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Machine Learning with H2O, Spark, and Python at Strata SJ 2015-by Cliff Click and Michal Malohlava - Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai - To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
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At the end of day, the only thing that data scientists want is tabular data for their analysis. They do not want to spend hours or days preparing data. How does a data engineer handle the massive amount of data that is being streamed at them from IoT devices and apps, and at the same time add structure to it so that data scientists can focus on finding insights and not preparing data? By the way, you need to do this within minutes (sometimes seconds). Oh… and there are a lot of other data sources that you need to ingest, and the current providers of data are changing their structure. GoPro has massive amounts of heterogeneous data being streamed from their consumer devices and applications, and they have developed the concept of “dynamic DDL” to structure their streamed data on the fly using Spark Streaming, Kafka, HBase, Hive and S3. The idea is simple: Add structure (schema) to the data as soon as possible; allow the providers of the data to dictate the structure; and automatically create event-based and state-based tables (DDL) for all data sources to allow data scientists to access the data via their lingua franca, SQL, within minutes.
Dynamic DDL: Adding Structure to Streaming Data on the Fly with David Winters...
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http://flink-forward.org/kb_sessions/multi-tenant-flink-as-a-service-on-yarn/ Since June 2016, Flink-as-a-service has been available to researchers and companies in Sweden from the Swedish ICT SICS Data Center at www.hops.site using the HopsWorks platform. Flink applications can be either deployed as jobs (batch or streaming) or written and run directly from Apache Zeppelin on YARN. Flink applications are run within a project on a YARN cluster with the novel property that Flink applications are metered and charged to projects. Projects are also securely isolated from each other and include support for project-specific Kafka topics that are protected from access by users that are not members of the project. Hopsworks is entirely UI-driven, is open-source, and Flink applications that include Kafka topics can be created in a few mouse clicks. In this talk we will discuss the challenges in building a metered version of Flink-as-a-Service for YARN, experiences with Flink-on-YARN, and some of the possibilities that Hopsworks opens up for building secure, multi-ten
Jim Dowling - Multi-tenant Flink-as-a-Service on YARN
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Learn how to deploy a managed Presto environment to interactively query log data on AWS Organizations often need to quickly analyze large amounts of data, such as logs, generated from a wide variety of sources and formats. However, traditional approaches require a lot of time and effort designing complex data transformation and loading processes; and configuring data warehouses. Using AWS, you can start querying your datasets within minutes In this webinar you will learn how you can deploy a managed Presto environment in minutes to interactively query log data using plain ANSI SQL. Presto is a popular open source SQL engine for running interactive analytic queries against data sources of all sizes. We will talk about common use cases and best practices for running Presto on Amazon EMR. Learning Objectives: • Learn how to deploy a managed Presto environment running on Amazon EMR • Understand best practices for running Presto on Amazon EMR, including use of Amazon EC2 Spot instances • Learn how other customers are using Presto to analyze large data sets
Running Fast, Interactive Queries on Petabyte Datasets using Presto - AWS Jul...
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Data can be viewed as the exhaust of online activity. With the rise of cloud-based data platforms, barriers to data storage and transfer have crumbled. The demand for creative applications and learning from those datasets has accelerated. Rapid acceleration can quickly accrue disorder, and disorderly data design can turn the deepest data lake into an impenetrable swamp. In this talk, I will discuss the evolution of the data science workflow at Expedia with a special emphasis on Learning to Rank problems. From the heroic early days of ad-hoc Spark exploration to our first production sort model on the cloud, we will explore the process of industrializing the workflow. Layered over our story, I will share some best practices and suggestions on how to keep your data productive, or even pull your organization out of the data swamp.
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Machine Learning with H2O, Spark, and Python at Strata 2015
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Presto in the cloud
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At the end of day, the only thing that data scientists want is tabular data for their analysis. They do not want to spend hours or days preparing data. How does a data engineer handle the massive amount of data that is being streamed at them from IoT devices and apps, and at the same time add structure to it so that data scientists can focus on finding insights and not preparing data? By the way, you need to do this within minutes (sometimes seconds). Oh… and there are a lot of other data sources that you need to ingest, and the current providers of data are changing their structure. GoPro has massive amounts of heterogeneous data being streamed from their consumer devices and applications, and they have developed the concept of “dynamic DDL” to structure their streamed data on the fly using Spark Streaming, Kafka, HBase, Hive and S3. The idea is simple: Add structure (schema) to the data as soon as possible; allow the providers of the data to dictate the structure; and automatically create event-based and state-based tables (DDL) for all data sources to allow data scientists to access the data via their lingua franca, SQL, within minutes.
Dynamic DDL: Adding Structure to Streaming Data on the Fly with David Winters...
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http://flink-forward.org/kb_sessions/multi-tenant-flink-as-a-service-on-yarn/ Since June 2016, Flink-as-a-service has been available to researchers and companies in Sweden from the Swedish ICT SICS Data Center at www.hops.site using the HopsWorks platform. Flink applications can be either deployed as jobs (batch or streaming) or written and run directly from Apache Zeppelin on YARN. Flink applications are run within a project on a YARN cluster with the novel property that Flink applications are metered and charged to projects. Projects are also securely isolated from each other and include support for project-specific Kafka topics that are protected from access by users that are not members of the project. Hopsworks is entirely UI-driven, is open-source, and Flink applications that include Kafka topics can be created in a few mouse clicks. In this talk we will discuss the challenges in building a metered version of Flink-as-a-Service for YARN, experiences with Flink-on-YARN, and some of the possibilities that Hopsworks opens up for building secure, multi-ten
Jim Dowling - Multi-tenant Flink-as-a-Service on YARN
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Learn how to deploy a managed Presto environment to interactively query log data on AWS Organizations often need to quickly analyze large amounts of data, such as logs, generated from a wide variety of sources and formats. However, traditional approaches require a lot of time and effort designing complex data transformation and loading processes; and configuring data warehouses. Using AWS, you can start querying your datasets within minutes In this webinar you will learn how you can deploy a managed Presto environment in minutes to interactively query log data using plain ANSI SQL. Presto is a popular open source SQL engine for running interactive analytic queries against data sources of all sizes. We will talk about common use cases and best practices for running Presto on Amazon EMR. Learning Objectives: • Learn how to deploy a managed Presto environment running on Amazon EMR • Understand best practices for running Presto on Amazon EMR, including use of Amazon EC2 Spot instances • Learn how other customers are using Presto to analyze large data sets
Running Fast, Interactive Queries on Petabyte Datasets using Presto - AWS Jul...
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Data can be viewed as the exhaust of online activity. With the rise of cloud-based data platforms, barriers to data storage and transfer have crumbled. The demand for creative applications and learning from those datasets has accelerated. Rapid acceleration can quickly accrue disorder, and disorderly data design can turn the deepest data lake into an impenetrable swamp. In this talk, I will discuss the evolution of the data science workflow at Expedia with a special emphasis on Learning to Rank problems. From the heroic early days of ad-hoc Spark exploration to our first production sort model on the cloud, we will explore the process of industrializing the workflow. Layered over our story, I will share some best practices and suggestions on how to keep your data productive, or even pull your organization out of the data swamp.
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Building a Real-Time Data Pipeline: Apache Kafka at LinkedIn
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SSR: Structured Streaming for R and Machine Learning
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Organizations from small startups to large enterprises are rapidly adopting Apache Spark on Amazon EMR in Amazon Web Services (AWS) to run streaming analytics, data science, machine learning, and batch processing workloads. These customers can quickly create big data architectures within minutes, and decouple compute and storage with Amazon S3 as a highly scalable, durable, and secure data lake, lower costs using Amazon EC2 Spot Instances and Auto Scaling, and utilize a wide range of encryption and access control features. In this session, we discuss how customers are using Spark on AWS and common architectures for easily running performant Spark clusters at scale and low cost with Amazon EMR.
