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Recorded at SpringOne2GX 2013 in Santa Clara, CA Speaker: Adam Shook This session assumes absolutely no knowledge of Apache Hadoop and will provide a complete introduction to all the major aspects of the Hadoop ecosystem of projects and tools. If you are looking to get up to speed on Hadoop, trying to work out what all the Big Data fuss is about, or just interested in brushing up your understanding of MapReduce, then this is the session for you. We will cover all the basics with detailed discussion about HDFS, MapReduce, YARN (MRv2), and a broad overview of the Hadoop ecosystem including Hive, Pig, HBase, ZooKeeper and more. Learn More about Spring XD at: http://projects.spring.io/spring-xd Learn More about Gemfire XD at: http://www.gopivotal.com/big-data/pivotal-hd
Hadoop - Just the Basics for Big Data Rookies (SpringOne2GX 2013)
Hadoop - Just the Basics for Big Data Rookies (SpringOne2GX 2013)
VMware Tanzu
Avro Data | Washington DC HUG
Avro Data | Washington DC HUG
Cloudera, Inc.
1. Serialization overview 2. Java libraries for serialization. 3. Benchmarking
Serialization and performance in Java
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Strannik_2013
Avro introduction
Avro introduction
Nanda8904648951
Some initial analysis of the Hadoop Stack using vProbes
Hadoop I/O Analysis
Hadoop I/O Analysis
Richard McDougall
This slide deck is used as an introduction to the internals of Hadoop MapReduce, as part of the Distributed Systems and Cloud Computing course I hold at Eurecom. Course website: http://michiard.github.io/DISC-CLOUD-COURSE/ Sources available here: https://github.com/michiard/DISC-CLOUD-COURSE
Hadoop Internals
Hadoop Internals
Pietro Michiardi
Event Stream Processing with Kafka and Samza, presented at Iowa Code Camp Fall 2014.
Event Stream Processing with Kafka and Samza
Event Stream Processing with Kafka and Samza
Zach Cox
Type safe, versioned, and rewindable stream processing with Apache {Avro, Kafka} and Scala.
Type safe, versioned, and rewindable stream processing with Apache {Avro, K...
Type safe, versioned, and rewindable stream processing with Apache {Avro, K...
Hisham Mardam-Bey
The only way to get where we need to be in security analysis is if we use Security Intelligence. This means working harder and understanding the big picture of your data.
Big Data, Security Intelligence, (And Why I Hate This Title)
Big Data, Security Intelligence, (And Why I Hate This Title)
Coastal Pet Products, Inc.
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 session, 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, best practices to quickly create Spark clusters using Amazon EMR, and ways to integrate Spark with other big data services in AWS. 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 Amazon DynamoDB, Amazon Redshift, Amazon Kinesis, and more.
Best Practices for Using Apache Spark on AWS
Best Practices for Using Apache Spark on AWS
Amazon Web Services
An introduction to Apache Avro
Avro intro
Avro intro
Randy Abernethy
Overview of Apache Avro just before 1.4 release
Apache Avro and You
Apache Avro and You
Eric Wendelin
Peter Wood started looking at Big Data as a solution for Advanced Threat Protection in 2013. This presentation examines how Big Data is being used for security in 2015, how this market is developing and how realistic vendor offerings are.
Big Data and Security - Where are we now? (2015)
Big Data and Security - Where are we now? (2015)
Peter Wood
Create a Colder Storage Tier for Hadoop & Spark Using IBM Elastic Storage Server & HDFS Transparency
Hadoop and Spark Analytics over Better Storage
Hadoop and Spark Analytics over Better Storage
Sandeep Patil
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3 apache-avro
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Thrift vs Protocol Buffers vs Avro - Biased Comparison
Chapter 8 big data and privacy
Chapter 8 big data and privacy
おひろめ会：Javaにおけるデータシリアライズ手法
おひろめ会：Javaにおけるデータシリアライズ手法
From a kafkaesque story to The Promised Land
From a kafkaesque story to The Promised Land
Hadoop - Just the Basics for Big Data Rookies (SpringOne2GX 2013)
Hadoop - Just the Basics for Big Data Rookies (SpringOne2GX 2013)
Avro Data | Washington DC HUG
Avro Data | Washington DC HUG
Serialization and performance in Java
Serialization and performance in Java
Avro introduction
Avro introduction
Hadoop I/O Analysis
Hadoop I/O Analysis
Hadoop Internals
Hadoop Internals
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Event Stream Processing with Kafka and Samza
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Type safe, versioned, and rewindable stream processing with Apache {Avro, K...
