The document discusses Netflix's use of Elasticsearch for querying log events. It describes how Netflix evolved from storing logs in files to using Elasticsearch to enable interactive exploration of billions of log events. It also summarizes some of Netflix's best practices for running Elasticsearch at scale, such as automatic sharding and replication, flexible schemas, and extensive monitoring.
Introduction to Elasticsearch with basics of LuceneRahul Jain
Rahul Jain gives an introduction to Elasticsearch and its basic concepts like term frequency, inverse document frequency, and boosting. He describes Lucene as a fast, scalable search library that uses inverted indexes. Elasticsearch is introduced as an open source search platform built on Lucene that provides distributed indexing, replication, and load balancing. Logstash and Kibana are also briefly described as tools for collecting, parsing, and visualizing logs in Elasticsearch.
ElasticSearch introduction talk. Overview of the API, functionality, use cases. What can be achieved, how to scale? What is Kibana, how it can benefit your business.
Elastic{ON} is the big conference organized by Elastic where new features and roadmap are announced. In this session, we will explore what's new in the Elastic stack
Elasticsearch is a free and open source distributed search and analytics engine. It allows documents to be indexed and searched quickly and at scale. Elasticsearch is built on Apache Lucene and uses RESTful APIs. Documents are stored in JSON format across distributed shards and replicas for fault tolerance and scalability. Elasticsearch is used by many large companies due to its ability to easily scale with data growth and handle advanced search functions.
Elasticsearch 1.1.0 includes several new features and improvements such as new aggregation types like cardinality and percentiles, significant terms aggregation, and improvements to terms and multi-field search. It also includes breaking changes to configuration, multi-fields, stopwords, and return values. New features for aggregations include bucketing and metrics aggregations as well as the ability to add sub-aggregations. Backup and restore capabilities were added through repositories and snapshots. The tribe feature allows federation across multiple clusters.
Deep Dive on ElasticSearch Meetup event on 23rd May '15 at www.meetup.com/abctalks
Agenda:
1) Introduction to NOSQL
2) What is ElasticSearch and why is it required
3) ElasticSearch architecture
4) Installation of ElasticSearch
5) Hands on session on ElasticSearch
Elasticsearch is a distributed, open source search and analytics engine. It allows storing and searching of documents of any schema in real-time. Documents are organized into indices which can contain multiple types of documents. Indices are partitioned into shards and replicas to allow horizontal scaling and high availability. The document consists of a JSON object which is indexed and can be queried using a RESTful API.
"ElasticSearch in action" by Thijs Feryn.
ElasticSearch is a really powerful search engine, NoSQL database & analytics engine. It is fast, it scales and it's a child of the Cloud/BigData generation. This talk will show you how to get things done using ElasticSearch. The focus is on doing actual work, creating actual queries and achieving actual results. Topics that will be covered: - Filters and queries - Cluster, shard and index management - Data mapping - Analyzers and tokenizers - Aggregations - ElasticSearch as part of the ELK stack - Integration in your code.
Log analysis using Logstash,ElasticSearch and KibanaAvinash Ramineni
This document provides an overview of Logstash, Elasticsearch, and Kibana for log analysis. It discusses how logging is used for troubleshooting, security, and monitoring. It then introduces Logstash as an open-source log collection and parsing tool. Elasticsearch is described as a search and analytics engine that indexes log data from Logstash. Kibana provides a web interface for visualizing and searching logs stored in Elasticsearch. The document concludes with discussing demo, installation, scaling, and deployment considerations for these log analysis tools.
Elasticsearch is a distributed, open source search and analytics engine built on Apache Lucene. It allows storing and searching of documents of any schema in JSON format. Documents are organized into indexes which can have multiple shards and replicas for scalability and high availability. Elasticsearch provides a RESTful API and can be easily extended with plugins. It is widely used for full-text search, structured search, analytics and more in applications requiring real-time search and analytics of large volumes of data.
Microservices, Continuous Delivery, and Elasticsearch at Capital OneNoriaki Tatsumi
This presentation focuses on the implementation of Continuous Delivery and Microservices principles in Capital One’s
cybersecurity data platform – which ingests ~6 TB of data every day, and where Elasticsearch is a core component.
Elasticsearch what is it ? How can I use it in my stack ? I will explain how to set up a working environment with Elasticsearch. The slides are in English.
Elasticsearch is an open source search engine based on Apache Lucene that allows users to search through and analyze data from any source. It uses a distributed and scalable architecture that enables near real-time search through a HTTP REST API. Elasticsearch supports schema-less JSON documents and is used by many large companies and websites due to its flexibility and performance.
Log Analytics with ELK Stack describes optimizing an ELK stack implementation for a mobile gaming company to reduce costs and scale data ingestion. Key optimizations included moving to spot instances, separating logs into different indexes based on type and retention needs, tuning Elasticsearch and Logstash configurations, and implementing a hot-warm architecture across different EBS volume types. These changes reduced overall costs by an estimated 80% while maintaining high availability and scalability.
Logging with Elasticsearch, Logstash & KibanaAmazee Labs
This document discusses logging with the ELK stack (Elasticsearch, Logstash, Kibana). It provides an overview of each component, how they work together, and demos their use. Elasticsearch is for search and indexing, Logstash centralizes and parses logs, and Kibana provides visualization. Tools like Curator help manage time-series data in Elasticsearch. The speaker demonstrates collecting syslog data with Logstash and viewing it in Kibana. The ELK stack provides centralized logging and makes queries like "check errors from yesterday between times" much easier.
Lessons Learned in Deploying the ELK Stack (Elasticsearch, Logstash, and Kibana)Cohesive Networks
Slides from the Chicago AWS user group on May 5th, 2016. Asaf Yigal, Co-Founder and VP Product at Logz.io, presented on using Elasticsearch, Logstash, and Kibana in Amazon Web Services.
"Setting up the increasingly-popular open-source ELK Stack (Elasticsearch, Logstash, and Kibana) on AWS might seem like an easy task, but we have gone through several iterations in our architecture and have made some mistakes in our deployments that have turned out to be common in the industry. In this talk, we will go through what we did and explain what worked and what failed -- and why. We will also provide a complete blueprint of how to set up ELK for production on AWS." ~ @asafyigal
An introduction to elasticsearch with a short demonstration on Kibana to present the search API. The slide covers:
- Quick overview of the Elastic stack
- indexation
- Analysers
- Relevance score
- One use case of elasticsearch
The query used for the Kibana demonstration can be found here:
https://github.com/melvynator/elasticsearch_presentation
This document summarizes a presentation on the Elastic Stack. It discusses the main components - Elasticsearch for storing and searching data, Logstash for ingesting data, Kibana for visualizing data. It provides examples of using Elasticsearch for search, analytics, and aggregations. It also briefly mentions new features across the Elastic Stack like update by query, ingest nodes, pipeline improvements, and APIs for management and metrics.