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Since mid-2016, Spark-as-a-Service has been available to researchers in Sweden from the Rise SICS ICE Data Center at www.hops.site. In this session, Dowling will discuss the challenges in building multi-tenant Spark structured streaming applications on YARN that are metered and easy-to-debug. The platform, called Hopsworks, is in an entirely UI-driven environment built with only open-source software. Learn how they use the ELK stack (Elasticsearch, Logstash and Kibana) for logging and debugging running Spark streaming applications; how they use Grafana and InfluxDB for monitoring Spark streaming applications; and, finally, how Apache Zeppelin can provide interactive visualizations and charts to end-users. This session will also show how Spark applications are run within a ‘project’ on a YARN cluster with the novel property that Spark applications are metered and charged to projects. Projects are securely isolated from each other and include support for project-specific Kafka topics. That is, Kafka topics are protected from access by users that are not members of the project. In addition, hear about the experiences of their users (over 150 users as of early 2017): how they manage their Kafka topics and quotas, patterns for how users share topics between projects, and the novel solutions for helping researchers debug and optimize Spark applications.hear about the experiences of their users (over 150 users as of early 2017): how they manage their Kafka topics and quotas, patterns for how users share topics between projects, and the novel solutions for helping researchers debug and optimize Spark applications.afka topics are protected from access by users that are not members of the project. We will also discuss the experiences of our users (over 150 users as of early 2017): how they manage their Kafka topics and quotas, patterns for how users share topics between projects, and our novel solutions for helping researchers debug and optimize Spark applications.
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In this session, we discuss how Spark and Presto complement the Netflix big data platform stack that started with Hadoop, and the use cases that Spark and Presto address. Also, we discuss how we run Spark and Presto on top of the Amazon EMR infrastructure; specifically, how we use Amazon S3 as our data warehouse and how we leverage Amazon EMR as a generic framework for data-processing cluster management.
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We are witnessing a proliferation of big data, which has lead to a zoo of data processing systems. Each system providing a different set of features. For example, Spark provides scalability to analytic tasks, but Java 8 Streams provides low-latency. Furthermore, complex applications, such as ETL and ML, are now requiring a mixture of platforms to perform tasks efficiently. In such complex data analytics pipelines, the use of multiple data processing system is not only for performance reasons, but also because of data diversity. Datasets often natively reside on different data formats and storage engines. Unfortunately, developers are left alone in the challenging tasks of: (1) choosing the right platform for their applications; and (2) performing tedious and costly data migration and integration tasks to obtain the results. In this talk, we will present Rheem, an open source scalable cross-platform system that frees developers from these burdens. Rheem provides an abstraction layer on top of Spark (and other processing platforms) with the aim of enabling cross-platform optimization and interoperability. It automatically selects the best data processing platforms for a given task and also handles the cross-platform execution. In particular, we will discuss how Rheem allows Spark to work in tandem with other platforms in order to achieve higher performance. We will also show how easy a developer can write complex applications on top of Rheem to seamlessly use multiple different data processing platforms according to their tasks at hand. Using Rheem developers do not have to worry about the integration or data migration between Spark and other platforms.
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The slides for HadoopCon 2014 in Taiwan.
Speed up Interactive Analytic Queries over Existing Big Data on Hadoop with P...
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hkbhadraa
Apache Spark has been a great technology for processing and analyzing Big Data. However, it is not accessible to business users, who don’t have technical or programming skills. In this talk, I’ll talk about recent efforts in the space of “Conversational analytics”. This paradigm allows any user to ask text and voice questions, in natural language, of their data to a bot and receive back a natural language and visual result. A key technology is natural language to SQL translation, where we translate natural language queries from a user into Spark SQL queries that can go against a Databricks system, and that can be easily trained on different schemas and databases. This NLP technology needs to be further combined with dialog management, natural-language generation/narration, data understanding and modeling, augmented analytics and automated visualization generation in order to achieve the goal of “Conversational Analytics”. Using such a technology, a user can ask, in plain English, “How many cases of Covid were there in the last 2 months in states that had no social distancing mandates by type of transmission”, and then dig deeper into the results in a conversational manner to uncover hidden insights from Covid datasets in a Spark instance. We believe that having access to such data and insights at their fingertips can help users make appropriate decisions quickly, improve data literacy and even overcome the scourge of fake news for the general public.
Natural Language Query and Conversational Interface to Apache Spark
Natural Language Query and Conversational Interface to Apache Spark
Databricks
Dask Tutorial at PyConDE / PyData Karlsruhe 2018. These were the introductory slides that mainly contain the link to Matthew Rocklin's Dask workshop at PyData NYC 2018 whereon this workshop was based.
Scalable Scientific Computing with Dask
Scalable Scientific Computing with Dask
Uwe Korn
Imagine we have Ada, our data science intern. Let's run through a very simple wordcount spark job, and find a handful of potential failure points. Dozens of failures can and should happen when running spark jobs on commodity hardware. Given the basic foundation for infrastructure-level expectations, this talk gives Ada tools to ensure her job isn’t caught dead. Once the simple example job runs reliably, with the potential to scale, our data scientist can apply the same toolset to focus on some more interesting algorithms. Turn SNAFUs into successes by anticipating and handling Infra failures gracefully. Note: this talk is a spark-focused extension of Part I, "Just Enough DevOps For Data Scientists" from Scale by The Bay 2018 https://www.youtube.com/watch?v=RqpnBl5NgW0&t=19s
Just enough DevOps for Data Scientists (Part II)
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Databricks
Slides used for the talk "Developing Apache Spark Jobs in .NET using Mobius" at dotnetfringe 20016 (http://lanyrd.com/2016/netfringe/sfcxpx). Apache Spark is an open source data processing framework built for big data processing and analytics. Ease of programming and high performance relative to the traditional big data tools and platforms and a unified API to solve a diverse set of complex data problems drove the rapid adoption of Spark in the industry. Apache Spark APIs in Scala, Java, Python and R cater to a wide range of big data professionals and a variety of functional roles. Mobius is an open source project that aims to bring Spark's rich set of capabilities to the .NET community. Mobius project added C# as another first-class programming language for Apache Spark and currently supports RDD, DataFrame and Streaming API. With Mobius, developers can build Spark jobs in C# and reuse their existing .NET libraries with Apache Spark. Mobius is open-sourced at http://github.com/Microsoft/Mobius. This project has received great support from the .NET community and positive feedback from the Spark enthusiasts
Developing apache spark jobs in .net using mobius
Developing apache spark jobs in .net using mobius
shareddatamsft
Data Pipeline with Kafka, This slide include Kafka Introduction, Topic / Partitions, Produce / Consumer, Quick Start, Offset Monitoring, Example Code, Camus
Data Pipeline with Kafka
Data Pipeline with Kafka
Peerapat Asoktummarungsri
From Single-Tenant Hadoop to 3000 Tenants in Apache Spark: Experiences from Watson Analytics
Spark Summit EU talk by Ruben Pulido Behar Veliqi
Spark Summit EU talk by Ruben Pulido Behar Veliqi
Spark Summit
Organizations need to perform increasingly complex analysis on data — streaming analytics, ad-hoc querying, and predictive analytics — in order to get better customer insights and actionable business intelligence. Apache Spark has recently emerged as the framework of choice to address many of these challenges. In this webinar, we show you how to use Apache Spark on AWS to implement and scale common big data use cases such as real-time data processing, interactive data science, predictive analytics, and more. We will talk about common architectures and best practices to quickly create Spark clusters using Amazon Elastic MapReduce (EMR), and ways to use Spark with Amazon Redshift, Amazon DynamoDB, Amazon Kinesis, and other big data applications in the Apache Hadoop ecosystem. Learning Objectives: Learn why Spark is great for ad-hoc interactive analysis and real-time stream processing How to deploy and tune scalable clusters running Spark on Amazon EMR How to use EMR File System (EMRFS) with Spark to query data directly in Amazon S3 Common architectures to leverage Spark with DynamoDB, Redshift, Kinesis, and more
AWS April 2016 Webinar Series - Best Practices for Apache Spark on AWS
AWS April 2016 Webinar Series - Best Practices for Apache Spark on AWS
Amazon Web Services
Supercharging ETL with Spark Slides from first Spark Meetup London
ETL with SPARK - First Spark London meetup
ETL with SPARK - First Spark London meetup
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Hadoop World 2009 talk on rapid prototyping of data intensive web applications with Hadoop, Hive, Amazon EC2, Python, and Ruby on Rails. Describes the process of building the open source trend tracking site trendingtopics.org
Prototyping Data Intensive Apps: TrendingTopics.org
Prototyping Data Intensive Apps: TrendingTopics.org
Peter Skomoroch
In this session, we introduce AWS Glue, provide an overview of its components, and share how you can use AWS Glue to automate discovering your data, cataloging it, and preparing it for analysis.
