This document summarizes Ryu Kobayashi's presentation on HDP2 and YARN operations. The presentation introduced YARN, the resource management framework in Hadoop 2.0, describing its architecture and how it differs from the previous MapReduce v1 framework. It highlighted important considerations for YARN resource management and potential bugs in older versions of Hadoop.
This document provides an overview of large scale graph analytics and JanusGraph. It discusses graph databases and their use cases. JanusGraph is presented as an open source graph database that can scale to billions of vertices and edges across multiple storage backends like HBase, Cassandra and Bigtable. It uses the TinkerPop framework and Gremlin query language. JanusGraph supports ACID transactions, external indices, and evolving schemas. Example graph queries are demonstrated using the Gremlin console.
My Data Journey with Python (SciPy 2015 Keynote)Wes McKinney
Wes McKinney gave a keynote talk at SciPy 2015 about his journey with Python for data analysis from 2007 to present day. He started as a mathematician with no exposure to Python or data analysis tools. His first job was at a quant hedge fund where he encountered frustrations with productivity due to extensive use of SQL and Excel. In 2008, he began experimenting with Python and created early versions of pandas to improve productivity for his projects. This led to open sourcing pandas in 2009 and evangelizing Python more broadly within his company and community.
Presto as a Service - Tips for operation and monitoringTaro L. Saito
- Presto as a Service in Treasure Data involves deploying Presto using blue-green deployments with no downtime and automatic error recovery of failed queries.
- Monitoring Presto involves using its JSON API to view queries and query plans as well as collecting Presto metrics with Fluentd and detecting anomalies.
- Benchmarking compares query performance between Presto versions by running predefined query sets and aggregating the results.
An investigation of how PostgreSQL and its latest capabilities (JSONB data type, GIN indices, Full Text Search) can be used to store, index and perform queries on structured Bibliographic Data such as MARC21/MARCXML, breaking the dependence on proprietary and arcane or obsolete software products.
Talk presented at FOSDEM 2016 in Brussels on 31/01/2016. This is a very practical & hands-on presentation with example code which is certainly not optimal ;)
Presto: SQL-on-Anything. Netherlands Hadoop User Group MeetupWojciech Biela
Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes. Presto was designed and written from the ground up for interactive analytics and approaches the speed of commercial data warehouses while scaling to the size of organizations like Facebook. One key feature in Presto is the ability to query data where it lives via an uniform ANSI SQL interface. Presto’s connector architecture creates an abstraction layer for anything that can be represented in a columnar or row-like format, such as HDFS, Amazon S3, Azure Storage, NoSQL stores, relational databases, Kafka streams and even proprietary data stores. Furthermore, a single Presto query can combine data from multiple sources, allowing for analytics across an entire organization.
Using Pluggable Apache Spark SQL Filters to Help GridPocket Users Keep Up wit...Spark Summit
Analyzing and comparing your energy consumption with that of other consumers provides healthy peer pressure and useful insight leading to energy conservation and impacting the bottom line. We helped GridPocket (http://www.gridpocket.com/), a smart grid company developing energy management applications for electricity water and gas utilities, implement high scale anonymized energy comparison queries with an order of magnitude lower cost and higher performance than was previously possible. IoT use cases like that of GridPocket are swamping our planet with data, and drive demand for analytics on extremely scalable and low cost storage. Enter Spark SQL over Object Storage: highly scalable and low cost storage which provides RESTful APIs to store and retrieve objects and their metadata. Key performance indicators (KPIs) of query performance and cost are the number of bytes shipped from Object Storage to Spark and the number of incurred REST requests. We propose Pluggable Spark SQL Filters, which extend the existing Spark SQL partitioning mechanism with an ability to dynamically filter irrelevant objects during query execution. Our approach handles any data format supported by Spark SQL (Parquet, JSON, csv etc.), and unlike pushdown compatible formats such as Parquet which require touching each object to determine its relevance, it avoids accessing irrelevant objects altogether. We developed a pluggable interface for developing and deploying Filters, and implemented GridPocket’s filter which screens objects according to their metadata, for example geo-spatial bounding boxes which describe the area covered by an object’s data points. This leads to drastically lower KPIs since there is no need to ship the entire dataset from Object Storage to Spark if you are only comparing yourself with your neighborhood. We demonstrate GridPocket analytics notebooks, report on our implementation and resulting 10-20x speedups, explain how to implement a Pluggable File Filter, and how we applied this to other use cases.
