A short presentation I gave on why Apache Spark is such an impressive analytics platform, particularly for R and Python users. I also discuss how academia can benefit from Amazon AWS implementation.
This document discusses Apache Spark, an open-source cluster computing framework. It provides an overview of Spark, including its main concepts like RDDs (Resilient Distributed Datasets) and transformations. Spark is presented as a faster alternative to Hadoop for iterative jobs and machine learning through its ability to keep data in-memory. Example code is shown for Spark's programming model in Scala and Python. The document concludes that Spark offers a rich API to make data analytics fast, achieving speedups of up to 100x over Hadoop in real applications.
This document provides an overview of Spark, including:
- Spark's processing model involves chopping live data streams into batches and treating each batch as an RDD to apply transformations and actions.
- Resilient Distributed Datasets (RDDs) are Spark's primary abstraction, representing an immutable distributed collection of objects that can be operated on in parallel.
- An example word count program is presented to illustrate how to create and manipulate RDDs to count the frequency of words in a text file.
Spark and Spark Streaming internals allow for low latency, fault tolerance, and diverse workloads. Spark uses a Resilient Distributed Dataset (RDD) model where data is partitioned across a cluster. A directed acyclic graph (DAG) is used to schedule tasks across stages in an optimized way. Spark Streaming runs streaming computations as small deterministic batch jobs by chopping live streams into batches and processing them using RDD transformations and actions.
Apache Spark is a fast and general engine for large-scale data processing. It provides a unified API for batch, interactive, and streaming data processing using in-memory primitives. A benchmark showed Spark was able to sort 100TB of data 3 times faster than Hadoop using 10 times fewer machines by keeping data in memory between jobs.
Transformations and actions a visual guide trainingSpark Summit
The document summarizes key Spark API operations including transformations like map, filter, flatMap, groupBy, and actions like collect, count, and reduce. It provides visual diagrams and examples to illustrate how each operation works, the inputs and outputs, and whether the operation is narrow or wide.
The document provides an overview of Apache Spark internals and Resilient Distributed Datasets (RDDs). It discusses:
- RDDs are Spark's fundamental data structure - they are immutable distributed collections that allow transformations like map and filter to be applied.
- RDDs track their lineage or dependency graph to support fault tolerance. Transformations create new RDDs while actions trigger computation.
- Operations on RDDs include narrow transformations like map that don't require data shuffling, and wide transformations like join that do require shuffling.
- The RDD abstraction allows Spark's scheduler to optimize execution through techniques like pipelining and cache reuse.
Apache Spark in Depth: Core Concepts, Architecture & InternalsAnton Kirillov
Slides cover Spark core concepts of Apache Spark such as RDD, DAG, execution workflow, forming stages of tasks and shuffle implementation and also describes architecture and main components of Spark Driver. The workshop part covers Spark execution modes , provides link to github repo which contains Spark Applications examples and dockerized Hadoop environment to experiment with
This document discusses Apache Spark, an open-source cluster computing framework. It provides an overview of Spark, including its main concepts like RDDs (Resilient Distributed Datasets) and transformations. Spark is presented as a faster alternative to Hadoop for iterative jobs and machine learning through its ability to keep data in-memory. Example code is shown for Spark's programming model in Scala and Python. The document concludes that Spark offers a rich API to make data analytics fast, achieving speedups of up to 100x over Hadoop in real applications.
This document provides an overview of Spark, including:
- Spark's processing model involves chopping live data streams into batches and treating each batch as an RDD to apply transformations and actions.
- Resilient Distributed Datasets (RDDs) are Spark's primary abstraction, representing an immutable distributed collection of objects that can be operated on in parallel.
- An example word count program is presented to illustrate how to create and manipulate RDDs to count the frequency of words in a text file.
Spark and Spark Streaming internals allow for low latency, fault tolerance, and diverse workloads. Spark uses a Resilient Distributed Dataset (RDD) model where data is partitioned across a cluster. A directed acyclic graph (DAG) is used to schedule tasks across stages in an optimized way. Spark Streaming runs streaming computations as small deterministic batch jobs by chopping live streams into batches and processing them using RDD transformations and actions.
Apache Spark is a fast and general engine for large-scale data processing. It provides a unified API for batch, interactive, and streaming data processing using in-memory primitives. A benchmark showed Spark was able to sort 100TB of data 3 times faster than Hadoop using 10 times fewer machines by keeping data in memory between jobs.
Transformations and actions a visual guide trainingSpark Summit
The document summarizes key Spark API operations including transformations like map, filter, flatMap, groupBy, and actions like collect, count, and reduce. It provides visual diagrams and examples to illustrate how each operation works, the inputs and outputs, and whether the operation is narrow or wide.
The document provides an overview of Apache Spark internals and Resilient Distributed Datasets (RDDs). It discusses:
- RDDs are Spark's fundamental data structure - they are immutable distributed collections that allow transformations like map and filter to be applied.
- RDDs track their lineage or dependency graph to support fault tolerance. Transformations create new RDDs while actions trigger computation.
- Operations on RDDs include narrow transformations like map that don't require data shuffling, and wide transformations like join that do require shuffling.
