From MapReduce to Apache Spark

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Doug Cutting's Spark Summit 2016 Keynote

Data & Analytics

1© Cloudera, Inc. All rights reserved.
From MapReduce to Apache Spark:
An Ecosystem Evolves
Doug Cutting (@cutting)
Chief Architect / Apache Hadoop Co-founder

2© Cloudera, Inc. All rights reserved.
The Merging Ecosystems

3© Cloudera, Inc. All rights reserved.
Genomics
(Broad Institute)
Physics
(CERN)
Healthcare Delivery
(CHOA)
The New Use Cases are Amazing

4© Cloudera, Inc. All rights reserved.
Bring Your Questions About Livy to the Cloudera Booth
Livy.io
• For submitting Spark jobs from any web/mobile app, with no
Spark client install — enabling new architectures and use
cases
• Provides multi-tenancy and fault tolerance to support
multiple users reliably
• Works in standalone mode, with YARN, or with Mesos
• No code changes needed
• Apache License. Initial contributors include employees of
Cloudera, Intel, and Microsoft + more wanted!
New Contribution: Livy, an Open Source REST Service for Spark (Alpha)

5© Cloudera, Inc. All rights reserved.
Thank you
@cutting
cloudera.com/spark

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Building cloud-enabled genomics workflows with Luigi and Docker

Building cloud-enabled genomics workflows with Luigi and Docker

Building cloud-enabled genomics workflows with Luigi and Docker

Data Science and CDSW

Data Science and CDSW

Data Science and CDSW

Similar to From MapReduce to Apache Spark (20)