A Day in the Life of a Druid Implementor and Druid's RoadmapItai Yaffe
This document summarizes a typical day for a Druid architect. It describes common tasks like evaluating production clusters, analyzing data and queries, and recommending optimizations. The architect asks stakeholders questions to understand usage and helps evaluate if Druid is a good fit. When advising on Druid, the architect considers factors like data sources, query types, and technology stacks. The document also provides tips on configuring clusters for performance and controlling segment size.
Lessons learned while taking Presto from alpha to production at Twitter. Presented at the Presto meetup at Facebook on 2015.03.22.
Video: https://www.facebook.com/prestodb/videos/531276353732033/
The document discusses several methods for getting data from Kafka into Hadoop, including batch tools like Camus, Sqoop2, and NiFi. It also covers streaming options like using Kafka as a source in Hive with the HiveKa storage handler, Spark Streaming, and Storm. The presenter is a software engineer and former consultant who now works at Cloudera on projects including Sqoop, Kafka, and Flume. They also maintain a blog on these topics and discuss setting up and using Kafka in Cloudera Manager.
AUTOMATED DATA EXPLORATION - Building efficient analysis pipelines with DaskVíctor Zabalza
# Talk given at PyCon UK 2017
The first step in any data-intensive project is understanding the available data. To this end, data scientists spend a significant part of their time carrying out data quality assessments and data exploration. In spite of this being a crucial step, it usually requires repeating a series of menial tasks before the data scientist gains an understanding ofthe dataset and can progress to the next steps in the project.
In this talk I will detail the inner workings of a Python package that we have built which automates this drudge work, enables efficient data exploration, and kickstarts data science projects. A summary is generated for each dataset, including:
- General information about the dataset, including data quality of each of the columns;
- Distribution of each of the columns through statistics and plots (histogram, CDF, KDE), optionally grouped by other categorical variables;
- 2D distribution between pairs of columns;
- Correlation coefficient matrix for all numerical columns.
Building this tool has provided a unique view into the full Python data stack, from the parallelised analysis of a dataframe within a Dask custom execution graph, to the interactive visualisation with Jupyter widgets and Plotly. During the talk, I will also introduce how Dask works, and demonstrate how to migrate data pipelines to take advantage of its scalable capabilities.
A real-time (lambda) architecture using Hadoop & Storm (NoSQL Matters Cologne...Nathan Bijnens
The document discusses the Lambda architecture, which handles both batch and real-time processing of data. It consists of three layers - a batch layer that handles batch views generation on Hadoop, a speed layer that handles real-time computation using Storm, and a serving layer that handles queries by merging batch and real-time views from Cassandra. The batch layer provides high-latency but unlimited computation, while the speed layer compensates for recent data with low-latency incremental updates. Together this provides a system that is fault-tolerant, scalable, and able to respond to queries in real-time.
Fast Data with Apache Ignite and Apache Spark with Christos ErotocritouSpark Summit
The document discusses Apache Ignite, a distributed in-memory platform that can be used with Apache Spark. It provides powerful APIs and flexible processing capabilities. Ignite allows for memory-centric storage and processing of data across clusters. It also integrates with Spark by allowing RDDs and DataFrames to be created from Ignite caches. This enables capabilities like running SQL queries and sharing data globally across Spark jobs. GridGain is a commercial distribution of Apache Ignite that adds enterprise features.
This document discusses Apache Arrow, an open source project that aims to standardize in-memory data representations to enable efficient data sharing across systems. It summarizes Arrow's goals of improving performance by 10-100x on many workloads through a common data layer, reducing serialization overhead. The document outlines Arrow's language bindings for Java, C++, Python, R, and Julia and efforts to integrate Arrow with systems like Spark, Drill and Impala to enable faster analytics. It encourages involvement in the Apache Arrow community.
Lens: Data exploration with Dask and Jupyter widgetsVíctor Zabalza
Lens is an open source Python library for automated data exploration of large datasets using Dask. It computes summary statistics and relationships between columns in a dataset. The results are serialized to JSON for interactive exploration through Jupyter widgets or a web UI. Dask allows the computations to run in parallel across a cluster for scalability. Lens integrates with the SherlockML platform to analyze all datasets uploaded.
This document provides an overview of SK Telecom's use of big data analytics and Spark. Some key points:
- SKT collects around 250 TB of data per day which is stored and analyzed using a Hadoop cluster of over 1400 nodes.
- Spark is used for both batch and real-time processing due to its performance benefits over other frameworks. Two main use cases are described: real-time network analytics and a network enterprise data warehouse (DW) built on Spark SQL.