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Big Data, Security Intelligence, (And Why I Hate This Title)
Best Practices for Using Apache Spark on AWS
Best Practices for Using Apache Spark on AWS
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Apache Avro and You
Apache Avro and You
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Big Data and Security - Where are we now? (2015)
Hadoop and Spark Analytics over Better Storage
Hadoop and Spark Analytics over Better Storage
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In the space of building products with data, either by dealing with huge amounts of data or by applying machine learning, many different ecosystems meet. Larger volumes of data have to be passed between these systems. The handling of the data is not only down to divide between systems written in Java that need to pass it on to the machine learning model in Python. When you take into account that you want to integrate with the existing business infrastructure, you also need to cater for legacy systems as well do you need to bring the large volumes of data to the user via UIs.
Berlin Buzzwords 2019 - Taming the language border in data analytics and scie...
Berlin Buzzwords 2019 - Taming the language border in data analytics and scie...
Uwe Korn
21-Jan-2022. Friday 9:45 AM — 10 min. DataMinutes. Apache Pulsar with MQTT for Edge Computing. https://datagrillen.com/dataminutes/ Apache Pulsar with MQTT for Edge Computing Lightning - 2022 Tim Spann
Data minutes #2 Apache Pulsar with MQTT for Edge Computing Lightning - 2022
Data minutes #2 Apache Pulsar with MQTT for Edge Computing Lightning - 2022
Timothy Spann
Update on Apache Arrow project and not-for-profit Ursa Labs org for 2019 https://ursalabs.org/. Active projects and development objectives
Ursa Labs and Apache Arrow in 2019
Ursa Labs and Apache Arrow in 2019
Wes McKinney
Aeolus is Comcast’s new internal Big Data system for providing access to an integrated view of a wide variety of high-quality, near-real-time and batch data. Such integration can enable data scientists to uncover otherwise hidden trends, anomalies, and powerful predictors of business successes and failures. But integrating data across silos in a large enterprise is fraught with peril. There typically are few standards on naming conventions and data representation, and spotty documentation at best. The old rule of thumb often applies: 70% of the analysts’ time goes into data wrangling, while only 30% goes toward the actual analyses and simulations. The goal of the Athene Data Governance Platform within Aeolus is to invert this ratio. This talk will explain how Comcast is using Apache Avro and Atlas for end-to-end data governance, the challenges faced, and methods used to address these challenges. Avro provides a lingua franca for data representation, data integration, and schema evolution. All data published for community consumption must have an associated avro schema in Atlas. Every step in its journey through Aeolus, in flight or at rest, is captured in Atlas. Atlas’ extensibility has allowed us to add or update various entity types (e.g., avro schemas, kafka topics, object store pseudo-directories) and lineage types (e.g., storing streaming data in object storage; embellishing and re-publishing streaming data; performing aggregations and other transformations on data at rest; and evolution of schemas with compatibility flags). Transformation services notify Atlas of lineage links via custom asynchronous kafka messaging. Atlas provides self-service data discovery and lineage browsing and querying, via full-text search, DSL query language, or gremlin graph query language. Example queries: “Where is data from kafka topic X stored?” “Display the journey of data currently stored in pseudo-directory X since it entered the Aeolus system”. “Show me all earlier versions of schema S, and whether they are forward/backward compatible with each other.”
End-to-end Data Governance with Apache Avro and Atlas
End-to-end Data Governance with Apache Avro and Atlas
DataWorks Summit
ApacheCon2022_Deep Dive into Building Streaming Applications with Apache Pulsar In this session I will get you started with real-time cloud native streaming programming with Java, Golang, Python and Apache NiFi. If there’s a preferred language that the attendees pick, we will focus only on that one. I will start off with an introduction to Apache Pulsar and setting up your first easy standalone cluster in docker. We will then go into terms and architecture so you have an idea of what is going on with your events. I will then show you how to produce and consume messages to and from Pulsar topics. As well as using some of the command line and REST interfaces to monitor, manage and do CRUD on things like tenants, namespaces and topics. We will discuss Functions, Sinks, Sources, Pulsar SQL, Flink SQL and Spark SQL interfaces. We also discuss why you may want to add protocols such as MoP (MQTT), AoP (AMQP/RabbitMQ) or KoP (Kafka) to your cluster. We will also look at WebSockets as a producer and consumer. I will demonstrate a simple web page that sends and receives Pulsar messages with basic JavaScript. After this session you will be able to build simple real-time streaming and messaging applications with your chosen language or tool of your choice. apache pulsar tim spann developer advocate streamnative datainmotion.dev
ApacheCon2022_Deep Dive into Building Streaming Applications with Apache Pulsar
ApacheCon2022_Deep Dive into Building Streaming Applications with Apache Pulsar
Timothy Spann
Short presentation on some techniques to gain performance of the NATS messaging system as it was rewritten in Go.
