SplunkLive! Washington DC May 2013 - Splunk Enterprise 5Splunk
This document provides an overview of Splunk Enterprise 5 software. The key points are:
1. Splunk Enterprise 5 provides faster reports that are up to 1000x faster through new report acceleration technology, easier to create dynamic drill-downs, and integrated PDF sharing capabilities.
2. It offers enterprise-scale resilience and high availability through features like index replication that allows indexed data to remain searchable even if an indexer fails.
3. The software includes enhanced modularity, interoperability and extensibility through tools like modular inputs that simplify adding new data sources, and APIs/SDKs that allow developers to integrate Splunk with other technologies.
Petit déjeuner OCTO Technology - Nouvelles Architectures Web Front-End et APIsOCTO Technology
Depuis deux ans, une nouvelle vague technologique submerge le paysage des applications Web : les architectures MV* côté client.
L’écosystème Web, enfin mature, offre l’opportunité d’avoir des interfaces riches et une meilleure expérience utilisateur grâce à la génération des écrans et la gestion des interactions côté client. En ne gérant plus l’affichage mais uniquement l’envoi des données brutes, le serveur se concentre sur des APIs métier mutualisables avec des applications mobiles notamment.
Venez découvrir au travers d’un retour d’expérience commun entre OCTO et ING Direct, acteur majeur de la banque en ligne en France, la réalisation d’une des toutes premières WebApp mobile multi-plateformes dans le milieu bancaire reposant sur ces nouvelles architectures Web.
Compte-rendu du petit-déjeuner : http://bit.ly/1g2nEnU
Hadoop World 2011: Big Data Architecture: Integrating Hadoop with Other Enter...Cloudera, Inc.
Recent research has pointed out the complementary nature of Hadoop and other data management solutions and the importance of leveraging existing systems, SQL, engineering, and operational skills, as well as incorporating novel uses of MapReduce to improve analytic processing. Come to this session to learn how companies optimize the use of Hadoop with other enterprise systems to improve overall analytical throughput and build new data-driven products. This session covers: ways to achieve high-performance integration between Hadoop and relational-based systems; Hadoop+NoSQL vs Hadoop+SQL architectures; high-speed, massively parallel data transfer to analytical platforms that can aggregate web log data with granular fact data; and strategies for freeing up capacity for more explorative, iterative analytics and ad hoc queries.
Petit Déjeuner Datastax 14-04-15 : Les nouvelles architectures de stockage et...OCTO Technology
Ces dernières années, nous avons assisté à une évolution majeure de l’écosystème des solutions de gestion de la donnée. Les usages ont également évolué tant sur les aspects analytiques que transactionnels : le batch J+1 n'est plus une fatalité !
Quels constats et quelles perspectives pour les SI traditionnels à l'heure où les technologies événementielles sont de plus en plus accessibles et adoptées ?
Les nouvelles architectures de stockage et traitement de la donnée
Face à l'accroissement du volume de données et de traitements ainsi que la course en avant vers des systèmes toujours plus temps réel, quelles problématiques rencontrent aujourd’hui les grandes DSI ? Toutes ces évolutions sont autant d’opportunités pour de nouvelles innovations dans les Systèmes d’Informations et relever les challenges d’aujourd’hui.
Du Big Data vers le SMART Data : Scénario d'un processusCHAKER ALLAOUI
Du Big Data vers le SMAR Data : Scénario d'un processus
Scénario d'une implémentation d'un processus de transformations des données Big Data vers des données exploitables et représentatives via des traitements des streaming, systèmes distibués, messages, stockage dans un environnement NoSQL, gestion avec un éco-système Big Data et présentation graphique et quantitative des données avec les technologies:
Apache Storm, Apache Zookeeper, Apache Kafka, Apache Cassandra, Apache Spark et Data-Driven Document.
Real Time search using Spark and ElasticsearchSigmoid
This document discusses using Spark Streaming and Elasticsearch to enable real-time search and analysis of streaming data. Spark Streaming processes and enriches streaming data and stores it in Elasticsearch for low-latency search and alerts. The elasticsearch-hadoop connector allows Spark jobs to read from and write to Elasticsearch, integrating the batch processing of Spark with the real-time search of Elasticsearch.