- The network DW consolidates data from over 130 legacy databases to enable thorough analysis of the entire network. Spark SQL, dynamic resource allocation in YARN, and integration with BI tools help meet requirements for timely processing and quick
This document discusses Presto, an interactive SQL query engine for big data. It describes how Presto is optimized to quickly query data stored in Parquet format at Uber. Key optimizations for Parquet include nested column pruning, columnar reads, predicate pushdown, dictionary pushdown, and lazy reads. Benchmark results show these optimizations improve Presto query performance. The document also provides an overview of Uber's analytics infrastructure, applications of Presto, and ongoing work to further optimize Presto and Hadoop.
This document discusses Presto, an open source distributed SQL query engine for interactive analysis of large datasets. It describes Presto's architecture including its coordinator, connectors, workers and storage plugins. Presto allows querying of multiple data sources simultaneously through its connector plugins for systems like Hive, Cassandra, PostgreSQL and others. Queries are executed in a pipelined fashion without disk I/O or waiting between stages for improved performance.
Presentation on Presto (http://prestodb.io) basics, design and Teradata's open source involvement. Presented on Sept 24th 2015 by Wojciech Biela and Łukasz Osipiuk at the #20 Warsaw Hadoop User Group meetup http://www.meetup.com/warsaw-hug/events/224872317
Plazma - Treasure Data’s distributed analytical database -Treasure Data, Inc.
This document summarizes Plazma, Treasure Data's distributed analytical database that can import 40 billion records per day. It discusses how Plazma reliably imports and processes large volumes of data through its scalable architecture with real-time and archive storage. Data is imported using Fluentd and processed using its column-oriented, schema-on-read design to enable fast queries. The document also covers Plazma's transaction API and how it is optimized for metadata operations.
Presto is a distributed SQL query engine that Treasure Data provides as a service. Taro Saito discussed the internals of the Presto service at Treasure Data, including how the TD Presto connector optimizes scan performance from storage systems and how the service manages multi-tenancy and resource allocation for customers. Key challenges in providing a database as a service were also covered, such as balancing cost and performance.
Application Timeline Server - Past, Present and FutureVARUN SAXENA
How YARN Application timeline server evolved from Application History Server to Application Timeline Server v1 to ATSv2 or ATS Next gen, which is currently under development.
This slide was present at Hadoop Big Data Meetup at eBay, Bangalore, India.
Vinod Kumar Vavilapalli and Jian He presented on Apache Hadoop YARN, the next generation architecture for Hadoop. They discussed YARN's role as a data operating system and resource management platform. They outlined YARN's current capabilities and highlighted several features in development, including resource manager high availability, the YARN timeline server, and improved scheduling. They also discussed how YARN enables new applications beyond MapReduce and the growing ecosystem of projects supported by YARN.
This is the presentation from the "Discover HDP 2.1: Apache Hadoop 2.4.0, YARN & HDFS" webinar on May 28, 2014. Rohit Bahkshi, a senior product manager at Hortonworks, and Vinod Vavilapalli, PMC for Apache Hadoop, discuss an overview of YARN in HDFS and new features in HDP 2.1. Those new features include: HDFS extended ACLs, HTTPs wire encryption, HDFS DataNode caching, resource manager high availability, application timeline server, and capacity scheduler pre-emption.
Accelerate Big Data Application Development with Cascading and HDP, Hortonwor...Hortonworks
Accelerate Big Data Application Development with Cascading and HDP, webinar hosted by Hortonworks and Concurrent. Visit Hortonworks.com/webinars to access the recording.
The document discusses graph databases and their properties. Graph databases are structured to store graph-based data by using nodes and edges to represent entities and their relationships. They are well-suited for applications with complex relationships between entities that can be modeled as graphs, such as social networks. Key graph database technologies mentioned include Neo4j, OrientDB, and TinkerPop which provides graph traversal capabilities.
Presto is a distributed SQL query engine that allows for interactive analysis of large datasets across various data sources. It was created at Facebook to enable interactive querying of data in HDFS and Hive, which were too slow for interactive use. Presto addresses problems with existing solutions like Hive being too slow, the need to copy data for analysis, and high costs of commercial databases. It uses a distributed architecture with coordinators planning queries and workers executing tasks quickly in parallel.
Slides for a short presentation I gave on AWS Lambda, which "lets you run code without provisioning or managing servers". Lambda is to running code as Amazon S3 is to storing objects.
A brief introduction to YARN: how and why it came into existence and how it fits together with this thing called Hadoop.
Focus given to architecture, availability, resource management and scheduling, migration from MR1 to MR2, job history and logging, interfaces, and applications.
- The document discusses Apache Hadoop YARN, including its past, present, and future.
- In the past, YARN started as a sub-project of Hadoop and had several alpha and beta releases before the first stable release in 2013.