Elasticsearch, Logstash, Kibana. Cool search, analytics, data mining and more...Oleksiy Panchenko
In the age of information and big data, ability to quickly and easily find a needle in a haystack is extremely important. Elasticsearch is a distributed and scalable search engine which provides rich and flexible search capabilities. Social networks (Facebook, LinkedIn), media services (Netflix, SoundCloud), Q&A sites (StackOverflow, Quora, StackExchange) and even GitHub - they all find data for you using Elasticsearch. In conjunction with Logstash and Kibana, Elasticsearch becomes a powerful log engine which allows to process, store, analyze, search through and visualize your logs.
Video: https://www.youtube.com/watch?v=GL7xC5kpb-c
Scripts for the Demo: https://github.com/opanchenko/morning-at-lohika-ELK
Logging is one of those things that everyone complains about, but doesn't dedicate time to. Of course, the first rule of logging is "do it". Without that, you have no visibility into system activities when investigations are required. But, the end goal is much, much more than this. Almost all applications require security audit logs for compliance; application logs for visibility across all cloud properties; and application tracing for tracking usage patterns and business intelligence. The latter is that magic sauce that helps businesses learn about their customer or in some cases the data is FOR the customer. Without a strategy this can get very messy, fast. In this session Michele will discuss design patterns for a sound logging and audit strategy; considerations for security and compliance; the benefits of a noSQL approach; and more.
Getting Started with Elastic Stack.
Detailed blog for the same
http://vikshinde.blogspot.co.uk/2017/08/elastic-stack-introduction.html
Elasticsearch is a distributed, open source search and analytics engine that allows full-text searches of structured and unstructured data. It is built on top of Apache Lucene and uses JSON documents. Elasticsearch can index, search, and analyze big volumes of data in near real-time. It is horizontally scalable, fault tolerant, and easy to deploy and administer.
This document discusses using the ELK stack (Elasticsearch, Logstash, Kibana) for log analysis. It describes the author's experience using Splunk and alternatives like Graylog and Elasticsearch before settling on the ELK stack. The key components - Logstash for input, Elasticsearch for storage and searching, and Kibana for the user interface - are explained. Troubleshooting tips are provided around checking that the components are running and communicating properly.
ElasticSearch - index server used as a document databaseRobert Lujo
Presentation held on 5.10.2014 on http://2014.webcampzg.org/talks/.
Although ElasticSearch (ES) primary purpose is to be used as index/search server, in its featureset ES overlaps with common NoSql database; better to say, document database.
Why this could be interesting and how this could be used effectively?
Talk overview:
- ES - history, background, philosophy, featureset overview, focus on indexing/search features
- short presentation on how to get started - installation, indexing and search/retrieving
- Database should provide following functions: store, search, retrieve -> differences between relational, document and search databases
- it is not unusual to use ES additionally as an document database (store and retrieve)
- an use-case will be presented where ES can be used as a single database in the system (benefits and drawbacks)
- what if a relational database is introduced in previosly demonstrated system (benefits and drawbacks)
ES is a nice and in reality ready-to-use example that can change perspective of development of some type of software systems.
The document introduces the ELK stack, which consists of Elasticsearch, Logstash, Kibana, and Beats. Beats ship log and operational data to Elasticsearch. Logstash ingests, transforms, and sends data to Elasticsearch. Elasticsearch stores and indexes the data. Kibana allows users to visualize and interact with data stored in Elasticsearch. The document provides descriptions of each component and their roles. It also includes configuration examples and demonstrates how to access Elasticsearch via REST.
DEVNET-1140 InterCloud Mapreduce and Spark Workload Migration and Sharing: Fi...Cisco DevNet
Data gravity is a reality when dealing with massive amounts and globally distributed systems. Processing this data requires distributed analytics processing across InterCloud. In this presentation we will share our real world experience with storing, routing, and processing big data workloads on Cisco Cloud Services and Amazon Web Services clouds.
Flink in Zalando's world of Microservices ZalandoHayley
Apache Flink Meetup at Zalando Technology, May 2016
By Javier Lopez & Mihail Vieru, Zalando
In this talk we present Zalando's microservices architecture and introduce Saiki – our next generation data integration and distribution platform on AWS. We show why we chose Apache Flink to serve as our stream processing framework and describe how we employ it for our current use cases: business process monitoring and continuous ETL. We then have an outlook on future use cases.
Elasticsearch is a distributed, open source search and analytics engine. It allows storing and searching of documents of any schema in real-time. Documents are organized into indices which can contain multiple types of documents. Indices are partitioned into shards and replicas to allow horizontal scaling and high availability. The document consists of a JSON object which is indexed and can be queried using a RESTful API.
"ElasticSearch in action" by Thijs Feryn.
ElasticSearch is a really powerful search engine, NoSQL database & analytics engine. It is fast, it scales and it's a child of the Cloud/BigData generation. This talk will show you how to get things done using ElasticSearch. The focus is on doing actual work, creating actual queries and achieving actual results. Topics that will be covered: - Filters and queries - Cluster, shard and index management - Data mapping - Analyzers and tokenizers - Aggregations - ElasticSearch as part of the ELK stack - Integration in your code.
Log analysis using Logstash,ElasticSearch and KibanaAvinash Ramineni
This document provides an overview of Logstash, Elasticsearch, and Kibana for log analysis. It discusses how logging is used for troubleshooting, security, and monitoring. It then introduces Logstash as an open-source log collection and parsing tool. Elasticsearch is described as a search and analytics engine that indexes log data from Logstash. Kibana provides a web interface for visualizing and searching logs stored in Elasticsearch. The document concludes with discussing demo, installation, scaling, and deployment considerations for these log analysis tools.
Elasticsearch is a distributed, open source search and analytics engine built on Apache Lucene. It allows storing and searching of documents of any schema in JSON format. Documents are organized into indexes which can have multiple shards and replicas for scalability and high availability. Elasticsearch provides a RESTful API and can be easily extended with plugins. It is widely used for full-text search, structured search, analytics and more in applications requiring real-time search and analytics of large volumes of data.
Microservices, Continuous Delivery, and Elasticsearch at Capital OneNoriaki Tatsumi
This presentation focuses on the implementation of Continuous Delivery and Microservices principles in Capital One’s
cybersecurity data platform – which ingests ~6 TB of data every day, and where Elasticsearch is a core component.
Elasticsearch what is it ? How can I use it in my stack ? I will explain how to set up a working environment with Elasticsearch. The slides are in English.