SplunkLive! Washington DC May 2013 - Splunk Enterprise 5Splunk
This document provides an overview of Splunk Enterprise 5 software. The key points are:
1. Splunk Enterprise 5 provides faster reports that are up to 1000x faster through new report acceleration technology, easier to create dynamic drill-downs, and integrated PDF sharing capabilities.
2. It offers enterprise-scale resilience and high availability through features like index replication that allows indexed data to remain searchable even if an indexer fails.
3. The software includes enhanced modularity, interoperability and extensibility through tools like modular inputs that simplify adding new data sources, and APIs/SDKs that allow developers to integrate Splunk with other technologies.
Petit déjeuner OCTO Technology - Nouvelles Architectures Web Front-End et APIsOCTO Technology
Depuis deux ans, une nouvelle vague technologique submerge le paysage des applications Web : les architectures MV* côté client.
L’écosystème Web, enfin mature, offre l’opportunité d’avoir des interfaces riches et une meilleure expérience utilisateur grâce à la génération des écrans et la gestion des interactions côté client. En ne gérant plus l’affichage mais uniquement l’envoi des données brutes, le serveur se concentre sur des APIs métier mutualisables avec des applications mobiles notamment.
Venez découvrir au travers d’un retour d’expérience commun entre OCTO et ING Direct, acteur majeur de la banque en ligne en France, la réalisation d’une des toutes premières WebApp mobile multi-plateformes dans le milieu bancaire reposant sur ces nouvelles architectures Web.
Compte-rendu du petit-déjeuner : http://bit.ly/1g2nEnU
Hadoop World 2011: Big Data Architecture: Integrating Hadoop with Other Enter...Cloudera, Inc.
Recent research has pointed out the complementary nature of Hadoop and other data management solutions and the importance of leveraging existing systems, SQL, engineering, and operational skills, as well as incorporating novel uses of MapReduce to improve analytic processing. Come to this session to learn how companies optimize the use of Hadoop with other enterprise systems to improve overall analytical throughput and build new data-driven products. This session covers: ways to achieve high-performance integration between Hadoop and relational-based systems; Hadoop+NoSQL vs Hadoop+SQL architectures; high-speed, massively parallel data transfer to analytical platforms that can aggregate web log data with granular fact data; and strategies for freeing up capacity for more explorative, iterative analytics and ad hoc queries.
Petit Déjeuner Datastax 14-04-15 : Les nouvelles architectures de stockage et...OCTO Technology
Ces dernières années, nous avons assisté à une évolution majeure de l’écosystème des solutions de gestion de la donnée. Les usages ont également évolué tant sur les aspects analytiques que transactionnels : le batch J+1 n'est plus une fatalité !
Quels constats et quelles perspectives pour les SI traditionnels à l'heure où les technologies événementielles sont de plus en plus accessibles et adoptées ?
Les nouvelles architectures de stockage et traitement de la donnée
Face à l'accroissement du volume de données et de traitements ainsi que la course en avant vers des systèmes toujours plus temps réel, quelles problématiques rencontrent aujourd’hui les grandes DSI ? Toutes ces évolutions sont autant d’opportunités pour de nouvelles innovations dans les Systèmes d’Informations et relever les challenges d’aujourd’hui.
Du Big Data vers le SMART Data : Scénario d'un processusCHAKER ALLAOUI
Du Big Data vers le SMAR Data : Scénario d'un processus
Scénario d'une implémentation d'un processus de transformations des données Big Data vers des données exploitables et représentatives via des traitements des streaming, systèmes distibués, messages, stockage dans un environnement NoSQL, gestion avec un éco-système Big Data et présentation graphique et quantitative des données avec les technologies:
Apache Storm, Apache Zookeeper, Apache Kafka, Apache Cassandra, Apache Spark et Data-Driven Document.
Real Time search using Spark and ElasticsearchSigmoid
This document discusses using Spark Streaming and Elasticsearch to enable real-time search and analysis of streaming data. Spark Streaming processes and enriches streaming data and stores it in Elasticsearch for low-latency search and alerts. The elasticsearch-hadoop connector allows Spark jobs to read from and write to Elasticsearch, integrating the batch processing of Spark with the real-time search of Elasticsearch.
Forrester CXNYC 2017 - Delivering great real-time cx is a true craftDataStax Academy
Companies today are innovating with real-time data to deliver truly amazing customer experiences in the moment. Real-time data management for real-time customer experience is core to staying ahead of competition and driving revenue growth. Join Trays to learn how Comcast is differentiating itself from it's own historical reputation with Customer Experience strategies.