- The RDD abstraction allows Spark's scheduler to optimize execution through techniques like pipelining and cache reuse.
Apache Spark in Depth: Core Concepts, Architecture & InternalsAnton Kirillov
Slides cover Spark core concepts of Apache Spark such as RDD, DAG, execution workflow, forming stages of tasks and shuffle implementation and also describes architecture and main components of Spark Driver. The workshop part covers Spark execution modes , provides link to github repo which contains Spark Applications examples and dockerized Hadoop environment to experiment with
This document provides an overview of Spark SQL and its architecture. Spark SQL allows users to run SQL queries over SchemaRDDs, which are RDDs with a schema and column names. It introduces a SQL-like query abstraction over RDDs and allows querying data in a declarative manner. The Spark SQL component consists of Catalyst, a logical query optimizer, and execution engines for different data sources. It can integrate with data sources like Parquet, JSON, and Cassandra.
Spark & Spark Streaming Internals - Nov 15 (1)Akhil Das
This document summarizes Spark and Spark Streaming internals. It discusses the Resilient Distributed Dataset (RDD) model in Spark, which allows for fault tolerance through lineage-based recomputation. It provides an example of log mining using RDD transformations and actions. It then discusses Spark Streaming, which provides a simple API for stream processing by treating streams as series of small batch jobs on RDDs. Key concepts discussed include Discretized Stream (DStream), transformations, and output operations. An example Twitter hashtag extraction job is outlined.
The document discusses Resilient Distributed Datasets (RDDs) in Spark. It explains that RDDs hold references to partition objects containing subsets of data across a cluster. When a transformation like map is applied to an RDD, a new RDD is created to store the operation and maintain a dependency on the original RDD. This allows chained transformations to be lazily executed together in jobs scheduled by Spark.
- Apache Spark is an open-source cluster computing framework that provides fast, general engine for large-scale data processing. It introduces Resilient Distributed Datasets (RDDs) that allow in-memory processing for speed.
- The document discusses Spark's key concepts like transformations, actions, and directed acyclic graphs (DAGs) that represent Spark job execution. It also summarizes Spark SQL, MLlib, and Spark Streaming modules.
- The presenter is a solutions architect who provides an overview of Spark and how it addresses limitations of Hadoop by enabling faster, in-memory processing using RDDs and a more intuitive API compared to MapReduce.
Apache Spark has emerged over the past year as the imminent successor to Hadoop MapReduce. Spark can process data in memory at very high speed, while still be able to spill to disk if required. Spark’s powerful, yet flexible API allows users to write complex applications very easily without worrying about the internal workings and how the data gets processed on the cluster.
Spark comes with an extremely powerful Streaming API to process data as it is ingested. Spark Streaming integrates with popular data ingest systems like Apache Flume, Apache Kafka, Amazon Kinesis etc. allowing users to process data as it comes in.
In this talk, Hari will discuss the basics of Spark Streaming, its API and its integration with Flume, Kafka and Kinesis. Hari will also discuss a real-world example of a Spark Streaming application, and how code can be shared between a Spark application and a Spark Streaming application. Each stage of the application execution will be presented, which can help understand practices while writing such an application. Hari will finally discuss how to write a custom application and a custom receiver to receive data from other systems.
Apache Spark Introduction and Resilient Distributed Dataset basics and deep diveSachin Aggarwal
We will give a detailed introduction to Apache Spark and why and how Spark can change the analytics world. Apache Spark's memory abstraction is RDD (Resilient Distributed DataSet). One of the key reason why Apache Spark is so different is because of the introduction of RDD. You cannot do anything in Apache Spark without knowing about RDDs. We will give a high level introduction to RDD and in the second half we will have a deep dive into RDDs.
This document provides an overview of Spark and its key components. Spark is a fast and general engine for large-scale data processing. It uses Resilient Distributed Datasets (RDDs) that allow data to be partitioned across clusters and cached in memory for fast performance. Spark is up to 100x faster than Hadoop for iterative jobs and provides a unified framework for batch processing, streaming, SQL, and machine learning workloads.
A really really fast introduction to PySpark - lightning fast cluster computi...Holden Karau
Apache Spark is a fast and general engine for distributed computing & big data processing with APIs in Scala, Java, Python, and R. This tutorial will briefly introduce PySpark (the Python API for Spark) with some hands-on-exercises combined with a quick introduction to Spark's core concepts. We will cover the obligatory wordcount example which comes in with every big-data tutorial, as well as discuss Spark's unique methods for handling node failure and other relevant internals. Then we will briefly look at how to access some of Spark's libraries (like Spark SQL & Spark ML) from Python. While Spark is available in a variety of languages this workshop will be focused on using Spark and Python together.
This document provides an overview of Apache Spark's architectural components through the life of simple Spark jobs. It begins with a simple Spark application analyzing airline on-time arrival data, then covers Resilient Distributed Datasets (RDDs), the cluster architecture, job execution through Spark components like tasks and scheduling, and techniques for writing better Spark applications like optimizing partitioning and reducing shuffle size.