Elasticsearch is an open source search engine based on Apache Lucene that allows users to search through and analyze data from any source. It uses a distributed and scalable architecture that enables near real-time search through a HTTP REST API. Elasticsearch supports schema-less JSON documents and is used by many large companies and websites due to its flexibility and performance.
Log Analytics with ELK Stack describes optimizing an ELK stack implementation for a mobile gaming company to reduce costs and scale data ingestion. Key optimizations included moving to spot instances, separating logs into different indexes based on type and retention needs, tuning Elasticsearch and Logstash configurations, and implementing a hot-warm architecture across different EBS volume types. These changes reduced overall costs by an estimated 80% while maintaining high availability and scalability.
Logging with Elasticsearch, Logstash & KibanaAmazee Labs
This document discusses logging with the ELK stack (Elasticsearch, Logstash, Kibana). It provides an overview of each component, how they work together, and demos their use. Elasticsearch is for search and indexing, Logstash centralizes and parses logs, and Kibana provides visualization. Tools like Curator help manage time-series data in Elasticsearch. The speaker demonstrates collecting syslog data with Logstash and viewing it in Kibana. The ELK stack provides centralized logging and makes queries like "check errors from yesterday between times" much easier.
Lessons Learned in Deploying the ELK Stack (Elasticsearch, Logstash, and Kibana)Cohesive Networks
Slides from the Chicago AWS user group on May 5th, 2016. Asaf Yigal, Co-Founder and VP Product at Logz.io, presented on using Elasticsearch, Logstash, and Kibana in Amazon Web Services.
"Setting up the increasingly-popular open-source ELK Stack (Elasticsearch, Logstash, and Kibana) on AWS might seem like an easy task, but we have gone through several iterations in our architecture and have made some mistakes in our deployments that have turned out to be common in the industry. In this talk, we will go through what we did and explain what worked and what failed -- and why. We will also provide a complete blueprint of how to set up ELK for production on AWS." ~ @asafyigal
An introduction to elasticsearch with a short demonstration on Kibana to present the search API. The slide covers:
- Quick overview of the Elastic stack
- indexation
- Analysers
- Relevance score
- One use case of elasticsearch
The query used for the Kibana demonstration can be found here:
https://github.com/melvynator/elasticsearch_presentation
This document summarizes a presentation on the Elastic Stack. It discusses the main components - Elasticsearch for storing and searching data, Logstash for ingesting data, Kibana for visualizing data. It provides examples of using Elasticsearch for search, analytics, and aggregations. It also briefly mentions new features across the Elastic Stack like update by query, ingest nodes, pipeline improvements, and APIs for management and metrics.
Elasticsearch, Logstash, Kibana. Cool search, analytics, data mining and more...Oleksiy Panchenko
In the age of information and big data, ability to quickly and easily find a needle in a haystack is extremely important. Elasticsearch is a distributed and scalable search engine which provides rich and flexible search capabilities. Social networks (Facebook, LinkedIn), media services (Netflix, SoundCloud), Q&A sites (StackOverflow, Quora, StackExchange) and even GitHub - they all find data for you using Elasticsearch. In conjunction with Logstash and Kibana, Elasticsearch becomes a powerful log engine which allows to process, store, analyze, search through and visualize your logs.
Video: https://www.youtube.com/watch?v=GL7xC5kpb-c
Scripts for the Demo: https://github.com/opanchenko/morning-at-lohika-ELK
Logging is one of those things that everyone complains about, but doesn't dedicate time to. Of course, the first rule of logging is "do it". Without that, you have no visibility into system activities when investigations are required. But, the end goal is much, much more than this. Almost all applications require security audit logs for compliance; application logs for visibility across all cloud properties; and application tracing for tracking usage patterns and business intelligence. The latter is that magic sauce that helps businesses learn about their customer or in some cases the data is FOR the customer. Without a strategy this can get very messy, fast. In this session Michele will discuss design patterns for a sound logging and audit strategy; considerations for security and compliance; the benefits of a noSQL approach; and more.
Getting Started with Elastic Stack.
Detailed blog for the same
http://vikshinde.blogspot.co.uk/2017/08/elastic-stack-introduction.html
Elasticsearch is a distributed, open source search and analytics engine that allows full-text searches of structured and unstructured data. It is built on top of Apache Lucene and uses JSON documents. Elasticsearch can index, search, and analyze big volumes of data in near real-time. It is horizontally scalable, fault tolerant, and easy to deploy and administer.
This document discusses using the ELK stack (Elasticsearch, Logstash, Kibana) for log analysis. It describes the author's experience using Splunk and alternatives like Graylog and Elasticsearch before settling on the ELK stack. The key components - Logstash for input, Elasticsearch for storage and searching, and Kibana for the user interface - are explained. Troubleshooting tips are provided around checking that the components are running and communicating properly.
ElasticSearch - index server used as a document databaseRobert Lujo
Presentation held on 5.10.2014 on http://2014.webcampzg.org/talks/.
Although ElasticSearch (ES) primary purpose is to be used as index/search server, in its featureset ES overlaps with common NoSql database; better to say, document database.
Why this could be interesting and how this could be used effectively?
Talk overview:
- ES - history, background, philosophy, featureset overview, focus on indexing/search features
- short presentation on how to get started - installation, indexing and search/retrieving
- Database should provide following functions: store, search, retrieve -> differences between relational, document and search databases
- it is not unusual to use ES additionally as an document database (store and retrieve)
- an use-case will be presented where ES can be used as a single database in the system (benefits and drawbacks)
- what if a relational database is introduced in previosly demonstrated system (benefits and drawbacks)
ES is a nice and in reality ready-to-use example that can change perspective of development of some type of software systems.
The document introduces the ELK stack, which consists of Elasticsearch, Logstash, Kibana, and Beats. Beats ship log and operational data to Elasticsearch. Logstash ingests, transforms, and sends data to Elasticsearch. Elasticsearch stores and indexes the data. Kibana allows users to visualize and interact with data stored in Elasticsearch. The document provides descriptions of each component and their roles. It also includes configuration examples and demonstrates how to access Elasticsearch via REST.
DEVNET-1140 InterCloud Mapreduce and Spark Workload Migration and Sharing: Fi...Cisco DevNet
Data gravity is a reality when dealing with massive amounts and globally distributed systems. Processing this data requires distributed analytics processing across InterCloud. In this presentation we will share our real world experience with storing, routing, and processing big data workloads on Cisco Cloud Services and Amazon Web Services clouds.
Flink in Zalando's world of Microservices ZalandoHayley
Apache Flink Meetup at Zalando Technology, May 2016
By Javier Lopez & Mihail Vieru, Zalando
In this talk we present Zalando's microservices architecture and introduce Saiki – our next generation data integration and distribution platform on AWS. We show why we chose Apache Flink to serve as our stream processing framework and describe how we employ it for our current use cases: business process monitoring and continuous ETL. We then have an outlook on future use cases.
