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brief presentation of tokyo cabinet to the Belgian Ruby User group
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Recommandé
tokyo cabinet database overview
Tokyo Cabinet
Tokyo Cabinet
André Mayer
RedHat built a distributed object storage solution named Ceph which first debuted ten years ago. Now we are seeing rapid developments in the industry and we want to take advantage of them. In this talk, we will briefly introduce Ceph, revisit the problems we are seeing when profiling its I/O performance with flash device, and explain why we want to embrace the future by switching to Seastar. We’ll share our experiences with the audience of how and when we are porting our software to this framework.
Scylla Summit 2018: Rebuilding the Ceph Distributed Storage Solution with Sea...
Scylla Summit 2018: Rebuilding the Ceph Distributed Storage Solution with Sea...
ScyllaDB
Ruby,no sql and tokyocabinet
Ruby,no sql and tokyocabinet
biaowei zhuang
Redis - From LAMP to NoSQL (CloudTW meetup-14)
Redis - From LAMP to NoSQL (CloudTW meetup-14)
York Tsai
Join us for a developer workshop where we’ll go hands-on to explore the affinities between Rust, the Tokio framework, and ScyllaDB. You’ll go live with our sample Rust application, built on our new, high performance native Rust client driver.
Build Low-Latency Applications in Rust on ScyllaDB
Build Low-Latency Applications in Rust on ScyllaDB
ScyllaDB
There are two key choices when scaling a NoSQL data store: choosing between a hash or a range based sharding and choosing the right sharding key. Any choice is a trade-off between scalability of read, append, and update workloads. In this talk I will present the standard scaling techniques, some non-universal sharding tricks, less obvious reasons for hotspots, as well as techniques to avoid them.
Avoiding Data Hotspots at Scale
Avoiding Data Hotspots at Scale
ScyllaDB
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
RedHat built a distributed object storage solution named Ceph which first debuted ten years ago. Now we are seeing rapid developments in the industry and we want to take advantage of them. In this talk, we will briefly introduce Ceph, revisit the problems we are seeing when profiling its I/O performance with flash device, and explain why we want to embrace the future by switching to Seastar. We’ll share our experiences with the audience of how and when we are porting our software to this framework.
Scylla Summit 2018: Rebuilding the Ceph Distributed Storage Solution with Sea...
Scylla Summit 2018: Rebuilding the Ceph Distributed Storage Solution with Sea...
ScyllaDB
Recommandé
tokyo cabinet database overview
Tokyo Cabinet
Tokyo Cabinet
André Mayer
RedHat built a distributed object storage solution named Ceph which first debuted ten years ago. Now we are seeing rapid developments in the industry and we want to take advantage of them. In this talk, we will briefly introduce Ceph, revisit the problems we are seeing when profiling its I/O performance with flash device, and explain why we want to embrace the future by switching to Seastar. We’ll share our experiences with the audience of how and when we are porting our software to this framework.
Scylla Summit 2018: Rebuilding the Ceph Distributed Storage Solution with Sea...
Scylla Summit 2018: Rebuilding the Ceph Distributed Storage Solution with Sea...
ScyllaDB
Ruby,no sql and tokyocabinet
Ruby,no sql and tokyocabinet
biaowei zhuang
Redis - From LAMP to NoSQL (CloudTW meetup-14)
Redis - From LAMP to NoSQL (CloudTW meetup-14)
York Tsai
Join us for a developer workshop where we’ll go hands-on to explore the affinities between Rust, the Tokio framework, and ScyllaDB. You’ll go live with our sample Rust application, built on our new, high performance native Rust client driver.
Build Low-Latency Applications in Rust on ScyllaDB
Build Low-Latency Applications in Rust on ScyllaDB
ScyllaDB
There are two key choices when scaling a NoSQL data store: choosing between a hash or a range based sharding and choosing the right sharding key. Any choice is a trade-off between scalability of read, append, and update workloads. In this talk I will present the standard scaling techniques, some non-universal sharding tricks, less obvious reasons for hotspots, as well as techniques to avoid them.
Avoiding Data Hotspots at Scale
Avoiding Data Hotspots at Scale
ScyllaDB
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
RedHat built a distributed object storage solution named Ceph which first debuted ten years ago. Now we are seeing rapid developments in the industry and we want to take advantage of them. In this talk, we will briefly introduce Ceph, revisit the problems we are seeing when profiling its I/O performance with flash device, and explain why we want to embrace the future by switching to Seastar. We’ll share our experiences with the audience of how and when we are porting our software to this framework.
Scylla Summit 2018: Rebuilding the Ceph Distributed Storage Solution with Sea...
Scylla Summit 2018: Rebuilding the Ceph Distributed Storage Solution with Sea...