Introduction to AWS Glue
Introduction to AWS Glue
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Ingesting data from the internet into Azure Data Lake and Visualizing in Power BI
Big Data Analytics from Azure Cloud to Power BI Mobile
Big Data Analytics from Azure Cloud to Power BI Mobile
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Building a Real-Time Data Pipeline: Apache Kafka at LinkedIn
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Amy W. Tang
Stepping beyond ETL in batches, large enterprises are looking at ways to generate more up-to-date insights. As we step into the age of Continuous Application, this session will explore the ever more popular Structure Streaming API in Apache Spark, its application to R, and building examples of machine learning use cases. Starting with an introduction to the high-level concepts, the session will dive into the core of the execution plan internals and examine how SparkR extends the existing system to add the streaming capability. Learn how to build various data science applications on data streams integrating with R packages to leverage the rich R ecosystem of 10k+ packages. Session hashtag: #SFdev2
SSR: Structured Streaming for R and Machine Learning
SSR: Structured Streaming for R and Machine Learning
felixcss
Organizations from small startups to large enterprises are rapidly adopting Apache Spark on Amazon EMR in Amazon Web Services (AWS) to run streaming analytics, data science, machine learning, and batch processing workloads. These customers can quickly create big data architectures within minutes, and decouple compute and storage with Amazon S3 as a highly scalable, durable, and secure data lake, lower costs using Amazon EC2 Spot Instances and Auto Scaling, and utilize a wide range of encryption and access control features. In this session, we discuss how customers are using Spark on AWS and common architectures for easily running performant Spark clusters at scale and low cost with Amazon EMR.
Analytics at Scale with Apache Spark on AWS with Jonathan Fritz
Analytics at Scale with Apache Spark on AWS with Jonathan Fritz
Databricks
Since mid-2016, Spark-as-a-Service has been available to researchers in Sweden from the Rise SICS ICE Data Center at www.hops.site. In this session, Dowling will discuss the challenges in building multi-tenant Spark structured streaming applications on YARN that are metered and easy-to-debug. The platform, called Hopsworks, is in an entirely UI-driven environment built with only open-source software. Learn how they use the ELK stack (Elasticsearch, Logstash and Kibana) for logging and debugging running Spark streaming applications; how they use Grafana and InfluxDB for monitoring Spark streaming applications; and, finally, how Apache Zeppelin can provide interactive visualizations and charts to end-users. This session will also show how Spark applications are run within a ‘project’ on a YARN cluster with the novel property that Spark applications are metered and charged to projects. Projects are securely isolated from each other and include support for project-specific Kafka topics. That is, Kafka topics are protected from access by users that are not members of the project. In addition, hear about the experiences of their users (over 150 users as of early 2017): how they manage their Kafka topics and quotas, patterns for how users share topics between projects, and the novel solutions for helping researchers debug and optimize Spark applications.hear about the experiences of their users (over 150 users as of early 2017): how they manage their Kafka topics and quotas, patterns for how users share topics between projects, and the novel solutions for helping researchers debug and optimize Spark applications.afka topics are protected from access by users that are not members of the project. We will also discuss the experiences of our users (over 150 users as of early 2017): how they manage their Kafka topics and quotas, patterns for how users share topics between projects, and our novel solutions for helping researchers debug and optimize Spark applications.
Structured-Streaming-as-a-Service with Kafka, YARN, and Tooling with Jim Dowling
Structured-Streaming-as-a-Service with Kafka, YARN, and Tooling with Jim Dowling
Databricks
In this session, we discuss how Spark and Presto complement the Netflix big data platform stack that started with Hadoop, and the use cases that Spark and Presto address. Also, we discuss how we run Spark and Presto on top of the Amazon EMR infrastructure; specifically, how we use Amazon S3 as our data warehouse and how we leverage Amazon EMR as a generic framework for data-processing cluster management.
(BDT303) Running Spark and Presto on the Netflix Big Data Platform
(BDT303) Running Spark and Presto on the Netflix Big Data Platform
Amazon Web Services
ApacheCon Big Data Europe 2016 talk. Hopsworks with secure Spark/Flink/Kafka as a service.
Hopsworks - Self-Service Spark/Flink/Kafka/Hadoop
Hopsworks - Self-Service Spark/Flink/Kafka/Hadoop
Jim Dowling
Hopsworks Multi-tenant hadoop-as-a-service
Strata Hadoop Hopsworks
Strata Hadoop Hopsworks
Jim Dowling
Spark Summit 2016 talk by Kaarthik Sivashanmugam (Microsoft)
Mobius: C# Language Binding For Spark
Mobius: C# Language Binding For Spark
Spark Summit
We are witnessing a proliferation of big data, which has lead to a zoo of data processing systems. Each system providing a different set of features. For example, Spark provides scalability to analytic tasks, but Java 8 Streams provides low-latency. Furthermore, complex applications, such as ETL and ML, are now requiring a mixture of platforms to perform tasks efficiently. In such complex data analytics pipelines, the use of multiple data processing system is not only for performance reasons, but also because of data diversity. Datasets often natively reside on different data formats and storage engines. Unfortunately, developers are left alone in the challenging tasks of: (1) choosing the right platform for their applications; and (2) performing tedious and costly data migration and integration tasks to obtain the results. In this talk, we will present Rheem, an open source scalable cross-platform system that frees developers from these burdens. Rheem provides an abstraction layer on top of Spark (and other processing platforms) with the aim of enabling cross-platform optimization and interoperability. It automatically selects the best data processing platforms for a given task and also handles the cross-platform execution. In particular, we will discuss how Rheem allows Spark to work in tandem with other platforms in order to achieve higher performance. We will also show how easy a developer can write complex applications on top of Rheem to seamlessly use multiple different data processing platforms according to their tasks at hand. Using Rheem developers do not have to worry about the integration or data migration between Spark and other platforms.
Interoperating a Zoo of Data Processing Platforms Using with Rheem Sebastian ...
Interoperating a Zoo of Data Processing Platforms Using with Rheem Sebastian ...
Databricks
The slides for HadoopCon 2014 in Taiwan.