Berlin Apache Flink Meetup, May 2016
In this talk we present Zalando's microservices architecture and introduce Saiki – our next generation data integration and distribution platform on AWS. We show why we chose Apache Flink to serve as our stream processing framework and describe how we employ it for our current use cases: business process monitoring and continuous ETL. We then have an outlook on future use cases.
By Javier Lopez & Mihail Vieru, Zalando, Zalando SE
The document summarizes the new features and improvements in Elastic Stack v5.0.0, including updates to Kibana, Elasticsearch, Logstash, and Beats. Key highlights include a redesigned Kibana interface, improved indexing performance in Elasticsearch, easier plugin development in Logstash, new data shippers and filtering capabilities in Beats, and expanded subscription support offerings. The Elastic Stack aims to help users build distributed applications and solve real problems through its integrated search, analytics, and data pipeline capabilities.
A Big Data Lake Based on Spark for BBVA Bank-(Oscar Mendez, STRATIO)Spark Summit
This document describes BBVA's implementation of a Big Data Lake using Apache Spark for log collection, storage, and analytics. It discusses:
1) Using Syslog-ng for log collection from over 2,000 applications and devices, distributing logs to Kafka.
2) Storing normalized logs in HDFS and performing analytics using Spark, with outputs to analytics, compliance, and indexing systems.
3) Choosing Spark because it allows interactive, batch, and stream processing with one system using RDDs, SQL, streaming, and machine learning.
Neo4j Database Overview document discusses:
1. Key components and ingredients of Neo4j including index-free adjacency and ACID foundation.
2. How Neo4j fits into the larger data ecosystem and common integration patterns.
3. Latest innovations in Neo4j 3.3 including performance improvements, security enhancements, and developer productivity features.
This talk covered the OpenStack basics that VMware Administrators need to be aware of to be successful in their deployments. We also had the Tesora team join us on stage to discuss the importance of Database-as-a-Service with the Trove project!
Scaling Security on 100s of Millions of Mobile Devices Using Apache Kafka® an...confluent
Lookout is a mobile cybersecurity company that ingests telemetry data from hundreds of millions of mobile devices to provide security scanning and apply corporate policies. They were facing scaling issues with their existing data pipeline and storage as the number of devices grew. They decided to use Apache Kafka and Confluent Platform for scalable data ingestion and ScyllaDB as the persistent store. Testing showed the new architecture could handle their target of 1 million devices with low latency and significantly lower costs compared to their previous DynamoDB-based solution. Key learnings included improving Kafka's default partitioner and working through issues during proof of concept testing with ScyllaDB.
Quali's cloud sandboxes easily integrate with popular tools like Ansible, Jenkins & JFrog to automate the workflow, increase release velocity & enhance quality.
Gs08 modernize your data platform with sql technologies wash dcBob Ward
The document discusses the challenges of modern data platforms including disparate systems, multiple tools, high costs, and siloed insights. It introduces the Microsoft Data Platform as a way to manage all data in a scalable and secure way, gain insights across data without movement, utilize existing skills and investments, and provide consistent experiences on-premises, in the cloud, and hybrid environments. Key elements of the Microsoft Data Platform include SQL Server, Azure SQL Database, Azure SQL Data Warehouse, Azure Data Lake, and Analytics Platform System.
MySQL shell is the MySQL client of the future. It will help you in your daily operations, whatever they are. It doesn't matter if you are a developer or an administrator, if you want to work with relational or non relational data, if you want to setup or monitor your cluster, if you want to work with SQL language or javascript or python.
Discover how MySQL shell will help you, no matter what you want to do with MySQL!
Connector/J Beyond JDBC: the X DevAPI for Java and MySQL as a Document StoreFilipe Silva
The document discusses Connector/J Beyond JDBC and the X DevAPI for Java and MySQL as a Document Store. It provides an agenda that includes an introduction to MySQL as a document store, an overview of the X DevAPI, and how the X DevAPI is implemented in Connector/J. The presentation aims to demonstrate the X DevAPI for developing CRUD-based applications and using MySQL as both a relational database and document store.
AWS Chicago 2016 Lessons Learned Deploying the ELK StackAWS Chicago
The document summarizes a presentation given by Asaf Yigal of logz.io about log analytics and the ELK stack. The key points covered include:
- An introduction to why log analytics is important and the various use cases it supports.
- An overview of the popular open source ELK stack for log analytics including Elasticsearch, Logstash, and Kibana.
- The challenges of installing and maintaining the ELK stack at production scale, including complex configuration, security, upgrades, and high availability.
- How logz.io's cloud-based ELK service addresses these challenges by providing a fully-managed, infinitely scalable, and production-ready log analytics platform.
OSCON 2013 - The Hitchiker’s Guide to Open Source Cloud ComputingMark Hinkle
And while the Hitchhiker’s Guide to the Galaxy (HHGTTG) is a wholly remarkable book it doesn’t cover the nuances of cloud computing. Whether you want to build a public, private or hybrid cloud there are free and open source tools that can help provide you a complete solution or help augment your existing Amazon or other hosted cloud solution. That’s why you need the Hitchhiker’s Guide to (Open Source) Cloud Computing (HHGTCC) or at least to attend this talk understand the current state of open source cloud computing. This talk will cover infrastructure-as-a-service, platform-as-a-service and developments in big data and how to more effectively deploy and manage open source flavors of these technologies. Specific the guide will cover:
Infrastructure-as-a-Service – The Systems Cloud – Get a comparison of the open source cloud platforms including OpenStack, Apache CloudStack, Eucalyptus and OpenNebula
Platform-as-a-Service – The Developers Cloud – Learn about the tools that abstract the complexity for developers and used to build portable auto-scaling applications ton CloudFoundry, OpenShift, Stackato and more.
Data-as-a-Service – The Analytics Cloud – Want to figure out the who, what, where, when and why of big data? You’ll get an overview of open source NoSQL databases and technologies like MapReduce to help parallelize data mining tasks and crunch massive data sets in the cloud.
Network-as-a-Service – The Network Cloud – The final pillar for truly fungible network infrastructure is network virtualization. We will give an overview of software-defined networking including OpenStack Quantum, Nicira, open Vswitch and others.
Finally this talk will provide an overview of the tools that can help you really take advantage of the cloud. Do you want to auto-scale to serve millions of web pages and scale back down as demand fluctuates. Are you interested in automating the total lifecycle of cloud computing environments You’ll learn how to combine these tools into tool chains to provide continuous deployment systems that will help you become agile and spend more time improving your IT rather than simply maintaining it.