ScyllaDB
Working with Shared Libraries in Perl
Working with Shared Libraries in Perl
Ido Kanner
Update on Crimson, the Seastarized Ceph
Update on Crimson - the Seastarized Ceph - Seastar Summit
Update on Crimson - the Seastarized Ceph - Seastar Summit
ScyllaDB
Gsummit apis-2013
Gsummit apis-2013
Gsummit apis-2013
Gluster.org
System software engineers have long been taught that disks are slow and sequential I/O is key to performance. With SSD drives I/O really got much faster but not simpler. In this brave new world of rocket-speed throughputs an engineer has to distinguish sustained workload from bursts, (still) take care about I/O buffer sizes, account for disks’ internal parallelism and study mixed I/O characteristics in advance. In this talk we will share some key performance measurements of the modern hardware we’re taking at ScyllaDB and our opinion about the implications for the database and system software design.
P99CONF — What We Need to Unlearn About Persistent Storage
P99CONF — What We Need to Unlearn About Persistent Storage
ScyllaDB
A better way to do distcp on hadoop? Use Gobblin!
Distcp gobblin
Distcp gobblin
Vasanth Rajamani
Alluxio Day VI October 12, 2021 https://www.alluxio.io/alluxio-day/ Speaker: Ke Wang, Facebook Zhenyu Song, Princeton University
Improve Presto Architectural Decisions with Shadow Cache
Improve Presto Architectural Decisions with Shadow Cache
Alluxio, Inc.
PXF - A Unified Access Framework for HDFS datasets
PXF BDAM 2016
PXF BDAM 2016
Shivram Mani
GlusterFS Presentation @FOSSCOMM2013
GlusterFS Presentation FOSSCOMM2013 HUA, Athens, GR
GlusterFS Presentation FOSSCOMM2013 HUA, Athens, GR
Theophanis Kontogiannis
In file systems, large sequential writes are more beneficial than small random writes, and hence many storage systems implement a log structured file system. In the same way, the cloud favors large objects more than small objects. Cloud providers place throttling limits on PUTs and GETs, and so it takes significantly longer time to upload a bunch of small objects than a large object of the aggregate size. Moreover, there are per-PUT calls associated with uploading smaller objects. In Netflix, a lot of media assets and their relevant metadata is generated and pushed to cloud. We would like to propose a strategy to compact these small objects into larger blobs before uploading them to Cloud. We will discuss how to select relevant smaller objects, and manage the indexing of these objects within the blob along with modification in reads, overwrites and deletes. Finally, we would showcase the potential impact of such a strategy on Netflix assets in terms of cost and performance.
Object Compaction in Cloud for High Yield
Object Compaction in Cloud for High Yield
ScyllaDB
This presentation was given by Doug Judd at BerlinBuzzwords 2010.
Hypertable Berlin Buzzwords
Hypertable Berlin Buzzwords
hypertable
Introduction To MongoDB Slides - Faro Dev Day, 2016
Introduction to mongo db
Introduction to mongo db
Lawrence Mwai
Xephon K is a time series database using Cassandra as main backend. We talk about how to model time series data in Cassandra and compare its throughput with InfluxDB and KairosDB
Xephon K A Time series database with multiple backends
Xephon K A Time series database with multiple backends
University of California, Santa Cruz
mypipe latches onto a MySQL server with binary log replication enabled and allows for the creation of pipes that can consume the replication stream and act on the data (primarily integrated with Apache Kafka). This presentation goes over mypipe's design and usage of Scala, Akka, Kafka, and Avro after which we look at some applications and possible use cases of mypipe in a data pipeline.
mypipe: Buffering and consuming MySQL changes via Kafka
mypipe: Buffering and consuming MySQL changes via Kafka
Hisham Mardam-Bey
Talk by Chris Mair during SFScon14: Schrödinger’s elephant: why PostgreSQL can solve all your database needs
SFScon14: Schrödinger’s elephant: why PostgreSQL can solve all your database ...
SFScon14: Schrödinger’s elephant: why PostgreSQL can solve all your database ...
South Tyrol Free Software Conference
Zero to 1 Billion+ Records: A True Story of Learning & Scaling GameChanger
Zero to 1 Billion+ Records: A True Story of Learning & Scaling GameChanger
MongoDB
In this session we will look over the various ways .NET is collecting memory, tips how to help GC perform better and tools that will save your day. This is a must attend session for those who still do not know how to troubleshoot memory issues. For the rest it is a nice refresh and new look of features in .NET 4.5. As usual there will be lots of demos.
.NET Memory Primer (Martin Kulov)
.NET Memory Primer (Martin Kulov)
ITCamp
Presto is a widely adopted federated SQL engine for federated querying across multiple data sources. With Presto, you can perform ad hoc querying of data in place. For today’s “data hacker”, Presto helps solve challenges around time to discovery and the amount of time it takes to do ad hoc analysis. In Level 101, you’ll get an overview of Presto, including: A high level overview of Presto & most common use cases The problems it solves and why you should use it A live, hands-on demo on getting Presto running on Docker Real world example: How Twitter uses Presto at scale
Level 101 for Presto: What is PrestoDB?
