explained, including how it works with different languages and data sources through its schema RDD abstraction. Features of Spark SQL are covered such as its integration with Spark programs, unified data access, compatibility with Hive, and standard connectivity.
Spark SQL Beyond Official Documentation
Spark SQL Beyond Official DocumentationDatabricks The document presents advanced concepts in Spark SQL, focusing on the use of statistics, data sorting, and performance optimization techniques. It covers how to compute and propagate statistics, the importance of enabling cost-based optimization, and methods for saving data in sorted states. The author emphasizes best practices for improving join performance and ensuring proper table analysis in partitioned datasets.
Hoodie - DataEngConf 2017
Hoodie - DataEngConf 2017Vinoth Chandar An Open Source Incremental Processing Framework called Hoodie is summarized. Key points:
- Hoodie provides upsert and incremental processing capabilities on top of a Hadoop data lake to enable near real-time queries while avoiding costly full scans.
- It introduces primitives like upsert and incremental pull to apply mutations and consume only changed data.
- Hoodie stores data on HDFS and provides different views like read optimized, real-time, and log views to balance query performance and data latency for analytical workloads.
- The framework is open source and built on Spark, providing horizontal scalability and leveraging existing Hadoop SQL query engines like Hive and Presto.
Hoodie: Incremental processing on hadoop
Hoodie: Incremental processing on hadoopPrasanna Rajaperumal The document discusses the incremental processing framework 'Hoodie' developed by Uber, detailing its architecture, use cases, and performance optimizations for large data sets within Hadoop. It highlights key features such as upserts, log streaming, and various storage types that enhance real-time data ingestion and processing efficiency. The presentation also covers future plans for Hoodie, including enhanced indexing and integration with analytics tools like Spark and Presto.
Apache spark - Architecture , Overview & libraries
Apache spark - Architecture , Overview & librariesWalaa Hamdy Assy This document provides an overview of Apache Spark, an open-source unified analytics engine for large-scale data processing. It discusses Spark's core APIs including RDDs and transformations/actions. It also covers Spark SQL, Spark Streaming, MLlib, and GraphX. Spark provides a fast and general engine for big data processing, with explicit operations for streaming, SQL, machine learning, and graph processing. The document includes installation instructions and examples of using various Spark components.
Apache Spark PDF
Apache Spark PDFNaresh Rupareliya Spark is a cluster computing framework designed to be fast, general-purpose, and able to handle a wide range of workloads including batch processing, iterative algorithms, interactive queries, and streaming. It is faster than Hadoop for interactive queries and complex applications by running computations in-memory when possible. Spark also simplifies combining different processing types through a single engine. It offers APIs in Java, Python, Scala and SQL and integrates closely with other big data tools like Hadoop. Spark is commonly used for interactive queries on large datasets, streaming data processing, and machine learning tasks.
HKOSCon18 - Chetan Khatri - Scaling TB's of Data with Apache Spark and Scala ...
HKOSCon18 - Chetan Khatri - Scaling TB's of Data with Apache Spark and Scala ...Chetan Khatri This document summarizes a presentation about scaling terabytes of data with Apache Spark and Scala. The key points are:
1) The presenter discusses how to use Apache Spark and Scala to process large scale data in a distributed manner across clusters. Spark operations like RDDs, DataFrames and Datasets are covered.
2) A case study is presented about reengineering a data processing platform for a retail business to improve performance. Changes included parallelizing jobs, tuning Spark hyperparameters, and building a fast data architecture using Spark, Kafka and data lakes.
3) Performance was improved through techniques like dynamic resource allocation in YARN, reducing memory and cores per executor to better utilize cluster resources, and processing data
Apache Spark Based Reliable Data Ingestion in Datalake with Gagan Agrawal
Apache Spark Based Reliable Data Ingestion in Datalake with Gagan AgrawalDatabricks The document discusses Paytm's data ingestion framework using Apache Spark for their digital payment platform, addressing traditional challenges like custom scripts for various data sources and managing schema changes. It outlines a structured ingestion approach, including full and incremental ingestion types, along with a centralized dataset management service for monitoring and dependency management. The Spark-based platform aims to reduce bugs, simplify transformations, and ensure data reliability through a unified architecture.
Introduction to Apache Spark
Introduction to Apache Sparkdatamantra Apache Spark is a fast, general engine for large-scale data processing. It provides unified analytics engine for batch, interactive, and stream processing using an in-memory abstraction called resilient distributed datasets (RDDs). Spark's speed comes from its ability to run computations directly on data stored in cluster memory and optimize performance through caching. It also integrates well with other big data technologies like HDFS, Hive, and HBase. Many large companies are using Spark for its speed, ease of use, and support for multiple workloads and languages.
Spark Core
Spark CoreTodd McGrath The document provides an introduction to Apache Spark, highlighting its advantages over Hadoop MapReduce and its extensive ecosystem for data processing. Key concepts include resilient distributed datasets (RDD), transformations, and actions, which are essential for efficient data interaction. For further learning resources, it references a website and a book on Spark.
Introduction to apache spark
Introduction to apache sparkMuktadiur Rahman The document introduces Apache Spark, a fast cluster computing framework for large-scale data processing, highlighting its speed advantages over Hadoop and its simple API across various programming languages. It covers the beginnings of Spark as a research project at UC Berkeley, its open-source journey, and its significant community contributions. Additionally, it explains the concept of Resilient Distributed Datasets (RDDs) and provides examples of how to create, transform, and persist RDDs in memory.
Meetup Oracle Database BCN: 2.1 Data Management Trends
Meetup Oracle Database BCN: 2.1 Data Management Trendsavanttic Consultoría Tecnológica The document discusses the evolution and trends in data management, focusing on Oracle's solutions for managing big data through their Big Data SQL platform. It highlights features such as unified data management, security models, and various analytical capabilities across data types and sources. Additionally, it covers Oracle's advanced analytics offerings, including machine learning and graph analysis, emphasizing their integration with traditional SQL and Hadoop environments.
Introduction to Apache Spark
Introduction to Apache Spark Juan Pedro Moreno The document introduces Apache Spark and its core concepts, including resilient distributed datasets (RDDs), transformations, and actions. It outlines Spark's architecture, data processing capabilities, and the functionality of Spark SQL and Spark Streaming. Additionally, it provides resources for further learning and practical workshops on the topic.
Lighting up Big Data Analytics with Apache Spark in Azure
Lighting up Big Data Analytics with Apache Spark in AzureJen Stirrup The document discusses Apache Spark, a powerful open-source processing engine for large-scale data analytics, emphasizing its speed and ease of use. It includes details on the architecture, components like Resilient Distributed Datasets (RDDs), and the advantages of using Spark over traditional systems such as Hadoop. Additionally, the document outlines its integration with Azure HDInsight for simplified deployment and processing of big data applications.
Introduction to SparkR
Introduction to SparkROlgun Aydın SparkR is an R package that provides an interface to Apache Spark to enable large scale data analysis from R. It introduces the concept of distributed data frames that allow users to manipulate large datasets using familiar R syntax. SparkR improves performance over large datasets by using lazy evaluation and Spark's relational query optimizer. It also supports over 100 functions on data frames for tasks like statistical analysis, string manipulation, and date operations.
Apache Spark on HDinsight Training
Apache Spark on HDinsight TrainingSynergetics Learning and Cloud Consulting The document provides an extensive overview of Apache Spark, outlining its capabilities as a powerful, open-source big data processing framework that supports batch and real-time analytics. It details Spark's architecture, including components like Spark Master and Worker Nodes, as well as the use of Resilient Distributed Datasets (RDDs) for efficient data processing. Additionally, the document discusses integration with Azure services and various use cases in industries such as finance and telecommunications.
Big_data_analytics_NoSql_Module-4_Session
Big_data_analytics_NoSql_Module-4_SessionRUHULAMINHAZARIKA - Apache Spark is an open-source cluster computing framework that is faster than Hadoop for batch processing and also supports real-time stream processing.