Part 2: A Visual Dive into Machine Learning and Deep Learning

Part 2: A Visual Dive into Machine Learning and Deep Learning

Part 2: A Visual Dive into Machine Learning and Deep Learning  

A practical introduction to Oracle NoSQL Database - OOW2014

A practical introduction to Oracle NoSQL Database - OOW2014

A practical introduction to Oracle NoSQL Database - OOW2014

Cloudera Analytics and Machine Learning Platform - Optimized for Cloud

Cloudera Analytics and Machine Learning Platform - Optimized for Cloud

Cloudera Analytics and Machine Learning Platform - Optimized for Cloud

Developing Oracle Fusion Middleware Applications in the Cloud

Developing Oracle Fusion Middleware Applications in the Cloud

Developing Oracle Fusion Middleware Applications in the Cloud

Applications on Hadoop

Applications on Hadoop

Applications on Hadoop

Oracle Cloud Café hybrid Cloud 19 mai 2016

Oracle Cloud Café hybrid Cloud 19 mai 2016

Oracle Cloud Café hybrid Cloud 19 mai 2016

OpenStack & MySQL

OpenStack & MySQL

OpenStack & MySQL

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DevOps (CI/CD) 실습

DevOps (CI/CD) 실습

Emerging trends in data analytics

Emerging trends in data analytics

Emerging trends in data analytics

Cloudera Big Data Integration Speedpitch at TDWI Munich June 2017

Cloudera Big Data Integration Speedpitch at TDWI Munich June 2017

Cloudera Big Data Integration Speedpitch at TDWI Munich June 2017

Kafka for DBAs

DevOps as a Pathway to AWS | AWS Public Sector Summit 2016

DevOps as a Pathway to AWS | AWS Public Sector Summit 2016

DevOps as a Pathway to AWS | AWS Public Sector Summit 2016

Analyzing Hadoop Data Using Sparklyr

Analyzing Hadoop Data Using Sparklyr

Analyzing Hadoop Data Using Sparklyr 

Fortifying Multi-Cluster Hybrid Cloud Data Lakes using Apache Knox

Fortifying Multi-Cluster Hybrid Cloud Data Lakes using Apache Knox

Fortifying Multi-Cluster Hybrid Cloud Data Lakes using Apache Knox

Conf42-Python-Building Apache NiFi 2.0 Python Processors

Conf42-Python-Building Apache NiFi 2.0 Python Processors

Conf42-Python-Building Apache NiFi 2.0 Python Processors

Spark One Platform Webinar

Spark One Platform Webinar

Spark One Platform Webinar

Spark in the Enterprise - 2 Years Later by Alan Saldich

Spark in the Enterprise - 2 Years Later by Alan Saldich

Spark in the Enterprise - 2 Years Later by Alan Saldich

GSJUG: Mastering Data Streaming Pipelines 09May2023

GSJUG: Mastering Data Streaming Pipelines 09May2023

GSJUG: Mastering Data Streaming Pipelines 09May2023

Building cloud-enabled genomics workflows with Luigi and Docker

Building cloud-enabled genomics workflows with Luigi and Docker

Building cloud-enabled genomics workflows with Luigi and Docker

Data Science and CDSW

Data Science and CDSW

Data Science and CDSW

More from Jen Aman

Building applications that can read and analyze a wide variety of data may change the way we do science and make business decisions. However, building such applications is challenging: real world data is expressed in natural language, images, or other “dark” data formats which are fraught with imprecision and ambiguity and so are difficult for machines to understand. This talk will describe Snorkel, whose goal is to make routine Dark Data and other prediction tasks dramatically easier. At its core, Snorkel focuses on a key bottleneck in the development of machine learning systems: the lack of large training datasets. In Snorkel, a user implicitly creates large training sets by writing simple programs that label data, instead of performing manual feature engineering or tedious hand-labeling of individual data items. We’ll provide a set of tutorials that will allow folks to write Snorkel applications that use Spark. Snorkel is open source on github and available from Snorkel.Stanford.edu.

Snorkel: Dark Data and Machine Learning with Christopher Ré

Snorkel: Dark Data and Machine Learning with Christopher Ré

Snorkel: Dark Data and Machine Learning with Christopher Ré

The combination of Deep Learning with Apache Spark has the potential for tremendous impact in many sectors of the industry. This webinar, based on the experience gained in assisting customers with the Databricks Virtual Analytics Platform, will present some best practices for building deep learning pipelines with Spark. Rather than comparing deep learning systems or specific optimizations, this webinar will focus on issues that are common to deep learning frameworks when running on a Spark cluster, including: * optimizing cluster setup; * configuring the cluster; * ingesting data; and * monitoring long-running jobs. We will demonstrate the techniques we cover using Google’s popular TensorFlow library. More specifically, we will cover typical issues users encounter when integrating deep learning libraries with Spark clusters. Clusters can be configured to avoid task conflicts on GPUs and to allow using multiple GPUs per worker. Setting up pipelines for efficient data ingest improves job throughput, and monitoring facilitates both the work of configuration and the stability of deep learning jobs.

Deep Learning on Apache® Spark™: Workflows and Best Practices

Deep Learning on Apache® Spark™: Workflows and Best Practices

Deep Learning on Apache® Spark™: Workflows and Best Practices

Spark Summit East Keynote by Ion Stoica A long-standing grand challenge in computing is to enable machines to act autonomously and intelligently: to rapidly and repeatedly take appropriate actions based on information in the world around them. To address this challenge, at UC Berkeley we are starting a new five year effort that focuses on the development of data-intensive systems that provide Real-Time Intelligence with Secure Execution (RISE). Following in the footsteps of AMPLab, RISELab is an interdisciplinary effort bringing together researchers across AI, robotics, security, and data systems. In this talk I’ll present our research vision and then discuss some of the applications that will be enabled by RISE technologies.

RISELab:Enabling Intelligent Real-Time Decisions

RISELab:Enabling Intelligent Real-Time Decisions

RISELab:Enabling Intelligent Real-Time Decisions

Spatial Analysis On Histological Images Using Spark

Spatial Analysis On Histological Images Using Spark

Spatial Analysis On Histological Images Using Spark

Massive Simulations In Spark: Distributed Monte Carlo For Global Health Forec...

Massive Simulations In Spark: Distributed Monte Carlo For Global Health Forec...

Massive Simulations In Spark: Distributed Monte Carlo For Global Health Forec...

Yggdrasil: Faster Decision Trees Using Column Partitioning In Spark

Yggdrasil: Faster Decision Trees Using Column Partitioning In Spark

Yggdrasil: Faster Decision Trees Using Column Partitioning In Spark

Time-Evolving Graph Processing On Commodity Clusters

Time-Evolving Graph Processing On Commodity Clusters

Time-Evolving Graph Processing On Commodity Clusters

Deploying Accelerators At Datacenter Scale Using Spark

Deploying Accelerators At Datacenter Scale Using Spark

Deploying Accelerators At Datacenter Scale Using Spark

Re-Architecting Spark For Performance Understandability

Re-Architecting Spark For Performance Understandability

Re-Architecting Spark For Performance Understandability

Re-Architecting Spark For Performance Understandability

Re-Architecting Spark For Performance Understandability

Re-Architecting Spark For Performance Understandability

Low Latency Execution For Apache Spark

Low Latency Execution For Apache Spark

Low Latency Execution For Apache Spark

Efficient State Management With Spark 2.0 And Scale-Out Databases

Efficient State Management With Spark 2.0 And Scale-Out Databases

Efficient State Management With Spark 2.0 And Scale-Out Databases

GPU Computing With Apache Spark And Python

GPU Computing With Apache Spark And Python

GPU Computing With Apache Spark And Python

Building Custom Machine Learning Algorithms With Apache SystemML

Building Custom Machine Learning Algorithms With Apache SystemML

Building Custom Machine Learning Algorithms With Apache SystemML

Spark at Bloomberg: Dynamically Composable Analytics

Spark at Bloomberg: Dynamically Composable Analytics

Spark at Bloomberg: Dynamically Composable Analytics

EclairJS = Node.Js + Apache Spark

EclairJS = Node.Js + Apache Spark

EclairJS = Node.Js + Apache Spark

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Spark: Interactive To Production

Spark: Interactive To Production

Scalable Deep Learning Platform On Spark In Baidu

Scalable Deep Learning Platform On Spark In Baidu

Scalable Deep Learning Platform On Spark In Baidu

Scaling Machine Learning To Billions Of Parameters

Scaling Machine Learning To Billions Of Parameters

Scaling Machine Learning To Billions Of Parameters

Embrace Sparsity At Web Scale: Apache Spark MLlib Algorithms Optimization For...