Introduction to DataStax Enterprise Graph DatabaseDataStax Academy
DataStax Enterprise (DSE) Graph is a built to manage, analyze, and search highly connected data. DSE Graph, built on NoSQL Apache Cassandra delivers continuous uptime along with predictable performance and scales for modern systems dealing with complex and constantly changing data.
Download DataStax Enterprise: Academy.DataStax.com/Download
Start free training for DataStax Enterprise Graph: Academy.DataStax.com/courses/ds332-datastax-enterprise-graph
Introduction to DataStax Enterprise Advanced Replication with Apache CassandraDataStax Academy
DataStax Enterprise Advanced Replication supports one-way distributed data replication from remote database clusters that might experience periods of network or internet downtime. Benefiting use cases that require a 'hub and spoke' architecture.
Learn more at http://www.datastax.com/2016/07/stay-100-connected-with-dse-advanced-replication
Advanced Replication docs – https://docs.datastax.com/en/latest-dse/datastax_enterprise/advRep/advRepTOC.html
This document discusses using Docker containers to run Cassandra clusters at Walmart. It proposes transforming existing Cassandra hardware into containers to better utilize unused compute. It also suggests building new Cassandra clusters in containers and migrating old clusters to double capacity on existing hardware and save costs. Benchmark results show Docker containers outperforming virtual machines on OpenStack and Azure in terms of reads, writes, throughput and latency for an in-house application.
The document discusses the evolution of Cassandra's data modeling capabilities over different versions of CQL. It covers features introduced in each version such as user defined types, functions, aggregates, materialized views, and storage attached secondary indexes (SASI). It provides examples of how to create user defined types, functions, materialized views, and SASI indexes in CQL. It also discusses when each feature should and should not be used.
Cisco has a large global IT infrastructure supporting many applications, databases, and employees. The document discusses Cisco's existing customer service and commerce systems (CSCC/SMS3) and some of the performance, scalability, and user experience issues. It then presents a proposed new architecture using modern technologies like Elasticsearch, Cassandra, and microservices to address these issues and improve agility, performance, scalability, uptime, and the user interface.
Data Modeling is the one of the first things to sink your teeth into when trying out a new database. That's why we are going to cover this foundational topic in enough detail for you to get dangerous. Data Modeling for relational databases is more than a touch different than the way it's approached with Cassandra. We will address the quintessential query-driven methodology through a couple of different use cases, including working with time series data for IoT. We will also demo a new tool to get you bootstrapped quickly with MovieLens sample data. This talk should give you the basics you need to get serious with Apache Cassandra.
Hear about how Coursera uses Cassandra as the core of its scalable online education platform. I'll discuss the strengths of Cassandra that we leverage, as well as some limitations that you might run into as well in practice.
In the second part of this talk, we'll dive into how best to effectively use the Datastax Java drivers. We'll dig into how the driver is architected, and use this understanding to develop best practices to follow. I'll also share a couple of interesting bug we've run into at Coursera.
This document promotes Datastax Academy and Certification resources for learning Cassandra including a three step process of learning Cassandra, getting certified, and profiting. It lists community evangelists like Luke Tillman, Patrick McFadin, Jon Haddad, and Duy Hai Doan who can provide help and resources.
Cassandra @ Netflix: Monitoring C* at Scale, Gossip and Tickler & PythonDataStax Academy
This document summarizes three presentations from a Cassandra Meetup:
1. Jason Cacciatore discussed monitoring Cassandra health at scale across hundreds of clusters and thousands of nodes using the reactive stream processing system Mantis.
2. Minh Do explained how Cassandra uses the gossip protocol for tasks like discovering cluster topology and sharing load information. Gossip also has limitations and race conditions that can cause problems.
3. Chris Kalantzis presented Cassandra Tickler, an open source tool he created to help repair operations that get stuck by running lightweight consistency checks on an old Cassandra version or a node with space issues.
Cassandra @ Sony: The good, the bad, and the ugly part 1DataStax Academy
This talk covers scaling Cassandra to a fast growing user base. Alex and Isaias will cover new best practices and how to work with the strengths and weaknesses of Cassandra at large scale. They will discuss how to adapt to bottlenecks while providing a rich feature set to the playstation community.