Deep Dive Into Catalyst: Apache Spark 2.0'S OptimizerSpark Summit
This document discusses Catalyst, the query optimizer in Apache Spark. It begins by explaining how Catalyst works at a high level, including how it abstracts user programs as trees and uses transformations and strategies to optimize logical and physical plans. It then provides more details on specific aspects like rule execution, ensuring requirements, and examples of optimizations. The document aims to help users understand how Catalyst optimizes queries automatically and provides tips on exploring its code and writing optimizations.
This document provides an overview of Apache Spark, including how it compares to Hadoop, the Spark ecosystem, Resilient Distributed Datasets (RDDs), transformations and actions on RDDs, the directed acyclic graph (DAG) scheduler, Spark Streaming, and the DataFrames API. Key points covered include Spark's faster performance versus Hadoop through its use of memory instead of disk, the RDD abstraction for distributed collections, common RDD operations, and Spark's capabilities for real-time streaming data processing and SQL queries on structured data.
This talk gives details about Spark internals and an explanation of the runtime behavior of a Spark application. It explains how high level user programs are compiled into physical execution plans in Spark. It then reviews common performance bottlenecks encountered by Spark users, along with tips for diagnosing performance problems in a production application.
Introduction to Spark ML Pipelines WorkshopHolden Karau
Introduction to Spark ML Pipelines Workshop slides - companion IJupyter notebooks in Python & Scala are available from my github at https://github.com/holdenk/spark-intro-ml-pipeline-workshop
Spark is a general engine for large-scale data processing. It introduces Resilient Distributed Datasets (RDDs) which allow in-memory caching for fault tolerance and act like familiar Scala collections for distributed computation across clusters. RDDs provide a programming model with transformations like map and reduce and actions to compute results. Spark also supports streaming, SQL, machine learning, and graph processing workloads.
This presentation show the main Spark characteristics, like RDD, Transformations and Actions.
I used this presentation for many Spark Intro workshops from Cluj-Napoca Big Data community : http://www.meetup.com/Big-Data-Data-Science-Meetup-Cluj-Napoca/
This document discusses Spark shuffle, which is an expensive operation that involves data partitioning, serialization/deserialization, compression, and disk I/O. It provides an overview of how shuffle works in Spark and the history of optimizations like sort-based shuffle and an external shuffle service. Key concepts discussed include shuffle writers, readers, and the pluggable block transfer service that handles data transfer. The document also covers shuffle-related configuration options and potential future work.
Here are the steps to complete the assignment:
1. Create RDDs to filter each file for lines containing "Spark":
val readme = sc.textFile("README.md").filter(_.contains("Spark"))
val changes = sc.textFile("CHANGES.txt").filter(_.contains("Spark"))
2. Perform WordCount on each:
val readmeCounts = readme.flatMap(_.split(" ")).map((_,1)).reduceByKey(_ + _)
val changesCounts = changes.flatMap(_.split(" ")).map((_,1)).reduceByKey(_ + _)
3. Join the two RDDs:
val joined = readmeCounts.join(changes
Introduction to Apache Spark. With an emphasis on the RDD API, Spark SQL (DataFrame and Dataset API) and Spark Streaming.
Presented at the Desert Code Camp:
http://oct2016.desertcodecamp.com/sessions/all
This document summarizes a presentation about productionizing streaming jobs with Spark Streaming. It discusses:
1. The lifecycle of a Spark streaming application including how data is received in batches and processed through transformations.
2. Best practices for aggregations including reducing over windows, incremental aggregation, and checkpointing.
3. How to achieve high throughput by increasing parallelism through more receivers and partitions.
4. Tips for debugging streaming jobs using the Spark UI and ensuring processing time is less than the batch interval.
This document provides an agenda and summaries for a meetup on introducing DataFrames and R on Apache Spark. The agenda includes overviews of Apache Spark 1.3, DataFrames, R on Spark, and large scale machine learning on Spark. There will also be discussions on news items, contributions so far, what's new in Spark 1.3, more data source APIs, what DataFrames are, writing DataFrames, and DataFrames with RDDs and Parquet. Presentations will cover Spark components, an introduction to SparkR, and Spark machine learning experiences.
Ten tools for ten big data areas 03_Apache SparkWill Du
Apache Spark is a fast and general-purpose cluster computing system for large-scale data processing. It provides functions for distributed processing of large datasets across clusters using a concept called resilient distributed datasets (RDDs). RDDs allow in-memory clustering computing to improve performance. Spark also supports streaming, SQL, machine learning, and graph processing.
This document provides an overview of Spark SQL and its architecture. Spark SQL allows users to run SQL queries over SchemaRDDs, which are RDDs with a schema and column names. It introduces a SQL-like query abstraction over RDDs and allows querying data in a declarative manner. The Spark SQL component consists of Catalyst, a logical query optimizer, and execution engines for different data sources. It can integrate with data sources like Parquet, JSON, and Cassandra.
Spark & Spark Streaming Internals - Nov 15 (1)Akhil Das
This document summarizes Spark and Spark Streaming internals. It discusses the Resilient Distributed Dataset (RDD) model in Spark, which allows for fault tolerance through lineage-based recomputation. It provides an example of log mining using RDD transformations and actions. It then discusses Spark Streaming, which provides a simple API for stream processing by treating streams as series of small batch jobs on RDDs. Key concepts discussed include Discretized Stream (DStream), transformations, and output operations. An example Twitter hashtag extraction job is outlined.