Level 101 for Presto: What is PrestoDB?
Ali LeClerc
In this presentation, Paul introduces InfluxDB, a distributed time series database that he open sourced based on the backend infrastructure at Errplane. He talks about why you'd want a database specifically for time series and he covers the API and some of the key features of InfluxDB, including: • Stores metrics (like Graphite) and events (like page views, exceptions, deploys) • No external dependencies (self contained binary) • Fast. Handles many thousands of writes per second on a single node • HTTP API for reading and writing data • SQL-like query language • Distributed to scale out to many machines • Built in aggregate and statistics functions • Built in downsampling
Introduction to InfluxDB, an Open Source Distributed Time Series Database by ...
Introduction to InfluxDB, an Open Source Distributed Time Series Database by ...
Hakka Labs
Data- How Does It Work-
Data- How Does It Work-
Boyang Niu
Hardware once reserved to HPC systems is entering the datacenter. Cyprien will describe an effort to help developers leverage its new capabilities. Its integration to H2O, along with tools like Caffe, is accelerating and making the platform more powerful. #h2ony
Caffe + H2O - By Cyprien noel
Caffe + H2O - By Cyprien noel
Sri Ambati
株式会社ゆめみ社内勉強会201004資料
SSDとTokyoTyrantやMySQLの性能検証
SSDとTokyoTyrantやMySQLの性能検証
勲 國府田
FutureRuby presentation on extending Tokyo Cabinet with Lua extensions. GitHub repo with sample code & extensions: http://bit.ly/wJpeG
Lean & Mean Tokyo Cabinet Recipes (with Lua) - FutureRuby '09
Lean & Mean Tokyo Cabinet Recipes (with Lua) - FutureRuby '09
Ilya Grigorik
Contenu connexe
Tendances
Working with Shared Libraries in Perl
Working with Shared Libraries in Perl
Ido Kanner
Update on Crimson, the Seastarized Ceph
Update on Crimson - the Seastarized Ceph - Seastar Summit
Update on Crimson - the Seastarized Ceph - Seastar Summit
ScyllaDB
Gsummit apis-2013
Gsummit apis-2013
Gsummit apis-2013
Gluster.org
System software engineers have long been taught that disks are slow and sequential I/O is key to performance. With SSD drives I/O really got much faster but not simpler. In this brave new world of rocket-speed throughputs an engineer has to distinguish sustained workload from bursts, (still) take care about I/O buffer sizes, account for disks’ internal parallelism and study mixed I/O characteristics in advance. In this talk we will share some key performance measurements of the modern hardware we’re taking at ScyllaDB and our opinion about the implications for the database and system software design.
P99CONF — What We Need to Unlearn About Persistent Storage
P99CONF — What We Need to Unlearn About Persistent Storage
ScyllaDB
A better way to do distcp on hadoop? Use Gobblin!
Distcp gobblin
Distcp gobblin
Vasanth Rajamani
Alluxio Day VI October 12, 2021 https://www.alluxio.io/alluxio-day/ Speaker: Ke Wang, Facebook Zhenyu Song, Princeton University
Improve Presto Architectural Decisions with Shadow Cache
Improve Presto Architectural Decisions with Shadow Cache
Alluxio, Inc.
PXF - A Unified Access Framework for HDFS datasets
PXF BDAM 2016
PXF BDAM 2016
Shivram Mani
GlusterFS Presentation @FOSSCOMM2013
GlusterFS Presentation FOSSCOMM2013 HUA, Athens, GR
GlusterFS Presentation FOSSCOMM2013 HUA, Athens, GR
Theophanis Kontogiannis
In file systems, large sequential writes are more beneficial than small random writes, and hence many storage systems implement a log structured file system. In the same way, the cloud favors large objects more than small objects. Cloud providers place throttling limits on PUTs and GETs, and so it takes significantly longer time to upload a bunch of small objects than a large object of the aggregate size. Moreover, there are per-PUT calls associated with uploading smaller objects. In Netflix, a lot of media assets and their relevant metadata is generated and pushed to cloud. We would like to propose a strategy to compact these small objects into larger blobs before uploading them to Cloud. We will discuss how to select relevant smaller objects, and manage the indexing of these objects within the blob along with modification in reads, overwrites and deletes. Finally, we would showcase the potential impact of such a strategy on Netflix assets in terms of cost and performance.
Object Compaction in Cloud for High Yield
Object Compaction in Cloud for High Yield
ScyllaDB
This presentation was given by Doug Judd at BerlinBuzzwords 2010.