Cassandra @ Sony: The good, the bad, and the ugly part 2DataStax Academy
The document discusses Cassandra's use by Sony Network Entertainment to handle the large amount of user and transaction data from the growing PlayStation Network. It describes how the relational database they previously used did not scale sufficiently, so they transitioned to using Cassandra in a denormalized and customized way. Some of the techniques discussed include caching user data locally on application servers, secondary indexing, and using a real-time indexer to enable personalized search by friends.
This document provides guidance on setting up server monitoring, application metrics, log aggregation, time synchronization, replication strategies, and garbage collection for a Cassandra cluster. Key recommendations include:
1. Use monitoring tools like Monit, Munin, Nagios, or OpsCenter to monitor processes, disk usage, and system performance. Aggregate all logs centrally with tools like Splunk, Logstash, or Greylog.
2. Install NTP to synchronize server times which are critical for consistency.
3. Use the NetworkTopologyStrategy replication strategy and avoid SimpleStrategy for production.
4. Avoid shared storage and focus on low latency and high throughput using multiple local disks.
5. Understand
This document discusses real time analytics using Spark and Spark Streaming. It provides an introduction to Spark and highlights limitations of Hadoop for real-time analytics. It then describes Spark's advantages like in-memory processing and rich APIs. The document discusses Spark Streaming and the Spark Cassandra Connector. It also introduces DataStax Enterprise which integrates Spark, Cassandra and Solr to allow real-time analytics without separate clusters. Examples of streaming use cases and demos are provided.
Introduction to Data Modeling with Apache CassandraDataStax Academy
This document provides an introduction to data modeling with Apache Cassandra. It discusses how Cassandra data models are designed based on the queries an application will perform, unlike relational databases which are designed based on normalization rules. Key aspects covered include avoiding joins by denormalizing data, using a partition key to group related data on nodes, and controlling the clustering order of columns. The document provides examples of modeling time series and tag data in Cassandra.
The document discusses different data storage options for small, medium, and large datasets. It argues that relational databases do not scale well for large datasets due to limitations with replication, normalization, sharding, and high availability. The document then introduces Apache Cassandra as a fast, distributed, highly available, and linearly scalable database that addresses these limitations through its use of a hash ring architecture and tunable consistency levels. It describes Cassandra's key features including replication, compaction, and multi-datacenter support.
Enabling Search in your Cassandra Application with DataStax EnterpriseDataStax Academy
This document provides an overview of using Datastax Enterprise (DSE) Search to enable full-text search capabilities in Cassandra applications. It discusses how DSE Search integrates Solr/Lucene indexing with the Cassandra database to allow searching of application data without requiring a separate search cluster, external ETL processes, or custom application code for data management. The document also includes examples of different types of searches that can be performed, such as filtering, faceting, geospatial searches, and joins. It concludes with basic steps for getting started with DSE Search such as creating a Solr core and executing search queries using CQL.
The document discusses common bad habits that can occur when working with Apache Cassandra and provides recommendations to avoid them. Specifically, it addresses issues like sliding back into a relational mindset when the data model is different, improperly benchmarking Cassandra systems, having slow client performance, and neglecting important operations tasks. The presentation provides guidance on how to approach data modeling, querying, benchmarking, driver usage, and operations management in a Cassandra-oriented way.
This document provides an overview and examples of modeling data in Apache Cassandra. It begins with an introduction to thinking about data models and queries before modeling, and emphasizes that Cassandra requires modeling around queries due to its limitations on joins and indexes. The document then provides examples of modeling user, video, and other entity data for a video sharing application to support common queries. It also discusses techniques for handling queries that could become hotspots, such as bucketing or adding random values. The examples illustrate best practices for data duplication, materialized views, and time series data storage in Cassandra.
The document discusses best practices for using Apache Cassandra, including:
- Topology considerations like replication strategies and snitches
- Booting new datacenters and replacing nodes
- Security techniques like authentication, authorization, and SSL encryption
- Using prepared statements for efficiency
- Asynchronous execution for request pipelining
- Batch statements and their appropriate uses
- Improving performance through techniques like the new row cache
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Forrester CXNYC 2017 - Delivering great real-time cx is a true craftDataStax Academy
Companies today are innovating with real-time data to deliver truly amazing customer experiences in the moment. Real-time data management for real-time customer experience is core to staying ahead of competition and driving revenue growth. Join Trays to learn how Comcast is differentiating itself from it's own historical reputation with Customer Experience strategies.