- Spark was created to be faster than Hadoop for interactive queries and iterative algorithms by keeping data in-memory when possible.
- Spark consists of Spark Core for the basic RDD API and also includes modules for SQL, streaming, machine learning, and graph processing. It can run on several cluster managers including YARN and Mesos.
More Related Content
What's hot (20)
Hoodie: How (And Why) We built an analytical datastore on Spark
Hoodie: How (And Why) We built an analytical datastore on SparkVinoth Chandar The document discusses Uber's development of an analytical datastore using Spark, addressing challenges with large-scale data ingestion and querying. It outlines the design choices for achieving scalability, low latency, and consistency while considering various storage formats and indexing options. Additionally, it introduces Hoodie, an open-source framework for managing and querying large datasets efficiently, and invites collaboration and inquiries on future enhancements.
Apache Spark Streaming
Apache Spark StreamingZahra Eskandari Spark Streaming allows for scalable, fault-tolerant stream processing of data ingested from sources like Kafka. It works by dividing the data streams into micro-batches, which are then processed using transformations like map, reduce, join using the Spark engine. This allows streaming aggregations, windows, and stream-batch joins to be expressed similarly to batch queries. The example shows a streaming word count application that receives text from a TCP socket, splits it into words, counts the words, and updates the result continuously.
A Thorough Comparison of Delta Lake, Iceberg and Hudi
A Thorough Comparison of Delta Lake, Iceberg and HudiDatabricks The document provides a detailed comparison of Delta Lake, Apache Iceberg, and Apache Hudi, focusing on their key features such as transaction support, data mutation, and streaming capabilities. Delta Lake offers strong integration with Spark, Iceberg excels in design abstraction for large datasets, and Hudi specializes in streaming data ingestion. The analysis covers aspects like schema evolution, performance optimization, and tooling, concluding that each technology has its unique strengths and use cases.
Managing ADLS gen2 using Apache Spark
Managing ADLS gen2 using Apache SparkDatabricks The document presents the 'Octopufs' management toolkit developed by Procter & Gamble for efficient data operations in Azure, including file copy, ACL management, and delta operations. It outlines use cases, design approaches, setup requirements, and key functionalities such as distributed file handling and multithreaded operations. The toolkit is open-sourced, available on GitHub, and emphasizes optimal performance and minimal disruption during data management tasks.
Apache Spark 101
Apache Spark 101Abdullah Çetin ÇAVDAR This document provides an overview of Apache Spark, including its goal of providing a fast and general engine for large-scale data processing. It discusses Spark's programming model, components like RDDs and DAGs, and how to initialize and deploy Spark on a cluster. Key aspects covered include RDDs as the fundamental data structure in Spark, transformations and actions, and storage levels for caching data in memory or disk.
Spark SQL
Spark SQLJoud Khattab The document summarizes Spark SQL, which is a Spark module for structured data processing. It introduces key concepts like RDDs, DataFrames, and interacting with data sources. The architecture of Spark SQL is explained, including how it works with different languages and data sources through its schema RDD abstraction. Features of Spark SQL are covered such as its integration with Spark programs, unified data access, compatibility with Hive, and standard connectivity.
Spark SQL Beyond Official Documentation
Spark SQL Beyond Official DocumentationDatabricks The document presents advanced concepts in Spark SQL, focusing on the use of statistics, data sorting, and performance optimization techniques. It covers how to compute and propagate statistics, the importance of enabling cost-based optimization, and methods for saving data in sorted states. The author emphasizes best practices for improving join performance and ensuring proper table analysis in partitioned datasets.
Hoodie - DataEngConf 2017
Hoodie - DataEngConf 2017Vinoth Chandar An Open Source Incremental Processing Framework called Hoodie is summarized. Key points:
- Hoodie provides upsert and incremental processing capabilities on top of a Hadoop data lake to enable near real-time queries while avoiding costly full scans.
- It introduces primitives like upsert and incremental pull to apply mutations and consume only changed data.
- Hoodie stores data on HDFS and provides different views like read optimized, real-time, and log views to balance query performance and data latency for analytical workloads.
- The framework is open source and built on Spark, providing horizontal scalability and leveraging existing Hadoop SQL query engines like Hive and Presto.
Hoodie: Incremental processing on hadoop
Hoodie: Incremental processing on hadoopPrasanna Rajaperumal The document discusses the incremental processing framework 'Hoodie' developed by Uber, detailing its architecture, use cases, and performance optimizations for large data sets within Hadoop. It highlights key features such as upserts, log streaming, and various storage types that enhance real-time data ingestion and processing efficiency. The presentation also covers future plans for Hoodie, including enhanced indexing and integration with analytics tools like Spark and Presto.
Apache spark - Architecture , Overview & libraries
Apache spark - Architecture , Overview & librariesWalaa Hamdy Assy This document provides an overview of Apache Spark, an open-source unified analytics engine for large-scale data processing. It discusses Spark's core APIs including RDDs and transformations/actions. It also covers Spark SQL, Spark Streaming, MLlib, and GraphX. Spark provides a fast and general engine for big data processing, with explicit operations for streaming, SQL, machine learning, and graph processing. The document includes installation instructions and examples of using various Spark components.
Apache Spark PDF
Apache Spark PDFNaresh Rupareliya Spark is a cluster computing framework designed to be fast, general-purpose, and able to handle a wide range of workloads including batch processing, iterative algorithms, interactive queries, and streaming. It is faster than Hadoop for interactive queries and complex applications by running computations in-memory when possible. Spark also simplifies combining different processing types through a single engine. It offers APIs in Java, Python, Scala and SQL and integrates closely with other big data tools like Hadoop. Spark is commonly used for interactive queries on large datasets, streaming data processing, and machine learning tasks.
HKOSCon18 - Chetan Khatri - Scaling TB's of Data with Apache Spark and Scala ...
HKOSCon18 - Chetan Khatri - Scaling TB's of Data with Apache Spark and Scala ...Chetan Khatri This document summarizes a presentation about scaling terabytes of data with Apache Spark and Scala. The key points are:
1) The presenter discusses how to use Apache Spark and Scala to process large scale data in a distributed manner across clusters. Spark operations like RDDs, DataFrames and Datasets are covered.
2) A case study is presented about reengineering a data processing platform for a retail business to improve performance. Changes included parallelizing jobs, tuning Spark hyperparameters, and building a fast data architecture using Spark, Kafka and data lakes.
3) Performance was improved through techniques like dynamic resource allocation in YARN, reducing memory and cores per executor to better utilize cluster resources, and processing data
Apache Spark Based Reliable Data Ingestion in Datalake with Gagan Agrawal
Apache Spark Based Reliable Data Ingestion in Datalake with Gagan AgrawalDatabricks The document discusses Paytm's data ingestion framework using Apache Spark for their digital payment platform, addressing traditional challenges like custom scripts for various data sources and managing schema changes. It outlines a structured ingestion approach, including full and incremental ingestion types, along with a centralized dataset management service for monitoring and dependency management. The Spark-based platform aims to reduce bugs, simplify transformations, and ensure data reliability through a unified architecture.
Introduction to Apache Spark
Introduction to Apache Sparkdatamantra Apache Spark is a fast, general engine for large-scale data processing. It provides unified analytics engine for batch, interactive, and stream processing using an in-memory abstraction called resilient distributed datasets (RDDs). Spark's speed comes from its ability to run computations directly on data stored in cluster memory and optimize performance through caching. It also integrates well with other big data technologies like HDFS, Hive, and HBase. Many large companies are using Spark for its speed, ease of use, and support for multiple workloads and languages.