- Currently, YARN supports features like rolling upgrades, long running services, node labels, and improved scheduling. The timeline service provides application history and monitoring.
- Going forward, plans include improving the timeline service, usability features, and moving to newer Java versions in upcoming Hadoop releases.
- The document discusses Apache Hadoop YARN, including its past, present, and future.
- In the past, YARN started as a sub-project of Hadoop and had several alpha and beta releases before the first stable release in 2013.
- Currently, YARN enables rolling upgrades, long running services, node labels, and improved cluster management features like preemption scheduling and fine-grained resource isolation.
Hideo Kimura from DeNA presented on the MBGA Open Platform and the Hermit gadget server. The key points are:
- The MBGA Open Platform uses OpenSocial 0.9 and allows third party developers to build gadgets and integrate them into social networks.
- Hermit is the gadget server implemented in Perl using PSGI and Plack. It uses pluggable modules and can handle high volumes of requests through lighttpd and FCGI.
- Future directions include supporting OpenSocial 1.0, developing template APIs, and integrating additional authentication methods.
Shrinking the container_zurich_july_2018Ewan Slater
The document discusses strategies for building lean container images, including making smaller individual services or applications, only including necessary dependencies, and using the smallest possible base images. It introduces tools like Smith and Crashcart that can be used to shrink existing container images by removing unnecessary files and dependencies. The goal is to improve security, reduce image size, and allow for more flexible deployment of containerized applications and microservices.
Architectural considerations for Hadoop Applicationshadooparchbook
The document discusses architectural considerations for Hadoop applications using a case study on clickstream analysis. It covers requirements for data ingestion, storage, processing, and orchestration. For data storage, it considers HDFS vs HBase, file formats, and compression formats. SequenceFiles are identified as a good choice for raw data storage as they allow for splittable compression.
Dragon: A Distributed Object Storage at Yahoo! JAPAN (WebDB Forum 2017 / E...Yahoo!デベロッパーネットワーク
The document discusses Dragon, an object storage system developed by Yahoo Japan. Dragon was built to address issues with their previous storage system and meet high performance, scalability, and availability requirements. Dragon uses a distributed architecture with API nodes, a storage cluster, and Cassandra as the metadata database. The storage cluster stores object data across multiple volume groups for redundancy and each volume group contains three storage nodes.
Accumulo Summit 2014: Accumulo with Distributed SQL queriesAccumulo Summit
SQL queries are often the #1 requested feature of key/value stores. Argyle will present our integration of Accumulo with Facebook’s PrestoDB distributed query engine. We will discuss:
· Data locality between PrestoDB and Accumulo
· Predicate pushdown for row keys
· Leveraging a secondary index for column based queries
The talk will include a live demonstration of big data benchmark queries.
Soft-Shake 2013 : Enabling Realtime Queries to End UsersBenoit Perroud
Since it became an Apache Top Level Project in early 2008, Hadoop has established itself as the de-facto industry standard for batch processing. The two layers composing its core, HDFS and MapReduce, are strong building blocks for data processing. Running data analysis and crunching petabytes of data is no longer fiction. But the MapReduce framework does have two major drawbacks: query latency and data freshness.
At the same time, businesses have started to exchange more and more data through REST API, leveraging HTTP words (GET, POST, PUT, DELETE) and URI (for instance http://company/api/v2/domain/identifier), pushing the need to read data in a random access style – from simple key/value to complex queries.
Enhancing the BigData stack with real time search capabilities is the next natural step for the Hadoop ecosystem, because the MapReduce framework was not designed with synchronous processing in mind.
There is a lot of traction today in this area and this talk will try to answer the question of how to fill in this gap with specific open-source components, ultimately building a dedicated platform that will enable real-time queries on Internet-scale data sets. After discussing the evolution of the deployments of common Hadoop platform, a hybrid approach called lambda architecture will be proposed. It will be demonstrated with concrete examples, discussing which technology could be a good match, and how they would interact together.
The document discusses strategies for scaling real-time applications to support 1 million concurrent users on the JVM. It recommends using microservices and embracing polyglot programming. It also provides examples of building blocks for distributed systems including consistent hashing, bloom filters, throttling with leaky bucket algorithms, and using Kafka for asynchronous data processing pipelines.
The document discusses architectural considerations for Hadoop applications based on a case study of clickstream analysis. It covers requirements for data ingestion, storage, processing, and orchestration. For data storage, it recommends storing raw clickstream data in HDFS using the Avro file format with Snappy compression. For processed data, it recommends using the Parquet columnar storage format to enable efficient analytical queries. The document also discusses partitioning strategies and HDFS directory layout design.