Introduction to DataStax Enterprise Graph DatabaseDataStax Academy
DataStax Enterprise (DSE) Graph is a built to manage, analyze, and search highly connected data. DSE Graph, built on NoSQL Apache Cassandra delivers continuous uptime along with predictable performance and scales for modern systems dealing with complex and constantly changing data.
Download DataStax Enterprise: Academy.DataStax.com/Download
Start free training for DataStax Enterprise Graph: Academy.DataStax.com/courses/ds332-datastax-enterprise-graph
Introduction to DataStax Enterprise Advanced Replication with Apache CassandraDataStax Academy
DataStax Enterprise Advanced Replication supports one-way distributed data replication from remote database clusters that might experience periods of network or internet downtime. Benefiting use cases that require a 'hub and spoke' architecture.
Learn more at http://www.datastax.com/2016/07/stay-100-connected-with-dse-advanced-replication
Advanced Replication docs – https://docs.datastax.com/en/latest-dse/datastax_enterprise/advRep/advRepTOC.html
This document discusses using Docker containers to run Cassandra clusters at Walmart. It proposes transforming existing Cassandra hardware into containers to better utilize unused compute. It also suggests building new Cassandra clusters in containers and migrating old clusters to double capacity on existing hardware and save costs. Benchmark results show Docker containers outperforming virtual machines on OpenStack and Azure in terms of reads, writes, throughput and latency for an in-house application.
The document discusses the evolution of Cassandra's data modeling capabilities over different versions of CQL. It covers features introduced in each version such as user defined types, functions, aggregates, materialized views, and storage attached secondary indexes (SASI). It provides examples of how to create user defined types, functions, materialized views, and SASI indexes in CQL. It also discusses when each feature should and should not be used.
Cisco has a large global IT infrastructure supporting many applications, databases, and employees. The document discusses Cisco's existing customer service and commerce systems (CSCC/SMS3) and some of the performance, scalability, and user experience issues. It then presents a proposed new architecture using modern technologies like Elasticsearch, Cassandra, and microservices to address these issues and improve agility, performance, scalability, uptime, and the user interface.
Data Modeling is the one of the first things to sink your teeth into when trying out a new database. That's why we are going to cover this foundational topic in enough detail for you to get dangerous. Data Modeling for relational databases is more than a touch different than the way it's approached with Cassandra. We will address the quintessential query-driven methodology through a couple of different use cases, including working with time series data for IoT. We will also demo a new tool to get you bootstrapped quickly with MovieLens sample data. This talk should give you the basics you need to get serious with Apache Cassandra.
Hear about how Coursera uses Cassandra as the core of its scalable online education platform. I'll discuss the strengths of Cassandra that we leverage, as well as some limitations that you might run into as well in practice.
In the second part of this talk, we'll dive into how best to effectively use the Datastax Java drivers. We'll dig into how the driver is architected, and use this understanding to develop best practices to follow. I'll also share a couple of interesting bug we've run into at Coursera.
This document promotes Datastax Academy and Certification resources for learning Cassandra including a three step process of learning Cassandra, getting certified, and profiting. It lists community evangelists like Luke Tillman, Patrick McFadin, Jon Haddad, and Duy Hai Doan who can provide help and resources.
Cassandra @ Netflix: Monitoring C* at Scale, Gossip and Tickler & PythonDataStax Academy
This document summarizes three presentations from a Cassandra Meetup:
1. Jason Cacciatore discussed monitoring Cassandra health at scale across hundreds of clusters and thousands of nodes using the reactive stream processing system Mantis.
2. Minh Do explained how Cassandra uses the gossip protocol for tasks like discovering cluster topology and sharing load information. Gossip also has limitations and race conditions that can cause problems.
3. Chris Kalantzis presented Cassandra Tickler, an open source tool he created to help repair operations that get stuck by running lightweight consistency checks on an old Cassandra version or a node with space issues.
Cassandra @ Sony: The good, the bad, and the ugly part 1DataStax Academy
This talk covers scaling Cassandra to a fast growing user base. Alex and Isaias will cover new best practices and how to work with the strengths and weaknesses of Cassandra at large scale. They will discuss how to adapt to bottlenecks while providing a rich feature set to the playstation community.