Hypertable Berlin Buzzwords
Hypertable Berlin Buzzwords
hypertable
Introduction To MongoDB Slides - Faro Dev Day, 2016
Introduction to mongo db
Introduction to mongo db
Lawrence Mwai
Xephon K is a time series database using Cassandra as main backend. We talk about how to model time series data in Cassandra and compare its throughput with InfluxDB and KairosDB
Xephon K A Time series database with multiple backends
Xephon K A Time series database with multiple backends
University of California, Santa Cruz
mypipe latches onto a MySQL server with binary log replication enabled and allows for the creation of pipes that can consume the replication stream and act on the data (primarily integrated with Apache Kafka). This presentation goes over mypipe's design and usage of Scala, Akka, Kafka, and Avro after which we look at some applications and possible use cases of mypipe in a data pipeline.
mypipe: Buffering and consuming MySQL changes via Kafka
mypipe: Buffering and consuming MySQL changes via Kafka
Hisham Mardam-Bey
Talk by Chris Mair during SFScon14: Schrödinger’s elephant: why PostgreSQL can solve all your database needs
SFScon14: Schrödinger’s elephant: why PostgreSQL can solve all your database ...
SFScon14: Schrödinger’s elephant: why PostgreSQL can solve all your database ...
South Tyrol Free Software Conference
Zero to 1 Billion+ Records: A True Story of Learning & Scaling GameChanger
Zero to 1 Billion+ Records: A True Story of Learning & Scaling GameChanger
MongoDB
In this session we will look over the various ways .NET is collecting memory, tips how to help GC perform better and tools that will save your day. This is a must attend session for those who still do not know how to troubleshoot memory issues. For the rest it is a nice refresh and new look of features in .NET 4.5. As usual there will be lots of demos.
.NET Memory Primer (Martin Kulov)
.NET Memory Primer (Martin Kulov)
ITCamp
Presto is a widely adopted federated SQL engine for federated querying across multiple data sources. With Presto, you can perform ad hoc querying of data in place. For today’s “data hacker”, Presto helps solve challenges around time to discovery and the amount of time it takes to do ad hoc analysis. In Level 101, you’ll get an overview of Presto, including: A high level overview of Presto & most common use cases The problems it solves and why you should use it A live, hands-on demo on getting Presto running on Docker Real world example: How Twitter uses Presto at scale
Level 101 for Presto: What is PrestoDB?
Level 101 for Presto: What is PrestoDB?
Ali LeClerc
In this presentation, Paul introduces InfluxDB, a distributed time series database that he open sourced based on the backend infrastructure at Errplane. He talks about why you'd want a database specifically for time series and he covers the API and some of the key features of InfluxDB, including: • Stores metrics (like Graphite) and events (like page views, exceptions, deploys) • No external dependencies (self contained binary) • Fast. Handles many thousands of writes per second on a single node • HTTP API for reading and writing data • SQL-like query language • Distributed to scale out to many machines • Built in aggregate and statistics functions • Built in downsampling
Introduction to InfluxDB, an Open Source Distributed Time Series Database by ...
Introduction to InfluxDB, an Open Source Distributed Time Series Database by ...
Hakka Labs
Data- How Does It Work-
Data- How Does It Work-
Boyang Niu
Hardware once reserved to HPC systems is entering the datacenter. Cyprien will describe an effort to help developers leverage its new capabilities. Its integration to H2O, along with tools like Caffe, is accelerating and making the platform more powerful. #h2ony
Caffe + H2O - By Cyprien noel
Caffe + H2O - By Cyprien noel
Sri Ambati
Tendances
(20)
Working with Shared Libraries in Perl
Working with Shared Libraries in Perl
Update on Crimson - the Seastarized Ceph - Seastar Summit
Update on Crimson - the Seastarized Ceph - Seastar Summit
Gsummit apis-2013
Gsummit apis-2013
P99CONF — What We Need to Unlearn About Persistent Storage
P99CONF — What We Need to Unlearn About Persistent Storage
Distcp gobblin
Distcp gobblin
Improve Presto Architectural Decisions with Shadow Cache
Improve Presto Architectural Decisions with Shadow Cache
PXF BDAM 2016
PXF BDAM 2016
GlusterFS Presentation FOSSCOMM2013 HUA, Athens, GR
GlusterFS Presentation FOSSCOMM2013 HUA, Athens, GR
Object Compaction in Cloud for High Yield
Object Compaction in Cloud for High Yield
Hypertable Berlin Buzzwords
Hypertable Berlin Buzzwords
Introduction to mongo db
Introduction to mongo db
Xephon K A Time series database with multiple backends
Xephon K A Time series database with multiple backends
mypipe: Buffering and consuming MySQL changes via Kafka
mypipe: Buffering and consuming MySQL changes via Kafka
SFScon14: Schrödinger’s elephant: why PostgreSQL can solve all your database ...