The document discusses Resilient Distributed Datasets (RDDs) in Spark. It explains that RDDs hold references to partition objects containing subsets of data across a cluster. When a transformation like map is applied to an RDD, a new RDD is created to store the operation and maintain a dependency on the original RDD. This allows chained transformations to be lazily executed together in jobs scheduled by Spark.
- Apache Spark is an open-source cluster computing framework that provides fast, general engine for large-scale data processing. It introduces Resilient Distributed Datasets (RDDs) that allow in-memory processing for speed.
- The document discusses Spark's key concepts like transformations, actions, and directed acyclic graphs (DAGs) that represent Spark job execution. It also summarizes Spark SQL, MLlib, and Spark Streaming modules.
- The presenter is a solutions architect who provides an overview of Spark and how it addresses limitations of Hadoop by enabling faster, in-memory processing using RDDs and a more intuitive API compared to MapReduce.
Apache Spark has emerged over the past year as the imminent successor to Hadoop MapReduce. Spark can process data in memory at very high speed, while still be able to spill to disk if required. Spark’s powerful, yet flexible API allows users to write complex applications very easily without worrying about the internal workings and how the data gets processed on the cluster.
Spark comes with an extremely powerful Streaming API to process data as it is ingested. Spark Streaming integrates with popular data ingest systems like Apache Flume, Apache Kafka, Amazon Kinesis etc. allowing users to process data as it comes in.
In this talk, Hari will discuss the basics of Spark Streaming, its API and its integration with Flume, Kafka and Kinesis. Hari will also discuss a real-world example of a Spark Streaming application, and how code can be shared between a Spark application and a Spark Streaming application. Each stage of the application execution will be presented, which can help understand practices while writing such an application. Hari will finally discuss how to write a custom application and a custom receiver to receive data from other systems.
Apache Spark Introduction and Resilient Distributed Dataset basics and deep diveSachin Aggarwal
We will give a detailed introduction to Apache Spark and why and how Spark can change the analytics world. Apache Spark's memory abstraction is RDD (Resilient Distributed DataSet). One of the key reason why Apache Spark is so different is because of the introduction of RDD. You cannot do anything in Apache Spark without knowing about RDDs. We will give a high level introduction to RDD and in the second half we will have a deep dive into RDDs.
This document provides an overview of Spark and its key components. Spark is a fast and general engine for large-scale data processing. It uses Resilient Distributed Datasets (RDDs) that allow data to be partitioned across clusters and cached in memory for fast performance. Spark is up to 100x faster than Hadoop for iterative jobs and provides a unified framework for batch processing, streaming, SQL, and machine learning workloads.
A really really fast introduction to PySpark - lightning fast cluster computi...Holden Karau
Apache Spark is a fast and general engine for distributed computing & big data processing with APIs in Scala, Java, Python, and R. This tutorial will briefly introduce PySpark (the Python API for Spark) with some hands-on-exercises combined with a quick introduction to Spark's core concepts. We will cover the obligatory wordcount example which comes in with every big-data tutorial, as well as discuss Spark's unique methods for handling node failure and other relevant internals. Then we will briefly look at how to access some of Spark's libraries (like Spark SQL & Spark ML) from Python. While Spark is available in a variety of languages this workshop will be focused on using Spark and Python together.
This document provides an overview of Apache Spark's architectural components through the life of simple Spark jobs. It begins with a simple Spark application analyzing airline on-time arrival data, then covers Resilient Distributed Datasets (RDDs), the cluster architecture, job execution through Spark components like tasks and scheduling, and techniques for writing better Spark applications like optimizing partitioning and reducing shuffle size.
Deep Dive Into Catalyst: Apache Spark 2.0'S OptimizerSpark Summit
This document discusses Catalyst, the query optimizer in Apache Spark. It begins by explaining how Catalyst works at a high level, including how it abstracts user programs as trees and uses transformations and strategies to optimize logical and physical plans. It then provides more details on specific aspects like rule execution, ensuring requirements, and examples of optimizations. The document aims to help users understand how Catalyst optimizes queries automatically and provides tips on exploring its code and writing optimizations.
This document provides an overview of Apache Spark, including how it compares to Hadoop, the Spark ecosystem, Resilient Distributed Datasets (RDDs), transformations and actions on RDDs, the directed acyclic graph (DAG) scheduler, Spark Streaming, and the DataFrames API. Key points covered include Spark's faster performance versus Hadoop through its use of memory instead of disk, the RDD abstraction for distributed collections, common RDD operations, and Spark's capabilities for real-time streaming data processing and SQL queries on structured data.
This talk gives details about Spark internals and an explanation of the runtime behavior of a Spark application. It explains how high level user programs are compiled into physical execution plans in Spark. It then reviews common performance bottlenecks encountered by Spark users, along with tips for diagnosing performance problems in a production application.
