Interactive analytics at scale with druid

•

15 gostaram•4,516 visualizações

How do you run analytics queries on a dataset that grows by billions of entries a day? What if you need to be able to drill into it, filter it or aggregate it by any available dimension? On demand, without precomputing, and with sub second latency? Oh and this isn't for an internal dashboard. The interface is customer-facing and is going to be accessed by thousands of clients. In this talk, I'll tell you how Criteo, one of the biggest ad tech firms in the world, uses Druid to build its new analytics platform. Druid is an open-source, real-time data store designed to power interactive applications at scale. I’ll walk you through its architecture, explain how it scales and how data is stored on disk and in memory to serve queries faster than you can blink.

Tecnologia

Device Publisher Cost
Iphone Google 0.1€
Android Yahoo 0.2€
Dimension Cost
Iphone 0.1€
Android 0.2€
Google 0.1€
Yahoo 0.1€
Iphone, Google 0.1€
Android, Yahoo 0,2€

Device Publisher Product Cost
Iphone Google Computer 0.1€
Android Yahoo Cloth 0.2€
Dimension Cost
Iphone 0.1€
Android 0.2€
Google 0.1€
Yahoo 0.1€
Computer 0.1€
Cloth 0.2€
Iphone, Google 0.1€
Iphone, Computer 0.1€
Google, Computer 0.1€
Android, Yahoo 0.2€
Android, Cloth 0.2€
Yahoo, Cloth 0.2€
Iphone, Google, Computer 0.1€
Android, Yahoo, Cloth 0.2€

SELECT sum(revenue) AS “Revenue",
sum(sales) AS “Sales"
FROM customer-insights
WHERE client_id = 2255 AND date BETWEEN "2014-08-01" AND "2014-08-08"
GROUP BY day(date)

${ queryType: "Timeseries", dataSource: "customer-insights", granularity: "day", aggregations: [ { type: "longSum", fieldName: "revenue", name: "Revenue" }, { type: "longSum ", fieldName: "sales", name: "Sales" } ], filter: { type: "selector", dimension: "client_id", value: 2255 }, intervals: ["2014-08-01T00:00/2014-08-08T00"] }$

${ queryType: "groupBy", dataSource: "customer-insights", granularity: "all", dimensions: ["device"], aggregations: [ { type: "longSum", fieldName: "clicks", name: "Clicks" }, { type: "longSum", fieldName: "cost", name: "Cost" } ], filter: { type: "selector", dimension: "client_id", value: 2255 }, intervals: ["2014-08-01T00:00/2014-08-08T00"] }$

Iphone Google 0.35€08:12:00
Android Yahoo 0.2€08:12:00
Iphone Google 0.1€08:12:37
Android Yahoo 0.2€08:12:38
Iphone Google 0.15€08:12:39
Iphone Google 0.1€08:12:40

Iphone Google 0.1€08:12:37
Android Yahoo 0.2€08:12:38
Iphone Google 0.15€08:13:02
Iphone Google 0.1€08:13:08
bob@mail.com
joe@mail.com
bob@mail.com
tony@mail.com

Iphone Google Computer 0.1€08:12:37
Android Yahoo Cloth 0.2€08:12:38
Iphone Google Computer 0.1€08:12:37
Android Yahoo Cloth 0.2€08:12:38

Iphone Google Computer 0.1€08:12:37
Android Yahoo Cloth 0.2€08:12:38

Google
Yahoo
Microsoft
Google
Yahoo
1
2
3
1
2

Google
Yahoo
Microsoft
Google
Yahoo
Google 1,0,0,0,1
Yahoo 0,1,0,1,0
Microsoft 0,0,1,0,0

Filter Time period Aggregated rows Time
Biggest advertiser 6 months 1.5M 225ms
Android devices 1 month 42M 750ms
Desktop 1 month 110M 910ms
RTB networks 7 months 753M 2.4s