High Performance Systems in Go - GopherCon 2014
High Performance Systems in Go - GopherCon 2014
Derek Collison
PyData Paris 2016 about the importance and recent developments on the Python side of Apache Arrow and Apache Parquet.
How Apache Arrow and Parquet boost cross-language interoperability
How Apache Arrow and Parquet boost cross-language interoperability
Uwe Korn
Scaling with Symfony2. Yes, you can do it :)
Scaling with Symfony - PHP UK
Scaling with Symfony - PHP UK
Ricard Clau
Delivered at SciPy 2018 -- July 11, 2018
Apache Arrow: Cross-language Development Platform for In-memory Data
Apache Arrow: Cross-language Development Platform for In-memory Data
Wes McKinney
Devfest uk & ireland using apache nifi with apache pulsar for fast data on-ramp 2022 As the Pulsar communities grows, more and more connectors will be added. To enhance the availability of sources and sinks and to make use of the greater Apache Streaming community, joining forces between Apache NiFi and Apache Pulsar is a perfect fit. Apache NiFi also adds the benefits of ELT, ETL, data crunching, transformation, validation and batch data processing. Once data is ready to be an event, NiFi can launch it into Pulsar at light speed. I will walk through how to get started, some use cases and demos and answer questions. https://www.devfest-uki.com/schedule https://linktr.ee/tspannhw
Devfest uk & ireland using apache nifi with apache pulsar for fast data on-r...
Devfest uk & ireland using apache nifi with apache pulsar for fast data on-r...
Timothy Spann
This slide show how can improve ceph storage performance using ssd and how to use ceph storage for container.
NAVER Ceph Storage on ssd for Container
NAVER Ceph Storage on ssd for Container
Jangseon Ryu
Deep Dive into Building Streaming Applications with Apache Pulsar philly ete apache pulsar with java, python, flink, nifi, spark
Deep Dive into Building Streaming Applications with Apache Pulsar
Deep Dive into Building Streaming Applications with Apache Pulsar
Timothy Spann
Real time cloud native open source streaming of any data to apache solr Utilizing Apache Pulsar and Apache NiFi we can parse any document in real-time at scale. We receive a lot of documents via cloud storage, email, social channels and internal document stores. We want to make all the content and metadata to Apache Solr for categorization, full text search, optimization and combination with other datastores. We will not only stream documents, but all REST feeds, logs and IoT data. Once data is produced to Pulsar topics it can instantly be ingested to Solr through Pulsar Solr Sink. Utilizing a number of open source tools, we have created a real-time scalable any document parsing data flow. We use Apache Tika for Document Processing with real-time language detection, natural language processing with Apache OpenNLP, Sentiment Analysis with Stanford CoreNLP, Spacy and TextBlob. We will walk everyone through creating an open source flow of documents utilizing Apache NiFi as our integration engine. We can convert PDF, Excel and Word to HTML and/or text. We can also extract the text to apply sentiment analysis and NLP categorization to generate additional metadata about our documents. We also will extract and parse images that if they contain text we can extract with TensorFlow and Tesseract.