Hadoop Application Architectures tutorial at Big DataService 2015hadooparchbook
This document outlines a presentation on architectural considerations for Hadoop applications. It introduces the presenters who are experts from Cloudera and contributors to Apache Hadoop projects. It then discusses a case study on clickstream analysis, how this was challenging before Hadoop due to data storage limitations, and how Hadoop provides a better solution by enabling active archiving of large volumes and varieties of data at scale. Finally, it covers some of the challenges in implementing Hadoop, such as choices around storage managers, data modeling and file formats, data movement workflows, metadata management, and data access and processing frameworks.
This document discusses Lattice, an open source Platform as a Service (PaaS) that was born from CloudFoundry. Lattice aims to make deploying and running containerized workloads easy through features like easy installation, clustering, scheduling, self-healing, load balancing, and log aggregation. The document provides an overview of Lattice's architecture and components like Diego for scheduling and X-Ray for visualization. It also demonstrates how to deploy Docker images and buildpacks, submit custom workloads, configure routing, and view logs using Lattice.
Raft protocol has been successfully used for consistent metadata replication; however, using it for data replication poses unique challenges. Apache Ratis is a RAFT implementation targeted at high throughput data replication problems. Apache Ratis is being successfully used as a consensus protocol for data stored in Ozone (object store) and Quadra(block device) to provide data throughput that saturates the network links and disk bandwidths.
Pluggable nature of Ratis renders it useful for multiple use cases including high availability, data or metadata replication, and ensuring consistency semantics.
This talk presents the design challenges to achieve high throughput and how Apache Ratis addresses them. We talk about specific optimizations that have been implemented to minimize overheads and scale up the throughput while maintaining correctness of the consistency protocol. The talk also explains how systems like Ozone take advantage of Ratis’s implementation choices to achieve scale. We will discuss the current performance numbers and also future optimizations. MUKUL KUMAR SINGH, Staff Software Engineer, Hortonworks and LOKESH JAIN, Software Engineer, Hortonworks
The document discusses the evolution of Pivotal Gemfire, now known as Apache Geode, from a proprietary product to an open source project. It provides an overview of Gemfire/Geode's capabilities including elastic scalability, high performance, and flexibility for developers. It also outlines Geode's role as a potential in-memory data exchange layer and integration point across modern data infrastructure technologies. Key aspects of Geode like its PDX serialization and asynchronous events are highlighted as building blocks that position it well for this role.
SaltConf14 - Craig Sebenik, LinkedIn - SaltStack at Web ScaleSaltStack
This talk will focus on the unique challenges of managing Web scale and an application stack that lives on tens of thousands of servers spread across multiple data centers. Learn more about LinkedIn's unique topology, about the development of an efficient build environment, and hear more about LinkedIn plans for a deployment system based on Salt. Also, all of the software that runs LinkedIn sends a LOT of data. In order to stay ahead of this tidal wave of data, the team must address scale challenges seen in very few environments through efficient use of monitoring and metrics systems. This talk will highlight best practices and user training necessary for the use of SaltStack in large environments.
SpagoBI 5 Demo Day and Workshop : Technology Applications and UsesSpagoWorld
These slides supported SpagoBI Labs' presentation of SpagoBI 5 ("Technology Applications and Uses" session), taking place in New York, NY on January 26th, and in Herndon, VA on January 28th, 2015. Further details on the event: http://bit.ly/1IzatIX
The new GDPR regulation went into effect on May 25th. While a majority of conversations have revolved around the security and IT aspects of the law, marketing teams will play a crucial role in helping organizations meet GDPR standards and playing a strategic role across the organization . Join us to learn more, engage with your peers and get prepared.
This webinar will cover:
- How complying with the GDPR will drive better marketing and raise the standard of the quality of your customer engagement
- The GDPR elements marketers must know about
- The elements of PII that will be affected and what marketers need to do about it
- A deep dive on how GDPR regulations will affect your marketing channels - email, programmatic advertising, cold calls, etc.
- Tactical marketing updates needed to meet GDPR guidelines
AR and VR by the Numbers: A Data First Approach to the Technology and MarketTreasure Data, Inc.
The document discusses trends in the augmented reality (AR) and virtual reality (VR) markets. It notes that the combined AR and VR market is estimated to reach $120 billion by 2020, with AR's market estimated at $89.9 billion and VR's at $29.9 billion. While VR growth is clear, the exact size is unclear. The document outlines challenges like the need for improved headsets and continued developer investment outside of mobile. It emphasizes that AR currently focuses on using data to project context and enable interaction with the real world, and that collecting user data is important for defining the experience.
