Learn about features with demos and announcements, from cross-cluster replication and frozen indices in Elasticsearch to Kibana Spaces and the ever-growing set of data integrations in Beats and Logstash.
Learn about features with demos and announcements, from cross-cluster replication and frozen indices in Elasticsearch to Kibana Spaces and the ever-growing set of data integrations in Beats and Logstash.
Análisis a fondo de roadmap del Elastic StackElasticsearch
Obtén información sobre las características mediante demostraciones y anuncios, desde replicación entre clusters e índices congelados en Elasticsearch hasta Kibana Spaces y el conjunto de integraciones de datos en constante crecimiento en Beats y Logstash.
Au cœur de la roadmap de la Suite ElasticElasticsearch
Découvrez les dernières fonctionnalités grâce à nos démos et annonces : réplication inter-clusters, indices gelés d'Elasticsearch, Kibana Spaces, et toujours plus d'intégrations de données dans Beats et Logstash.
Descubre las características disponibles con demostraciones: la replicación entre clústeres, los índices bloqueados de Elasticsearch, los espacios de Kibana y los datos de integraciones en Beats y Logstash.
Descubre las mas recientes y futuras características del Stack: gestión del ciclo de vida de los datos para arquitecturas hot/warm/cold con DataStreams, mejoras en uso de memoria y disco, mejoras en el enrutado de las consultas; Analítica de datos multi lenguaje con query cDSL, SQL, KQL, PromQL y EQL; el nuevo sistema de Alertas y Acciones.
Apache Druid®: A Dance of Distributed ProcessesImply
This document summarizes the key components and collaborations in Apache Druid. It describes Zookeeper's role in coordination, the Overlord's role in task management, the Broker's role in query routing, and the Middle Manager's role in ingestion and indexing. It provides diagrams illustrating how these components work together to ingest and store distributed data, and answer queries in a scalable way.
Move your on prem data to a lake in a Lake in CloudCAMMS
With the boom in data; the volume and its complexity, the trend is to move data to the cloud. Where and How do we do this? Azure gives you the answer. In this session, I will give you an introduction to Azure Data Lake and Azure Data Factory, and why they are good for the type of problem we are talking about. You will learn how large datasets can be stored on the cloud, and how you could transport your data to this store. The session will briefly cover Azure Data Lake as the modern warehouse for data on the cloud,
Hermes: Free the Data! Distributed Computing with MongoDBMongoDB
Moving data throughout an organization is an art form. Whether mastering the art of ETL or building micro services, we are often left with either business logic embedded where it doesn't belong or monolithic apps that do too much. In this talk, we will show you how we built a persisted messaging bus to ‘Free the Data’ from the apps, making it available across the organization without having to write custom ETL code. This in turn makes it possible for business apps to be standalone, testable and more reliable. We will discuss the basic architecture and how it works, go through some code samples (server side and client side), and present some statistics and visualizations.
Learn about features with demos and announcements, from cross-cluster replication and frozen indices in Elasticsearch to Kibana Spaces and the ever-growing set of data integrations in Beats and Logstash.
Análisis a fondo de roadmap del Elastic StackElasticsearch
Obtén información sobre las características mediante demostraciones y anuncios, desde replicación entre clusters e índices congelados en Elasticsearch hasta Kibana Spaces y el conjunto de integraciones de datos en constante crecimiento en Beats y Logstash.
Au cœur de la roadmap de la Suite ElasticElasticsearch
Découvrez les dernières fonctionnalités grâce à nos démos et annonces : réplication inter-clusters, indices gelés d'Elasticsearch, Kibana Spaces, et toujours plus d'intégrations de données dans Beats et Logstash.
Descubre las características disponibles con demostraciones: la replicación entre clústeres, los índices bloqueados de Elasticsearch, los espacios de Kibana y los datos de integraciones en Beats y Logstash.
Descubre las mas recientes y futuras características del Stack: gestión del ciclo de vida de los datos para arquitecturas hot/warm/cold con DataStreams, mejoras en uso de memoria y disco, mejoras en el enrutado de las consultas; Analítica de datos multi lenguaje con query cDSL, SQL, KQL, PromQL y EQL; el nuevo sistema de Alertas y Acciones.
Apache Druid®: A Dance of Distributed ProcessesImply
This document summarizes the key components and collaborations in Apache Druid. It describes Zookeeper's role in coordination, the Overlord's role in task management, the Broker's role in query routing, and the Middle Manager's role in ingestion and indexing. It provides diagrams illustrating how these components work together to ingest and store distributed data, and answer queries in a scalable way.
Move your on prem data to a lake in a Lake in CloudCAMMS
With the boom in data; the volume and its complexity, the trend is to move data to the cloud. Where and How do we do this? Azure gives you the answer. In this session, I will give you an introduction to Azure Data Lake and Azure Data Factory, and why they are good for the type of problem we are talking about. You will learn how large datasets can be stored on the cloud, and how you could transport your data to this store. The session will briefly cover Azure Data Lake as the modern warehouse for data on the cloud,
Hermes: Free the Data! Distributed Computing with MongoDBMongoDB
Moving data throughout an organization is an art form. Whether mastering the art of ETL or building micro services, we are often left with either business logic embedded where it doesn't belong or monolithic apps that do too much. In this talk, we will show you how we built a persisted messaging bus to ‘Free the Data’ from the apps, making it available across the organization without having to write custom ETL code. This in turn makes it possible for business apps to be standalone, testable and more reliable. We will discuss the basic architecture and how it works, go through some code samples (server side and client side), and present some statistics and visualizations.