Speed up Interactive Analytic Queries over Existing Big Data on Hadoop with P...
Speed up Interactive Analytic Queries over Existing Big Data on Hadoop with P...
viirya
Big Data Batch Layer implementation with Amazon Web Services Cloud Platform, Apache Spark, Hadoop, Apache Cassandra, AngularJS, Java Restful Web Services. This can be extended to implement real world use cases.
Big data Lambda Architecture - Batch Layer Hands On
Big data Lambda Architecture - Batch Layer Hands On
hkbhadraa
Apache Spark has been a great technology for processing and analyzing Big Data. However, it is not accessible to business users, who don’t have technical or programming skills. In this talk, I’ll talk about recent efforts in the space of “Conversational analytics”. This paradigm allows any user to ask text and voice questions, in natural language, of their data to a bot and receive back a natural language and visual result. A key technology is natural language to SQL translation, where we translate natural language queries from a user into Spark SQL queries that can go against a Databricks system, and that can be easily trained on different schemas and databases. This NLP technology needs to be further combined with dialog management, natural-language generation/narration, data understanding and modeling, augmented analytics and automated visualization generation in order to achieve the goal of “Conversational Analytics”. Using such a technology, a user can ask, in plain English, “How many cases of Covid were there in the last 2 months in states that had no social distancing mandates by type of transmission”, and then dig deeper into the results in a conversational manner to uncover hidden insights from Covid datasets in a Spark instance. We believe that having access to such data and insights at their fingertips can help users make appropriate decisions quickly, improve data literacy and even overcome the scourge of fake news for the general public.
Natural Language Query and Conversational Interface to Apache Spark
Natural Language Query and Conversational Interface to Apache Spark
Databricks
Dask Tutorial at PyConDE / PyData Karlsruhe 2018. These were the introductory slides that mainly contain the link to Matthew Rocklin's Dask workshop at PyData NYC 2018 whereon this workshop was based.
Scalable Scientific Computing with Dask
Scalable Scientific Computing with Dask
Uwe Korn
Imagine we have Ada, our data science intern. Let's run through a very simple wordcount spark job, and find a handful of potential failure points. Dozens of failures can and should happen when running spark jobs on commodity hardware. Given the basic foundation for infrastructure-level expectations, this talk gives Ada tools to ensure her job isn’t caught dead. Once the simple example job runs reliably, with the potential to scale, our data scientist can apply the same toolset to focus on some more interesting algorithms. Turn SNAFUs into successes by anticipating and handling Infra failures gracefully. Note: this talk is a spark-focused extension of Part I, "Just Enough DevOps For Data Scientists" from Scale by The Bay 2018 https://www.youtube.com/watch?v=RqpnBl5NgW0&t=19s
Just enough DevOps for Data Scientists (Part II)
Just enough DevOps for Data Scientists (Part II)
Databricks
Slides used for the talk "Developing Apache Spark Jobs in .NET using Mobius" at dotnetfringe 20016 (http://lanyrd.com/2016/netfringe/sfcxpx). Apache Spark is an open source data processing framework built for big data processing and analytics. Ease of programming and high performance relative to the traditional big data tools and platforms and a unified API to solve a diverse set of complex data problems drove the rapid adoption of Spark in the industry. Apache Spark APIs in Scala, Java, Python and R cater to a wide range of big data professionals and a variety of functional roles. Mobius is an open source project that aims to bring Spark's rich set of capabilities to the .NET community. Mobius project added C# as another first-class programming language for Apache Spark and currently supports RDD, DataFrame and Streaming API. With Mobius, developers can build Spark jobs in C# and reuse their existing .NET libraries with Apache Spark. Mobius is open-sourced at http://github.com/Microsoft/Mobius. This project has received great support from the .NET community and positive feedback from the Spark enthusiasts
Developing apache spark jobs in .net using mobius
Developing apache spark jobs in .net using mobius
shareddatamsft
Data Pipeline with Kafka, This slide include Kafka Introduction, Topic / Partitions, Produce / Consumer, Quick Start, Offset Monitoring, Example Code, Camus
Data Pipeline with Kafka
Data Pipeline with Kafka
Peerapat Asoktummarungsri
From Single-Tenant Hadoop to 3000 Tenants in Apache Spark: Experiences from Watson Analytics
Spark Summit EU talk by Ruben Pulido Behar Veliqi
Spark Summit EU talk by Ruben Pulido Behar Veliqi
Spark Summit
Organizations need to perform increasingly complex analysis on data — streaming analytics, ad-hoc querying, and predictive analytics — in order to get better customer insights and actionable business intelligence. Apache Spark has recently emerged as the framework of choice to address many of these challenges. In this webinar, we show you how to use Apache Spark on AWS to implement and scale common big data use cases such as real-time data processing, interactive data science, predictive analytics, and more. We will talk about common architectures and best practices to quickly create Spark clusters using Amazon Elastic MapReduce (EMR), and ways to use Spark with Amazon Redshift, Amazon DynamoDB, Amazon Kinesis, and other big data applications in the Apache Hadoop ecosystem. Learning Objectives: Learn why Spark is great for ad-hoc interactive analysis and real-time stream processing How to deploy and tune scalable clusters running Spark on Amazon EMR How to use EMR File System (EMRFS) with Spark to query data directly in Amazon S3 Common architectures to leverage Spark with DynamoDB, Redshift, Kinesis, and more
AWS April 2016 Webinar Series - Best Practices for Apache Spark on AWS
AWS April 2016 Webinar Series - Best Practices for Apache Spark on AWS
Amazon Web Services
Supercharging ETL with Spark Slides from first Spark Meetup London
ETL with SPARK - First Spark London meetup
ETL with SPARK - First Spark London meetup
Rafal Kwasny
Hadoop World 2009 talk on rapid prototyping of data intensive web applications with Hadoop, Hive, Amazon EC2, Python, and Ruby on Rails. Describes the process of building the open source trend tracking site trendingtopics.org
Prototyping Data Intensive Apps: TrendingTopics.org
Prototyping Data Intensive Apps: TrendingTopics.org
Peter Skomoroch
La actualidad más candente
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Building a Real-Time Data Pipeline: Apache Kafka at LinkedIn
Building a Real-Time Data Pipeline: Apache Kafka at LinkedIn
SSR: Structured Streaming for R and Machine Learning
SSR: Structured Streaming for R and Machine Learning
Analytics at Scale with Apache Spark on AWS with Jonathan Fritz
Analytics at Scale with Apache Spark on AWS with Jonathan Fritz
Structured-Streaming-as-a-Service with Kafka, YARN, and Tooling with Jim Dowling
Structured-Streaming-as-a-Service with Kafka, YARN, and Tooling with Jim Dowling
(BDT303) Running Spark and Presto on the Netflix Big Data Platform
(BDT303) Running Spark and Presto on the Netflix Big Data Platform
Hopsworks - Self-Service Spark/Flink/Kafka/Hadoop
Hopsworks - Self-Service Spark/Flink/Kafka/Hadoop
Strata Hadoop Hopsworks
Strata Hadoop Hopsworks
Mobius: C# Language Binding For Spark
Mobius: C# Language Binding For Spark
Interoperating a Zoo of Data Processing Platforms Using with Rheem Sebastian ...
Interoperating a Zoo of Data Processing Platforms Using with Rheem Sebastian ...
Speed up Interactive Analytic Queries over Existing Big Data on Hadoop with P...
Speed up Interactive Analytic Queries over Existing Big Data on Hadoop with P...