An overview of Customer Data Platforms (CDP) with the industry leader who coined the term, David Raab. Find out how to use Live Customer Data to create a better customer experience and how Live Data Management can give you a competitive edge with a 360 degree view of your clients.
Learn:
- The definition and requirements for Customer Data Platforms
- The differences between Customer Data Platforms and comparative technologies such as Data Warehousing and Marketing Automation
- Reference architectures/approaches to building CDP
- How Treasure Data is used to build Customer Data Platforms
And here's the song: https://youtu.be/RalMozVq55A
In this hands-on webinar we will cover how to leverage the Treasure Data Javascript SDK library to ensure user stitching of web data into the Treasure Data Customer Data Platform to provide a holistic view of prospects and customers.
We will demo the native SDK, as well as deploying the SDK inside of Adobe DTM and Google Tag Manager.
Hands-On: Managing Slowly Changing Dimensions Using TD WorkflowTreasure Data, Inc.
In this hands-on webinar we'll explore the data warehousing concept of Slowly Changing Dimensions (SCDs) and common use cases for managing SCDs when dealing with customer data. This webinar will demonstrate different methods for tracking SCDs in a data warehouse, and how Treasure Data Workflow can be used to create robust data pipelines to handle these processes.
Brand Analytics Management: Measuring CLV Across Platforms, Devices and AppsTreasure Data, Inc.
Gaming companies with multiple products often struggle to calculate accurate Customer Lifetime Value (CLTV) across their portfolio. This is because user data is often analyzed in silos so companies are unable to get a clear picture of ROI and CLTV across platforms, devices and apps.
In this webinar we’ll look at how you can apply a holistic and complete approach to your CLTV and ROI through the lens of gaming companies, though this technique is applicable for any company who has products spanning platforms.
We’ll also explore:
How the integral power of data in business has shifted over the past 10 years.
Discover the current technologies and processes used to analyze data across different platforms by combining multiple data streams, looking at examples in brand and portfolio-based LTV.
How to process and centralize dozens of varying data streams.
Nicolas Nadeau will speak from his extensive experience and show how leveraging data from multiple product strategies spanning many platforms can be highly beneficial for your company.
Do you know what your top ten 'happy' customers look like? Would you like to find ten more just like them? Come learn how to leverage 1st & 3rd party data to map your customer journey and drive users down a path where every interaction is personalized, fun, & data-driven. No more detractors, power your Customer Experience with data!
In this webinar you will learn:
-When, why, and how to leverage 1st, 2nd, and 3rd party data
-Tips & Tricks for marketers to become more data driven when launching their campaigns
-Why all marketers needs a 360 degree customer view
The reality is virtual, but successful VR games still require cold, hard data. For wildly popular games like Survios’ Raw Data, the first VR-exclusive game to reach #1 on Steam’s Global Top Sellers list, data and analytics are the key to success.
And now online gaming companies have the full-stack analytics infrastructure and tools to measure every aspect of a virtual reality game and its ecosystem in real time. You can keep tabs on lag, which ruins a VR experience, improve gameplay and identify issues before they become showstoppers, and create fully personalized, completely immersive experiences that blow minds and boost adoption, and more. All with the right tools.
Make success a reality: Register now for our latest interactive VB Live event, where we’ll tap top experts in the industry to share insights into turning data into winning VR games.
Attendees will:
* Understand the role of VR in online gaming
* Find out how VR company Survios successfully leverages the Exostatic analytics infrastructure for commercial and gaming success
* Discover how to deploy full-stack analytics infrastructure and tools
Speakers:
Nicolas Nadeau, President, Exostatic
Kiyoto Tamura, VP Marketing, Treasure Data
Ben Solganik, Producer, Survios
Stewart Rogers, Director of Marketing Technology, VentureBeat
Wendy Schuchart, Moderator, VentureBeat
The document discusses how marketers can better leverage customer data to improve the customer experience. It provides tips from various experts on developing a robust data strategy, asking the right questions of data to uncover insights, owning customer data to stay compliant with regulations, and how IoT can be used to inform and deploy customer experience solutions. The overall message is that marketers need to stop data from being fragmented and better connect customer touchpoints to deliver personalized experiences.
Harnessing Data for Better Customer Experience and Company SuccessTreasure Data, Inc.
As big data has exploded, the ability for companies to easily leverage it has imploded. Organizations are drowning in their own information, unable to see the forest through the trees, while the big players consistently outperform in their ability to deliver a great customer experience, faster, cheaper…As a result, the vast majority of companies are scrambling to catch up and become more agile, data-driven, to use their data more effectively so they can attract and retain their elusive customers...