[Finally, for those of you that are Douglas Adams fans please accept the deepest apologies for bad analogies to the HHGTTG.]
The document discusses Oracle Cloud Infrastructure, which provides modern cloud computing technology including availability domains, non-blocking networks, off-box IO virtualization, and direct-attached NVMe storage. This infrastructure delivers high availability, high performance, and high scalability. The cloud infrastructure offers flexible compute options from VMs to bare metal servers, NVMe-based storage, and virtual private networks. Case studies show it can accelerate rendering workloads by 2-10x and support high performance computing for financial firms.
- Oracle Database Cloud Service provides Oracle Database software in a cloud environment, including features like Real Application Clusters (RAC) and Data Guard.
- It offers different service levels from a free developer tier to a managed Exadata service. The Exadata service provides extreme database performance on cloud infrastructure.
- New offerings include the Oracle Database Exadata Cloud Service, which provides the full Exadata platform as a cloud service for large, mission-critical workloads.
Red hat's updates on the cloud & infrastructure strategyOrgad Kimchi
Red Hat presented its cloud and infrastructure strategy, focusing on Red Hat Cloud Suite which includes OpenStack for the software platform, OpenShift for DevOps and containers, and CloudForms for cloud management. OpenStack provides massive scalability for infrastructure and removes vendor lock-in. OpenShift enables developers and operations to build, deploy, and manage containerized applications from development to production on any infrastructure including physical, virtual, private and public clouds. CloudForms allows for managing containers and OpenShift deployments across hybrid cloud environments.
Scale Your Load Balancer from 0 to 1 million TPS on AzureAvi Networks
For years, enterprises have relied on appliance-based (hardware or virtual) load balancers. Unfortunately, these legacy ADCs are inflexible at scale, costly due to overprovisioning for peak traffic, and slow to respond to changes or security incidents.
These problems are amplified as applications migrate to the cloud. In contrast, the Avi Vantage Platform not only elastically scales up and down based on real-time traffic patterns, but also offers ludicrous scale at a fraction of the cost.
Watch this webinar to see how Avi can scale up and down quickly on the Microsoft Azure Cloud.
- Configure load balancing on Azure to scale up from 0 to 1 million transactions per second (TPS) and down in under 10 minutes
- Learn why hardware or virtual appliances are not an option for modern load balancing in public clouds
- Understand how Avi’s elastic scale dramatically lowers TCO and enhances security, including DDoS attacks
Watch the full webinar: https://info.avinetworks.com/webinars-ludicrous-scale-on-azure
Cisco’s Cloud Strategy, including our acquisition of CliQr Cisco Canada
At Partner Summit we made a series of exciting announcements in our Cloud portfolio, including our acquisition of CliQr. Join us to learn about these new announcements and an understanding of Cisco’s Cloud Strategy.
- How does CliQr fit into our existing Cloud portfolio (Metapod, APIC, Enterprise Cloud Suite, Cloud Consumption-as-a-Service)?
- How does our Cloud portfolio today meet the needs of our customers? What problems are we solving?
- How does our portfolio today position us for the world of Containers and Microservices?
Join us for a presentation of how these announcements fit into our current environment and what they mean to your longer-term strategy.
Ingestion in data pipelines with Managed Kafka Clusters in Azure HDInsightMicrosoft Tech Community
This document provides an overview of Apache Kafka on Azure HDInsight, including its key features such as 99.9% availability, support for various development tools, enterprise security features, integration with other Azure services, and examples of how it is used by customers for real-time analytics and streaming workloads. It also includes diagrams illustrating how Kafka works and call-outs about Kafka's scalability, fault tolerance, and pub-sub model.
Hi! I'm an ex-B2B Marketing VP, Copywriter & Graphic Designer with 10 years of experience. I've written & designed 100+ brochures and crafted a winning methodology that combines creative and reader-friendly visualization concepts to make ingesting information interesting and engaging. I combine my experience and skills to create cutting-edge-looking brochures that also tell a compelling story.
I deliver the highest design quality and proactively bring fresh ideas to every job I take. But the real added value I bring is my vast marketing experience and copywriting skills, which you get at no extra cost. I go far beyond existing briefs, providing my two cents on content/copy/design best practices and how to best deliver them.
Cybowall is committed to protecting organizations of all sizes. Whether securing the IP reputations of some of the largest Service Provider networks in the world.
AML Transaction Monitoring Tuning WebinarIdan Tohami
Poorly defined thresholds have a number of key impacts on a bank’s operations and compliance departments. Often times, analysts spend considerable time investigating useless alerts which increases operational costs significantly and causes a delay in regulatory filings. Also, the absence of risk-focused thresholds may cause potential money laundering patterns to go un-detected which poses higher monitoring risk to the bank.
Learn how financial institutions can leverage advanced analytics techniques to improve the productivity of the rules by setting up appropriate thresholds. Our speaker will also discuss how to leverage automation techniques for alert investigation in order to reduce the effort spent on false positives, thereby giving more time for the investigations to focus on true suspicious activities.
Topics covered:
- Regulatory Implications
- Managing AML Risks and Emerging Typologies
- Developing Targeted Detection Scenarios
- Customer Segmentation/Population Groups
- Understanding Normal and Outliers
- Operational Improvement through automation
Robotic Process Automation (RPA) Webinar - By Matrix-IFSIdan Tohami
(1) RPA can automate repetitive tasks in financial crime compliance like AML/KYC to reduce manual work and costs. It allows focusing investigator time on more complex cases.
(2) The document discusses how RPA can enhance operations throughput by automating tasks like external data retrieval and form filling. A case study shows an organization improved alerts processed per day from 200 to 1200 using RPA.
(3) The presentation recommends organizations first assess their operations to identify automation opportunities, then start with a pilot RPA project and scale up based on proven value and ROI. RPA benefits include faster processes, accuracy, and scalability with business needs.
Open Banking / PSD2 & GDPR Regulations and How They Are Changing Fraud & Fina...Idan Tohami
The purpose of this webinar is to help Financial Institutions understand the implications of financial crime and fraud prevention, and get ready to review and upgrade their systems accordingly where required.
Topics covered:
-Overview of GDPR and PSD2 regulations with respect to Financial Crime
-Implications of each the regulations on Fraud and Financial Crime (FFC)
-The challenges and opportunities offered by those regulations
-Which steps should Financial Institutions take to mitigate the cost of FFC
Robotic Automation Process (RPA) Webinar - By Matrix-IFSIdan Tohami
Anshul Arora presented Matrix-ifs' RPA solution which talked about
- Integrating AML, Fraud and Cyber-security Investigations
- Eliminate Manual Time Consuming Tasks Using Automation
- Proactive Investigations - System Triggering using AI and Machine Learning Trends
Public cloud spending is growing rapidly, with the public cloud market expected to reach $236 billion by 2020. While public cloud platforms are growing the fastest, cloud and on-premises environments still need to co-exist. There are different hybrid models organizations can choose from based on their environment, tiers, load requirements, and cloud readiness. A hybrid multi-cloud environment provides capabilities across infrastructure, security, integration, service operation, and service transition to manage applications and data across on-premises and multiple cloud platforms.