Introduction to Spark ML Pipelines WorkshopHolden Karau
Introduction to Spark ML Pipelines Workshop slides - companion IJupyter notebooks in Python & Scala are available from my github at https://github.com/holdenk/spark-intro-ml-pipeline-workshop
Spark is a general engine for large-scale data processing. It introduces Resilient Distributed Datasets (RDDs) which allow in-memory caching for fault tolerance and act like familiar Scala collections for distributed computation across clusters. RDDs provide a programming model with transformations like map and reduce and actions to compute results. Spark also supports streaming, SQL, machine learning, and graph processing workloads.
This presentation show the main Spark characteristics, like RDD, Transformations and Actions.
I used this presentation for many Spark Intro workshops from Cluj-Napoca Big Data community : http://www.meetup.com/Big-Data-Data-Science-Meetup-Cluj-Napoca/
This document discusses Spark shuffle, which is an expensive operation that involves data partitioning, serialization/deserialization, compression, and disk I/O. It provides an overview of how shuffle works in Spark and the history of optimizations like sort-based shuffle and an external shuffle service. Key concepts discussed include shuffle writers, readers, and the pluggable block transfer service that handles data transfer. The document also covers shuffle-related configuration options and potential future work.
Here are the steps to complete the assignment:
1. Create RDDs to filter each file for lines containing "Spark":
val readme = sc.textFile("README.md").filter(_.contains("Spark"))
val changes = sc.textFile("CHANGES.txt").filter(_.contains("Spark"))
2. Perform WordCount on each:
val readmeCounts = readme.flatMap(_.split(" ")).map((_,1)).reduceByKey(_ + _)
val changesCounts = changes.flatMap(_.split(" ")).map((_,1)).reduceByKey(_ + _)
3. Join the two RDDs:
val joined = readmeCounts.join(changes
Introduction to Apache Spark. With an emphasis on the RDD API, Spark SQL (DataFrame and Dataset API) and Spark Streaming.
Presented at the Desert Code Camp:
http://oct2016.desertcodecamp.com/sessions/all
This document summarizes a presentation about productionizing streaming jobs with Spark Streaming. It discusses:
1. The lifecycle of a Spark streaming application including how data is received in batches and processed through transformations.
2. Best practices for aggregations including reducing over windows, incremental aggregation, and checkpointing.
3. How to achieve high throughput by increasing parallelism through more receivers and partitions.
4. Tips for debugging streaming jobs using the Spark UI and ensuring processing time is less than the batch interval.
This document provides an agenda and summaries for a meetup on introducing DataFrames and R on Apache Spark. The agenda includes overviews of Apache Spark 1.3, DataFrames, R on Spark, and large scale machine learning on Spark. There will also be discussions on news items, contributions so far, what's new in Spark 1.3, more data source APIs, what DataFrames are, writing DataFrames, and DataFrames with RDDs and Parquet. Presentations will cover Spark components, an introduction to SparkR, and Spark machine learning experiences.
Ten tools for ten big data areas 03_Apache SparkWill Du
Apache Spark is a fast and general-purpose cluster computing system for large-scale data processing. It provides functions for distributed processing of large datasets across clusters using a concept called resilient distributed datasets (RDDs). RDDs allow in-memory clustering computing to improve performance. Spark also supports streaming, SQL, machine learning, and graph processing.
Spark - The Ultimate Scala Collections by Martin OderskySpark Summit
Spark is a domain-specific language for working with collections that is implemented in Scala and runs on a cluster. While similar to Scala collections, Spark differs in that it is lazy and supports additional functionality for paired data. Scala can learn from Spark by adding views to make laziness clearer, caching for persistence, and pairwise operations. Types are important for Spark as they prevent logic errors and help with programming complex functional operations across a cluster.
This document summarizes IBM's announcement of a major commitment to advance Apache Spark. It discusses IBM's investments in Spark capabilities, including log processing, graph analytics, stream processing, machine learning, and unified data access. Key reasons for interest in Spark include its performance (up to 100x faster than Hadoop for some tasks), productivity gains, ability to leverage existing Hadoop investments, and continuous community improvements. The document also provides an overview of Spark's architecture, programming model using resilient distributed datasets (RDDs), and common use cases like interactive querying, batch processing, analytics, and stream processing.
This document provides an introduction to Apache Spark presented by Vincent Poncet of IBM. It discusses how Spark is a fast, general-purpose cluster computing system for large-scale data processing. It is faster than MapReduce, supports a wide range of workloads, and is easier to use with APIs in Scala, Python, and Java. The document also provides an overview of Spark's execution model and its core API called resilient distributed datasets (RDDs).
En esta charla miraremos al futuro introduciendo Spark como alternativa al clásico motor de Hadoop MapReduce. Describiremos las diferencias más importantes frente al mismo, se detallarán los componentes principales que componen el ecosistema Spark, e introduciremos conceptos básicos que permitan empezar con el desarrollo de aplicaciones básicas sobre el mismo.
Spark Intro @ analytics big data summitSujee Maniyam
The document discusses Apache Spark, an open-source cluster computing framework. It provides an overview of Spark's architecture and capabilities. Spark can be used for batch processing, streaming, and machine learning. It is faster than Hadoop for iterative jobs and large-scale data processing when data fits in memory. The document demonstrates Spark through examples in the Spark shell and a word count job.