Apache Druid ingests and enables instant query on many billions of events in real-time. But how? In this talk, each of the components of an Apache Druid cluster is described – along with the data and query optimisations at its core – that unlock fresh, fast data for all.
MoPub, a Twitter company, provides monetization solutions for mobile app publishers and developers around the globe. MoPub receives over 33 Billion ad requests per day generating over 200TB of raw logs every day. We built MoPub Analytics as the analytics platform, using Druid + Imply for our end users who are Publishers, Demand side partners and Internal users.
We will talk about the architecture of the analytics platform, our Druid cluster setup, hardware choices, monitoring, use cases, limiting factors, challenges with lookups and solutions we used.
Watch video:https://imply.io/virtual-druid-summit/analytics-over-terabytes-of-data-at-twitter-apache-druid
This document summarizes an open source scalable log analytics solution. The solution uses Lumberjack for log collection, Logstash for indexing and filtering logs, Redis for buffering, Elasticsearch for indexing and searching logs, MongoDB for document storage, and Kibana with D3.js for visualization. Logs are collected from servers by Lumberjack, sent to Logstash for processing, indexed by Elasticsearch for searching, stored in MongoDB for retrieval, and visualized through dashboards and reports in Kibana. The solution allows for real-time log analysis, flexible searching and filtering, and scales horizontally as needs grow.
This document discusses Apache Dremio, an open source data virtualization platform that provides self-service SQL access to data sources like Elasticsearch, MongoDB, HDFS, and relational databases. It aims to make data analytics faster by avoiding the need for data staging, warehouses, cubes, and extracts. Dremio uses techniques like reflections, pushdowns, and a universal relational algebra to optimize queries and leverage caches. It is based on projects like Apache Drill, Calcite, Arrow, and Parquet and can be deployed on Hadoop or the cloud. The presentation includes a demo of using Dremio to create datasets, curate/prepare data, accelerate queries with reflections, and manage resources.
One of the most popular use cases for Apache Druid is building data applications. Data applications exist to deliver data into the hands of everyone on a team in a business, and are used by these teams to make faster, better decisions. To fulfill this role, they need to support granular drill down, because the devil is in the details, but also be extremely fast, because otherwise people won't use them!
In this talk, Gian Merlino will cover:
*The unique technical challenges of powering data-driven applications
*What attributes of Druid make it a good platform for data applications
*Some real-world data applications powered by Druid
Building a Real-Time Gaming Analytics Service with Apache DruidImply
At GameAnalytics we receive and process real time behavioural data from more than 100 million daily active users, helping thousands of game studios and developers understand user behaviour and improve their games. In this talk, you will learn how we managed to migrate our legacy backend system from using an in-house built streaming analytics service to Apache Druid, and the lessons learned along the way. By adopting Druid, we have been able to reduce development costs, increase reliability of our systems and implement new features that would have not been possible with our old stack. We will provide an overview of our approach to schema design, segments optimization, creation of our query layer, caching and datasources optimisation, which can help you better understand how you can successfully use Druid as a key component on your data processing and reporting infrastructure.
Archmage, Pinterest’s Real-time Analytics Platform on DruidImply
In this talk, we will talk about:
1) the motivation of switching from Hbase backed analytics system to Druid
2) the architecture design of Druid as a platform in Pinterest (Archmage, Hadoop, Kafka) including a query interface, Archmage, a thrift service in front of Druid which exposes a thrift api to company-wise clients, handles Druid broker hosts discovery, serves as a relay to broker hosts to abstract the async HTTP connection and provides query optimizations transparent to clients including directly translating fixed pattern SQL to Druid native JSON queries to save planning time. In addition, we’ll cover the production Hadoop batch and Kafka real time ingestion pipeline setup and the reason we picked a pull-based solution instead of a push-based solution for real time ingestion.
3) We will also talk about the use cases currently running in production on this platform including their data volume, QPS, Druid cluster setup, the unique challenges we met while onboarding and how we addressed them with extensive tunings to meet SLA and lessons learned for use cases including: partner insights, which provides partners with stats on organic pins; realtime spam detection, which detects user login related anomaly events and pin related spamming events like pin creation and repin; and migrating the backend from Presto to Druid for Ads related experiments data analysis.
Gian will offer his reflections on the Druid journey to date, plus describe his vision for what Druid will become. He will lay out the near-term Druid roadmap and take your questions.
Watch video: https://imply.io/virtual-druid-summit/apache-druid-vision-and-roadmap-gian-merlino
Our secure remote connectivity tool provides full video recording of all work our engineers perform on client systems. We have requirements to analyze the video log to detect suspicious activity, provide forensic and root cause analysis capabilities. Some of the obvious use cases include detection of credit card patterns or personally identifiable information (PII) as well as malicious activity like dropping database objects. We need to process hundreds of gigabytes per day representing thousands of hours of video. Our solution leverages a variety of Hadoop components to perform optical text recognition and indexing, keyboard and mouse movement analysis as well as integration with variety of other data sources such as our monitoring, documentation, ticketing and communication systems. We will present our complete architecture starting from multi-source data ingestion through data processing and analysis up to the end user interface, reporting and integration layer.
Peter Marshall, Technology Evangelist at Imply
Abstract: Apache Druid® can revolutionise business decision-making with a view of the freshest of fresh data in web, mobile, desktop, and data science notebooks. In this talk, we look at key activities to integrate into Apache Druid POCs, discussing common hurdles and signposting to important information.