The document discusses CloudZone's path to helping customers adopt AWS cloud services. It describes AWS' global infrastructure including regions and availability zones. CloudZone provides assessments, governance, workload reviews, and implementation to help customers migrate systems to AWS cloud. Ongoing services include cost optimization and managed services. Two customer case studies are presented: a Ministry of Health using AWS for big data healthcare research, and a manufacturer using AWS for worldwide connectivity of factory data collection.
The document discusses how enterprises are accelerating their journey to the cloud. It notes that change has become more dynamic and that transformation can take years during which the patient/enterprise needs to remain conscious. It discusses how the traditional IT model lacks agility to keep pace with startups. Adopting capabilities of startups can help but bridging the gap is not simple. AWS provides services that can help enterprises and startups bridge this gap. Moving to the cloud allows enterprises to focus on their core mission rather than IT operations. It also discusses how enterprises can become more agile like startups through practices like DevOps and continuous delivery. The document also discusses how the cloud makes it feasible for enterprises to move to the next generation
This document provides an overview of Google Cloud Fundamentals. It introduces Andrew Liaskovski as the teacher and covers various Google Cloud topics including migration, security, DevOps, big data, and disaster recovery services. It also discusses CloudZone's full service package including consulting, managed services, and professional services. The rest of the document focuses on specific Google Cloud products and services such as Compute Engine, App Engine, Container Engine, Cloud Storage, Cloud SQL, networking, big data, and machine learning.
This document provides instructions for deploying the necessary environments and tools for a data analytics lab. It includes setting up a Hortonworks sandbox cluster on Azure, creating an Azure data science virtual machine, and optional configurations for Azure Data Lake and SQL Data Warehouse. Completing these steps ensures students have all required software and access installed prior to the lab. The document estimates completion of the prerequisite setup should take less than 30 minutes.
Cloud Regulations and Security Standards by Ran AdlerIdan Tohami
The document discusses regulations and standards related to cloud computing and privacy. It outlines various regulations including GDPR, Ramot (Israeli privacy authority), and Privacy Shield. It also discusses standards such as ISO 27017 and 27018 which provide guidance on information security controls for cloud computing. The document suggests that cloud computing raises risks regarding confidentiality but can improve availability and integrity if proper security policies and frameworks are implemented.
Azure Logic Apps by Gil Gross, CloudZoneIdan Tohami
This document discusses Azure Logic Apps and serverless computing. It defines key cloud computing models like IaaS, PaaS, and serverless. Serverless computing is running code without dedicated servers. Logic Apps allow automating workflows between cloud services without coding by using connectors. Popular Logic Apps connectors include FTP, HTTP, and Office 365. Logic Apps are billed per action and examples of pricing are provided. Advanced uses of Logic Apps include orchestrating API apps, data validation, transformation, and connectivity between cloud and on-premises systems.
AWS Fundamentals @Back2School by CloudZoneIdan Tohami
This document provides an overview of an AWS Fundamentals course. The course objectives are to teach attendees how to navigate the AWS Management Console, understand foundational AWS services like EC2, VPC, S3, and EBS, manage security and access with IAM, use database services like DynamoDB and RDS, and manage resources with services like Auto Scaling, ELB, and CloudWatch. The agenda covers introductions to AWS, foundational services, security and IAM, databases, and management tools.
Mobile App Development Company in Saudi ArabiaSteve Jonas
EmizenTech is a globally recognized software development company, proudly serving businesses since 2013. With over 11+ years of industry experience and a team of 200+ skilled professionals, we have successfully delivered 1200+ projects across various sectors. As a leading Mobile App Development Company In Saudi Arabia we offer end-to-end solutions for iOS, Android, and cross-platform applications. Our apps are known for their user-friendly interfaces, scalability, high performance, and strong security features. We tailor each mobile application to meet the unique needs of different industries, ensuring a seamless user experience. EmizenTech is committed to turning your vision into a powerful digital product that drives growth, innovation, and long-term success in the competitive mobile landscape of Saudi Arabia.
Enhancing ICU Intelligence: How Our Functional Testing Enabled a Healthcare I...Impelsys Inc.
Impelsys provided a robust testing solution, leveraging a risk-based and requirement-mapped approach to validate ICU Connect and CritiXpert. A well-defined test suite was developed to assess data communication, clinical data collection, transformation, and visualization across integrated devices.
Artificial Intelligence is providing benefits in many areas of work within the heritage sector, from image analysis, to ideas generation, and new research tools. However, it is more critical than ever for people, with analogue intelligence, to ensure the integrity and ethical use of AI. Including real people can improve the use of AI by identifying potential biases, cross-checking results, refining workflows, and providing contextual relevance to AI-driven results.
News about the impact of AI often paints a rosy picture. In practice, there are many potential pitfalls. This presentation discusses these issues and looks at the role of analogue intelligence and analogue interfaces in providing the best results to our audiences. How do we deal with factually incorrect results? How do we get content generated that better reflects the diversity of our communities? What roles are there for physical, in-person experiences in the digital world?
Book industry standards are evolving rapidly. In the first part of this session, we’ll share an overview of key developments from 2024 and the early months of 2025. Then, BookNet’s resident standards expert, Tom Richardson, and CEO, Lauren Stewart, have a forward-looking conversation about what’s next.
Link to recording, presentation slides, and accompanying resource: https://bnctechforum.ca/sessions/standardsgoals-for-2025-standards-certification-roundup/
Presented by BookNet Canada on May 6, 2025 with support from the Department of Canadian Heritage.
HCL Nomad Web – Best Practices und Verwaltung von Multiuser-Umgebungenpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-nomad-web-best-practices-und-verwaltung-von-multiuser-umgebungen/
HCL Nomad Web wird als die nächste Generation des HCL Notes-Clients gefeiert und bietet zahlreiche Vorteile, wie die Beseitigung des Bedarfs an Paketierung, Verteilung und Installation. Nomad Web-Client-Updates werden “automatisch” im Hintergrund installiert, was den administrativen Aufwand im Vergleich zu traditionellen HCL Notes-Clients erheblich reduziert. Allerdings stellt die Fehlerbehebung in Nomad Web im Vergleich zum Notes-Client einzigartige Herausforderungen dar.