Interactive analytics at scale with druid

Mais conteúdo relacionado

Mais procurados

MongoDB 4.0 새로운 기능 소개

Ha-Yang(White) Moon

Today, companies are using various channels to communicate with their customers. As a consequence, a lot of data is created, more and more also outside of the traditional IT infrastructure of an enterprise. This data often does not have a common format and they are continuously created with ever increasing volume. With Internet of Things (IoT) and their sensors, the volume as well as the velocity of data just gets more extreme. To achieve a complete and consistent view of a customer, all these customer-related information has to be included in a 360 degree view in a real-time or near-real-time fashion. By that, the Customer Hub will become the Customer Event Hub. It constantly shows the actual view of a customer over all his interaction channels and provides an enterprise the basis for a substantial and effective customer relation. In this presentation the value of such a platform is shown and how it can be implemented using DataStax Enterprise as the backend.

Customer Event Hub – a modern Customer 360° view with DataStax Enterprise (DSE)

Guido Schmutz

Presented by: Saurabh Kashikar Abstract: MongoDB’s basic unit of storage is a document. Documents can represent rich, schema-free data structures, meaning that we have several viable alternatives to the normalized, relational model. If you are new to MongoDB, learn the schema design basics in this introductory session. This session will help you model basic relationships in MongoDB. What You Will Learn: - The fundamentals of the MongoDB document model. - How to model 1-1 and one-to-many (1-N) relationships in MongoDB. - How to model many-to-many (N-N) relationships in MongoDB.

[MongoDB.local Bengaluru 2018] Jumpstart: Introduction to Schema Design

MongoDB

Graphs in Action

Neo4j

MongoDB .local Chicago 2019: MongoDB – Powering the new age data demands

MongoDB

Check Out our Rich Python Portfolio: Leaders in Python & Django‎

Zealous System

[WSO2Con Asia 2018] Patterns for Building Streaming Apps

WSO2

Jumpstart: BI Connector & Tableau Speaker: Ronan Bohan, Solutions Architect, MongoDB Speaker: Viady Krishnan Level: 100 (Beginner) Track: Jumpstart Get started with the BI connector and Tableau in this introductory session. We will give you insight into how you can view your MongoDB data in traditional BI tools and an overview of connecting Tableau with MongoDB. After attending this session, students should be able connect their analytics tool of choice to a MongoDB data store using the BI connector, secure their client connection, and know how to enable authentication. Audience members should be familiar with analytics tools like Tableau to do business analytics, and know how to set up and run analytics in a BI tool. This session will use Tableau as an example. This is a Jumpstart session, held before the keynotes, designed to give you an overview of MongoDB basics so you can dive into more advanced technical sessions later in the day. What You Will Learn: - How to connect your analytics tool of choice to a MongoDB data store using the BI connector. - How to view MongoDB data in Tableau or another BI tool. - How to secure your client connection to MongoDB.

MongoDB BI Connector & Tableau

MongoDB

Security Information and Event Management with Kafka, Kafka Connect, KSQL and...

confluent

MongoDB .local Toronto 2019: MongoDB – Powering the new age data demands

MongoDB

View this talk here: https://www.confluent.io/online-talks/connecting-apache-kafka-to-cash-lyndon-hedderly Real-time data has value. But how do you quantify that value in order to create a business case for becoming data, or event driven? This talk explores why valuing Kafka is important - but covers some of the problems in quantifying the value of a data infrastructure platform. Despite the challenges, we will explore some examples of where we have attributed a quantified monetary amount to Kafka across specific business use cases, within Retail, Banking and Automotive. Whether organizations are using data to create new business products and services, improving user experiences, increasing productivity, or managing risk, we’ll see that fast and interconnected data, or ‘event streaming’ is increasingly important. We will conclude with the five steps to creating a business case around Kafka use cases.