Bio: Peter Marshall (https://petermarshall.io) is an Apache Druid Technology Evangelist at Imply (http://imply.io/), a company founded by original developers of Apache Druid. He has 20 years architecture experience in CRM, EDRM, ERP, EIP, Digital Services, Security, BI, Analytics, and MDM. He is TOGAF certified and has a BA degree in Theology and Computer Studies from the University of Birmingham in the United Kingdom.
This document discusses Big Data solutions in Microsoft Azure. It introduces Azure cloud services and provides an overview of Big Data and how it differs from traditional databases. It then outlines Microsoft's Big Data solutions built on Hortonworks Data Platform, including HDInsight which allows running Hadoop on Azure. HDInsight supports various data storage options and processing tools like Hive, Pig, and Storm. The document also covers designing HDInsight clusters and Azure Data Lake for unlimited storage of structured and unstructured data.
Hype, buzzword, threat; however you want to characterize it, the Internet of Things (IoT) is here.
IoT scenarios that were hypothetical only a few years ago are real today. Still thinking along the line of fleet management and temperature measurements? You’re out. Endless possibilities of IoT applications are surfacing every day, from the connected cow (huh?) to things that monitor and analyze your daily life (really?).
In this webinar, we will discuss architecture of IoT data management solutions and the challenges that arise. We will explore how MongoDB features provide solutions to those problems. Time permitting, we will demonstrate an IoT Cloud service built on top of MongoDB.
This document discusses Apache Arrow, an open source cross-language development platform for in-memory analytics. It provides an overview of Arrow's goals of being cross-language compatible, optimized for modern CPUs, and enabling interoperability between systems. Key components include core C++/Java libraries, integrations with projects like Pandas and Spark, and common message patterns for sharing data. The document also describes how Arrow is implemented in practice in systems like Dremio's Sabot query engine.
Data Analytics and Processing at Snap - Druid Meetup LA - September 2018Charles Allen
Charles Allen covers data processing, analytics, and insights systems at Snap. Strength points for Druid use cases are called out as are differences in some of the processing systems used.
This is the slide collection from the second talk from:
https://www.meetup.com/druidio-la/events/254080924/
MongoDB in the Middle of a Hybrid Cloud and Polyglot Persistence ArchitectureMongoDB
The Sage Data Cloud enables next-generation cloud and mobile services via a Hybrid Cloud and Polyglot Persistence Architecture. Come learn how MongoDB and other cloud data stores make this a reality, and get an insight into our learnings and operations.
How Netflix Uses Druid in Real-time to Ensure a High Quality Streaming Experi...Imply
Ensuring a consistently great Netflix experience while continuously pushing innovative technology updates is no easy feat.
We'll look at how Netflix turns log streams into real-time metrics to provide visibility into how devices are performing in the field. Including sharing some of the lessons learned around optimizing Druid to handle our load.
A Non-Standard use Case of Hadoop: High Scale Image Processing and AnalyticsDataWorks Summit
1. The Hadoop Image Processing (HIP) pipeline acquires vehicle images, identifies updates, generates URLs, crops and resizes images, copies them to asset servers, and removes duplicates.
2. It uses HBase for image storage and archiving, MapReduce for image processing, Kafka for publishing to asset servers, OpenCV for image processing, and Avro for data serialization.
3. Performance testing showed HIP scales linearly and is at least 10x faster than the previous system, and using cascading downloads provided a 20% performance gain.
Descubre las características disponibles con demostraciones: la replicación entre clústeres, los índices bloqueados de Elasticsearch, los espacios de Kibana y los datos de integraciones en Beats y Logstash.
Apache Druid ingests and enables instant query on many billions of events in real-time. But how? In this talk, each of the components of an Apache Druid cluster is described – along with the data and query optimisations at its core – that unlock fresh, fast data for all.
MoPub, a Twitter company, provides monetization solutions for mobile app publishers and developers around the globe. MoPub receives over 33 Billion ad requests per day generating over 200TB of raw logs every day. We built MoPub Analytics as the analytics platform, using Druid + Imply for our end users who are Publishers, Demand side partners and Internal users.
We will talk about the architecture of the analytics platform, our Druid cluster setup, hardware choices, monitoring, use cases, limiting factors, challenges with lookups and solutions we used.
Watch video:https://imply.io/virtual-druid-summit/analytics-over-terabytes-of-data-at-twitter-apache-druid
This document summarizes an open source scalable log analytics solution. The solution uses Lumberjack for log collection, Logstash for indexing and filtering logs, Redis for buffering, Elasticsearch for indexing and searching logs, MongoDB for document storage, and Kibana with D3.js for visualization. Logs are collected from servers by Lumberjack, sent to Logstash for processing, indexed by Elasticsearch for searching, stored in MongoDB for retrieval, and visualized through dashboards and reports in Kibana. The solution allows for real-time log analysis, flexible searching and filtering, and scales horizontally as needs grow.
This document discusses Apache Dremio, an open source data virtualization platform that provides self-service SQL access to data sources like Elasticsearch, MongoDB, HDFS, and relational databases. It aims to make data analytics faster by avoiding the need for data staging, warehouses, cubes, and extracts. Dremio uses techniques like reflections, pushdowns, and a universal relational algebra to optimize queries and leverage caches. It is based on projects like Apache Drill, Calcite, Arrow, and Parquet and can be deployed on Hadoop or the cloud. The presentation includes a demo of using Dremio to create datasets, curate/prepare data, accelerate queries with reflections, and manage resources.
One of the most popular use cases for Apache Druid is building data applications. Data applications exist to deliver data into the hands of everyone on a team in a business, and are used by these teams to make faster, better decisions. To fulfill this role, they need to support granular drill down, because the devil is in the details, but also be extremely fast, because otherwise people won't use them!