Spark Core
Spark CoreTodd McGrath The document provides an introduction to Apache Spark, highlighting its advantages over Hadoop MapReduce and its extensive ecosystem for data processing. Key concepts include resilient distributed datasets (RDD), transformations, and actions, which are essential for efficient data interaction. For further learning resources, it references a website and a book on Spark.
Introduction to apache spark
Introduction to apache sparkMuktadiur Rahman The document introduces Apache Spark, a fast cluster computing framework for large-scale data processing, highlighting its speed advantages over Hadoop and its simple API across various programming languages. It covers the beginnings of Spark as a research project at UC Berkeley, its open-source journey, and its significant community contributions. Additionally, it explains the concept of Resilient Distributed Datasets (RDDs) and provides examples of how to create, transform, and persist RDDs in memory.
Meetup Oracle Database BCN: 2.1 Data Management Trends
Meetup Oracle Database BCN: 2.1 Data Management Trendsavanttic Consultoría Tecnológica The document discusses the evolution and trends in data management, focusing on Oracle's solutions for managing big data through their Big Data SQL platform. It highlights features such as unified data management, security models, and various analytical capabilities across data types and sources. Additionally, it covers Oracle's advanced analytics offerings, including machine learning and graph analysis, emphasizing their integration with traditional SQL and Hadoop environments.
Introduction to Apache Spark
Introduction to Apache Spark Juan Pedro Moreno The document introduces Apache Spark and its core concepts, including resilient distributed datasets (RDDs), transformations, and actions. It outlines Spark's architecture, data processing capabilities, and the functionality of Spark SQL and Spark Streaming. Additionally, it provides resources for further learning and practical workshops on the topic.
Lighting up Big Data Analytics with Apache Spark in Azure
Lighting up Big Data Analytics with Apache Spark in AzureJen Stirrup The document discusses Apache Spark, a powerful open-source processing engine for large-scale data analytics, emphasizing its speed and ease of use. It includes details on the architecture, components like Resilient Distributed Datasets (RDDs), and the advantages of using Spark over traditional systems such as Hadoop. Additionally, the document outlines its integration with Azure HDInsight for simplified deployment and processing of big data applications.
Introduction to SparkR
Introduction to SparkROlgun Aydın SparkR is an R package that provides an interface to Apache Spark to enable large scale data analysis from R. It introduces the concept of distributed data frames that allow users to manipulate large datasets using familiar R syntax. SparkR improves performance over large datasets by using lazy evaluation and Spark's relational query optimizer. It also supports over 100 functions on data frames for tasks like statistical analysis, string manipulation, and date operations.
Similar to Data processing with spark in r & python (20)
Apache Spark on HDinsight Training
Apache Spark on HDinsight TrainingSynergetics Learning and Cloud Consulting The document provides an extensive overview of Apache Spark, outlining its capabilities as a powerful, open-source big data processing framework that supports batch and real-time analytics. It details Spark's architecture, including components like Spark Master and Worker Nodes, as well as the use of Resilient Distributed Datasets (RDDs) for efficient data processing. Additionally, the document discusses integration with Azure services and various use cases in industries such as finance and telecommunications.
Big_data_analytics_NoSql_Module-4_Session
Big_data_analytics_NoSql_Module-4_SessionRUHULAMINHAZARIKA - Apache Spark is an open-source cluster computing framework that is faster than Hadoop for batch processing and also supports real-time stream processing.
- Spark was created to be faster than Hadoop for interactive queries and iterative algorithms by keeping data in-memory when possible.
- Spark consists of Spark Core for the basic RDD API and also includes modules for SQL, streaming, machine learning, and graph processing. It can run on several cluster managers including YARN and Mesos.
Processing Large Data with Apache Spark -- HasGeek
Processing Large Data with Apache Spark -- HasGeekVenkata Naga Ravi This document provides an overview of Apache Spark, highlighting its advantages over traditional big data processing frameworks like Hadoop, including faster processing speeds and improved fault tolerance. It discusses Spark's architecture, key components such as resilient distributed datasets (RDDs) and Spark SQL, as well as integrations with various data sources and libraries for machine learning and graph processing. The document also touches on data science processes and optimizations for effective big data analytics.
Extending Apache Spark SQL Data Source APIs with Join Push Down with Ioana De...
Extending Apache Spark SQL Data Source APIs with Join Push Down with Ioana De...Databricks This document summarizes a presentation on extending Spark SQL Data Sources APIs with join push down. The presentation discusses how join push down can significantly improve query performance by reducing data transfer and exploiting data source capabilities like indexes. It provides examples of join push down in enterprise data pipelines and SQL acceleration use cases. The presentation also outlines the challenges of network speeds and exploiting data source capabilities, and how join push down addresses these challenges. Future work discussed includes building a cost model for global optimization across data sources.
Large Scale Data Analytics with Spark and Cassandra on the DSE Platform
Large Scale Data Analytics with Spark and Cassandra on the DSE PlatformDataStax Academy The document discusses the advantages of using Spark for large-scale data analytics compared to Hadoop, highlighting its efficient in-memory processing and simplified programming model. It elaborates on the Spark Cassandra connector for optimized data handling and the benefits of streaming analytics with Spark's fault-tolerant architecture. Additionally, it emphasizes the development of declarative code and the utilization of resilient distributed datasets (RDDs) for enhanced performance and scalability in data processing.
Big data processing with Apache Spark and Oracle Database
Big data processing with Apache Spark and Oracle DatabaseMartin Toshev The document discusses Apache Spark, a powerful framework for distributed big data processing, emphasizing its capabilities, architecture, and comparison to other technologies like MapReduce and Hadoop. It details the use of Spark with Oracle RDBMS as a data source, including methods for both batch and real-time data processing. It highlights the flexibility of Spark's streaming capabilities, supported cluster managers, and practical examples of data transformation and manipulation.
Unit II Real Time Data Processing tools.pptx
Unit II Real Time Data Processing tools.pptxRahul Borate Apache Spark is a lightning-fast cluster computing framework designed for real-time processing. It overcomes limitations of Hadoop by running 100 times faster in memory and 10 times faster on disk. Spark uses resilient distributed datasets (RDDs) that allow data to be partitioned across clusters and cached in memory for faster processing.
Apache spark its place within a big data stack
Apache spark its place within a big data stackJunjun Olympia Spark is a fast, large-scale data processing engine that can be 10-100x faster than Hadoop MapReduce. It is commonly used to capture and extract data from various sources, transform the data by handling data quality issues and computing derived fields, and then store the data in files, databases, or data warehouses to enable querying, analysis, and visualization of the data. Spark provides a unified framework for these functions and is an essential part of the modern big data stack.
Big Data training
Big Data trainingvishal192091 The document provides an overview of big data concepts and frameworks. It discusses the dimensions of big data including volume, velocity, variety, veracity, value and variability. It then describes the traditional approach to data processing and its limitations in dealing with large, complex data. Hadoop and its core components HDFS and YARN are introduced as the solution. Spark is presented as a faster alternative to Hadoop for processing large datasets in memory. Other frameworks like Hive, Pig and Presto are also briefly mentioned.
Apache Spark for RDBMS Practitioners: How I Learned to Stop Worrying and Lov...
Apache Spark for RDBMS Practitioners: How I Learned to Stop Worrying and Lov...Databricks Luca Canali, a data engineer at CERN, shares experiences with Apache Spark for large-scale data processing, particularly for high energy physics applications. The presentation highlights using Spark for analytics across various data sources while optimizing performance and addressing challenges like data extraction and query execution from relational databases. Canali emphasizes the benefits of integrating Spark into traditional RDBMS environments to handle growing data workloads and the valuable insights gained from this transition.