Apache Spark - Intro to Large-scale recommendations with Apache Spark and PythonChristian Perone
This document provides an introduction to Apache Spark and collaborative filtering. It discusses big data and the limitations of MapReduce, then introduces Apache Spark including Resilient Distributed Datasets (RDDs), transformations, actions, and DataFrames. It also covers Spark Machine Learning (ML) libraries and algorithms such as classification, regression, clustering, and collaborative filtering.
Holden Karau walks attendees through a number of common mistakes that can keep your Spark programs from scaling and examines solutions and general techniques useful for moving beyond a proof of concept to production.
Topics include:
Working with key/value data
Replacing groupByKey for awesomeness
Key skew: your data probably has it and how to survive
Effective caching and checkpointing
Considerations for noisy clusters
Functional transformations with Spark Datasets: getting the benefits of Catalyst with the ease of functional development
How to make our code testable
This document provides an overview of Apache Spark and machine learning using Spark. It introduces the speaker and objectives. It then covers Spark concepts including its architecture, RDDs, transformations and actions. It demonstrates working with RDDs and DataFrames. Finally, it discusses machine learning libraries available in Spark like MLib and how Spark can be used for supervised machine learning tasks.
http://bit.ly/1BTaXZP – Hadoop has been a huge success in the data world. It’s disrupted decades of data management practices and technologies by introducing a massively parallel processing framework. The community and the development of all the Open Source components pushed Hadoop to where it is now.
That's why the Hadoop community is excited about Apache Spark. The Spark software stack includes a core data-processing engine, an interface for interactive querying, Sparkstreaming for streaming data analysis, and growing libraries for machine-learning and graph analysis. Spark is quickly establishing itself as a leading environment for doing fast, iterative in-memory and streaming analysis.
This talk will give an introduction the Spark stack, explain how Spark has lighting fast results, and how it complements Apache Hadoop.
Keys Botzum - Senior Principal Technologist with MapR Technologies
Keys is Senior Principal Technologist with MapR Technologies, where he wears many hats. His primary responsibility is interacting with customers in the field, but he also teaches classes, contributes to documentation, and works with engineering teams. He has over 15 years of experience in large scale distributed system design. Previously, he was a Senior Technical Staff Member with IBM, and a respected author of many articles on the WebSphere Application Server as well as a book.
Your data is getting bigger while your boss is getting anxious to have insights! This tutorial covers Apache Spark that makes data analytics fast to write and fast to run. Tackle big datasets quickly through a simple API in Python, and learn one programming paradigm in order to deploy interactive, batch, and streaming applications while connecting to data sources incl. HDFS, Hive, JSON, and S3.
The state of analytics has changed dramatically over the last few years. Hadoop is now commonplace, and the ecosystem has evolved to include new tools such as Spark, Shark, and Drill, that live alongside the old MapReduce-based standards. It can be difficult to keep up with the pace of change, and newcomers are left with a dizzying variety of seemingly similar choices. This is compounded by the number of possible deployment permutations, which can cause all but the most determined to simply stick with the tried and true. In this talk I will introduce you to a powerhouse combination of Cassandra and Spark, which provides a high-speed platform for both real-time and batch analysis.
This document provides an overview of Apache Spark and compares it to Hadoop MapReduce. It defines big data and explains that Spark is a solution for processing large datasets in parallel. Spark improves on MapReduce by allowing in-memory computation using Resilient Distributed Datasets (RDDs) which makes it faster, especially for iterative jobs. Spark is also easier to program with rich APIs. While MapReduce is tolerant, Spark caching improves performance. Both are widely used but Spark sees more adoption for real-time applications due to its speed.
This document provides an introduction to Apache Spark, including its history and key concepts. It discusses how Spark was developed in response to big data processing needs at Google and how it builds upon earlier systems like MapReduce. The document then covers Spark's core abstractions like RDDs and DataFrames/Datasets and common transformations and actions. It also provides an overview of Spark SQL and how to deploy Spark applications on a cluster.
ScalaTo July 2019 - No more struggles with Apache Spark workloads in productionChetan Khatri
Scala Toronto July 2019 event at 500px.
Pure Functional API Integration
Apache Spark Internals tuning
Performance tuning
Query execution plan optimisation
Cats Effects for switching execution model runtime.
Discovery / experience with Monix, Scala Future.
Similar to Survey of Spark for Data Pre-Processing and Analytics (20)
This document provides an overview of databases and tools relevant to systems immunology. It discusses several freely available and licensed databases containing gene expression, drug, pathway, and disease data. Issues with third party data like cleanup requirements and need for downloadability are also covered. Examples are given of integrating data from sources like GEO, DrugBank, Connectivity Map, and ImmPort to enable meta-analyses addressing immunological questions.
Managing experiment data using Excel and FriendsYannick Pouliot
This document provides a summary of a training session on managing experiment data using Excel and related tools. The training covered essential Excel functions like conditional formatting, named ranges, pivot tables, and querying web data or databases. It demonstrated how to use MS Query to directly query databases and retrieve results in Excel. Resources for further learning about Excel, Access, SQL, and related topics were also provided. The goal was to teach practical skills for better organizing and analyzing experimental data using common office tools.