Real time cloud native open source streaming of any data to apache solr
Real time cloud native open source streaming of any data to apache solr
Timothy Spann
OSS EU: Deep Dive into Building Streaming Applications with Apache Pulsar In this session I will get you started with real-time cloud native streaming programming with Java, Golang, Python and Apache NiFi. If there’s a preferred language that the attendees pick, we will focus only on that one. I will start off with an introduction to Apache Pulsar and setting up your first easy standalone cluster in docker. We will then go into terms and architecture so you have an idea of what is going on with your events. I will then show you how to produce and consume messages to and from Pulsar topics. As well as using some of the command line and REST interfaces to monitor, manage and do CRUD on things like tenants, namespaces and topics. We will discuss Functions, Sinks, Sources, Pulsar SQL, Flink SQL and Spark SQL interfaces. We also discuss why you may want to add protocols such as MoP (MQTT), AoP (AMQP/RabbitMQ) or KoP (Kafka) to your cluster. We will also look at WebSockets as a producer and consumer. I will demonstrate a simple web page that sends and receives Pulsar messages with basic JavaScript. After this session you will be able to build simple real-time streaming and messaging applications with your chosen language or tool of your choice. apache pulsar
OSS EU: Deep Dive into Building Streaming Applications with Apache Pulsar
OSS EU: Deep Dive into Building Streaming Applications with Apache Pulsar
Timothy Spann
@LaraConf Taiwan 2019
High Concurrency Architecture and Laravel Performance Tuning
High Concurrency Architecture and Laravel Performance Tuning
Albert Chen
Slides on Apache Arrow development from DataEngConf Barcelona 2018
Apache Arrow at DataEngConf Barcelona 2018
Apache Arrow at DataEngConf Barcelona 2018
Wes McKinney
Ways you can scale your software and organization with Wordnik's Swagger framework
Scaling with swagger
Scaling with swagger
Tony Tam
NoSQL afternoon in Japan Kumofs & MessagePack
NoSQL afternoon in Japan Kumofs & MessagePack
Sadayuki Furuhashi
NoSQL afternoon in Japan kumofs & MessagePack
NoSQL afternoon in Japan kumofs & MessagePack
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DBCC 2021 - FLiP Stack for Cloud Data Lakes With Apache Pulsar, Apache NiFi, Apache Flink. The FLiP(N) Stack for Event processing and IoT. With StreamNative Cloud. DBCC International – Friday 15.10.2021 Powered by Apache Pulsar, StreamNative provides a cloud-native, real-time messaging and streaming platform to support multi-cloud and hybrid cloud strategies.
DBCC 2021 - FLiP Stack for Cloud Data Lakes
DBCC 2021 - FLiP Stack for Cloud Data Lakes
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Berlin Buzzwords 2019 - Taming the language border in data analytics and scie...
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Data minutes #2 Apache Pulsar with MQTT for Edge Computing Lightning - 2022
Data minutes #2 Apache Pulsar with MQTT for Edge Computing Lightning - 2022
Ursa Labs and Apache Arrow in 2019
Ursa Labs and Apache Arrow in 2019
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ApacheCon2022_Deep Dive into Building Streaming Applications with Apache Pulsar
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Scaling with Symfony - PHP UK
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Deep Dive into Building Streaming Applications with Apache Pulsar
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OSS EU: Deep Dive into Building Streaming Applications with Apache Pulsar
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Apache Arrow at DataEngConf Barcelona 2018
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Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
sammart93
Enterprise Knowledge’s Urmi Majumder, Principal Data Architecture Consultant, and Fernando Aguilar Islas, Senior Data Science Consultant, presented "Driving Behavioral Change for Information Management through Data-Driven Green Strategy" on March 27, 2024 at Enterprise Data World (EDW) in Orlando, Florida. In this presentation, Urmi and Fernando discussed a case study describing how the information management division in a large supply chain organization drove user behavior change through awareness of the carbon footprint of their duplicated and near-duplicated content, identified via advanced data analytics. Check out their presentation to gain valuable perspectives on utilizing data-driven strategies to influence positive behavioral shifts and support sustainability initiatives within your organization. In this session, participants gained answers to the following questions: - What is a Green Information Management (IM) Strategy, and why should you have one? - How can Artificial Intelligence (AI) and Machine Learning (ML) support your Green IM Strategy through content deduplication? - How can an organization use insights into their data to influence employee behavior for IM? - How can you reap additional benefits from content reduction that go beyond Green IM?
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...
Enterprise Knowledge
Read about the journey the Adobe Experience Manager team has gone through in order to become and scale API-first throughout the organisation.
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
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Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Driving Behavioral Change for Information Management through Data-Driven Gree...
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Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
Serialization (Avro, Message Pack, Kryo)
1.
Serialization Avro, Message Pack,
Kryo Han O Seok
2.
What is Serialization?
3.
What is Serialization?
4.
5.
Avro • Apache Foundation •
JSON defineded Schema
6.
Avro • Created by
Doug Cutting, the Creator of Hadoop • Data is always accompanied by a schema - Support for dynamic typing-code generation is not required
7.
Performance of Avro •
Avro is not the fastest, But is in the top half
8.
Message Pack • Rich
data structures - JSON • Interface Definition Language(IDL) - thrift • Create Schema Based Annotaion • RPC Sync,Async Support Event-Driven I/O
9.
Format of Message
Pack
10.
Performance of Message Pack
11.
Kryo • Google Code •
Easy to Collect Serializers Case By Class • Support Compression • Kryo TCP & UDP client/server library
12.
BenchmarkingV2 • http://code.google.com/p/thrift-protobuf- compare/wiki/BenchmarkingV2
13.
Thanks :)
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