Apache Spark - Lightning Fast Cluster Computing - Hyderabad Scalability Meetup
Apache Spark - Lightning Fast Cluster Computing - Hyderabad Scalability MeetupHyderabad Scalability Meetup This document provides an introduction and overview of Apache Spark, a lightning-fast cluster computing framework. It discusses Spark's ecosystem, how it differs from Hadoop MapReduce, where it shines well, how easy it is to install and start learning, includes some small code demos, and provides additional resources for information. The presentation introduces Spark and its core concepts, compares it to Hadoop MapReduce in areas like speed, usability, tools, and deployment, demonstrates how to use Spark SQL with an example, and shows a visualization demo. It aims to provide attendees with a high-level understanding of Spark without being a training class or workshop.
Dec6 meetup spark presentation
Dec6 meetup spark presentationRamesh Mudunuri This document provides an introduction and overview of Apache Spark, a lightning-fast cluster computing framework. It discusses Spark's ecosystem, how it differs from Hadoop MapReduce, where it shines well, how easy it is to install and start learning, includes some small code demos, and provides additional resources for information. The presentation introduces Spark and its core concepts, compares it to Hadoop MapReduce in areas like speed, usability, tools, and deployment, demonstrates how to use Spark SQL with an example, and shows a visualization demo. It aims to provide attendees with a high-level understanding of Spark without being a training class or workshop.
Large Scale Machine learning with Spark
Large Scale Machine learning with SparkMd. Mahedi Kaysar Spark is an open source cluster computing framework that allows processing of large datasets across clusters of computers using a simple programming model. It provides high-level APIs in Java, Scala, Python and R.
Typical machine learning workflows in Spark involve loading data, preprocessing, feature engineering, training models, evaluating performance, and tuning hyperparameters. Spark MLlib provides algorithms for common tasks like classification, regression, clustering and collaborative filtering.
The document provides an example of building a spam filtering application in Spark. It involves reading email data, extracting features using tokenization and hashing, training a logistic regression model, evaluating performance on test data, and tuning hyperparameters via cross validation.
Big Data Processing with Apache Spark 2014
Big Data Processing with Apache Spark 2014mahchiev This document provides an overview of Apache Spark, a framework for large-scale data processing. It discusses what big data is, the history and advantages of Spark, and Spark's execution model. Key concepts explained include Resilient Distributed Datasets (RDDs), transformations, actions, and MapReduce algorithms like word count. Examples are provided to illustrate Spark's use of RDDs and how it can improve on Hadoop MapReduce.
2014-10-20 Large-Scale Machine Learning with Apache Spark at Internet of Thin...
2014-10-20 Large-Scale Machine Learning with Apache Spark at Internet of Thin...DB Tsai This document discusses machine learning techniques for large-scale datasets using Apache Spark. It provides an overview of Spark's machine learning library (MLlib), describing algorithms like logistic regression, linear regression, collaborative filtering, and clustering. It also compares Spark to traditional Hadoop MapReduce, highlighting how Spark leverages caching and iterative algorithms to enable faster machine learning model training.
Jump Start on Apache Spark 2.2 with Databricks
Jump Start on Apache Spark 2.2 with DatabricksAnyscale The document outlines a workshop on Apache Spark 2.2, focusing on its architecture, features, and the use of DataFrames and Datasets. It emphasizes the introduction of structured streaming and the unified analytics platform provided by Databricks to simplify big data processing. Key improvements in Spark 2.x, such as the Catalyst optimizer and SparkSession, are highlighted to enhance performance and ease of use.
APACHE SPARK.pptx
APACHE SPARK.pptxDeepaThirumurugan Spark is a fast, general-purpose cluster computing system. It provides high-level APIs in Java, Scala, Python and R for distributed tasks including SQL, streaming, and machine learning. Spark improves on MapReduce by keeping data in-memory, allowing iterative algorithms to run faster than disk-based approaches. Resilient Distributed Datasets (RDDs) are Spark's fundamental data structure, acting as a fault-tolerant collection of elements that can be operated on in parallel.
Paris Data Geek - Spark Streaming
Paris Data Geek - Spark Streaming Djamel Zouaoui This document discusses Spark Streaming and its use for near real-time ETL. It provides an overview of Spark Streaming, how it works internally using receivers and workers to process streaming data, and an example use case of building a recommender system to find matches using both batch and streaming data. Key points covered include the streaming execution model, handling data receipt and job scheduling, and potential issues around data loss and (de)serialization.
Spark and Couchbase: Augmenting the Operational Database with Spark
Spark and Couchbase: Augmenting the Operational Database with SparkSpark Summit The document discusses integrating Couchbase NoSQL with Apache Spark for augmenting operational databases with analytics. It outlines architectural alignment between Couchbase and Spark, including automatic data sharding and locality, data streaming replication from Couchbase to Spark, predicate pushdown to Couchbase global indexes from Spark, and flexible schemas. Integration points discussed include using the Couchbase data locality hints in Spark, limitations on predicate pushdown for Couchbase views and N1QL, and using the Couchbase change data capture protocol for low-latency data streaming into Spark Streaming.
Apache Spark - Lightning Fast Cluster Computing - Hyderabad Scalability Meetup
Apache Spark - Lightning Fast Cluster Computing - Hyderabad Scalability MeetupHyderabad Scalability Meetup
Ad
More from Maloy Manna, PMP® (10)
Data Modeling in Hadoop - Essentials for building data driven applications
Data Modeling in Hadoop - Essentials for building data driven applicationsMaloy Manna, PMP® The document discusses data modeling in Hadoop, emphasizing its advantages over traditional RDBMS, such as 'schema on read' which allows for flexible data organization and processing. It covers key topics including schema design, storage formats, data warehousing, and managing change in data-driven applications. The webinar aims to provide insights on improving data processing and metadata management in Hadoop.
From Big Data to AI
From Big Data to AIMaloy Manna, PMP® The document discusses the integration of artificial intelligence (AI) and machine learning (ML) in building competitive applications utilizing big data architectures. It emphasizes the importance of aligning AI initiatives with business objectives, detailing the evolution of data analytics, various ML use cases across industries, and the technical aspects of implementing ML solutions. Additionally, it highlights the role of deep learning and APIs in enhancing AI capabilities.
Pre processing big data
Pre processing big dataMaloy Manna, PMP® This document discusses techniques for pre-processing big data to improve the quality of analysis. It covers exploring and cleaning data by handling missing values, reducing noise, and reducing dimensions. Data transformation techniques are also discussed, such as standardizing, aggregating, and joining data. Finally, the document emphasizes that data preparation is a key factor in model quality and generating insights from trusted data.
Data Visualization in Data Science
Data Visualization in Data ScienceMaloy Manna, PMP® The document discusses the importance of data visualization in data science, highlighting its role at various stages of the data science process and how it aids in understanding data and engaging audiences. It covers visual encoding techniques, biases in visualization, and the tools available for creating effective visualizations. Additionally, it outlines design principles and narrative structures essential for conveying insights through data.
Coursera Data Analysis and Statistical Inference 2014
Coursera Data Analysis and Statistical Inference 2014Maloy Manna, PMP® Maloy Manna successfully completed the online Coursera course "Data Analysis and Statistical Inference" offered by Duke University with distinction on November 19, 2014. The course introduced students to core statistical concepts such as exploratory data analysis, statistical inference and modeling, basic probability, and statistical computing, as taught by Dr. Mine Çetinkaya-Rundel, Assistant Professor of the Practice of Statistical Science at Duke University.
Coursera Getting and Cleaning Data 2014
Coursera Getting and Cleaning Data 2014Maloy Manna, PMP® Maloy Manna successfully completed the Coursera course "Getting and Cleaning Data" offered by Johns Hopkins University with distinction in September 2014. The course covered obtaining data from various sources like the web, APIs, databases and colleagues as well as basics of cleaning and organizing data into a complete dataset including raw data, processing instructions, codebooks and processed data. The course was instructed by Jeffrey Leek, Roger Peng and Brian Caffo from the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health.