This document provides an overview of a workshop on essential UNIX skills for biologists. The workshop covered basic UNIX commands like ls, cat, grep, find, and uniq. It explained concepts like redirection, metacharacters, and stringing commands with pipes. The document also discussed accessing UNIX on Mac, Windows by installing programs like UnxUtils or Cygwin, and dual booting. Resources like UNIX command references and ebooks from Lane Library were provided.
A guided SQL tour of bioinformatics databasesYannick Pouliot
This document provides an overview of a guided tour of SQL querying bioinformatics databases. The tour begins with a brief review of relational databases and SQL. It then demonstrates querying the Ensembl and BioWarehouse databases using SQL, walking through the database schemas and providing example queries. Resources for connecting to databases remotely and setting up data source names are also included. The goal is to introduce bioinformaticians to directly querying bioinformatics databases using SQL.
1. The document discusses automatically assigning ontological IDs to cell populations identified in studies using FLOCKMapper.
2. It describes the system components used, including a study dataset, FLOCK for analysis, the Cell Ontology and ImmPort ontologies for IDs, and HIPC definitions converted to a computable form.
3. It explains the mapping process involves identifying an ontological class that maps to a given HIPC class definition, addressing mismatches, and codifying the mappings as views and functions.
Why The Cloud Is A Computational Biologist's Best FriendYannick Pouliot
This document discusses the author's experience using Amazon Cloud services. It provides an overview of Amazon Cloud's flexible computing power and storage available for rent. The author finds Amazon to be the clear leader in cloud providers, with computing power that feels similar to a local cluster but with more flexibility. Amazon Cloud offers storage, computing instances of various types and operating systems, and tools for managing and distributing instances. While there are some limitations and costs to consider, the author believes the cloud can provide vast computing power affordably.
There’s No Avoiding It: Programming Skills You’ll NeedYannick Pouliot
The document discusses the importance of programming skills for bioresearchers. It argues that just as pipetting skills are essential, programming skills are now equally important. It recommends moving away from solely using Excel for data storage and analysis and instead using relational databases and programming. The document outlines free and cheap software, algorithms, and cloud computing resources available to help researchers learn programming. It emphasizes that programming is necessary to address both small and large problems as off-the-shelf software will fail for real science applications.
Ontologies for Semantic Normalization of Immunological DataYannick Pouliot
This document discusses using ontologies to semantically normalize immunological data from the Human Immune Profiling Consortium (HIPC). 57 ontologies covering domains like anatomy, disease, pathways were evaluated. Text from HIPC datasets and protocols was annotated using these ontologies, with the NCI Thesaurus, Medical Subject Headings, and Gene Ontology mapping to the most terms. Many failures were due to missing commercial reagent terms. The conclusions are that ImmPort, the HIPC data repository, could adopt ontology-based encoding with additions to ontologies and text pre-processing.
Predicting Adverse Drug Reactions Using PubChem Screening DataYannick Pouliot
This document discusses predicting adverse drug reactions using PubChem screening data. It aims to determine if specific classes of adverse drug reactions can be identified from patterns of compound reactivity in PubChem bioassay screens. The document outlines the hypothesis that drugs with increased frequency of tissue-specific adverse drug reactions can be identified from their bioassay screening patterns. It then presents results of predictive modeling for different system organ classes, showing areas under the curve for various models and highlighting top assays correlated with specific adverse event classes. Lessons learned are discussed around database and data loading challenges.
Repositioning Old Drugs For New Indications Using Computational ApproachesYannick Pouliot
Topiramate was identified as a potential drug candidate for inflammatory bowel disease (IBD) using a computational approach. Gene expression profiles of drugs and disease states were analyzed to find drugs that induced the reciprocal signature of IBD tissues compared to normal tissues. Topiramate decreased diarrhea in a rat model of IBD and counter-expressed genes observed in microarray data. This provides proof that drugs affecting gene expression anti-correlated to disease patterns may treat symptoms.
Databases, Web Services and Tools For Systems ImmunologyYannick Pouliot
This document provides an overview of databases, web services, tools, and computing resources needed for systems immunology. It discusses the importance of having a clear hypothesis, statistical understanding, large datasets from different levels of biology, software tools, programming expertise, and computing power. Specific databases, tools, and programming languages discussed include ImmPort, Stanford's HIMC database, MySQL, GenePattern, Galaxy, Weka, R, Perl, Python, and Amazon Cloud computing. The document provides recommendations and resources for learning statistics, data mining, programming languages, and using cloud computing resources.
8 Surprising Reasons To Meditate 40 Minutes A Day That Can Change Your Life.pptxHolistified Wellness
We’re talking about Vedic Meditation, a form of meditation that has been around for at least 5,000 years. Back then, the people who lived in the Indus Valley, now known as India and Pakistan, practised meditation as a fundamental part of daily life. This knowledge that has given us yoga and Ayurveda, was known as Veda, hence the name Vedic. And though there are some written records, the practice has been passed down verbally from generation to generation.