SFScon14: Schrödinger’s elephant: why PostgreSQL can solve all your database ...
Zero to 1 Billion+ Records: A True Story of Learning & Scaling GameChanger
Zero to 1 Billion+ Records: A True Story of Learning & Scaling GameChanger
.NET Memory Primer (Martin Kulov)
.NET Memory Primer (Martin Kulov)
Level 101 for Presto: What is PrestoDB?
Level 101 for Presto: What is PrestoDB?
Introduction to InfluxDB, an Open Source Distributed Time Series Database by ...
Introduction to InfluxDB, an Open Source Distributed Time Series Database by ...
Data- How Does It Work-
Data- How Does It Work-
Caffe + H2O - By Cyprien noel
Caffe + H2O - By Cyprien noel
En vedette
株式会社ゆめみ社内勉強会201004資料
SSDとTokyoTyrantやMySQLの性能検証
SSDとTokyoTyrantやMySQLの性能検証
勲 國府田
FutureRuby presentation on extending Tokyo Cabinet with Lua extensions. GitHub repo with sample code & extensions: http://bit.ly/wJpeG
Lean & Mean Tokyo Cabinet Recipes (with Lua) - FutureRuby '09
Lean & Mean Tokyo Cabinet Recipes (with Lua) - FutureRuby '09
Ilya Grigorik
tokyotalk
tokyotalk
Hiroshi Ono
Tokyocabinet
Tokyocabinet
guestf96ccd
First step guide of Tokyo Cabinet & Tokto Tyrant.
Tokyo Cabinet & Tokyo Tyrant
Tokyo Cabinet & Tokyo Tyrant
輝 子安
Tokyo Cabinet is a library of routines for managing a database. The database is a simple data file containing records, each is a pair of a key and a value. Every key and value is serial bytes with variable length. Both binary data and character string can be used as a key and a value. There is neither concept of data tables nor data types. Records are organized in hash table, B+ tree, or fixed-length array.
Introduction to Tokyo Products
Introduction to Tokyo Products
Mikio Hirabayashi
En vedette
(6)
SSDとTokyoTyrantやMySQLの性能検証
SSDとTokyoTyrantやMySQLの性能検証
Lean & Mean Tokyo Cabinet Recipes (with Lua) - FutureRuby '09
Lean & Mean Tokyo Cabinet Recipes (with Lua) - FutureRuby '09
tokyotalk
tokyotalk
Tokyocabinet
Tokyocabinet
Tokyo Cabinet & Tokyo Tyrant
Tokyo Cabinet & Tokyo Tyrant
Introduction to Tokyo Products
Introduction to Tokyo Products
Similaire à Tokyo Cabinet
Introduction to Apache Marmotta, given at ISWC2014 in Riva del Garda.
Apache Marmotta - Introduction
Apache Marmotta - Introduction
Sebastian Schaffert
Redis Introduction and customized framework base on StackExchange.Redis but update to using singleton pattern and JSON Configuration Mapping with Redis Instance Group and Name concept.
Redis tutoring
Redis tutoring
Chen-Tien Tsai
Understanding of Oracle GoldenGate 12c and it's features, typologies, architecture, concepts etc.
Understanding Oracle GoldenGate 12c
Understanding Oracle GoldenGate 12c
IT Help Desk Inc
An overview of the various Apache Technologies to help you build your own Big Data solution. A talk given at Berlin Buzzwords, in June 2013.
The other Apache technologies your big data solution needs!
The other Apache technologies your big data solution needs!
gagravarr
Apache Hudi is a data lake platform, that provides streaming primitives (upserts/deletes/change streams) on top of data lake storage. Hudi powers very large data lakes at Uber, Robinhood and other companies, while being pre-installed on four major cloud platforms. Hudi supports exactly-once, near real-time data ingestion from Apache Kafka to cloud storage, which is typically used in-place of a S3/HDFS sink connector to gain transactions and mutability. While this approach is scalable and battle-tested, it can only ingest data in mini batches, leading to lower data freshness. In this talk, we introduce a Kafka Connect Sink Connector for Apache Hudi, which writes data straight into Hudi's log format, making the data immediately queryable, while Hudi's table services like indexing, compaction, clustering work behind the scenes, to further re-organize for better query performance.
Streaming Data Lakes using Kafka Connect + Apache Hudi | Vinoth Chandar, Apac...
Streaming Data Lakes using Kafka Connect + Apache Hudi | Vinoth Chandar, Apac...
HostedbyConfluent
Talk about NLP processing on top of Hadoop using Behemoth by Julien of Digital Pebble
Digital Pebble Behemoth
Digital Pebble Behemoth
Steve Loughran
CouchDB: A NoSQL database
CouchDB: A NoSQL database
Rubyc Slides
NoSQL: Why, When, and How
NoSQL: Why, When, and How
BigBlueHat
Cloud Native London talk about the control layer of Hopsworks.ai and our choice of cloud native services. We built our own multi-tenant services as cloud native services, for the most part.