Coursera Exploratory Data Analysis 2014
Coursera Exploratory Data Analysis 2014Maloy Manna, PMP® Maloy Manna successfully completed an online course in Exploratory Data Analysis from Johns Hopkins University with distinction in September 2014. The course covered exploratory data summarization techniques and visualization methods used before modeling, including plotting in R and common techniques for high-dimensional data. The course was led by professors Roger D. Peng, Jeffrey Leek, and Brian Caffo from the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health.
Coursera R Programming 2014
Coursera R Programming 2014Maloy Manna, PMP® Maloy Manna successfully completed the Coursera course "R Programming" from Johns Hopkins University with distinction. The course covered practical issues in statistical computing including programming in R, reading data into R, accessing packages, writing functions, debugging, profiling code, and organizing and commenting code. The certificate was signed by Roger D. Peng, Jeffrey Leek, and Brian Caffo of Johns Hopkins Bloomberg School of Public Health.
Coursera The Data Scientist's Toolbox 2014
Coursera The Data Scientist's Toolbox 2014Maloy Manna, PMP® Maloy Manna completed the Johns Hopkins University's Data Scientist's Toolbox course on July 8, 2014. The course provided an overview of data analysis concepts and practical tools such as version control and R programming. This statement of accomplishment does not imply enrollment or confer any academic credit from Johns Hopkins University.
Ad
Recently uploaded (20)
Publishing Your Memoir with Brooke Warner
Publishing Your Memoir with Brooke WarnerBrooke Warner Brooke Warner presents on getting published - traditional, hybrid, and self-publishing.
www.memoirnation.com
Assisting Individuals and Families to Promote and Maintain Health – Unit 7 | ...
Assisting Individuals and Families to Promote and Maintain Health – Unit 7 | ...RAKESH SAJJAN This PowerPoint presentation is based on Unit 7 – Assisting Individuals and Families to Promote and Maintain Their Health, a core topic in Community Health Nursing – I for 5th Semester B.Sc Nursing students, as per the Indian Nursing Council (INC) guidelines.
The unit emphasizes the nurse’s role in family-centered care, early detection of health problems, health promotion, and appropriate referrals, especially in the context of home visits and community outreach. It also strengthens the student’s understanding of nursing responsibilities in real-life community settings.
📘 Key Topics Covered in the Presentation:
Introduction to family health care: needs, principles, and objectives
Assessment of health needs of individuals, families, and groups
Observation and documentation during home visits and field assessments
Identifying risk factors: environmental, behavioral, genetic, and social
Conducting growth and development monitoring in infants and children
Recording and observing:
Milestones of development
Menstrual health and reproductive cycle
Temperature, blood pressure, and vital signs
General physical appearance and personal hygiene
Social assessment: understanding family dynamics, occupation, income, living conditions
Health education and counseling for individuals and families
Guidelines for early detection and referral of communicable and non-communicable diseases
Maintenance of family health records and individual health cards
Assisting families with:
Maternal and child care
Elderly and chronic disease management
Hygiene and nutrition guidance
Utilization of community resources – referral linkages, support services, and local health programs
Role of nurse in coordinating care, advocating for vulnerable individuals, and empowering families
Promoting self-care and family participation in disease prevention and health maintenance
This presentation is highly useful for:
Nursing students preparing for internal exams, university theory papers, or community postings
Health educators conducting family teaching sessions
Students conducting fieldwork and project work during community postings
Public health nurses and outreach workers dealing with preventive, promotive, and rehabilitative care
It’s structured in a step-by-step format, featuring tables, case examples, and simplified explanations tailored for easy understanding and classroom delivery.
Chalukyas of Gujrat, Solanki Dynasty NEP.pptx
Chalukyas of Gujrat, Solanki Dynasty NEP.pptxDr. Ravi Shankar Arya Mahila P. G. College, Banaras Hindu University, Varanasi, India. This presentation has been made keeping in mind the students of undergraduate and postgraduate level. In this slide try to present the brief history of Chaulukyas of Gujrat up to Kumarpala To keep the facts in a natural form and to display the material in more detail, the help of various books, websites and online medium has been taken. Whatever medium the material or facts have been taken from, an attempt has been made by the presenter to give their reference at the end.
Chaulukya or Solanki was one of the Rajputs born from Agnikul. In the Vadnagar inscription, the origin of this dynasty is told from Brahma's Chauluk or Kamandalu. They ruled in Gujarat from the latter half of the tenth century to the beginning of the thirteenth century. Their capital was in Anahilwad. It is not certain whether it had any relation with the Chalukya dynasty of the south or not. It is worth mentioning that the name of the dynasty of the south was 'Chaluky' while the dynasty of Gujarat has been called 'Chaulukya'. The rulers of this dynasty were the supporters and patrons of Jainism.
LAZY SUNDAY QUIZ "A GENERAL QUIZ" JUNE 2025 SMC QUIZ CLUB, SILCHAR MEDICAL CO...
LAZY SUNDAY QUIZ "A GENERAL QUIZ" JUNE 2025 SMC QUIZ CLUB, SILCHAR MEDICAL CO...Ultimatewinner0342 🧠 Lazy Sunday Quiz | General Knowledge Trivia by SMC Quiz Club – Silchar Medical College
Presenting the Lazy Sunday Quiz, a fun and thought-provoking general knowledge quiz created by the SMC Quiz Club of Silchar Medical College & Hospital (SMCH). This quiz is designed for casual learners, quiz enthusiasts, and competitive teams looking for a diverse, engaging set of questions with clean visuals and smart clues.
🎯 What is the Lazy Sunday Quiz?
The Lazy Sunday Quiz is a light-hearted yet intellectually rewarding quiz session held under the SMC Quiz Club banner. It’s a general quiz covering a mix of current affairs, pop culture, history, India, sports, medicine, science, and more.
Whether you’re hosting a quiz event, preparing a session for students, or just looking for quality trivia to enjoy with friends, this PowerPoint deck is perfect for you.
📋 Quiz Format & Structure
Total Questions: ~50
Types: MCQs, one-liners, image-based, visual connects, lateral thinking
Rounds: Warm-up, Main Quiz, Visual Round, Connects (optional bonus)
Design: Simple, clear slides with answer explanations included
Tools Needed: Just a projector or screen – ready to use!
🧠 Who Is It For?
College quiz clubs
School or medical students
Teachers or faculty for classroom engagement
Event organizers needing quiz content
Quizzers preparing for competitions
Freelancers building quiz portfolios
💡 Why Use This Quiz?
Ready-made, high-quality content
Curated with lateral thinking and storytelling in mind
Covers both academic and pop culture topics
Designed by a quizzer with real event experience
Usable in inter-college fests, informal quizzes, or Sunday brain workouts
📚 About the Creators
This quiz has been created by Rana Mayank Pratap, an MBBS student and quizmaster at SMC Quiz Club, Silchar Medical College. The club aims to promote a culture of curiosity and smart thinking through weekly and monthly quiz events.
🔍 SEO Tags:
quiz, general knowledge quiz, trivia quiz, SlideShare quiz, college quiz, fun quiz, medical college quiz, India quiz, pop culture quiz, visual quiz, MCQ quiz, connect quiz, science quiz, current affairs quiz, SMC Quiz Club, Silchar Medical College
📣 Reuse & Credit
You’re free to use or adapt this quiz for your own events or sessions with credit to:
SMC Quiz Club – Silchar Medical College & Hospital
Curated by: Rana Mayank Pratap
Health Care Planning and Organization of Health Care at Various Levels – Unit...
Health Care Planning and Organization of Health Care at Various Levels – Unit...RAKESH SAJJAN This comprehensive PowerPoint presentation is prepared for B.Sc Nursing 5th Semester students and covers Unit 2 of Community Health Nursing – I based on the Indian Nursing Council (INC) syllabus. The unit focuses on the planning, structure, and functioning of health care services at various levels in India. It is especially useful for nursing educators and students preparing for university exams, internal assessments, or professional teaching assignments.