In this joint deck by 451 Research and Treasure Data, you will learn how to enable your line of business team to own their own data (instead of relying on IT) to be able to:
- deliver a single, persistent view of your customer based on behavior data
- make that data accessible to the right people at the right time
- Increase organizational effectiveness by (finally) breaking down silos with data
- enable powerful marketing tools to enhance the customer experience
How to make your open source project MATTER
Let’s face it: most open source projects die. “For every Rails, Docker and React, there are thousands of projects that never take off. They die in the lonely corners of GitHub, only to be discovered by bots scanning for SSH private keys.
Over the last 5 years, I worked on and off on marketing a piece of infrastructure middleware called Fluentd. We tried many things to ensure that it did not die: From speaking at events, speaking to strangers, giving away stickers, making people install Fluentd on their laptop. Most everything I tried had a small, incremental effect, but there were several initiatives/hacks that raised Fluentd’s awareness to the next level. As I listed up these “ideas that worked”, I noticed the common thread: they all brought Fluentd into a new ecosystem via packaging.”
* 행사 정보 :2016년 10월 14일 MARU180 에서 진행된 '데이터야 놀자' 1day 컨퍼런스 발표 자료
* 발표자 : Dylan Ko (고영혁) Data Scientist / Data Architect at Treasure Data
* 발표 내용
- 데이터사이언티스트 고영혁 소개
- Treasure Data (트레저데이터) 소개
- 데이터로 돈 버는 글로벌 사례 #1
>> MUJI : 전통적 리테일에서 데이터 기반 O2O
- 데이터로 돈 버는 글로벌 사례 #2
>> WISH : 개인화&자동화를 통한 쇼핑 최적화
- 데이터로 돈 버는 글로벌 사례 #3
>> Oisix : 머신러닝으로 이탈고객 예측&방지
- 데이터로 돈 버는 글로벌 사례 #4
>> 워너브로스 : 프로세스 자동화로 시간과 돈 절약
- 데이터로 돈 버는 글로벌 사례 #5
>> Dentsu 등의 애드테크(Adtech) 회사들
- 데이터로 돈을 벌고자 할 때 반드시 체크해야 하는 것
Keynote on Fluentd Meetup Summer
Related Slide
- Fluentd ServerEngine Integration & Windows Support http://www.slideshare.net/RittaNarita/fluentd-meetup-2016-serverengine-integration-windows-support
- Fluentd v0.14 Plugin API Details http://www.slideshare.net/tagomoris/fluentd-v014-plugin-api-details
This document provides an introduction and overview of Hivemall, an open source machine learning library built as a collection of Hive UDFs. It begins with background on the presenter, Makoto Yui, and then covers the following key points:
- What Hivemall is and its vision of bringing machine learning capabilities to SQL users
- Popular algorithms supported in current and upcoming versions, such as random forest, factorization machines, gradient boosted trees
- Real-world use cases at companies such as for click-through rate prediction, user profiling, and churn detection
- How to use algorithms like random forest, matrix factorization, and factorization machines from Hive queries
- The development roadmap, with plans to support NLP
This document summarizes Johan Gustavsson's presentation on scalable Hadoop in the cloud. It discusses (1) replacing an on-premise Hadoop cluster with Plazma storage on S3 and job execution in containers, (2) how jobs are isolated either through individual JobClients or resource pools, and (3) ongoing architecture changes through the Patchset Treasure Data initiative to support multiple Hadoop versions and improve high availability of job submission services.
Muga Nishizawa discusses Embulk, an open-source bulk data loader. Embulk loads records from various sources to various targets in parallel using plugins. Treasure Data customers use Embulk to upload different file formats and data sources to their TD database. While Embulk is focused on bulk loading, TD also develops additional tools to generate Embulk configurations, manage loads over time, and scale Embulk using a MapReduce executor on Hadoop clusters for very large data loads.
John Hammink's Talk at Great Wide Open 2016. We discuss: 1.) the need for data analytics infrastructure that can scale exponentially and 2.) what such an infrastructure must contain and finally 3.) the need for an infrastructure to be able to handle un - and semi-structured data.
Treasure Data: Move your data from MySQL to Redshift with (not much more tha...Treasure Data, Inc.
This document discusses migrating data from MySQL to Amazon Redshift. It describes MySQL and Redshift, and some of the challenges of migrating between the two systems, such as incompatible schemas and manual processes. The proposed solution is to use a cloud data lake with schema-on-read to store JSON event data, which can then be loaded into Redshift, a cloud data warehouse with schema-on-write, providing an automated way to migrate data between different systems and schemas.