In this talk, Gian Merlino will cover:
*The unique technical challenges of powering data-driven applications
*What attributes of Druid make it a good platform for data applications
*Some real-world data applications powered by Druid
Building a Real-Time Gaming Analytics Service with Apache DruidImply
At GameAnalytics we receive and process real time behavioural data from more than 100 million daily active users, helping thousands of game studios and developers understand user behaviour and improve their games. In this talk, you will learn how we managed to migrate our legacy backend system from using an in-house built streaming analytics service to Apache Druid, and the lessons learned along the way. By adopting Druid, we have been able to reduce development costs, increase reliability of our systems and implement new features that would have not been possible with our old stack. We will provide an overview of our approach to schema design, segments optimization, creation of our query layer, caching and datasources optimisation, which can help you better understand how you can successfully use Druid as a key component on your data processing and reporting infrastructure.
Archmage, Pinterest’s Real-time Analytics Platform on DruidImply
In this talk, we will talk about:
1) the motivation of switching from Hbase backed analytics system to Druid
2) the architecture design of Druid as a platform in Pinterest (Archmage, Hadoop, Kafka) including a query interface, Archmage, a thrift service in front of Druid which exposes a thrift api to company-wise clients, handles Druid broker hosts discovery, serves as a relay to broker hosts to abstract the async HTTP connection and provides query optimizations transparent to clients including directly translating fixed pattern SQL to Druid native JSON queries to save planning time. In addition, we’ll cover the production Hadoop batch and Kafka real time ingestion pipeline setup and the reason we picked a pull-based solution instead of a push-based solution for real time ingestion.
3) We will also talk about the use cases currently running in production on this platform including their data volume, QPS, Druid cluster setup, the unique challenges we met while onboarding and how we addressed them with extensive tunings to meet SLA and lessons learned for use cases including: partner insights, which provides partners with stats on organic pins; realtime spam detection, which detects user login related anomaly events and pin related spamming events like pin creation and repin; and migrating the backend from Presto to Druid for Ads related experiments data analysis.
Gian will offer his reflections on the Druid journey to date, plus describe his vision for what Druid will become. He will lay out the near-term Druid roadmap and take your questions.
Watch video: https://imply.io/virtual-druid-summit/apache-druid-vision-and-roadmap-gian-merlino
Our secure remote connectivity tool provides full video recording of all work our engineers perform on client systems. We have requirements to analyze the video log to detect suspicious activity, provide forensic and root cause analysis capabilities. Some of the obvious use cases include detection of credit card patterns or personally identifiable information (PII) as well as malicious activity like dropping database objects. We need to process hundreds of gigabytes per day representing thousands of hours of video. Our solution leverages a variety of Hadoop components to perform optical text recognition and indexing, keyboard and mouse movement analysis as well as integration with variety of other data sources such as our monitoring, documentation, ticketing and communication systems. We will present our complete architecture starting from multi-source data ingestion through data processing and analysis up to the end user interface, reporting and integration layer.
Peter Marshall, Technology Evangelist at Imply
Abstract: Apache Druid® can revolutionise business decision-making with a view of the freshest of fresh data in web, mobile, desktop, and data science notebooks. In this talk, we look at key activities to integrate into Apache Druid POCs, discussing common hurdles and signposting to important information.
Bio: Peter Marshall (https://petermarshall.io) is an Apache Druid Technology Evangelist at Imply (http://imply.io/), a company founded by original developers of Apache Druid. He has 20 years architecture experience in CRM, EDRM, ERP, EIP, Digital Services, Security, BI, Analytics, and MDM. He is TOGAF certified and has a BA degree in Theology and Computer Studies from the University of Birmingham in the United Kingdom.
This document discusses Big Data solutions in Microsoft Azure. It introduces Azure cloud services and provides an overview of Big Data and how it differs from traditional databases. It then outlines Microsoft's Big Data solutions built on Hortonworks Data Platform, including HDInsight which allows running Hadoop on Azure. HDInsight supports various data storage options and processing tools like Hive, Pig, and Storm. The document also covers designing HDInsight clusters and Azure Data Lake for unlimited storage of structured and unstructured data.
Hype, buzzword, threat; however you want to characterize it, the Internet of Things (IoT) is here.
IoT scenarios that were hypothetical only a few years ago are real today. Still thinking along the line of fleet management and temperature measurements? You’re out. Endless possibilities of IoT applications are surfacing every day, from the connected cow (huh?) to things that monitor and analyze your daily life (really?).
In this webinar, we will discuss architecture of IoT data management solutions and the challenges that arise. We will explore how MongoDB features provide solutions to those problems. Time permitting, we will demonstrate an IoT Cloud service built on top of MongoDB.
This document discusses Apache Arrow, an open source cross-language development platform for in-memory analytics. It provides an overview of Arrow's goals of being cross-language compatible, optimized for modern CPUs, and enabling interoperability between systems. Key components include core C++/Java libraries, integrations with projects like Pandas and Spark, and common message patterns for sharing data. The document also describes how Arrow is implemented in practice in systems like Dremio's Sabot query engine.
Data Analytics and Processing at Snap - Druid Meetup LA - September 2018Charles Allen
Charles Allen covers data processing, analytics, and insights systems at Snap. Strength points for Druid use cases are called out as are differences in some of the processing systems used.