Embrace Sparsity At Web Scale: Apache Spark MLlib Algorithms Optimization For...

Embrace Sparsity At Web Scale: Apache Spark MLlib Algorithms Optimization For...

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Snorkel: Dark Data and Machine Learning with Christopher Ré

Snorkel: Dark Data and Machine Learning with Christopher Ré

Snorkel: Dark Data and Machine Learning with Christopher Ré

Deep Learning on Apache® Spark™: Workflows and Best Practices

Deep Learning on Apache® Spark™: Workflows and Best Practices

Deep Learning on Apache® Spark™: Workflows and Best Practices

RISELab:Enabling Intelligent Real-Time Decisions

RISELab:Enabling Intelligent Real-Time Decisions

RISELab:Enabling Intelligent Real-Time Decisions

Spatial Analysis On Histological Images Using Spark

Spatial Analysis On Histological Images Using Spark

Spatial Analysis On Histological Images Using Spark

Massive Simulations In Spark: Distributed Monte Carlo For Global Health Forec...

Massive Simulations In Spark: Distributed Monte Carlo For Global Health Forec...

Massive Simulations In Spark: Distributed Monte Carlo For Global Health Forec...

Yggdrasil: Faster Decision Trees Using Column Partitioning In Spark

Yggdrasil: Faster Decision Trees Using Column Partitioning In Spark

Yggdrasil: Faster Decision Trees Using Column Partitioning In Spark

Time-Evolving Graph Processing On Commodity Clusters

Time-Evolving Graph Processing On Commodity Clusters

Time-Evolving Graph Processing On Commodity Clusters

Deploying Accelerators At Datacenter Scale Using Spark

Deploying Accelerators At Datacenter Scale Using Spark

Deploying Accelerators At Datacenter Scale Using Spark

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Re-Architecting Spark For Performance Understandability

Re-Architecting Spark For Performance Understandability

Re-Architecting Spark For Performance Understandability

Re-Architecting Spark For Performance Understandability

Re-Architecting Spark For Performance Understandability

Low Latency Execution For Apache Spark

Low Latency Execution For Apache Spark

Low Latency Execution For Apache Spark

Efficient State Management With Spark 2.0 And Scale-Out Databases

Efficient State Management With Spark 2.0 And Scale-Out Databases

Efficient State Management With Spark 2.0 And Scale-Out Databases

GPU Computing With Apache Spark And Python

GPU Computing With Apache Spark And Python

GPU Computing With Apache Spark And Python

Building Custom Machine Learning Algorithms With Apache SystemML

Building Custom Machine Learning Algorithms With Apache SystemML

Building Custom Machine Learning Algorithms With Apache SystemML

Spark at Bloomberg: Dynamically Composable Analytics

Spark at Bloomberg: Dynamically Composable Analytics

Spark at Bloomberg: Dynamically Composable Analytics

EclairJS = Node.Js + Apache Spark

EclairJS = Node.Js + Apache Spark

EclairJS = Node.Js + Apache Spark

Spark: Interactive To Production

Spark: Interactive To Production

Spark: Interactive To Production

Scalable Deep Learning Platform On Spark In Baidu

Scalable Deep Learning Platform On Spark In Baidu

Scalable Deep Learning Platform On Spark In Baidu

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Scaling Machine Learning To Billions Of Parameters

Scaling Machine Learning To Billions Of Parameters

Embrace Sparsity At Web Scale: Apache Spark MLlib Algorithms Optimization For...

Embrace Sparsity At Web Scale: Apache Spark MLlib Algorithms Optimization For...

Embrace Sparsity At Web Scale: Apache Spark MLlib Algorithms Optimization For...