Big data Lambda Architecture - Batch Layer Hands On
Big data Lambda Architecture - Batch Layer Hands On
Natural Language Query and Conversational Interface to Apache Spark
Natural Language Query and Conversational Interface to Apache Spark
Scalable Scientific Computing with Dask
Scalable Scientific Computing with Dask
Just enough DevOps for Data Scientists (Part II)
Just enough DevOps for Data Scientists (Part II)
Developing apache spark jobs in .net using mobius
Developing apache spark jobs in .net using mobius
Data Pipeline with Kafka
Data Pipeline with Kafka
Spark Summit EU talk by Ruben Pulido Behar Veliqi
Spark Summit EU talk by Ruben Pulido Behar Veliqi
AWS April 2016 Webinar Series - Best Practices for Apache Spark on AWS
AWS April 2016 Webinar Series - Best Practices for Apache Spark on AWS
ETL with SPARK - First Spark London meetup
ETL with SPARK - First Spark London meetup
Prototyping Data Intensive Apps: TrendingTopics.org
Prototyping Data Intensive Apps: TrendingTopics.org
Similar a Hw09 Building Data Intensive Apps A Closer Look At Trending Topics.Org
In this session, we introduce AWS Glue, provide an overview of its components, and share how you can use AWS Glue to automate discovering your data, cataloging it, and preparing it for analysis.
Introduction to AWS Glue
Introduction to AWS Glue
Amazon Web Services
Ingesting data from the internet into Azure Data Lake and Visualizing in Power BI
Big Data Analytics from Azure Cloud to Power BI Mobile
Big Data Analytics from Azure Cloud to Power BI Mobile
Roy Kim
AWS Glue is a fully managed, serverless extract, transform, and load (ETL) service that makes it easy to move data between data stores. AWS Glue simplifies and automates the difficult and time consuming tasks of data discovery, conversion mapping, and job scheduling so you can focus more of your time querying and analyzing your data using Amazon Redshift Spectrum and Amazon Athena. In this session, we introduce AWS Glue, provide an overview of its components, and share how you can use AWS Glue to automate discovering your data, cataloging it, and preparing it for analysis.
BDA311 Introduction to AWS Glue
BDA311 Introduction to AWS Glue
Amazon Web Services
Hadoop Summit 2010 - Developers Track Honu - A Large Scale Streaming Data Collection and Processing Pipeline Jerome Boulon, Netflix
Honu - A Large Scale Streaming Data Collection and Processing Pipeline__Hadoo...
Honu - A Large Scale Streaming Data Collection and Processing Pipeline__Hadoo...
Yahoo Developer Network
Gives an overview of a typical Data Lake Reference Architecture and AWS Big Data Services
AWS Big Data Landscape
AWS Big Data Landscape
Crishantha Nanayakkara
Learn about the only solution to instantly provision a full-featured ETL environment running on AWS for less than your Sunday newspaper!
Big Data Goes Airborne. Propelling Your Big Data Initiative with Ironcluster ...
Big Data Goes Airborne. Propelling Your Big Data Initiative with Ironcluster ...
Precisely
Behind the growing interest in Generate AI and LLM-based enterprise applications lies an expanded set of requirements for data integrations and ML orchestration. Enterprises want to use proprietary data to power LLM-based applications that create new business value, but they face challenges in moving beyond experimentation. The pipelines that power these models need to run reliably at scale, bringing together data from many sources and reacting continuously to changing conditions. This talk focuses on the design patterns for using Apache Airflow to support LLM applications created using private enterprise data. We’ll go through a real-world example of what this looks like, as well as a proposal to improve Airflow and to add additional Airflow Providers to make it easier to interact with LLMs such as the ones from OpenAI (such as GPT4) and the ones on HuggingFace, while working with both structured and unstructured data. In short, this shows how these Airflow patterns enable reliable, traceable, and scalable LLM applications within the enterprise. https://airflowsummit.org/sessions/2023/keynote-llm/
Building and deploying LLM applications with Apache Airflow
Building and deploying LLM applications with Apache Airflow
Kaxil Naik
https://github.com/intel-analytics/analytics-zoo (Analytics Zoo for Apache Spark and BigDL)
Build Deep Learning Applications for Big Data Platforms (CVPR 2018 tutorial)
Build Deep Learning Applications for Big Data Platforms (CVPR 2018 tutorial)
Jason Dai
Hadoop World 2011: Building Web Analytics Processing on Hadoop at CBS Interac...
Hadoop World 2011: Building Web Analytics Processing on Hadoop at CBS Interac...
Cloudera, Inc.
This introductory level talk is about Apache Flink: a multi-purpose Big Data analytics framework leading a movement towards the unification of batch and stream processing in the open source. With the many technical innovations it brings along with its unique vision and philosophy, it is considered the 4 G (4th Generation) of Big Data Analytics frameworks providing the only hybrid (Real-Time Streaming + Batch) open source distributed data processing engine supporting many use cases: batch, streaming, relational queries, machine learning and graph processing. In this talk, you will learn about: 1. What is Apache Flink stack and how it fits into the Big Data ecosystem? 2. How Apache Flink integrates with Hadoop and other open source tools for data input and output as well as deployment? 3. Why Apache Flink is an alternative to Apache Hadoop MapReduce, Apache Storm and Apache Spark. 4. Who is using Apache Flink? 5. Where to learn more about Apache Flink?
Apache-Flink-What-How-Why-Who-Where-by-Slim-Baltagi
Apache-Flink-What-How-Why-Who-Where-by-Slim-Baltagi
Slim Baltagi
Big Data, a recent phenomenon. Everyone talks about it, but do you really know what Big Data is? Join our four-part series about Big Data and you will get answers to your questions! We will cover Introduction to Big Data and available platforms which we can use to deal with Big Data. And in the end, we are going to give you an insight into the possible future of dealing with Big Data. Spark, Flink, Presto and many others. This is just a sample of frameworks which are used in real companies and we will talk about some of them. In the previous episode of this Big Data series, we talked about the basic information concerning Big Data. This presentation, however, will be much more technical as we will be covering the most popular platforms you can use to deal with Big Data 2.0 Systems and learn about the key differences between these platforms. Let’s go! #CHEDTEB www.chedteb.eu
Available platforms for Big Data 2.0
Available platforms for Big Data 2.0
Petr Novotný
The next release of Apache Spark will be 2.0, marking a big milestone for the project. In this talk, I’ll cover how the community has grown to reach this point, and some of the major features in 2.0. The largest additions are performance improvements for Datasets, DataFrames and SQL through Project Tungsten, as well as a new Structured Streaming API that provides simpler and more powerful stream processing. I’ll also discuss a bit of what’s in the works for future versions.
Spark Summit San Francisco 2016 - Matei Zaharia Keynote: Apache Spark 2.0
Spark Summit San Francisco 2016 - Matei Zaharia Keynote: Apache Spark 2.0
Databricks
Bored and Frustrated with traditional data warehousing methodologies? Find out the latest agile data ware housing methodologies here.
Agile data warehousing
Agile data warehousing
Sneha Challa
Presented at Open Source India 2016, at a workshop titled: Building a Data Lake using Apache Hadoop: A Proof of Concept
Datalake Architecture
Datalake Architecture
TechYugadi IT Solutions & Consulting
Learning Objectives: - Discover dark data that you are currently not analyzing. - Analyze dark data without moving it into your data warehouse. - Visualize the results of your dark data analytics.