This is the slide collection from the second talk from:
https://www.meetup.com/druidio-la/events/254080924/
MongoDB in the Middle of a Hybrid Cloud and Polyglot Persistence ArchitectureMongoDB
The Sage Data Cloud enables next-generation cloud and mobile services via a Hybrid Cloud and Polyglot Persistence Architecture. Come learn how MongoDB and other cloud data stores make this a reality, and get an insight into our learnings and operations.
How Netflix Uses Druid in Real-time to Ensure a High Quality Streaming Experi...Imply
Ensuring a consistently great Netflix experience while continuously pushing innovative technology updates is no easy feat.
We'll look at how Netflix turns log streams into real-time metrics to provide visibility into how devices are performing in the field. Including sharing some of the lessons learned around optimizing Druid to handle our load.
A Non-Standard use Case of Hadoop: High Scale Image Processing and AnalyticsDataWorks Summit
1. The Hadoop Image Processing (HIP) pipeline acquires vehicle images, identifies updates, generates URLs, crops and resizes images, copies them to asset servers, and removes duplicates.
2. It uses HBase for image storage and archiving, MapReduce for image processing, Kafka for publishing to asset servers, OpenCV for image processing, and Avro for data serialization.
3. Performance testing showed HIP scales linearly and is at least 10x faster than the previous system, and using cascading downloads provided a 20% performance gain.
Descubre las características disponibles con demostraciones: la replicación entre clústeres, los índices bloqueados de Elasticsearch, los espacios de Kibana y los datos de integraciones en Beats y Logstash.
Learn about how to reduce public cloud storage costs on the AWS and Azure marketplaces with SoftNAS Senior Director of Product Marketing, John Bedrick.
Data is gravity. Your workloads and processing is dependent on where your data is and how it is stored. With AWS, you have a host of storage options and the key to successfully leverage them is to know when to use which option. This session will explain in details about each of the AWS Storage offerings along with data ingestion optins into the Cloud using Snowball and Snowmobile
Marc Trimuschat,
Head - Business Developement, AWS Storage, AWS APAC
This document discusses a webinar on data lakes and analytics hosted by Karlos Correia and Claudio Chiba, AWS solutions architects for the public sector. The agenda covers what a data lake is, why organizations use data lakes, how data lakes expand traditional analytics approaches, and the benefits of data lakes such as centralized data storage and schema-on-read capabilities. Amazon S3 and AWS analytics services are positioned as enabling technologies for building data lakes.
This document discusses real-time analytics on streaming data. It describes why real-time data streaming and analytics are important due to the perishable nature of data value over time. It then covers key components of real-time analytics systems including data sources, stream storage, stream ingestion, stream processing, and stream delivery. Finally, it discusses streaming data processing techniques like filtering, enriching, and converting streaming data.
Analyze your Data Lake, Fast @ Any Scale - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
-Learn how to automatically discover, catalog, and prepare your data for analytics
-Understand how to query data in your data lake without having to transform or load the data into your data warehouse
-See how to analyze data in both your data lake and data warehouse
(ARC311) Decoding The Genetic Blueprint Of Life On A Cloud EcosystemAmazon Web Services
Thermo Fisher Scientific, a world leader in biotechnology, has built a new polymerase chain reaction (PCR) system for DNA sequencing. Designed for low- to midlevel throughput laboratories that conduct real time PCR experiments, the system runs on individual QuantStudio devices. These devices are connected to Thermo Fisher’s cloud computing platform, which is built on AWS using Amazon EC2, Amazon DynamoDB, and Amazon S3. With this single platform, applied and clinical researchers can learn, analyze, share, collaborate, and obtain support. Researchers worldwide can now collaborate online in real time and access their data wherever and whenever necessary. Laboratories can also share experimental conditions and results with their partners while providing a uniform experience for every user and helping to minimize training and errors. The net result is increased collaboration, faster time to market, fewer errors, and lower cost. We have architected a solution that uses Amazon EMR, DynamoDB, Amazon Elasticache, and S3. In this presentation, we share our architecture, lessons learned, best design patterns for NoSQL, strategies for leveraging EMR with DynamoDB, and a flexible solution that our scientist use. We also share our next step in architecture evolution.
Calculating dynamic pricing, estimated travel times or detecting fraud in real time. These are all the cases where realtime analytics create the differentiation between experiences. Redis comes with built in types to enable realtime processing of complex analytics with data types like sorted sets, hyperloglog, bloom and cuckoo filters and more.
AWS Analytics Immersion Day - Build BI System from Scratch (Day1, Day2 Full V...Sungmin Kim
How to build Business Intelligence System from scratch on AWS (Day1, Day2)
------------------------------------------------------------------------------------------
2020-03-18(수)~19(목) 2일 동안 온라인으로 진행한 Online AWS Analytics Immersion Day 전체 발표 자료 입니다.
BI(Business Intelligence) 시스템을 설계하는 과정에서 AWS Analytics 서비스들을 어떻게 활용할 수 있는지 설명 드리고자 만든 자료 입니다.
Target Audience
-------------------
Online Analytics Immersion Day는 다음과 같은 고객을 대상으로 진행됩니다.
- AWS Analytics Services (ex. Kinesis, Athena, Redshift, EMR, etc)의 기본 개념을 알고 있지만, 이러한 서비스 활용 방법 및 데이터 분석 시스템 구축 과정이 궁금하신 분
- 데이터 분석 시스템을 구축한 경험은 있지만, 자신이 만든 시스템을 아키텍처 관점에서
어떻게 평가하고 확인할 수 있는지 궁금하신 분
Implementation of Dense Storage Utilizing HDDs with SSDs and PCIe Flash Acc...Red_Hat_Storage
At Red Hat Storage Day New York on 1/19/16, Red Hat partner Seagate presented on how to implement dense storage using HDDs with SSDs and PCIe flash accelerator cards.