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如何办理英国诺森比亚大学毕业证（NU毕业证书）成绩单原件一模一样

如何办理英国诺森比亚大学毕业证（NU毕业证书）成绩单原件一模一样

如何办理英国诺森比亚大学毕业证（NU毕业证书）成绩单原件一模一样

Yilin Xia (yilinx2@illinois.edu), Shawn Bowers (bowers@gonzaga.edu), Lan Li (lanl2@illinois.edu), and Bertram Ludäscher (ludaesch@illinois.edu) Presented at IDCC-2024 in Edinburg. ABSTRACT. We propose a new approach for modeling and reconciling conflicting data cleaning actions. Such conflicts arise naturally in collaborative data curation settings where multiple experts work independently and then aim to put their efforts together to improve and accelerate data cleaning. The key idea of our approach is to model conflicting updates as a formal argumentation framework (AF). Such argumentation frameworks can be automatically analyzed and solved by translating them to a logic program PAF whose declarative semantics yield a transparent solution with many desirable properties, e.g., uncontroversial updates are accepted, unjustified ones are rejected, and the remaining ambiguities are exposed and presented to users for further analysis. After motivating the problem, we introduce our approach and illustrate it with a detailed running example introducing both well-founded and stable semantics to help understand the AF solutions. We have begun to develop open source tools and Jupyter notebooks that demonstrate the practicality of our approach. In future work we plan to develop a toolkit for conflict resolution that can be used in conjunction with OpenRefine, a popular interactive data cleaning tool.

Reconciling Conflicting Data Curation Actions: Transparency Through Argument...

Reconciling Conflicting Data Curation Actions: Transparency Through Argument...

Reconciling Conflicting Data Curation Actions: Transparency Through Argument...

Bertram Ludäscher

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Jual obat aborsi Bandung ( 085657271886 ) Cytote pil telat bulan penggugur ka...

Jual obat aborsi Bandung ( 085657271886 ) Cytote pil telat bulan penggugur ka...

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Building Real-Time Pipelines With FLaNK Timothy Spann, Principal Developer Advocate, Streaming - Cloudera Future of Data meetup, startup grind, AI Camp The combination of Apache Flink, Apache NiFi, and Apache Kafka for building real-time data processing pipelines is extremely powerful, as demonstrated by this case study using the FLaNK-MTA project. The project leverages these technologies to process and analyze real-time data from the New York City Metropolitan Transportation Authority (MTA). FLaNK-MTA demonstrates how to efficiently collect, transform, and analyze high-volume data streams, enabling timely insights and decision-making. Apache NiFi Apache Kafka Apache Flink Apache Iceberg LLM Generative AI Slack Postgresql

DATA SUMMIT 24 Building Real-Time Pipelines With FLaNK

DATA SUMMIT 24 Building Real-Time Pipelines With FLaNK

DATA SUMMIT 24 Building Real-Time Pipelines With FLaNK

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RESEARCH-FINAL-DEFENSE-PPT-TEMPLATE.pptx

RESEARCH-FINAL-DEFENSE-PPT-TEMPLATE.pptx

RESEARCH-FINAL-DEFENSE-PPT-TEMPLATE.pptx

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怎样办理圣地亚哥州立大学毕业证（SDSU毕业证书）成绩单学校原版复制

怎样办理圣地亚哥州立大学毕业证（SDSU毕业证书）成绩单学校原版复制

怎样办理圣地亚哥州立大学毕业证（SDSU毕业证书）成绩单学校原版复制

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Statistics notes ,it includes mean to index numbers

Statistics notes ,it includes mean to index numbers

Statistics notes ,it includes mean to index numbers

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Reconciling Conflicting Data Curation Actions: Transparency Through Argument...

Reconciling Conflicting Data Curation Actions: Transparency Through Argument...

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DATA SUMMIT 24 Building Real-Time Pipelines With FLaNK

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RESEARCH-FINAL-DEFENSE-PPT-TEMPLATE.pptx

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怎样办理圣地亚哥州立大学毕业证（SDSU毕业证书）成绩单学校原版复制

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Statistics notes ,it includes mean to index numbers

Statistics notes ,it includes mean to index numbers

Statistics notes ,it includes mean to index numbers

From MapReduce to Apache Spark

1. 1© Cloudera, Inc. All rights reserved. From MapReduce to Apache Spark: An Ecosystem Evolves Doug Cutting (@cutting) Chief Architect / Apache Hadoop Co-founder

2. 2© Cloudera, Inc. All rights reserved. The Merging Ecosystems

3. 3© Cloudera, Inc. All rights reserved. Genomics (Broad Institute) Physics (CERN) Healthcare Delivery (CHOA) The New Use Cases are Amazing

4. 4© Cloudera, Inc. All rights reserved. Bring Your Questions About Livy to the Cloudera Booth Livy.io • For submitting Spark jobs from any web/mobile app, with no Spark client install — enabling new architectures and use cases • Provides multi-tenancy and fault tolerance to support multiple users reliably • Works in standalone mode, with YARN, or with Mesos • No code changes needed • Apache License. Initial contributors include employees of Cloudera, Intel, and Microsoft + more wanted! New Contribution: Livy, an Open Source REST Service for Spark (Alpha)

5. 5© Cloudera, Inc. All rights reserved. Thank you @cutting cloudera.com/spark