Cassandra @ Sony: The good, the bad, and the ugly part 2DataStax Academy
The document discusses Cassandra's use by Sony Network Entertainment to handle the large amount of user and transaction data from the growing PlayStation Network. It describes how the relational database they previously used did not scale sufficiently, so they transitioned to using Cassandra in a denormalized and customized way. Some of the techniques discussed include caching user data locally on application servers, secondary indexing, and using a real-time indexer to enable personalized search by friends.
This document provides guidance on setting up server monitoring, application metrics, log aggregation, time synchronization, replication strategies, and garbage collection for a Cassandra cluster. Key recommendations include:
1. Use monitoring tools like Monit, Munin, Nagios, or OpsCenter to monitor processes, disk usage, and system performance. Aggregate all logs centrally with tools like Splunk, Logstash, or Greylog.
2. Install NTP to synchronize server times which are critical for consistency.
3. Use the NetworkTopologyStrategy replication strategy and avoid SimpleStrategy for production.
4. Avoid shared storage and focus on low latency and high throughput using multiple local disks.
5. Understand
This document discusses real time analytics using Spark and Spark Streaming. It provides an introduction to Spark and highlights limitations of Hadoop for real-time analytics. It then describes Spark's advantages like in-memory processing and rich APIs. The document discusses Spark Streaming and the Spark Cassandra Connector. It also introduces DataStax Enterprise which integrates Spark, Cassandra and Solr to allow real-time analytics without separate clusters. Examples of streaming use cases and demos are provided.
Introduction to Data Modeling with Apache CassandraDataStax Academy
This document provides an introduction to data modeling with Apache Cassandra. It discusses how Cassandra data models are designed based on the queries an application will perform, unlike relational databases which are designed based on normalization rules. Key aspects covered include avoiding joins by denormalizing data, using a partition key to group related data on nodes, and controlling the clustering order of columns. The document provides examples of modeling time series and tag data in Cassandra.
The document discusses different data storage options for small, medium, and large datasets. It argues that relational databases do not scale well for large datasets due to limitations with replication, normalization, sharding, and high availability. The document then introduces Apache Cassandra as a fast, distributed, highly available, and linearly scalable database that addresses these limitations through its use of a hash ring architecture and tunable consistency levels. It describes Cassandra's key features including replication, compaction, and multi-datacenter support.
Enabling Search in your Cassandra Application with DataStax EnterpriseDataStax Academy
This document provides an overview of using Datastax Enterprise (DSE) Search to enable full-text search capabilities in Cassandra applications. It discusses how DSE Search integrates Solr/Lucene indexing with the Cassandra database to allow searching of application data without requiring a separate search cluster, external ETL processes, or custom application code for data management. The document also includes examples of different types of searches that can be performed, such as filtering, faceting, geospatial searches, and joins. It concludes with basic steps for getting started with DSE Search such as creating a Solr core and executing search queries using CQL.
The document discusses common bad habits that can occur when working with Apache Cassandra and provides recommendations to avoid them. Specifically, it addresses issues like sliding back into a relational mindset when the data model is different, improperly benchmarking Cassandra systems, having slow client performance, and neglecting important operations tasks. The presentation provides guidance on how to approach data modeling, querying, benchmarking, driver usage, and operations management in a Cassandra-oriented way.
This document provides an overview and examples of modeling data in Apache Cassandra. It begins with an introduction to thinking about data models and queries before modeling, and emphasizes that Cassandra requires modeling around queries due to its limitations on joins and indexes. The document then provides examples of modeling user, video, and other entity data for a video sharing application to support common queries. It also discusses techniques for handling queries that could become hotspots, such as bucketing or adding random values. The examples illustrate best practices for data duplication, materialized views, and time series data storage in Cassandra.
The document discusses best practices for using Apache Cassandra, including:
- Topology considerations like replication strategies and snitches
- Booting new datacenters and replacing nodes
- Security techniques like authentication, authorization, and SSL encryption
- Using prepared statements for efficiency
- Asynchronous execution for request pipelining
- Batch statements and their appropriate uses
- Improving performance through techniques like the new row cache
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Essentials of Automations: Exploring Attributes & Automation ParametersSafe Software
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.