The content of this presentation includes:
Historical development of health planning in India
Detailed study of various health committees: Bhore, Mudaliar, Kartar Singh, Shrivastava Committee, etc.
Overview of major health commissions
In-depth understanding of Five-Year Plans and their impact on health care
Community participation and stakeholder involvement in health care planning
Structure of health care delivery system at central, state, district, and peripheral levels
Concepts and implementation of Primary Health Care (PHC) and Sustainable Development Goals (SDGs)
Introduction to Comprehensive Primary Health Care (CPHC) and Health and Wellness Centers (HWCs)
Expanded role of Mid-Level Health Providers (MLHPs) and Community Health Providers (CHPs)
Explanation of national health policies: NHP 1983, 2002, and 2017
Key national missions and schemes including:
National Health Mission (NHM)
National Rural Health Mission (NRHM)
National Urban Health Mission (NUHM)
Ayushman Bharat – Pradhan Mantri Jan Arogya Yojana (PM-JAY)
Universal Health Coverage (UHC) and India’s commitment to equitable health care
This presentation is ideal for:
Nursing students (B.Sc, GNM, Post Basic)
Nursing tutors and faculty
Health educators
Competitive exam aspirants in nursing and public health
It is organized in a clear, point-wise format with relevant terminologies and a focus on applied knowledge. The slides can also be used for community health demonstrations, teaching sessions, and revision guides.
Community Health Nursing Approaches, Concepts, Roles & Responsibilities – Uni...
Community Health Nursing Approaches, Concepts, Roles & Responsibilities – Uni...RAKESH SAJJAN This PowerPoint presentation is based on Unit 6 – Community Health Nursing Approaches, Concepts, Roles & Responsibilities of Community Health Nursing Personnel, designed for B.Sc Nursing 5th Semester students under the subject Community Health Nursing – I, following the syllabus of the Indian Nursing Council (INC).
This unit focuses on the various approaches in community health, the organizational framework, and the responsibilities of different levels of nursing staff in the healthcare system. It emphasizes the real-world application of nursing principles to provide comprehensive and preventive care to the community.
📘 Key Areas Covered in this Presentation:
Introduction to the concept of community health nursing
Approaches to community health:
Nursing Process Approach
Epidemiological Approach
Evidence-Based Approach
Problem-Solving Approach
Nursing Theories in Community Health Practice
Explanation of teamwork and intersectoral coordination
Concept of primary health care and its application in community nursing
Levels of health care delivery – primary, secondary, and tertiary care
Home visit process: principles, planning, implementation, and follow-up
Use of community bag and record maintenance
Roles and responsibilities of:
Auxiliary Nurse Midwives (ANMs)
Community Health Officers (CHOs)
Staff Nurses
ASHA workers
Public Health Nurses (PHNs)
Documentation and reporting in community settings
Promotion of health education, immunization, maternal and child health, and nutritional support
Role of nurse in disease surveillance, outbreak control, and health promotion
Ethical principles in community nursing
Coordination with health team members and village health committees
This presentation is useful for:
Nursing students preparing for university theory exams, class tests, or viva
Nursing educators conducting lectures or field discussions
Interns and trainees working in PHCs, sub-centers, or community settings
Community nurses and health educators involved in rural and urban outreach
The content is simplified, clear, and enhanced with point-wise explanations, flowcharts, and field-related examples for better retention and application.
Gibson "Secrets to Changing Behaviour in Scholarly Communication: A 2025 NISO...
Gibson "Secrets to Changing Behaviour in Scholarly Communication: A 2025 NISO...National Information Standards Organization (NISO) This presentation was provided by Jennifer Gibson of Dryad, during the second session of our 2025 NISO training series "Secrets to Changing Behavior in Scholarly Communications." Session Two was held June 12, 2025.
SCHIZOPHRENIA OTHER PSYCHOTIC DISORDER LIKE Persistent delusion/Capgras syndr...
SCHIZOPHRENIA OTHER PSYCHOTIC DISORDER LIKE Persistent delusion/Capgras syndr...parmarjuli1412 SCHIZOPHRENIA INCLUDED TOPIC IS INTRODUCTION, DEFINITION OF GENERAL TERM IN PSYCHIATRIC, THEN DIFINITION OF SCHIZOPHRENIA, EPIDERMIOLOGY, ETIOLOGICAL FACTORS, CLINICAL FEATURE(SIGN AND SYMPTOMS OF SCHIZOPHRENIA), CLINICAL TYPES OF SCHIZOPHRENIA, DIAGNOSIS, INVESTIGATION, TREATMENT MODALITIES(PHARMACOLOGICAL MANAGEMENT, PSYCHOTHERAPY, ECT, PSYCHO-SOCIO-REHABILITATION), NURSING MANAGEMENT(ASSESSMENT,DIAGNOSIS,NURSING INTERVENTION,AND EVALUATION), OTHER PSYCHOTIC DISORDER LIKE Persistent delusion/Capgras syndrome(The Delusion of Doubles)/Acute and Transient Psychotic Disorders/Induced Delusional Disorders/Schizoaffective Disorder /CAPGRAS SYNDROME(DELUSION OF DOUBLE), GERIATRIC CONSIDERATION, FOLLOW UP, HOMECARE AND REHABILITATION OF THE PATIENT,
LDMMIA Yoga S10 Free Workshop Grad Level
LDMMIA Yoga S10 Free Workshop Grad LevelLDM & Mia eStudios This is complete for June 17th. For the weekend of Summer Solstice
June 20th-22nd.
6/17/25: “My now Grads, You’re doing well. I applaud your efforts to continue. We all are shifting to new paradigm realities. Its rough, there’s good and bad days/weeks. However, Reiki with Yoga assistance, does work.”
6/18/25: "For those planning the Training Program Do Welcome. Happy Summer 2k25. You are not ignored and much appreciated. Our updates are ongoing and weekly since Spring. I Hope you Enjoy the Practitioner Grad Level. There's more to come. We will also be wrapping up Level One. So I can work on Levels 2 topics. Please see documents for any news updates. Also visit our websites. Every decade I release a Campus eMap. I will work on that for summer 25. We have 2 old libraries online thats open. https://ldmchapels.weebly.com "
Your virtual attendance is appreciated. No admissions or registration needed.
We hit over 5k views for Spring Workshops and Updates-TY.
As a Guest Student,
You are now upgraded to Grad Level.
See Uploads for “Student Checkins” & “S9”. Thx.
Happy Summer 25.
These are also timeless.
Thank you for attending our workshops.
If you are new, do welcome.
For visual/Video style learning see our practitioner student status.
This is listed under our new training program. Updates ongoing levels 1-3 this summer. We just started Session 1 for level 1.
These are optional programs. I also would like to redo our library ebooks about Hatha and Money Yoga. THe Money Yoga was very much energy healing without the Reiki Method. An updated ebook/course will be done this year. These Projects are for *all fans, followers, teams, and Readers. TY for being presenting.
Q1_ENGLISH_PPT_WEEK 1 power point grade 3 Quarter 1 week 1
Q1_ENGLISH_PPT_WEEK 1 power point grade 3 Quarter 1 week 1jutaydeonne Grade 3 Quarter 1 Week 1 English part 2
This is why students from these 44 institutions have not received National Se...
This is why students from these 44 institutions have not received National Se...Kweku Zurek This is why students from these 44 institutions have not received National Service PIN codes (LIST)
YSPH VMOC Special Report - Measles Outbreak Southwest US 6-14-2025.pptx
YSPH VMOC Special Report - Measles Outbreak Southwest US 6-14-2025.pptxYale School of Public Health - The Virtual Medical Operations Center (VMOC) BLUF:
The Texas outbreak has slowed down, but sporadic cases continue to emerge in Kansas, Oklahoma, and New Mexico.