Connecting Apache Kafka to Cash

confluent

MongoDB Evenings Houston: What's the Scoop on MongoDB and Hadoop? by Jake Ang...

MongoDB

MongoDB ClickStream and Visualization

Cameron Sim

Voxxed Days Cluj - Powering interactive data analysis with Google BigQuery

Márton Kodok

On large-scale web sites, users leave thousands of traces every second. Businesses need to process and interpret these traces in real-time to be able to react on the behavior of their users. In this talk, Andreas will show a real world example of the power of a modern open-source stack. He will walk you through the design of a real-time clickstream analysis PAAS solution based on Apache Spark, Kafka, Parquet and HDFS, explain our decision making and present our lessons learned.

Clickstream Analysis With Apache Spark

Andreas Zitzelsberger

Every scientist who needs big data analytics to save millions of lives should have that power. Powering Interactive Data Analysis require massive architecture, and know-how to build a fast real-time computing system. You will learn how Google BigQuery solves this problem by enabling super-fast, SQL queries against petabytes of data using the processing power of Google’s infrastructure. After this session you will be able to work with BigQuery, do streaming inserts, write User Defined Functions in Javascript, and several use cases for everyday developer: funnel analytics, behavioral analytics, exploring unstructured data. You will be able to run arbitrary queries on open-data such as historical data about Github commits, Stackoverflow Q&A data, or analysing Reddit comments to find out books the community talks about.

GDG DevFest Ukraine - Powering Interactive Data Analysis with Google BigQuery

Márton Kodok

MongoDB - General Purpose Database

Ashnikbiz

Modern architectures are moving away from a "one size fits all" approach. We are well aware that we need to use the best tools for the job. Given the large selection of options available today, chances are that you will end up managing data in MongoDB for your operational workload and with Spark for your high speed data processing needs. Description: When we model documents or data structures there are some key aspects that need to be examined not only for functional and architectural purposes but also to take into consideration the distribution of data nodes, streaming capabilities, aggregation and queryability options and how we can integrate the different data processing software, like Spark, that can benefit from subtle but substantial model changes. A clear example is when embedding or referencing documents and their implications on high speed processing. Over the course of this talk we will detail the benefits of a good document model for the operational workload. As well as what type of transformations we should incorporate in our document model to adjust for the high speed processing capabilities of Spark. We will look into the different options that we have to connect these two different systems, how to model according to different workloads, what kind of operators we need to be aware of for top performance and what kind of design and architectures we should put in place to make sure that all of these systems work well together. Over the course of the talk we will showcase different libraries that enable the integration between spark and MongoDB, such as MongoDB Hadoop Connector, Stratio Connector and MongoDB Spark Native Connector. By the end of the talk I expect the attendees to have an understanding of: How they connect their MongoDB clusters with Spark Which use cases show a net benefit for connecting these two systems What kind of architecture design should be considered for making the most of Spark + MongoDB How documents can be modeled for better performance and operational process, while processing these data sets stored in MongoDB. The talk is suitable for: Developers that want to understand how to leverage Spark Architects that want to integrate their existing MongoDB cluster and have real time high speed processing needs Data scientists that know about Spark, are playing with Spark and want to integrate with MongoDB for their persistency layer

Spark and MongoDB

Norberto Leite

[MongoDB.local Bengaluru 2018] The Path to Truly Understanding Your MongoDB Data

MongoDB

Thu-310pm-Impetus-SachinAndAjay

Ajay Shriwastava

Mais procurados (20)

MongoDB 4.0 새로운 기능 소개

Customer Event Hub – a modern Customer 360° view with DataStax Enterprise (DSE)

[MongoDB.local Bengaluru 2018] Jumpstart: Introduction to Schema Design

Graphs in Action

MongoDB .local Chicago 2019: MongoDB – Powering the new age data demands

Check Out our Rich Python Portfolio: Leaders in Python & Django‎

[WSO2Con Asia 2018] Patterns for Building Streaming Apps

MongoDB BI Connector & Tableau

Security Information and Event Management with Kafka, Kafka Connect, KSQL and...