Cell Therapy Expansion and Challenges in Autoimmune DiseaseHealth Advances
There is increasing confidence that cell therapies will soon play a role in the treatment of autoimmune disorders, but the extent of this impact remains to be seen. Early readouts on autologous CAR-Ts in lupus are encouraging, but manufacturing and cost limitations are likely to restrict access to highly refractory patients. Allogeneic CAR-Ts have the potential to broaden access to earlier lines of treatment due to their inherent cost benefits, however they will need to demonstrate comparable or improved efficacy to established modalities.
In addition to infrastructure and capacity constraints, CAR-Ts face a very different risk-benefit dynamic in autoimmune compared to oncology, highlighting the need for tolerable therapies with low adverse event risk. CAR-NK and Treg-based therapies are also being developed in certain autoimmune disorders and may demonstrate favorable safety profiles. Several novel non-cell therapies such as bispecific antibodies, nanobodies, and RNAi drugs, may also offer future alternative competitive solutions with variable value propositions.
Widespread adoption of cell therapies will not only require strong efficacy and safety data, but also adapted pricing and access strategies. At oncology-based price points, CAR-Ts are unlikely to achieve broad market access in autoimmune disorders, with eligible patient populations that are potentially orders of magnitude greater than the number of currently addressable cancer patients. Developers have made strides towards reducing cell therapy COGS while improving manufacturing efficiency, but payors will inevitably restrict access until more sustainable pricing is achieved.
Despite these headwinds, industry leaders and investors remain confident that cell therapies are poised to address significant unmet need in patients suffering from autoimmune disorders. However, the extent of this impact on the treatment landscape remains to be seen, as the industry rapidly approaches an inflection point.
- Video recording of this lecture in English language: https://youtu.be/kqbnxVAZs-0
- Video recording of this lecture in Arabic language: https://youtu.be/SINlygW1Mpc
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Muktapishti is a traditional Ayurvedic preparation made from Shoditha Mukta (Purified Pearl), is believed to help regulate thyroid function and reduce symptoms of hyperthyroidism due to its cooling and balancing properties. Clinical evidence on its efficacy remains limited, necessitating further research to validate its therapeutic benefits.
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Basavarajeeyam is a Sreshta Sangraha grantha (Compiled book ), written by Neelkanta kotturu Basavaraja Virachita. It contains 25 Prakaranas, First 24 Chapters related to Rogas& 25th to Rasadravyas.
share - Lions, tigers, AI and health misinformation, oh my!.pptxTina Purnat
• Pitfalls and pivots needed to use AI effectively in public health
• Evidence-based strategies to address health misinformation effectively
• Building trust with communities online and offline
• Equipping health professionals to address questions, concerns and health misinformation
• Assessing risk and mitigating harm from adverse health narratives in communities, health workforce and health system
Histololgy of Female Reproductive System.pptxAyeshaZaid1
Dive into an in-depth exploration of the histological structure of female reproductive system with this comprehensive lecture. Presented by Dr. Ayesha Irfan, Assistant Professor of Anatomy, this presentation covers the Gross anatomy and functional histology of the female reproductive organs. Ideal for students, educators, and anyone interested in medical science, this lecture provides clear explanations, detailed diagrams, and valuable insights into female reproductive system. Enhance your knowledge and understanding of this essential aspect of human biology.
Local Advanced Lung Cancer: Artificial Intelligence, Synergetics, Complex Sys...Oleg Kshivets
Overall life span (LS) was 1671.7±1721.6 days and cumulative 5YS reached 62.4%, 10 years – 50.4%, 20 years – 44.6%. 94 LCP lived more than 5 years without cancer (LS=2958.6±1723.6 days), 22 – more than 10 years (LS=5571±1841.8 days). 67 LCP died because of LC (LS=471.9±344 days). AT significantly improved 5YS (68% vs. 53.7%) (P=0.028 by log-rank test). Cox modeling displayed that 5YS of LCP significantly depended on: N0-N12, T3-4, blood cell circuit, cell ratio factors (ratio between cancer cells-CC and blood cells subpopulations), LC cell dynamics, recalcification time, heparin tolerance, prothrombin index, protein, AT, procedure type (P=0.000-0.031). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and N0-12 (rank=1), thrombocytes/CC (rank=2), segmented neutrophils/CC (3), eosinophils/CC (4), erythrocytes/CC (5), healthy cells/CC (6), lymphocytes/CC (7), stick neutrophils/CC (8), leucocytes/CC (9), monocytes/CC (10). Correct prediction of 5YS was 100% by neural networks computing (error=0.000; area under ROC curve=1.0).
TEST BANK For Community Health Nursing A Canadian Perspective, 5th Edition by...Donc Test
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It facilitates two types of operations: transformation and action.
A transformation is an operation such as filter(), map(), or union() on an RDD that yields another RDD.
An action is an operation such as count(), first(), take(n), or collect() that triggers a computation, returns a value back to the Master, or writes to a stable storage system.
More later on transformations and actions
Note: no type definition
http://www.scala-lang.org/
A lot of transformations are lazy: NOT run until an action is requested