Elsewhere in the US, we continue to see signs of acceleration due to outbreaks outside the Southwest (North Dakota, Montana, and Colorado) and travel-related cases. Measles exposures due to travel are expected to pose a significant challenge throughout the summer.
The U.S. is on track to exceed its 30-year high for measles cases (1,274) within the next two weeks.
Here is the latest update:
CURRENT CASE COUNT: 919
•Texas: 744 (+2) (55% of cases are in Gaines County).
•New Mexico: 81 (83% of cases are from Lea County).
•Oklahoma: 20 (+2)
•Kansas: 74 (+5) (38.89% of the cases are from Gray County).
HOSPITALIZATIONS: 104
• Texas: 96 (+2) – This accounts for 13% of all cases in Texas.
• New Mexico: 7 – This accounts for 9.47% of all cases in New Mexico.
• Kansas: 3 – This accounts for 5.08% of all cases in the state of Kansas.
DEATHS: 3
•Texas: 2 – This is 0.27% of all cases in Texas.
•New Mexico: 1 – This is 1.23% of all cases in New Mexico.
US NATIONAL CASE COUNT: 1,197
INTERNATIONAL SPREAD
•Mexico: 2337 (+257), 5 fatalities
‒Chihuahua, Mexico: 2,179 (+239) cases, 4 fatalities, 7 currently hospitalized.
•Canada: 3,207 (+208), 1 fatality
‒Ontario Outbreak, Canada: 2,115 (+74) cases, 158 hospitalizations, 1 fatality.
‒Alberta, Canada: 879(+118) cases, 5 currently hospitalized.
LDMMIA Practitioner Level Orientation Updates
LDMMIA Practitioner Level Orientation UpdatesLDM & Mia eStudios See our 2 Starter PDFs within a Compressed, Zip Drive. Within Shop. Videos will be available Before the Weekend of 6/14th. For the US, Happy Fathers Day Weekend. (Our readers/teams are global.) Also, our content remains timeless for Future Grad Students seeking updates.
After about a Year or 10, I retire older content. Literally up to under 10 yrs. We will be 19 yrs old this Aug for Love and Divinity in Motion (LDM). How old are we? So funny. Our oldest profile is X, formerly Twitter. From our old Apple Podcast Years.
https://ldm-mia.creator-spring.com
Session/Lesson 1 -Intro
REIKI- YOGA “ORIENTATION”
It helps to understand the text behind anything. This improves our performance and confidence.
Your training will be mixed media. Includes Rehab Intro and Meditation vods, all sold separately.
Editing our Vods & New Shop. Retail under $30 per item.
*Store Fees will apply. *Digital Should be low cost.
Thank you for attending our free workshops. Those can be used with any Reiki Yoga training package. Traditional Reiki does host rules and ethics. Its silent and within the JP Culture/Area/Training/Word of Mouth. It allows remote healing but there’s limits for practitioners and masters. We are not allowed to share certain secrets/tools. Some content is designed only for “Masters”...
Next Upload will be our Video package for Session 1. Prices will be affordable as possible. Thx for becoming a "Practitioner Level" Student.
Updates so far, are every week for spring. Summer should be a similar schedule. Thx for visitings, attending, and following LDMMIA.
Social Media:
https://x.com/OnlineDrLeZ
and
https://www.instagram.com/chelleofsl/
Plate Tectonic Boundaries and Continental Drift Theory
Plate Tectonic Boundaries and Continental Drift TheoryMarie This 28 slide presentation covers the basics of plate tectonics and continental drift theory. It is an effective introduction into a full plate tectonics unit study, but does not cover faults, stress, seismic waves, or seafloor spreading.
To download PDF, visit The Homeschool Daily. We will be uploading more slideshows to follow this one. Blessings, Marie
THE PSYCHOANALYTIC OF THE BLACK CAT BY EDGAR ALLAN POE (1).pdf
THE PSYCHOANALYTIC OF THE BLACK CAT BY EDGAR ALLAN POE (1).pdfnabilahk908 Psychoanalytic Analysis of The Black Cat by Edgar Allan Poe explores the deep psychological dimensions of the narrator’s disturbed mind through the lens of Sigmund Freud’s psychoanalytic theory. According to Freud (1923), the human psyche is structured into three components: the Id, which contains primitive and unconscious desires; the Ego, which operates on the reality principle and mediates between the Id and the external world; and the Superego, which reflects internalized moral standards.
In this story, Poe presents a narrator who experiences a psychological breakdown triggered by repressed guilt, aggression, and internal conflict. This analysis focuses not only on the gothic horror elements of the narrative but also on the narrator’s mental instability and emotional repression, demonstrating how the imbalance of these three psychic forces contributes to his downfall.
Code Profiling in Odoo 18 - Odoo 18 Slides
Code Profiling in Odoo 18 - Odoo 18 SlidesCeline George Profiling in Odoo identifies slow code and resource-heavy processes, ensuring better system performance. Odoo code profiling detects bottlenecks in custom modules, making it easier to improve speed and scalability.
LDM Recording Presents Yogi Goddess by LDMMIA
LDM Recording Presents Yogi Goddess by LDMMIALDM & Mia eStudios A bonus dept update. Happy Summer 25 almost. Do Welcome or Welcome back. Our 10th Free workshop will be released the end of this week, June 20th Weekend. All Materials/updates/Workshops are timeless for future students.
6/17/25: “My now Grads, You’re doing well. I applaud your efforts to continue. We all are shifting to new paradigm realities. Its rough, there’s good and bad days/weeks. However, Reiki with Yoga assistance, does work.”
6/18/25: "For those planning the Training Program Do Welcome. Happy Summer 2k25. You are not ignored and much appreciated. Our updates are ongoing and weekly since Spring. I Hope you Enjoy the Practitioner Grad Level. There's more to come. We will also be wrapping up Level One. So I can work on Levels 2 topics. Please see documents for any news updates. Also visit our websites. Every decade I release a Campus eMap. I will work on that for summer 25. We have 2 old libraries online thats open. https://ldmchapels.weebly.com "
A Safe House,
sanctuary of virtual relaxation and rejuvenation.
By ©YogiGoddess of ©LDMMIA, ©LDMYoga.
♥Teacher Dept: (Rev Dr) Leslie Moore, ND Yoga (Aide/LPN Trained), Metaphysician,
Using Reiki Practitioner/Master Level Trained.
#yogigoddess @YogiGoddessVEVO
♥LDMMIA & Depts: are fusing the fan clubs so do welcome.
We are timeless and a safe haven / Cyber Space. That’s the design of our Fan/Reader/Loyal Blog.♥
LDM HQ Est. in Ann Arbor, MI 2005.
- Moved to Detroit in 2006,
- Expanded online 2007-2024+
- Became a Beatz Studio in 2009 as Yogi Goddess. After our Apple Podcast
- Relocated to Mount Pleasant MI for College The Pandemic Ending.
- Endemic - Present; Moved back to assist Family in Metro Detroit.
Practitioner Student. Level/Session 2
* The Review & Topics:
* All virtual, adult, education students must be over 18 years to attend LDMMIA eClasses and vStudio Thx.
* Please refer to our Free Workshops anytime for review/notes.
*Tech: Products Sold Separately are for Uploading Size Reasons, THX.
MIA TECH: Videos under Copyright including our Music Video for Yogi Goddess, Can only be picked up vs shop downloaded. We are under vydia.com
Pickup will be our Youtube, for unlisted Playlist.
We do have another Vod for Session 2, Level 1.
After that we move on to Session 3.
Levels 1-3 should be done by August to Sept.
LDM Recording, Yogi Goddess Bio (ReverbNation)
Organization functions as a Studio 1st. We are a Media Co, Private Sector, and Global Listed.
Imagine we are 2 different studios. One for Yoga, the Other for Music Beatz. We are also Vevo TV for Smart TV and Youtube, 2 platforms. The audience differs.