Building Hopsworks, a cloud-native managed feature store for machine learning
Building Hopsworks, a cloud-native managed feature store for machine learning
Jim Dowling
Alluxio Community Office Hour Aug 27, 2019 Speakers: Bin Fan Nakkul Sreenivas
Building a Cloud Native Stack with EMR Spark, Alluxio, and S3
Building a Cloud Native Stack with EMR Spark, Alluxio, and S3
Alluxio, Inc.
An overview of the various Apache Technologies to help you build your own Big Data solution
The other Apache Technologies your Big Data solution needs
The other Apache Technologies your Big Data solution needs
gagravarr
Amazon EMR is a managed Hadoop service that makes it easy for customers to use big data frameworks and applications like Hadoop, Spark, and Presto to analyze data stored in HDFS or on Amazon S3 , Amazon’s highly scalable object storage service. In this webinar, we will introduce the latest release of Amazon EMR. With Amazon EMR release 5.0, customers can now launch the latest versions of popular open source frameworks including Apache Spark 2.0, Hive 2.1, Presto 0.151, Tez 0.8.4, and Apache Hadoop 2.7.2. We will walk through a demo to show you how to deploy a Hadoop environment within minutes. We will cover common use cases and best practices to lower costs using Amazon S3 as your data store and Amazon EC2 Spot Instances, which allow you to bid on space Amazon computing capacity. Learning Objectives: • Describe the new features and updated frameworks in Amazon EMR 5.0 • Learn best practices and real-world applications for Amazon EMR • Understand how to use EC2 Spot pricing to save costs • Explain the advantages of decoupling storage and compute with Amazon S3 as storage layer for EMR workloads
Introducing Amazon EMR Release 5.0 - August 2016 Monthly Webinar Series
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Amazon Web Services
Introduction to Redis and how to use it from Ruby. Talk presented at EuRuKo 2013 Athens and sponsored by teowaki
Fun with Ruby and Redis
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javier ramirez
Running Production CDC Ingestion Pipelines With Balaji Varadarajan and Pritam K Dey | Current 2022 Robinhood’s mission is to democratize finance for all. Data driven decision making is key to achieving this goal. Data needed are hosted in various OLTP databases. Replicating this data near real time in a reliable fashion to data lakehouse powers many critical use cases for the company. In Robinhood, CDC is not only used for ingestion to data-lake but is also being adopted for inter-system message exchanges between different online micro services. . In this talk, we will describe the evolution of change data capture based ingestion in Robinhood not only in terms of the scale of data stored and queries made, but also the use cases that it supports. We will go in-depth into the CDC architecture built around our Kafka ecosystem using open source system Debezium and Apache Hudi. We will cover online inter-system message exchange use-cases along with our experience running this service at scale in Robinhood along with lessons learned.
Running Production CDC Ingestion Pipelines With Balaji Varadarajan and Pritam...
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HostedbyConfluent
03 net saturday anton samarskyy ''document oriented databases for the .net pl...
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DneprCiklumEvents
My EuRuKo 2010 talk about how to build your own web framework using existing Rack components.
Building web framework with Rack
Building web framework with Rack
sickill
Building highly efficient data lakes using Apache Hudi (Incubating) Even with the exponential growth in data volumes, ingesting/storing/managing big data remains unstandardized & in-efficient. Data lakes are a common architectural pattern to organize big data and democratize access to the organization. In this talk, we will discuss different aspects of building honest data lake architectures, pin pointing technical challenges and areas of inefficiency. We will then re-architect the data lake using Apache Hudi (Incubating), which provides streaming primitives right on top of big data. We will show how upserts & incremental change streams provided by Hudi help optimize data ingestion and ETL processing. Further, Apache Hudi manages growth, sizes files of the resulting data lake using purely open-source file formats, also providing for optimized query performance & file system listing. We will also provide hands-on tools and guides for trying this out on your own data lake. Speaker: Vinoth Chandar (Uber) Vinoth is Technical Lead at Uber Data Infrastructure Team
SF Big Analytics 20190612: Building highly efficient data lakes using Apache ...
SF Big Analytics 20190612: Building highly efficient data lakes using Apache ...
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What you need to know about ceph
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Emma Haruka Iwao
Setup a 100T mobile cluster in 30 minutes
Ceph Day Tokyo - Bring Ceph to Enterprise
Ceph Day Tokyo - Bring Ceph to Enterprise
Ceph Community
Technology Stack Discussion
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Similaire à Tokyo Cabinet
(20)
Apache Marmotta - Introduction
Apache Marmotta - Introduction
Redis tutoring
Redis tutoring
Understanding Oracle GoldenGate 12c
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The other Apache technologies your big data solution needs!