Begleiten Sie Christoph und Marc, während sie demonstrieren, wie der Fehlerbehebungsprozess in HCL Nomad Web vereinfacht werden kann, um eine reibungslose und effiziente Benutzererfahrung zu gewährleisten.
In diesem Webinar werden wir effektive Strategien zur Diagnose und Lösung häufiger Probleme in HCL Nomad Web untersuchen, einschließlich
- Zugriff auf die Konsole
- Auffinden und Interpretieren von Protokolldateien
- Zugriff auf den Datenordner im Cache des Browsers (unter Verwendung von OPFS)
- Verständnis der Unterschiede zwischen Einzel- und Mehrbenutzerszenarien
- Nutzung der Client Clocking-Funktion
AI and Data Privacy in 2025: Global TrendsInData Labs
In this infographic, we explore how businesses can implement effective governance frameworks to address AI data privacy. Understanding it is crucial for developing effective strategies that ensure compliance, safeguard customer trust, and leverage AI responsibly. Equip yourself with insights that can drive informed decision-making and position your organization for success in the future of data privacy.
This infographic contains:
-AI and data privacy: Key findings
-Statistics on AI data privacy in the today’s world
-Tips on how to overcome data privacy challenges
-Benefits of AI data security investments.
Keep up-to-date on how AI is reshaping privacy standards and what this entails for both individuals and organizations.
What is Model Context Protocol(MCP) - The new technology for communication bw...Vishnu Singh Chundawat
The MCP (Model Context Protocol) is a framework designed to manage context and interaction within complex systems. This SlideShare presentation will provide a detailed overview of the MCP Model, its applications, and how it plays a crucial role in improving communication and decision-making in distributed systems. We will explore the key concepts behind the protocol, including the importance of context, data management, and how this model enhances system adaptability and responsiveness. Ideal for software developers, system architects, and IT professionals, this presentation will offer valuable insights into how the MCP Model can streamline workflows, improve efficiency, and create more intuitive systems for a wide range of use cases.
AI Changes Everything – Talk at Cardiff Metropolitan University, 29th April 2...Alan Dix
Talk at the final event of Data Fusion Dynamics: A Collaborative UK-Saudi Initiative in Cybersecurity and Artificial Intelligence funded by the British Council UK-Saudi Challenge Fund 2024, Cardiff Metropolitan University, 29th April 2025
https://alandix.com/academic/talks/CMet2025-AI-Changes-Everything/
Is AI just another technology, or does it fundamentally change the way we live and think?
Every technology has a direct impact with micro-ethical consequences, some good, some bad. However more profound are the ways in which some technologies reshape the very fabric of society with macro-ethical impacts. The invention of the stirrup revolutionised mounted combat, but as a side effect gave rise to the feudal system, which still shapes politics today. The internal combustion engine offers personal freedom and creates pollution, but has also transformed the nature of urban planning and international trade. When we look at AI the micro-ethical issues, such as bias, are most obvious, but the macro-ethical challenges may be greater.
At a micro-ethical level AI has the potential to deepen social, ethnic and gender bias, issues I have warned about since the early 1990s! It is also being used increasingly on the battlefield. However, it also offers amazing opportunities in health and educations, as the recent Nobel prizes for the developers of AlphaFold illustrate. More radically, the need to encode ethics acts as a mirror to surface essential ethical problems and conflicts.
At the macro-ethical level, by the early 2000s digital technology had already begun to undermine sovereignty (e.g. gambling), market economics (through network effects and emergent monopolies), and the very meaning of money. Modern AI is the child of big data, big computation and ultimately big business, intensifying the inherent tendency of digital technology to concentrate power. AI is already unravelling the fundamentals of the social, political and economic world around us, but this is a world that needs radical reimagining to overcome the global environmental and human challenges that confront us. Our challenge is whether to let the threads fall as they may, or to use them to weave a better future.
Linux Support for SMARC: How Toradex Empowers Embedded DevelopersToradex
Toradex brings robust Linux support to SMARC (Smart Mobility Architecture), ensuring high performance and long-term reliability for embedded applications. Here’s how:
• Optimized Torizon OS & Yocto Support – Toradex provides Torizon OS, a Debian-based easy-to-use platform, and Yocto BSPs for customized Linux images on SMARC modules.
• Seamless Integration with i.MX 8M Plus and i.MX 95 – Toradex SMARC solutions leverage NXP’s i.MX 8 M Plus and i.MX 95 SoCs, delivering power efficiency and AI-ready performance.
• Secure and Reliable – With Secure Boot, over-the-air (OTA) updates, and LTS kernel support, Toradex ensures industrial-grade security and longevity.
• Containerized Workflows for AI & IoT – Support for Docker, ROS, and real-time Linux enables scalable AI, ML, and IoT applications.
• Strong Ecosystem & Developer Support – Toradex offers comprehensive documentation, developer tools, and dedicated support, accelerating time-to-market.
With Toradex’s Linux support for SMARC, developers get a scalable, secure, and high-performance solution for industrial, medical, and AI-driven applications.
Do you have a specific project or application in mind where you're considering SMARC? We can help with Free Compatibility Check and help you with quick time-to-market
For more information: https://www.toradex.com/computer-on-modules/smarc-arm-family
Big Data Analytics Quick Research Guide by Arthur MorganArthur Morgan
This is a Quick Research Guide (QRG).
QRGs include the following:
- A brief, high-level overview of the QRG topic.
- A milestone timeline for the QRG topic.
- Links to various free online resource materials to provide a deeper dive into the QRG topic.
- Conclusion and a recommendation for at least two books available in the SJPL system on the QRG topic.
QRGs planned for the series:
- Artificial Intelligence QRG
- Quantum Computing QRG
- Big Data Analytics QRG
- Spacecraft Guidance, Navigation & Control QRG (coming 2026)
- UK Home Computing & The Birth of ARM QRG (coming 2027)
Any questions or comments?
- Please contact Arthur Morgan at [email protected].
100% human made.
DevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptxJustin Reock
Building 10x Organizations with Modern Productivity Metrics
10x developers may be a myth, but 10x organizations are very real, as proven by the influential study performed in the 1980s, ‘The Coding War Games.’
Right now, here in early 2025, we seem to be experiencing YAPP (Yet Another Productivity Philosophy), and that philosophy is converging on developer experience. It seems that with every new method we invent for the delivery of products, whether physical or virtual, we reinvent productivity philosophies to go alongside them.
But which of these approaches actually work? DORA? SPACE? DevEx? What should we invest in and create urgency behind today, so that we don’t find ourselves having the same discussion again in a decade?