Our Biz income are Media monetization within The Entertainment genre. This includes the category of Yoga, Reiki, ASMR, and Music Beatz. Any other tips, donations, B2C sales/Student Tuition are extra. The Biz gifts are appreciated. (We have been given a few $K for random emergencies.)
Chalukyas of Gujrat, Solanki Dynasty NEP.pptx
Chalukyas of Gujrat, Solanki Dynasty NEP.pptxDr. Ravi Shankar Arya Mahila P. G. College, Banaras Hindu University, Varanasi, India.
Gibson "Secrets to Changing Behaviour in Scholarly Communication: A 2025 NISO...
Gibson "Secrets to Changing Behaviour in Scholarly Communication: A 2025 NISO...National Information Standards Organization (NISO)
YSPH VMOC Special Report - Measles Outbreak Southwest US 6-14-2025.pptx
YSPH VMOC Special Report - Measles Outbreak Southwest US 6-14-2025.pptxYale School of Public Health - The Virtual Medical Operations Center (VMOC)
Data processing with spark in r & python
- 1. Data processing with Spark in R & Python Maloy Manna linkedin.com/in/maloy @itsmaloy biguru.wordpress.com
- 2. Abstract With ever increasing adoption by vendors and enterprises, Spark is fast becoming the de facto big data platform. As a general purpose data processing engine, Spark can be used in both R and Python programs. In this webinar, we’ll see how to use Spark to process data from various sources in R and Python and how new tools like Spark SQL and data frames make it easy to perform structured data processing.
- 3. Speaker profile Maloy Manna Data science engineering AXA Data Innovation Lab • Building data driven products and services for over 15 years • Worked in Thomson Reuters, Infosys, TCS and data science startup Saama linkedin.com/in/maloy @itsmaloy biguru.wordpress.com
- 4. Agenda • Overview of Spark • Data processing operations • RDD operations – Transformations, Actions • Spark SQL – DataFrames – DataFrame operations • Spark R • Useful Tips • References
- 5. Overview of Spark • Fast, general-purpose engine for large-scale data processing • Smarter than Hadoop in utilizing memory • Faster than MapReduce in memory & on disk • Can run on Hadoop, or standalone; can access data in HDFS, Cassandra, Hive / any Hadoop data source • Provides high-level APIs in Scala, Java, Python & R • Supports high-level tools like Spark SQL for structured data processing
- 6. Using Spark for data science & big data • Data science lifecycle • 50% – 80% of time spent in data preparation stage • Automation is key to efficiency • R & Python already have packages & libraries for data processing • Apache Spark adds more power to R & Python big data wrangling
- 7. Data processing Getting data to the right format for analysis: • Data manipulations • Data tidying • Data visualization reshaping formatting cleaning Transformations munging Wrangling carpentry manipulation cleaning processing
- 8. Data processing - operations • Reshaping data Change layout (rows/columns “shape”) of dataset • Subset data Select rows or columns • Group data Group data by categories, summarize values • Make new variables Compute and append new columns, drop old columns • Combine data sets Joins, append rows/columns, set operations
- 9. • Driver program runs main function • RDD (resilient distributed datasets) and shared variables help in parallel execution • Cluster manager distributes code and manages data in RDDs Spark for data processing
- 10. Installing and using Spark • Install pre-compiled package http://spark.apache.org/downloads.html • Build from source code http://spark.apache.org/docs/latest/building-spark.html • Run Spark on Amazon EC2 or use Databricks Spark notebooks (Python / R) http://spark.apache.org/docs/latest/ec2-scripts.html | www.databricks.com/registration • Run as Docker image https://hub.docker.com/r/sequenceiq/spark/
- 11. • Download pre-compiled release version • Choose “pre-built for Hadoop 2.6 and later” • Unpack/untar package • Try out the Python interactive shell bin/pyspark • Ensure JAVA_HOME is set bin/sparkR Installing Spark
- 12. Using Spark in Python • Import Spark classes • Create SparkContext object (driver program) and initialize it • In practice, use the spark-submit script to launch applications on a cluster, using configurable options and including dependencies • Once a SparkContext is available, it can be used to build RDDs.
- 13. RDD: Transformations & Actions • RDD is immutable, distributed data structure – Each RDD is split into multiple partitions • Can be created in 2 ways: – Loading external dataset or – Distributing a collection of objects in driver • RDDs support 2 different types of operations: – Transformations (construct new RDD) – Actions (compute result based on RDD)
- 14. RDD: Transformations & Actions Transformations No (lazy) evaluations New RDD returned Examples: ⁻ map ⁻ filter ⁻ flatMap ⁻ groupByKey ⁻ reduceByKey ⁻ aggregateByKey ⁻ union ⁻ join ⁻ coalesce Actions Evaluations done New value returned Examples: ⁻ reduce ⁻ collect ⁻ count ⁻ first ⁻ take ⁻ countByKey ⁻ foreach ⁻ saveAsTextFile ⁻ saveAsSequenceFile
- 15. Create RDDs • Creating distributed datasets – From any storage source supported by Hadoop • Use SparkContext methods: – Support directories, compressed files, wildcards
- 16. Loading data • Loading text files • Loading unstructured JSON files • Loading sequence files
- 17. Loading data • Loading csv files • Loading csv files in full
- 18. Saving data • Saving text files • Saving unstructured JSON files • Saving csv files
- 19. Spark SQL • Spark’s interface for working with structured and semi-structured data • Can load data from JSON, Hive, Parquet • Can query using SQL • Can be combined with regular code e.g. Python / Java inside Spark application • Provides “DataFrames” (SchemaRDD < v1.3) • Like RDDs, DataFrames are evaluated “lazily”
- 20. Using Spark SQL • HiveContext (or SQLContext for a stripped- down version) based on SparkContext • Construct a SQLContext: • Basic query:
- 21. Spark SQL: DataFrames • Spark SQL provides DataFrames as programming abstractions • A DataFrame is a distributed collection of data organized into named columns • Conceptually equivalent to relational table • Familiar syntax (R dplyr / Pandas) but scales to PBs • Entry-point remains SQLContext
- 22. Spark SQL: DataFrame Operations • Selecting rows, columns • Grouping / aggregation • Running SQL queries • Window functions
- 23. • Reading JSON data into dataframe in Python • Reading JSON data into dataframe in R DataFrames – Data Operations
- 24. • Generic load/save – Python – R • Default data source parquet – Can be changed by manually specifying format DataFrames – Saving data
- 25. SparkR • R package providing light-weight front-end to use Apache Spark from R • Entry point in SparkContext • With SQLContext, dataframes can be created from local R data frames, Hive tables or other Spark data sources • Introduced with Spark 1.4
- 26. SparkR: Creating DataFrames • From local data frames • From data sources like JSON • From Hive tables
- 27. Useful tips • Use Spark SQL dataframes to write less code. Easier to avoid closure problems. • Be aware of closure issues while working in cluster mode. Use accumulator variables instead of locally defined methods • Utilize Spark SQL capability to automatically infer schema of JSON datasets SQLContext.read.json • Other than using command-line, IDEs like IntelliJ IDEA community edition can be used for free
- 28. References • Spark pages: http://spark.apache.org/ • Databricks blog: https://databricks.com/blog • Spark summit: https://spark-summit.org/ • Additional Spark packages at: http://spark-packages.org/ • Example scripts: • https://github.com/apache/spark/blob/master/examples/src/main/pytho n/sql.py • https://github.com/apache/spark/blob/master/examples/src/main/r/data -manipulation.R • https://github.com/apache/spark/blob/master/examples/src/main/r/data frame.R • API docs: http://spark.apache.org/docs/latest/api/python/index.html • http://spark.apache.org/docs/latest/api/R/index.html • Using SparkR in Rstudio: http://www.r-bloggers.com/how-to-use-sparkr- within-rstudio/