This document discusses migrating data from MySQL to Amazon Redshift. It describes MySQL and Redshift, and some of the challenges of migrating between the two systems, such as incompatible schemas and manual processes. The proposed solution is to use a cloud data lake with schema-on-read to store JSON event data, which can then be loaded into Redshift, a cloud data warehouse with schema-on-write, providing an automated way to migrate data between different systems and schemas.
This presentation describes the common issues when doing application logging and introduce how to solve most of the problems through the implementation of an unified logging layer with Fluentd.
internship project presentation for reference.pptxSaieJadhav1
Purpose :- Debian-based Linux distribution for advanced Penetration Testing and Security Auditing.- Developed by Offensive Security, known for security training.- Released in March 2013, a rebuild of BackTrack Linux following Debian standards.Maintained by Offensive Security:- Offensive Security oversees Kali Linux, ensuring it includes the latest security tools.- Known for certifications like OSCP (Offensive Security Certified Professional).Usage:- Penetration Testing: Simulates cyber-attacks to find system vulnerabilities.- Security Research: Helps researchers understand exploits and security techniques.- Computer Forensics: Used in digital crime investigations and data recovery.
Agricultural Profitability through Resilience: Smallholder Farmers' Strategie...IJAEMSJORNAL
This study investigated the knowledge strategies and coping utilized by smallholder farmers in Guimba, Nueva Ecija to reduce and adjust to the effects of climate change. Smallholder farmers, who are frequently susceptible to climate change, utilize various traditional and innovative methods to strengthen their ability to withstand and recover from these consequences. Based on the results of this study, farmers in Guimba, Nueva Ecija demonstrate a profound comprehension of the adverse weather conditions, such as typhoons, droughts, and excessive rainfall, which they ascribe to climate change. While they have a fundamental understanding of climate change and its effects, their knowledge of scientific intricacies is restricted, indicating a need for information that is particular to the context. Although farmers possess knowledge about climate change, they are not actively engaging in proactive actions to adapt to it. Instead, they rely on reactive coping mechanisms. This highlights the necessity for targeted educational and communicative endeavors to promote the acceptance and implementation of approaches. Furthermore, the absence of available resources poses a significant barrier to achieving successful adaptation, highlighting the importance of pushing for inexpensive and feasible measures for adaptation. Farmers recognize the benefits of agroforestry and have started integrating the growth of fruit trees, particularly mangoes, into their coping techniques.
Maintaining asset integrity, maximising performance, and reaching peak production outcomes all depend on effective defect elimination management.
The difficulties of locating, comprehending, and resolving asset flaws will be examined in this article, with a focus on the significance of having a complete awareness of their nature, implications, and possible outcomes.
The significance of maintenance departments creating a framework for integrating defect elimination and bad actor analysis into their proactive maintenance strategies, enabling them to make more informed decisions, becomes clear when we delve into the subtleties of defect analysis.
Defects in an asset are characterised as flaws, inadequacies, or departures from standard operating characteristics or design specifications.
The kind of defect, where it is located on the assembly of the asset, and what happens when it exists determine how one defect impacts the performance of the asset as a whole.
For example, a minor corrosion patch on a panel that is readily replaceable and does not pose a structural risk to the asset overall is unlikely to have an adverse effect on the asset's longevity, performance, or safety.
Effective Defect Elimination Management is critical for maintaining asset integrity, optimizing performance, and achieving peak production results. It involves identifying, understanding, and addressing asset defects systematically.
Understanding asset defects requires accurate identification and comprehensive documentation in the CMMS, including risk assessments that evaluate both the consequence and likelihood of defects leading to failures.
Defect Elimination Management (DEM) is a comprehensive approach that goes beyond traditional maintenance practices, focusing on root cause analysis and implementing long-term solutions to prevent defect recurrence.
"Bad Actors" in defect elimination refer to equipment, systems, or components that consistently underperform, require frequent maintenance, or cause repeated operational reliability or quality issues.
Advanced diagnostic tools and technologies, such as vibration analysis systems, infrared thermography, and AI-based analytics, have transformed the way asset defects are identified and managed.
Quality and timely repairs and clear business processes for managing defects are crucial, along with maintaining quality maintenance history data to provide valuable insights for future defect elimination processes.
To learn more, you can read my article via this link: https://www.cmmssuccess.com/defect-elimination-management/
Manufacturing is the process of converting raw materials into finished goods through various production methods. Historically, manufacturing occurred on a small scale through apprenticeships or putting-out systems, but the Industrial Revolution led to large-scale manufacturing using machines powered by steam engines