Amazon Redshift is a fast, fully managed data warehousing service that allows customers to analyze petabytes of structured data, at one-tenth the cost of traditional data warehousing solutions. It provides massively parallel processing across multiple nodes, columnar data storage for efficient queries, and automatic backups and recovery. Customers have seen up to 100x performance improvements over legacy systems when using Redshift for applications like log and clickstream analytics, business intelligence reporting, and real-time analytics.
Traditional data warehouses become expensive and slow down as the volume of your data grows. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it easy to analyze all of your data using existing business intelligence tools for 1/10th the traditional cost. This session will provide an introduction to Amazon Redshift and cover the essentials you need to deploy your data warehouse in the cloud so that you can achieve faster analytics and save costs.
This document summarizes best practices for running Elasticsearch in production environments. It covers anti-patterns to avoid like arbitrary keys and heavy updating. It discusses resource usage and distributed problems like memory usage, out of memory errors, and network glitches. It also provides guidance on security practices, client concerns, and strategies for changing clusters.
Video in french at https://www.youtube.com/watch?v=9LNnNh63rBI
Sizing an Elasticsearch cluster has to consider many dimensions. In this presentation we go through the different elements and features you should consider to handle big and varying loads of log data.
Introduction to Storage on AWS - AWS Summit Cape Town 2017Amazon Web Services
With AWS, you can choose the right storage service for the right use case. This session shows the range of AWS choices that are available to you: Amazon S3, Amazon EBS, Amazon EFS, Amazon Glacier and Cloud Data Migration solutions.
Elasticsearch in production New York Meetup at Twitter October 2014beiske
Elasticsearch easily lets you develop amazing things, and it has gone to great lengths to make Lucene's features readily available in a distributed setting. However, when it comes to running Elasticsearch in production, you still have a fairly complicated system on your hands: a system with high demands on network stability, a huge appetite for memory, and a system that assumes all users are trustworthy. This talk will cover some of the lessons we've learned from securing and herding hundreds of Elasticsearch clusters.
Elasticsearch in production Boston Meetup October 2014beiske
Elasticsearch easily lets you develop amazing things, and it has gone to great lengths to make Lucene's features readily available in a distributed setting. However, when it comes to running Elasticsearch in production, you still have a fairly complicated system on your hands: a system with high demands on network stability, a huge appetite for memory, and a system that assumes all users are trustworthy. This talk will cover some of the lessons we've learned from securing and herding hundreds of Elasticsearch clusters.
Three Steps to Modern Media Asset Management with Active ArchiveAvere Systems
This document discusses a three step approach to modern media asset management with an active archive:
1) Using object storage like Cleversafe for scalable, low-cost archive storage that is geo-dispersed for resilience.
2) Making the archive easily accessible using tools like Avere to provide NAS simplicity and performance.
3) Managing large quantities of media assets using asset management tools like CatDV for ingest, metadata, search, collaboration and workflows.
An introduction to Elasticsearch's advanced relevance ranking toolboxElasticsearch
The hallmark of a great search experience is always delivering the most relevant results, quickly, to every user. The difficulty lies behind the scenes in making that happen elegantly and at a scale. From App Search’s intuitive drag and drop interface to the advanced relevance capabilities built into the core of Elasticsearch — Elastic offers a range of tools for developers to tune relevance ranking and create incredible search experiences. In this session, we’ll explore some of Elasticsearch’s advanced relevance ranking features, such as dense vector fields, BM25F, ranking evaluation, and more. Plus we’ll give you some ideas for how these features are being used by other Elastic users to create world-class, category defining search experiences.
Eze Castle Integration is a managed service provider (MSP), cloud service provider (CSP), and internet service provider (ISP) that delivers services to more than 1,000 clients around the world. Different departments within Eze Castle have devised their own log aggregation solutions in order to provide visibility, meet regulatory compliance requirements, conduct cybersecurity investigations, and help engineers with troubleshooting infrastructure issues. In 2019, they partnered with Elastic to consolidate the data generated from different systems into a single pane of glass. And thanks to the ease of deployment on Elastic Cloud, professional consultation services from Elastic engineers, and on-demand training courses available on Elastic Learning, Eze Castle was able to go from proof-of-concept to a fully functioning ""Eze Managed SIEM"" product within a month!
Learn about Eze Castle's journey with Elastic and how they grew Eze Managed SIEM from zero to 100 customers In less than 14 months.
Cómo crear excelentes experiencias de búsqueda en sitios webElasticsearch
Descubre lo fácil que es crear búsquedas relevantes y enriquecidas en sitios web de cara al público para impulsar las conversiones, incrementar el consumo de contenido y ayudar a los visitantes a encontrar lo que necesitan. Realiza un recorrido por las herramientas de Elastic a las que puedes sacar partido para transformar con facilidad tu sitio web, lo que incluye nuestro nuevo y potente rastreador web.
Te damos la bienvenida a una nueva forma de realizar búsquedas Elasticsearch
1) The document introduces ElasticON Solution Series, which provides out-of-the-box personalized, centralized, and secure organizational search across internal and external sources.
2) It discusses how Elastic Enterprise Search can improve productivity, satisfaction, collaboration, and decision making by connecting all applications and content with a single scalable search platform.
3) The solution achieves this through intuitive search features, powerful analytics and visualization tools, simplified administration, and security certifications to ensure data protection.