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Streaming Data Lakes using Kafka Connect + Apache Hudi | Vinoth Chandar, Apac...
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Digital Pebble Behemoth
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CouchDB: A NoSQL database
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NoSQL: Why, When, and How
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Building Hopsworks, a cloud-native managed feature store for machine learning
Building Hopsworks, a cloud-native managed feature store for machine learning
Building a Cloud Native Stack with EMR Spark, Alluxio, and S3
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The other Apache Technologies your Big Data solution needs
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Talk euroclojure 2017
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Description of the actor model. Which libraries implement it in Ruby? What are the successful actor libraries in other
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exploring the actor concurrency primitives in ruby and pointing out options in other languages.
Ruby hollywood nordic
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Ruby goes to hollywood
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Ruby hollywood
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talk about concurrency in Ruby, different concurrency models, examples from other languages.
Concurrency: Rubies, plural
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Talk about concurrency in Ruby, and comparison with a few languages
Concurrency
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improved version of 12 hours to rate a rails application - modified after feedback from Euruko and ScotRuby. As presented at Railsconf
12 hours to rate a rails application
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Talk given at Euruko 2010: the talk describes how to quickly evaluate the quality of a Rails codebase. Ruby metrics are explained in detail.
how to rate a Rails application
how to rate a Rails application
ehuard
In some situations, it's useful to be able to evaluate a Rails application quickly. I talk about how I work to get the most data as possible to get a good picture of whether an application is well-maintained, and will be easy to maintain later.
12 Hours To Rate A Rails Application
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thought experiment about how to find objects in the home quicky using technology (while tidying up and putting the objects in the right place is obviously the correct solution)
Barcamp Ghent2009
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ehuard
realtimeweb, webhooks, juggernaut,hype
The real-time web
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ehuard
The internet of thing is hot. This talk describes the trends that led to this phenomenon. Augmented reality links online content to physical object - i talk about the different ways this can happen. Then i talk about physical computing: making things talk, using Arduino, mainly.
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OAuth protocol - keeping your password to yourself in sharing of resources between sites.
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Euroclojure 2017
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Ruby goes to Hollywood
Ruby goes to Hollywood
Ruby hollywood nordic
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Ruby goes to hollywood
Ruby goes to hollywood
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Concurrency
Concurrency
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12 hours to rate a rails application
12 hours to rate a rails application
how to rate a Rails application
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These are the slides delivered in a workshop at Data Innovation Summit Stockholm April 2024, by Kristof Neys and Jonas El Reweny.
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Neo4j
The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.
Artificial Intelligence: Facts and Myths
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Joaquim Jorge
Terragrunt, Terraspace, Terramate, terra... whatever. What is wrong with Terraform so people keep on creating wrappers and solutions around it? How OpenTofu will affect this dynamic? In this presentation, we will look into the fundamental driving forces behind a zoo of wrappers. Moreover, we are going to put together a wrapper ourselves so you can make an educated decision if you need one.
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AWS Community Day CPH - Three problems of Terraform
Andrey Devyatkin
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
The Digital Insurer
This presentation explores the impact of HTML injection attacks on web applications, detailing how attackers exploit vulnerabilities to inject malicious code into web pages. Learn about the potential consequences of such attacks and discover effective mitigation strategies to protect your web applications from HTML injection vulnerabilities. for more information visit https://bostoninstituteofanalytics.org/category/cyber-security-ethical-hacking/
HTML Injection Attacks: Impact and Mitigation Strategies
HTML Injection Attacks: Impact and Mitigation Strategies
Boston Institute of Analytics
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
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
Webinar Recording: https://www.panagenda.com/webinars/why-teams-call-analytics-is-critical-to-your-entire-business Nothing is as frustrating and noticeable as being in an important call and being unable to see or hear the other person. Not surprising then, that issues with Teams calls are among the most common problems users call their helpdesk for. Having in depth insight into everything relevant going on at the user’s device, local network, ISP and Microsoft itself during the call is crucial for good Microsoft Teams Call quality support. To ensure a quick and adequate solution and to ensure your users get the most out of their Microsoft 365. But did you know that ‘bad calls’ are also an excellent indicator of other problems arising? Precisely because it is so noticeable!? Like the canary in the mine, bad calls can be early indicators of problems. Problems that might otherwise not have been noticed for a while but can have a big impact on productivity and satisfaction. Join this session by Christoph Adler to learn how true Microsoft Teams call quality analytics helped other organizations troubleshoot bad calls and identify and fix problems that impacted Teams calls or the use of Microsoft365 in general. See what it can do to keep your users happy and productive! In this session we will cover - Why CQD data alone is not enough to troubleshoot call problems - The importance of attributing call problems to the right call participant - What call quality analytics can do to help you quickly find, fix-, and prevent problems - Why having retrospective detailed insights matters - Real life examples of how others have used Microsoft Teams call quality monitoring to problem shoot problems with their ISP, network, device health and more.