AI EngineHost Review: Revolutionary USA Datacenter-Based Hosting with NVIDIA ...SOFTTECHHUB
I started my online journey with several hosting services before stumbling upon Ai EngineHost. At first, the idea of paying one fee and getting lifetime access seemed too good to pass up. The platform is built on reliable US-based servers, ensuring your projects run at high speeds and remain safe. Let me take you step by step through its benefits and features as I explain why this hosting solution is a perfect fit for digital entrepreneurs.
Designing Low-Latency Systems with Rust and ScyllaDB: An Architectural Deep DiveScyllaDB
Want to learn practical tips for designing systems that can scale efficiently without compromising speed?
Join us for a workshop where we’ll address these challenges head-on and explore how to architect low-latency systems using Rust. During this free interactive workshop oriented for developers, engineers, and architects, we’ll cover how Rust’s unique language features and the Tokio async runtime enable high-performance application development.
As you explore key principles of designing low-latency systems with Rust, you will learn how to:
- Create and compile a real-world app with Rust
- Connect the application to ScyllaDB (NoSQL data store)
- Negotiate tradeoffs related to data modeling and querying
- Manage and monitor the database for consistently low latencies
Role of Data Annotation Services in AI-Powered ManufacturingAndrew Leo
From predictive maintenance to robotic automation, AI is driving the future of manufacturing. But without high-quality annotated data, even the smartest models fall short.
Discover how data annotation services are powering accuracy, safety, and efficiency in AI-driven manufacturing systems.
Precision in data labeling = Precision on the production floor.
Quantum Computing Quick Research Guide by Arthur MorganArthur Morgan
This is a Quick Research Guide (QRG).
QRGs include the following:
- A brief, high-level overview of the QRG topic.
- A milestone timeline for the QRG topic.
- Links to various free online resource materials to provide a deeper dive into the QRG topic.
- Conclusion and a recommendation for at least two books available in the SJPL system on the QRG topic.
QRGs planned for the series:
- Artificial Intelligence QRG
- Quantum Computing QRG
- Big Data Analytics QRG
- Spacecraft Guidance, Navigation & Control QRG (coming 2026)
- UK Home Computing & The Birth of ARM QRG (coming 2027)
Any questions or comments?
- Please contact Arthur Morgan at [email protected].
100% human made.
6. 2010
1st Elastic{ON} user conference
Company name changed to “Elastic”
Found acquired (now Elastic Cloud)
Packetbeat team joins Elastic (now Beats)
Total cumulative downloads 45M
2012 2013 2014 2015 2016
6
20. Logs Logs Logs,
many devices,
many systems
More than 40% of our
customers use our products
for operational log analysis
21. We collect more than
1.2 TB logs every day from
our infrastructure, web servers,
and applications.
21
22. We analyze more than 400
Million events a day to
maximize our manufacturing
processes and increase
efficiency across our teams.
22
23. Sniff sniff sniff,
find the bad actors
in your data
200% YoY growth in security
use cases with our products
24. We analyze piles of data:
13B AMP queries/day
600B emails/day
16B web requests/day
24
25. We are on track to achieve our
goal to handle more than 20 PB
of data to serve over 100
technical and business teams at
scale across the globe.
25
26. The Elastic Stack:
A foundation to solve
many use cases
75% of our customers use
our products for more than
one use case
SEARCH
SECURIT
CUSTOM APPS
METRICS
OPERATIONAL
ANALYTICS
LOG ANALYSIS
35. 35
March 7-9, 2017
Pier 48
San Francisco, CA
2,500 attendees
3rd Annual Elastic User Conference
REGISTER TO ATTEND:
https://www.elastic.co/elasticon/conf/2017/sf/registration
43. Fast, Safe Scripting Language
43
• Secure and production-safe
• Significantly faster than Groovy
• Familiar syntax
• Can be used in various places:
• Ingest node pipeline, function scoring,
scripted result filtering, watch conditions,
and more
Say “Heya” to Painless
46. Resiliency and Safety Improvements
46
• We saw some common problems when getting
started or new users on a multi-tenant environment
• Bootstrap checks
• Circuit breakers
• Safeguards
47. Faster, more normalized DSL
47
• Completion Suggester v2
• Percolation is now a normal query
• Profile API expansion to include aggregations and
not just queries
48. Beyond 5.0
48
• Higher timestamp resolution (great for logging use cases)
• More improvements on resiliency
• Build on BKD: range fields, geo
• Increased performance for append-only time series use cases
• Native RESTful Java client
59. Window into the Elastic Stack
59
Console (formerly Sense) is a default app
60. Window into the Elastic Stack
60
Monitoring app now includes Kibana monitoring
* requires X-Pack
61. Window into the Elastic Stack
61
New UI to manage users and roles
* requires X-Pack
62. Share the Kibana <3
62
Create reports of your visualizations and dashboards
* requires X-Pack
63. Beyond 5.0
63
• Kibana is the Window into the Elastic Stack — management and visualization
• Embrace more diversity: New user interfaces, visualizations, and dev management tools
• Kibana for everyone — developers, technical, non-technical business users
• “Unexpected apps”
66. X-pack X-pack
Nodes (X)
Logstash
Messaging
Queue
Kafka
Redis
Elasticsearch
Master Nodes (3)
Data Nodes - Warm (X)
Instances (X)
Kibana
Custom UI
Datastore Web APIs
Social Sensors
Log Files
Beats
Metrics
Wire Data your{beat}
Hadoop Ecosystem
ES-Hadoop
Ingest Nodes (X)
Data Nodes - Hot (X)
Authentication Notification
LDAP AD SSO
67. Say Heya to Ingest Node
67
Process incoming data directly in Elasticsearch
I
N
G
E
S
T
68. Logstash: Goodbye Black Box!
68
logstash:9600/_node
Node Info
Node Stats
Plugins
Hot Threads
Monitoring API
Debug active pipelines
with new logging API
Component level logging
granularity
Log4j2 Internal
Logging
70. Logstash: Plugin Features
70
Developers can
generate new
plugins in seconds
Kafka 0.10 Support
Basic Auth & SSL/TLS
Plugin GeneratorKafka Support++
Kinesis Input
Protobuf Codec
Dissect Filter
IPv6 Support with
GeoIP2
New Plugins
71. 71
Elasticsearch Kibana
ES-Hadoop
Backup Elasticsearch with HDFS
Efficiently move data between
Elasticsearch & Hadoop
Elasticsearch-Hadoop 5.0
Spark 2.0 & Better
Streaming Support
Ingest Node
Pipeline Integration
Elasticsearch 5.0
Parallel Reader
72. Beyond 5.0 (Beats)
72
• Moar modules in Metricbeat
• Moar Beats
• Even easier getting started experience
• Centralized configuration & monitoring