Tackle Your Dark Data Challenge with AWS Glue - AWS Online Tech Talks
Tackle Your Dark Data Challenge with AWS Glue - AWS Online Tech Talks
Amazon Web Services
Talk from Dremio's Subsurface conference on March 3, 2022. Discusses applications of Arrow and Voltron Data's new Enterprise Subscription offering
Apache Arrow: Open Source Standard Becomes an Enterprise Necessity
Apache Arrow: Open Source Standard Becomes an Enterprise Necessity
Wes McKinney
Learn how to use API Platform and Symfony to create super easily rich web and mobile applications relying on React (JS) for their presentational layer. In just a few minutes, we will create a hypermedia API thanks to API Platform, Symfony and Doctrine. We will do it step by step, and the API will be 100% functional with support for pagination, validation, filters, resources embedding. The API will be automatically documented using Swagger and Hydra and beautiful user interface for developers will be available. HTTP cache, authorization and authentication can then be added in a breath. Then, we will introduce all new client-side tools for API Platform: * A fully featured JavaScript (Single Page App) administration system with a modern user interface (Material Design) ; built on top of Admin On Rest (React and Redux). This admin is builded dynamically thanks to the API discoverability (Hydra). * A raw React, Redux and React Router code generator to bootstrap fully-featured Single Page Applications and native mobile apps thanks to the API documentation exposed by API Platform (client-side and server-side validation, on fields error, Twitter Bootstrap compatibility, a11y support...)
API Platform 2.1: when Symfony meets ReactJS (Symfony Live 2017)
API Platform 2.1: when Symfony meets ReactJS (Symfony Live 2017)
Les-Tilleuls.coop
Understanding hadoop ecosystem.
Hadoop Big Data A big picture
Hadoop Big Data A big picture
J S Jodha
Describe the Hadoop features provided in Windows Azure HDInsight
Windows Azure HDInsight Service
Windows Azure HDInsight Service
Neil Mackenzie
We will explore the strengths and limitations of Hadoop for analyzing large data sets and review the growing ecosystem of tools for augmenting, extending, or replacing Hadoop MapReduce. We will introduce the Amazon Elastic MapReduce (EMR) platform as the big data foundation for Hadoop and beyond by providing specific examples of running Machine Learning (Mahout), Graph Analytics (Giraph), and Statistical Analysis (R) on EMR. We will discuss also big data analytics and visualization of results with Amazon Redshift + third party business intelligence tools, as well as typical end-to-end Big Data workflow on AWS. We will conclude with real-world examples from ICAO of Big Data analytics for aviation safety data on AWS. The integrated Safety Trend Analysis and Reporting System (iSTARS) is a web based system linking a collection of safety datasets and related web application to perform online safety and risk analysis. It uses AWS EC2, S3, EMR and related partner tools for continuous data aggregation and filtering.
(BDT302) Big Data Beyond Hadoop: Running Mahout, Giraph, and R on Amazon EMR ...
(BDT302) Big Data Beyond Hadoop: Running Mahout, Giraph, and R on Amazon EMR ...
Amazon Web Services
Similar a Hw09 Building Data Intensive Apps A Closer Look At Trending Topics.Org
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Introduction to AWS Glue
Introduction to AWS Glue
Big Data Analytics from Azure Cloud to Power BI Mobile
Big Data Analytics from Azure Cloud to Power BI Mobile
BDA311 Introduction to AWS Glue
BDA311 Introduction to AWS Glue
Honu - A Large Scale Streaming Data Collection and Processing Pipeline__Hadoo...
Honu - A Large Scale Streaming Data Collection and Processing Pipeline__Hadoo...
AWS Big Data Landscape
AWS Big Data Landscape
Big Data Goes Airborne. Propelling Your Big Data Initiative with Ironcluster ...
Big Data Goes Airborne. Propelling Your Big Data Initiative with Ironcluster ...
Building and deploying LLM applications with Apache Airflow
Building and deploying LLM applications with Apache Airflow
Build Deep Learning Applications for Big Data Platforms (CVPR 2018 tutorial)
Build Deep Learning Applications for Big Data Platforms (CVPR 2018 tutorial)
Hadoop World 2011: Building Web Analytics Processing on Hadoop at CBS Interac...
Hadoop World 2011: Building Web Analytics Processing on Hadoop at CBS Interac...
Apache-Flink-What-How-Why-Who-Where-by-Slim-Baltagi
Apache-Flink-What-How-Why-Who-Where-by-Slim-Baltagi
Available platforms for Big Data 2.0
Available platforms for Big Data 2.0
Spark Summit San Francisco 2016 - Matei Zaharia Keynote: Apache Spark 2.0
Spark Summit San Francisco 2016 - Matei Zaharia Keynote: Apache Spark 2.0
Agile data warehousing
Agile data warehousing
Datalake Architecture
Datalake Architecture
Tackle Your Dark Data Challenge with AWS Glue - AWS Online Tech Talks
Tackle Your Dark Data Challenge with AWS Glue - AWS Online Tech Talks
Apache Arrow: Open Source Standard Becomes an Enterprise Necessity
Apache Arrow: Open Source Standard Becomes an Enterprise Necessity
API Platform 2.1: when Symfony meets ReactJS (Symfony Live 2017)
API Platform 2.1: when Symfony meets ReactJS (Symfony Live 2017)
Hadoop Big Data A big picture
Hadoop Big Data A big picture
Windows Azure HDInsight Service
Windows Azure HDInsight Service
(BDT302) Big Data Beyond Hadoop: Running Mahout, Giraph, and R on Amazon EMR ...
(BDT302) Big Data Beyond Hadoop: Running Mahout, Giraph, and R on Amazon EMR ...
Más de Cloudera, Inc.
Partner Webinar for updates and news January 25th 2022
Partner Briefing_January 25 (FINAL).pptx
Partner Briefing_January 25 (FINAL).pptx
Cloudera, Inc.
This annual program recognizes organizations who are moving swiftly towards the future and building innovative solutions by making what was impossible yesterday, possible today. The winning organizations' implementations demonstrate outstanding achievements in fulfilling their mission, technical advancement, and overall impact. The 2021 Data Impact Awards recognize organizations' achievements with the Cloudera Data Platform in seven categories: Data Lifecycle Connection Data for Enterprise AI Cloud Innovation Security & Governance Leadership People First Data for Good Industry Transformation
Cloudera Data Impact Awards 2021 - Finalists
Cloudera Data Impact Awards 2021 - Finalists
Cloudera, Inc.
Cloudera is proud to present the 2020 Data Impact Awards Finalists. This annual program recognizes organizations running the Cloudera platform for the applications they've built and the impact their data projects have on their organizations, their industries, and the world. Nominations were evaluated by a panel of independent thought-leaders and expert industry analysts, who then selected the finalists and winners. Winners exemplify the most-cutting edge data projects and represent innovation and leadership in their respective industries.
2020 Cloudera Data Impact Awards Finalists
2020 Cloudera Data Impact Awards Finalists
Cloudera, Inc.
Cloudera Enterprise Data Cloud Event Vienna 1 Oct. 2019
Edc event vienna presentation 1 oct 2019
Edc event vienna presentation 1 oct 2019
Cloudera, Inc.
Cloudera Fast Forward Labs’ latest research report and prototype explore learning with limited labeled data. This capability relaxes the stringent labeled data requirement in supervised machine learning and opens up new product possibilities. It is industry invariant, addresses the labeling pain point and enables applications to be built faster and more efficiently.
Machine Learning with Limited Labeled Data 4/3/19
Machine Learning with Limited Labeled Data 4/3/19
Cloudera, Inc.
In this session, we will cover how to move beyond structured, curated reports based on known questions on known data, to an ad-hoc exploration of all data to optimize business processes and into the unknown questions on unknown data, where machine learning and statistically motivated predictive analytics are shaping business strategy.