Tirez pleinement parti d'Elastic grâce à Elastic CloudElasticsearch
Découvrez pourquoi Elastic Cloud est la solution idéale pour exploiter toutes les offres d'Elastic. Bénéficiez d'une flexibilité d'achat et de déploiement au sein de Google Cloud, de Microsoft Azure, d'Amazon Web Services ou des trois à la fois. Apprenez quels avantages vous apporte une offre de service géré et déterminez la solution qui vous permet de la gérer par vous-même grâce à des outils intégrés d'automatisation et d'orchestration. Et ce n'est pas tout ! Familiarisez-vous avec les fonctionnalités qui peuvent vous aider à scaler vos opérations au fur et à mesure de l'évolution de votre déploiement, à stocker vos données d'une manière rentable et à optimiser vos recherches. Ainsi, vous n'aurez plus à abandonner de données et obtiendrez les informations exploitables dont vous avez besoin pour assurer le fonctionnement de votre entreprise.
Comment transformer vos données en informations exploitablesElasticsearch
Découvrez des fonctionnalités stratégiques de la Suite Elastic, notamment Elasticsearch, un moteur de données incomparable, et Kibana, véritable fenêtre ouverte sur la Suite Elastic.
Dans cette session, vous apprendrez à :
injecter des données dans la Suite Elastic ;
stocker des données ;
analyser des données ;
exploiter des données.
Plongez au cœur de la recherche dans tous ses états.Elasticsearch
À l'instar de la plupart des entreprises modernes, vos équipes utilisent probablement plus de 10 applications hébergées dans le cloud chaque jour, mais passent aussi bien trop de temps à chercher les informations dont elles ont besoin dans ces outils. Grâce aux fonctionnalités prêtes à l'emploi d'Elastic Workplace Search, découvrez combien il est facile de mettre le contenu pertinent à portée de la main de vos équipes grâce à une recherche unifiée sur l'ensemble des applications qu'elles utilisent pour faire leur travail.
Modernising One Legal Se@rch with Elastic Enterprise Search [Customer Story]Elasticsearch
Knowledge management needs in the legal sector, why Linklaters decided to move away from its legacy KM search engine, Kin+Carta's management of the migration process, and how the switch revitalised a well-established system and opened up new possibilities for its future development.
An introduction to Elasticsearch's advanced relevance ranking toolboxElasticsearch
The hallmark of a great search experience is always delivering the most relevant results, quickly, to every user. The difficulty lies behind the scenes in making that happen elegantly and at a scale. From App Search’s intuitive drag and drop interface to the advanced relevance capabilities built into the core of Elasticsearch — Elastic offers a range of tools for developers to tune relevance ranking and create incredible search experiences. In this session, we’ll explore some of Elasticsearch’s advanced relevance ranking features, such as dense vector fields, BM25F, ranking evaluation, and more. Plus we’ll give you some ideas for how these features are being used by other Elastic users to create world-class, category defining search experiences.
Like most modern organizations, your teams are likely using upwards of 10 cloud-based applications on a daily basis, but spending far too many hours a day searching for the information they need across all of them. With the out-of-the-box capabilities of Elastic Workplace Search, see how easy it is to put relevant content right at your teams’ fingertips with unified search across all the apps they rely on to get work done.
Building great website search experiencesElasticsearch
Discover how easy it is to create rich, relevant search on public facing websites that drives conversion, increases content consumption, and helps visitors find what they need. Get a tour of the Elastic tools you can leverage to easily transform your website, including our powerful new web crawler.
Keynote: Harnessing the power of Elasticsearch for simplified searchElasticsearch
Get an overview of the innovation Elastic is bringing to the Enterprise Search landscape, and learn how you can harness these capabilities across your technology landscape to make the power of search work for you.
Cómo transformar los datos en análisis con los que tomar decisionesElasticsearch
Descubre las áreas de características estratégicas de Elastic Stack: Elasticsearch, un motor de datos inigualable y Kibana, la ventana que da acceso a Elastic Stack.
En la sesión hablaremos sobre:
Cómo incorporar datos a Elastic Stack
Almacenamiento de datos
Análisis de los datos
Actuar en función de los datos
Explore relève les défis Big Data avec Elastic Cloud Elasticsearch
Spécialisée dans le développement et la gestion de solutions de veille documentaire et commerciale, Explore offre à ses clients une lecture précise et organisée de l’actualités des marchés et projets sur leurs territoires d'intervention. Afin de rendre leur offre plus agile et performante, Explore a choisi l’offre Elastic Cloud hébergée sur Microsoft Azure. Découvrez comment les équipes de production et de développement sont désormais en mesure de mieux exploiter les données pour les clients d’Explore et gagnent du temps sur la gestion de leur infrastructure.
Comment transformer vos données en informations exploitablesElasticsearch
Découvrez des fonctionnalités stratégiques de la Suite Elastic, notamment Elasticsearch, un moteur de données incomparable, et Kibana, véritable fenêtre ouverte sur la Suite Elastic.
Dans cette session, vous apprendrez à :
injecter des données dans la Suite Elastic ;
stocker des données ;
analyser des données ;
exploiter des données.
Transforming data into actionable insightsElasticsearch
Learn about the strategic feature areas of the Elastic Stack—Elasticsearch, a data engine like no other, and Kibana, the window into the Elastic Stack.
The session will cover:
Bringing data into the Elastic Stack
Storing data
Analyzing data
Acting on data
"Elastic enables the world’s leading organization to exceed their business objectives and power their mission-critical systems by eliminating data silos, connecting the dots, and transforming data of all types into actionable insights.