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
panagenda
Imagine a world where information flows as swiftly as thought itself, making decision-making as fluid as the data driving it. Every moment is critical, and the right tools can significantly boost your organization’s performance. The power of real-time data automation through FME can turn this vision into reality. Aimed at professionals eager to leverage real-time data for enhanced decision-making and efficiency, this webinar will cover the essentials of real-time data and its significance. We’ll explore: FME’s role in real-time event processing, from data intake and analysis to transformation and reporting An overview of leveraging streams vs. automations FME’s impact across various industries highlighted by real-life case studies Live demonstrations on setting up FME workflows for real-time data Practical advice on getting started, best practices, and tips for effective implementation Join us to enhance your skills in real-time data automation with FME, and take your operational capabilities to the next level.
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
Safe Software
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
Radu Cotescu
MINDCTI Revenue Release Quarter 1 2024
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024
MIND CTI
How to get Oracle DBA Job as fresher.
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Strategies for Landing an Oracle DBA Job as a Fresher
Remote DBA Services
A Principled Technologies deployment guide Conclusion Deploying VMware Cloud Foundation 5.1 on next gen Dell PowerEdge servers brings together critical virtualization capabilities and high-performing hardware infrastructure. Relying on our hands-on experience, this deployment guide offers a comprehensive roadmap that can guide your organization through the seamless integration of advanced VMware cloud solutions with the performance and reliability of Dell PowerEdge servers. In addition to the deployment efficiency, the Cloud Foundation 5.1 and PowerEdge solution delivered strong performance while running a MySQL database workload. By leveraging VMware Cloud Foundation 5.1 and PowerEdge servers, you could help your organization embrace cloud computing with confidence, potentially unlocking a new level of agility, scalability, and efficiency in your data center operations.
Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...
Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...
Principled Technologies
Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024
The Digital Insurer
writing some innovation for development and search
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
sudhanshuwaghmare1
The value of a flexible API Management solution for Open Banking Steve Melan, Manager for IT Innovation and Architecture - State's and Saving's Bank of Luxembourg 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 value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...
apidays
Created by Mozilla Research in 2012 and now part of Linux Foundation Europe, the Servo project is an experimental rendering engine written in Rust. It combines memory safety and concurrency to create an independent, modular, and embeddable rendering engine that adheres to web standards. Stewardship of Servo moved from Mozilla Research to the Linux Foundation in 2020, where its mission remains unchanged. After some slow years, in 2023 there has been renewed activity on the project, with a roadmap now focused on improving the engine’s CSS 2 conformance, exploring Android support, and making Servo a practical embeddable rendering engine. In this presentation, Rakhi Sharma reviews the status of the project, our recent developments in 2023, our collaboration with Tauri to make Servo an easy-to-use embeddable rendering engine, and our plans for the future to make Servo an alternative web rendering engine for the embedded devices industry. (c) Embedded Open Source Summit 2024 April 16-18, 2024 Seattle, Washington (US) https://events.linuxfoundation.org/embedded-open-source-summit/ https://ossna2024.sched.com/event/1aBNF/a-year-of-servo-reboot-where-are-we-now-rakhi-sharma-igalia
A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?
Igalia
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
Stay safe, grab a drink and join us virtually for our upcoming "GenAI Risks & Security" Meetup to hear about how to uncover critical GenAI risks and vulnerabilities, AI security considerations in every company, and how a CISO should navigate through GenAI Risks.
GenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdf
lior mazor
Explore the top 10 most downloaded games on the Play Store in 2024, reflecting the latest gaming trends. As a premier game development company in India, we're committed to crafting innovative and engaging gaming experiences. Partner with us to bring your game ideas to life and captivate audiences worldwide. Visit here:- https://www.synarionit.com/game-development-company-in-india.html
Top 10 Most Downloaded Games on Play Store in 2024
Top 10 Most Downloaded Games on Play Store in 2024
SynarionITSolutions
Dernier
(20)
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
HTML Injection Attacks: Impact and Mitigation Strategies
HTML Injection Attacks: Impact and Mitigation Strategies
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
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...
Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...
Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...
A Year of the Servo Reboot: Where Are We Now?
A Year of the Servo Reboot: Where Are We Now?
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CV
GenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdf
Top 10 Most Downloaded Games on Play Store in 2024
Top 10 Most Downloaded Games on Play Store in 2024
Tokyo Cabinet
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Tokyo Cabinet Elise
Huard – BRUG meeting 27/08/2009
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http://www.slideshare.net/estraier/introduction-to-tokyo-products?src=embed
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FAST 0.7s to
store 10 6 in hash table 1.6s to store 10 6 in Btree table
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http://www.igvita.com/2009/07/13/extending-tokyo-cabinet-db-with-lua/
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