Data Driven With the Cloudera Modern Data Warehouse 3.19.19
Data Driven With the Cloudera Modern Data Warehouse 3.19.19
Cloudera, Inc.
Watch this webinar to understand how Hortonworks DataFlow (HDF) has evolved into the new Cloudera DataFlow (CDF). Learn about key capabilities that CDF delivers such as - -Powerful data ingestion powered by Apache NiFi -Edge data collection by Apache MiNiFi -IoT-scale streaming data processing with Apache Kafka -Enterprise services to offer unified security and governance from edge-to-enterprise
Introducing Cloudera DataFlow (CDF) 2.13.19
Introducing Cloudera DataFlow (CDF) 2.13.19
Cloudera, Inc.
Cloudera’s Data Science Workbench (CDSW) is available for Hortonworks Data Platform (HDP) clusters for secure, collaborative data science at scale. During this webinar, we provide an introductory tour of CDSW and a demonstration of a machine learning workflow using CDSW on HDP.
Introducing Cloudera Data Science Workbench for HDP 2.12.19
Introducing Cloudera Data Science Workbench for HDP 2.12.19
Cloudera, Inc.
Join Cloudera as we outline how we use Cloudera technology to strengthen sales engagement, minimize marketing waste, and empower line of business leaders to drive successful outcomes.
Shortening the Sales Cycle with a Modern Data Warehouse 1.30.19
Shortening the Sales Cycle with a Modern Data Warehouse 1.30.19
Cloudera, Inc.
Learn how organizations are deriving unique customer insights, improving product and services efficiency, and reducing business risk with a modern big data architecture powered by Cloudera on Azure. In this webinar, you see how fast and easy it is to deploy a modern data management platform—in your cloud, on your terms.
Leveraging the cloud for analytics and machine learning 1.29.19
Leveraging the cloud for analytics and machine learning 1.29.19
Cloudera, Inc.
Join us to learn about the challenges of legacy data warehousing, the goals of modern data warehousing, and the design patterns and frameworks that help to accelerate modernization efforts.
Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19
Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19
Cloudera, Inc.
Learn how organizations are deriving unique customer insights, improving product and services efficiency, and reducing business risk with a modern big data architecture powered by Cloudera on AWS. In this webinar, you see how fast and easy it is to deploy a modern data management platform—in your cloud, on your terms.
Leveraging the Cloud for Big Data Analytics 12.11.18
Leveraging the Cloud for Big Data Analytics 12.11.18
Cloudera, Inc.
Explore new trends and use cases in data warehousing including exploration and discovery, self-service ad-hoc analysis, predictive analytics and more ways to get deeper business insight. Modern Data Warehousing Fundamentals will show how to modernize your data warehouse architecture and infrastructure for benefits to both traditional analytics practitioners and data scientists and engineers.
Modern Data Warehouse Fundamentals Part 3
Modern Data Warehouse Fundamentals Part 3
Cloudera, Inc.
Explore new trends and use cases in data warehousing including exploration and discovery, self-service ad-hoc analysis, predictive analytics and more ways to get deeper business insight. Modern Data Warehousing Fundamentals will show how to modernize your data warehouse architecture and infrastructure for benefits to both traditional analytics practitioners and data scientists and engineers.
Modern Data Warehouse Fundamentals Part 2
Modern Data Warehouse Fundamentals Part 2
Cloudera, Inc.
Explore new trends and use cases in data warehousing including exploration and discovery, self-service ad-hoc analysis, predictive analytics and more ways to get deeper business insight. Modern Data Warehousing Fundamentals will show how to modernize your data warehouse architecture and infrastructure for benefits to both traditional analytics practitioners and data scientists and engineers.
Modern Data Warehouse Fundamentals Part 1
Modern Data Warehouse Fundamentals Part 1
Cloudera, Inc.
Cloudera SDX is by no means no restricted to just the platform; it extends well beyond. In this webinar, we show you how Bardess Group’s Zero2Hero solution leverages the shared data experience to coordinate Cloudera, Trifacta, and Qlik to deliver complete customer insight.
Extending Cloudera SDX beyond the Platform
Extending Cloudera SDX beyond the Platform
Cloudera, Inc.
Join Cloudera Fast Forward Labs Research Engineer, Mike Lee Williams, to hear about their latest research report and prototype on Federated Learning. Learn more about what it is, when it’s applicable, how it works, and the current landscape of tools and libraries.
Federated Learning: ML with Privacy on the Edge 11.15.18
Federated Learning: ML with Privacy on the Edge 11.15.18
Cloudera, Inc.
451 Research Analyst Sheryl Kingstone, and Cloudera’s Steve Totman recently discussed how a growing number of organizations are replacing legacy Customer 360 systems with Customer Insights Platforms.
Analyst Webinar: Doing a 180 on Customer 360
Analyst Webinar: Doing a 180 on Customer 360
Cloudera, Inc.
In this webinar, you will learn how Cloudera and BAH riskCanvas can help you build a modern AML platform that reduces false positive rates, investigation costs, technology sprawl, and regulatory risk.
Build a modern platform for anti-money laundering 9.19.18
Build a modern platform for anti-money laundering 9.19.18
Cloudera, Inc.
How can companies integrate data science into their businesses more effectively? Watch this recorded webinar and demonstration to hear more about operationalizing data science with Cloudera Data Science Workbench on Cazena’s fully-managed cloud platform.
Introducing the data science sandbox as a service 8.30.18
Introducing the data science sandbox as a service 8.30.18
Cloudera, Inc.
Más de Cloudera, Inc.
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Partner Briefing_January 25 (FINAL).pptx
Partner Briefing_January 25 (FINAL).pptx
Cloudera Data Impact Awards 2021 - Finalists
Cloudera Data Impact Awards 2021 - Finalists
2020 Cloudera Data Impact Awards Finalists
2020 Cloudera Data Impact Awards Finalists
Edc event vienna presentation 1 oct 2019
Edc event vienna presentation 1 oct 2019
Machine Learning with Limited Labeled Data 4/3/19
Machine Learning with Limited Labeled Data 4/3/19
Data Driven With the Cloudera Modern Data Warehouse 3.19.19
Data Driven With the Cloudera Modern Data Warehouse 3.19.19
Introducing Cloudera DataFlow (CDF) 2.13.19
Introducing Cloudera DataFlow (CDF) 2.13.19
Introducing Cloudera Data Science Workbench for HDP 2.12.19
Introducing Cloudera Data Science Workbench for HDP 2.12.19
Shortening the Sales Cycle with a Modern Data Warehouse 1.30.19
Shortening the Sales Cycle with a Modern Data Warehouse 1.30.19
Leveraging the cloud for analytics and machine learning 1.29.19
Leveraging the cloud for analytics and machine learning 1.29.19
Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19
Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19
Leveraging the Cloud for Big Data Analytics 12.11.18
Leveraging the Cloud for Big Data Analytics 12.11.18
Modern Data Warehouse Fundamentals Part 3
Modern Data Warehouse Fundamentals Part 3
Modern Data Warehouse Fundamentals Part 2
Modern Data Warehouse Fundamentals Part 2
Modern Data Warehouse Fundamentals Part 1
Modern Data Warehouse Fundamentals Part 1
Extending Cloudera SDX beyond the Platform
Extending Cloudera SDX beyond the Platform
Federated Learning: ML with Privacy on the Edge 11.15.18
Federated Learning: ML with Privacy on the Edge 11.15.18
Analyst Webinar: Doing a 180 on Customer 360
Analyst Webinar: Doing a 180 on Customer 360
Build a modern platform for anti-money laundering 9.19.18
Build a modern platform for anti-money laundering 9.19.18
Introducing the data science sandbox as a service 8.30.18
Introducing the data science sandbox as a service 8.30.18
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ICT role in 21 century education. How to ICT help in education
presentation ICT roal in 21st century education
presentation ICT roal in 21st century education
jfdjdjcjdnsjd
Following the popularity of "Cloud Revolution: Exploring the New Wave of Serverless Spatial Data," we're thrilled to announce this much-anticipated encore webinar. In this sequel, we'll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR. Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios. Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects. Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you're building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Safe Software
Whatsapp Number Escorts Call girls 8617370543 Available 24x7 Navi Mumbai Call Girls Service Offer Genuine VIP Model Escorts Call Girls in Your Budget. Navi Mumbai Call Girls Service Provide Real Call Girls Number. Make Your Sexual Pleasure Memorable with Our Navi Mumbai Call Girls at Affordable Price. Top VIP Escorts Call Girls, High Profile Independent Escorts Call Girls, Housewife Women Escorts Call Girl, College Girls Escorts Call Girls, Russian Escorts Call girls Service in Your Budget.