Come learn how the power of search can help you quickly surface relevant insights at scale. Whether you are an executive looking to reduce operational costs, a department head striving to do more with fewer tools, or engineer monitoring and protecting your IT environment, this session is for you. "
Empowering agencies using Elastic as a Service inside GovernmentElasticsearch
It has now been four years since the beta release of Elastic Cloud Enterprise which kicked off a wave of the Elastic public sector community running Elastic as a service within Government rather than utilizing purely hosted solutions. Fast forward to 2021 and we have multiple options for multiple mission needs. Learn top tips from Elastic architects and their experience enabling their teams with the automation and provisioning of Elastic tech to change the game in how government delivers solutions.
The opportunities and challenges of data for public goodElasticsearch
The document discusses data for public good and the opportunities and challenges involved. It notes that data infrastructure is needed to deliver public good through data. There are almost endless opportunities to use data for public services, policy, and citizen benefits. However, challenges include legacy systems, data silos, unclear governance, and risk aversion. As a case study, it outlines how the UK Census 2021 addressed index faced challenges but showed progress on using data better, with lessons for continued public sector transformation.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
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.
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
Northern Engraving | Modern Metal Trim, Nameplates and Appliance PanelsNorthern Engraving
What began over 115 years ago as a supplier of precision gauges to the automotive industry has evolved into being an industry leader in the manufacture of product branding, automotive cockpit trim and decorative appliance trim. Value-added services include in-house Design, Engineering, Program Management, Test Lab and Tool Shops.
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
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).
High performance Serverless Java on AWS- GoTo Amsterdam 2024Vadym Kazulkin
Java is for many years one of the most popular programming languages, but it used to have hard times in the Serverless community. Java is known for its high cold start times and high memory footprint, comparing to other programming languages like Node.js and Python. In this talk I'll look at the general best practices and techniques we can use to decrease memory consumption, cold start times for Java Serverless development on AWS including GraalVM (Native Image) and AWS own offering SnapStart based on Firecracker microVM snapshot and restore and CRaC (Coordinated Restore at Checkpoint) runtime hooks. I'll also provide a lot of benchmarking on Lambda functions trying out various deployment package sizes, Lambda memory settings, Java compilation options and HTTP (a)synchronous clients and measure their impact on cold and warm start times.
Must Know Postgres Extension for DBA and Developer during MigrationMydbops
Mydbops Opensource Database Meetup 16
Topic: Must-Know PostgreSQL Extensions for Developers and DBAs During Migration
Speaker: Deepak Mahto, Founder of DataCloudGaze Consulting
Date & Time: 8th June | 10 AM - 1 PM IST
Venue: Bangalore International Centre, Bangalore
Abstract: Discover how PostgreSQL extensions can be your secret weapon! This talk explores how key extensions enhance database capabilities and streamline the migration process for users moving from other relational databases like Oracle.
Key Takeaways:
* Learn about crucial extensions like oracle_fdw, pgtt, and pg_audit that ease migration complexities.
* Gain valuable strategies for implementing these extensions in PostgreSQL to achieve license freedom.
* Discover how these key extensions can empower both developers and DBAs during the migration process.
* Don't miss this chance to gain practical knowledge from an industry expert and stay updated on the latest open-source database trends.
Mydbops Managed Services specializes in taking the pain out of database management while optimizing performance. Since 2015, we have been providing top-notch support and assistance for the top three open-source databases: MySQL, MongoDB, and PostgreSQL.
Our team offers a wide range of services, including assistance, support, consulting, 24/7 operations, and expertise in all relevant technologies. We help organizations improve their database's performance, scalability, efficiency, and availability.
Contact us: info@mydbops.com
Visit: https://www.mydbops.com/
Follow us on LinkedIn: https://in.linkedin.com/company/mydbops
For more details and updates, please follow up the below links.
Meetup Page : https://www.meetup.com/mydbops-databa...
Twitter: https://twitter.com/mydbopsofficial
Blogs: https://www.mydbops.com/blog/
Facebook(Meta): https://www.facebook.com/mydbops/
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!
Session 1 - Intro to Robotic Process Automation.pdfUiPathCommunity
👉 Check out our full 'Africa Series - Automation Student Developers (EN)' page to register for the full program:
https://bit.ly/Automation_Student_Kickstart
In this session, we shall introduce you to the world of automation, the UiPath Platform, and guide you on how to install and setup UiPath Studio on your Windows PC.
📕 Detailed agenda:
What is RPA? Benefits of RPA?
RPA Applications
The UiPath End-to-End Automation Platform
UiPath Studio CE Installation and Setup
💻 Extra training through UiPath Academy:
Introduction to Automation
UiPath Business Automation Platform
Explore automation development with UiPath Studio
👉 Register here for our upcoming Session 2 on June 20: Introduction to UiPath Studio Fundamentals: https://community.uipath.com/events/details/uipath-lagos-presents-session-2-introduction-to-uipath-studio-fundamentals/
36. Elasticsearch is built for speed
• Every field is indexed
• Indexes built at ingest
• Denormalized data, no joins
• Distributed execution
But needs disk, cpu, memory!
42. Find users who:
• in the previous 12 months
• have used an application
• on today’s Malicious Apps list
• with param “powershell.exe”
43. Find users who:
• in the previous 12 months
• have used an application
• on today’s Malicious Apps list
• with param “powershell.exe”
Limited Join
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44. Find users who:
• in the previous 12 months
• have used an application
• on today’s Malicious Apps list
• with param “powershell.exe”
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