Navi Mumbai Call Girls 🥰 8617370543 Service Offer VIP Hot Model
Navi Mumbai Call Girls 🥰 8617370543 Service Offer VIP Hot Model
Deepika Singh
Manulife - Insurer Transformation Award 2024
Manulife - Insurer Transformation Award 2024
Manulife - Insurer Transformation Award 2024
The Digital Insurer
Join our latest Connector Corner webinar to discover how UiPath Integration Service revolutionizes API-centric automation in a 'Quote to Cash' process—and how that automation empowers businesses to accelerate revenue generation. A comprehensive demo will explore connecting systems, GenAI, and people, through powerful pre-built connectors designed to speed process cycle times. Speakers: James Dickson, Senior Software Engineer Charlie Greenberg, Host, Product Marketing Manager
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
DianaGray10
Presented by Mike Hicks
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How to Troubleshoot Apps for the Modern Connected Worker
ThousandEyes
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
The Digital Insurer
This project focuses on implementing real-time object detection using Raspberry Pi and OpenCV. Real-time object detection is a critical aspect of computer vision applications, allowing systems to identify and locate objects within a live video stream instantly.
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CV
Khem
Three things you will take away from the session: • How to run an effective tenant-to-tenant migration • Best practices for before, during, and after migration • Tips for using migration as a springboard to prepare for Copilot in Microsoft 365 Main ideas: Migration Overview: The presentation covers the current reality of cross-tenant migrations, the triggers, phases, best practices, and benefits of a successful tenant migration Considerations: When considering a migration, it is important to consider the migration scope, performance, customization, flexibility, user-friendly interface, automation, monitoring, support, training, scalability, data integrity, data security, cost, and licensing structure Next Wave: The next wave of change includes the launch of Copilot, which requires businesses to be prepared for upcoming changes related to Copilot and the cloud, and to consolidate data and tighten governance ShareGate: ShareGate can help with pre-migration analysis, configurable migration tool, and automated, end-user driven collaborative governance
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
sammart93
The Good, the Bad and the Governed - Why is governance a dirty word? David O'Neill, Chief Operating Officer - APIContext Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
apidays
Scaling API-first – The story of a global engineering organization Ian Reasor, Senior Computer Scientist - Adobe Radu Cotescu, Senior Computer Scientist - Adobe Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
apidays
writing some innovation for development and search
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
sudhanshuwaghmare1
Copy of the slides presented by Matt Robison to the SFWelly Salesforce user group community on May 2 2024. The audience was truly international with attendees from at least 4 different countries joining online. Matt is an expert in data cloud and this was a brilliant session.
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
Anna Loughnan Colquhoun
This reviewer is for the second quarter of Empowerment Technology / ICT in Grade 11
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
MadyBayot
In the thrilling conclusion to 2023, ransomware groups had a banner year, really outdoing themselves in the "make everyone's life miserable" department. LockBit 3.0 took gold in the hacking olympics, followed by the plucky upstarts Clop and ALPHV/BlackCat. Apparently, 48% of organizations were feeling left out and decided to get in on the cyber attack action. Business services won the "most likely to get digitally mugged" award, with education and retail nipping at their heels. Hackers expanded their repertoire beyond boring old encryption to the much more exciting world of extortion. The US, UK and Canada took top honors in the "countries most likely to pay up" category. Bitcoins were the currency of choice for discerning hackers, because who doesn't love untraceable money?
Ransomware_Q4_2023. The report. [EN].pdf
Ransomware_Q4_2023. The report. [EN].pdf
Overkill Security
FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024
The Digital Insurer
Abhishek Deb(1), Mr Abdul Kalam(2) M. Des (UX) , School of Design, DIT University , Dehradun. This paper explores the future potential of AI-enabled smartphone processors, aiming to investigate the advancements, capabilities, and implications of integrating artificial intelligence (AI) into smartphone technology. The research study goals consist of evaluating the development of AI in mobile phone processors, analyzing the existing state as well as abilities of AI-enabled cpus determining future patterns as well as chances together with reviewing obstacles as well as factors to consider for more growth.
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
debabhi2
45-60 minute session deck from introducing Google Apps Script to developers, IT leadership, and other technical professionals.
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
wesley chun
In this session, we will delve into strategic approaches for optimizing knowledge management within Microsoft 365, amidst the evolving landscape of Copilot. From leveraging automatic metadata classification and permission governance with SharePoint Premium, to unlocking Viva Engage for the cultivation of knowledge and communities, you will gain actionable insights to bolster your organization's knowledge-sharing initiatives. In this session, we will also explore how to facilitate solutions to enable your employees to find answers and expertise within Microsoft 365. You will leave equipped with practical techniques and a deeper understanding of how there is more to effective knowledge management than just enabling Copilot, but building actual solutions to prepare the knowledge that Copilot and your employees can use.
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Drew Madelung
DBX 1Q24 Investor Presentation
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor Presentation
Dropbox
Último
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presentation ICT roal in 21st century education
presentation ICT roal in 21st century education
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Navi Mumbai Call Girls 🥰 8617370543 Service Offer VIP Hot Model
Navi Mumbai Call Girls 🥰 8617370543 Service Offer VIP Hot Model
Manulife - Insurer Transformation Award 2024
Manulife - Insurer Transformation Award 2024
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CV
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
Ransomware_Q4_2023. The report. [EN].pdf
Ransomware_Q4_2023. The report. [EN].pdf
FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor Presentation
Hw09 Building Data Intensive Apps A Closer Look At Trending Topics.Org
1.
Prototyping Data Intensive
Apps: TrendingTopics.org 09/29/09 1 Pete Skomoroch Research Scientist at LinkedIn Consultant at Data Wrangling @peteskomoroch
2.
3.
4.
TrendingTopics.org
5.
Daily Pageview Timeline
Charts
6.
Detects Rising Trends
with Hadoop
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8.
Technology Stack
9.
Application Data Flow
10.
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14.
Python Streaming for
Daily Timelines
15.
Streaming: Filter &
Aggregate Logs
16.
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Fixing Wiki Redirects
with Hive JOIN
18.
Trend Detection With
Hadoop
19.
Hourly Data: “Java”
vs. “Hangover”
20.
21.
Call Python Trend
Mapper from Hive
22.
Simple Trend Calculation
in Python
23.
Use Trend Scores
to Rank Articles
24.
25.
Hooking It All
Together
26.
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32.
33.
LinkedIn A
nalytics Team We’re Hiring
34.
35.
Backup slides
36.
Wikipedia Views ~
Google Searches “ Dwight Howard” on TrendingTopics “ Dwight Howard” on Google Trends
37.
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