MongoDB .local Toronto 2019: MongoDB – Powering the new age data demands

Connecting Apache Kafka to Cash

MongoDB Evenings Houston: What's the Scoop on MongoDB and Hadoop? by Jake Ang...

MongoDB ClickStream and Visualization

Voxxed Days Cluj - Powering interactive data analysis with Google BigQuery

Clickstream Analysis With Apache Spark

GDG DevFest Ukraine - Powering Interactive Data Analysis with Google BigQuery

MongoDB - General Purpose Database

Spark and MongoDB

[MongoDB.local Bengaluru 2018] The Path to Truly Understanding Your MongoDB Data

Thu-310pm-Impetus-SachinAndAjay

Destaque

Scalable Real-time analytics using Druid

DataWorks Summit/Hadoop Summit

Aggregated queries with Druid on terrabytes and petabytes of data

Rostislav Pashuto

Real-time analytics with Druid at Appsflyer

Michael Spector

Druid realtime indexing

Seoeun Park

The case study is about ViralGains - a US based video marketing platform. The presentation was delivered by me (Salil Kalia) at Great Indian Developer Summit (GIDS) 2016. This is a piece of a great work that we have done at TO THE NEW Digital with our customer, ViralGains. Here, I show-cased Druid (http://druid.io) and the supporting technologies (Kafka/Zookeeper) to demonstrate how it helped us in building a stable realtime analytics system, in capturing hundreds of millions of analytics events per day. When it comes to Ad industry - it becomes very important to be precise or close to precision because money is involved at every step (even for a single ad impression). The case study included a demo and a short talk on their journey of moving from Redis to Cassandra and finally ending up on Druid with an outstanding performance.

Case Study: Realtime Analytics with Druid

Salil Kalia

Data Analytics with Druid

Yousun Jeong

Real-time Analytics with Apache Flink and Druid

Jan Graßegger

Druid at SF Big Analytics 2015-12-01

gianmerlino

Druid at Hadoop Ecosystem

Slim Bouguerra

Open Source Lambda Architecture with Hadoop, Kafka, Samza and Druid

DataWorks Summit

July 2014 HUG : Pushing the limits of Realtime Analytics using Druid

Yahoo Developer Network

Pulsar: Real-time Analytics at Scale with Kafka, Kylin and Druid

Tony Ng

Most dev teams have a very simple conception of software architecture. They start with a database, a user interface, and add layers of (DRY) mess between the two. Then they have a performance problem so they add memcache and mongodb (Now we're webscale!). Months or years later, releasing new features in production gets harder and riskier: it's time for a rewrite. Let's use AnguNodeMvcDoop to solve all our maintainability issues! And the cycle starts over... In this talk we'll see that software architecture is not defined by the technologies you use but by the high level principles you set. We'll see what really matters to make your information system maintainable and how to get there progressively!

Ncrafts.io - Refactor your software architecture

Julien Lavigne du Cadet

How do you monitor performance for one of your clients on a specific user segmentation when dealing with billions of events a day ? With over 2 billion ads served and 230Tb of data processed a day, we at Criteo have a comprehensive need for an interactive analytics stack. And by interactive, we mean a querying system with dynamic filtering to drill down over multiple dimensions, answering within sub-second latency. This session will take you on our journey with Druid, ""an open-source data store designed for real-time exploratory analytics on large data sets"". We will explore Druid's architecture and noticeable concepts, how relevant they are for some use cases and how it really performs.

Gregorry Letribot - Druid at Criteo - NoSQL matters 2015

NoSQLmatters

Monitoring @ scale over diverse data sources @ PayPal - Druid, TSDB, Hadoop

Senthil Pandurangan

Using druid for interactive count distinct queries at scale @ nmc

Ido Shilon

Big Data Day LA 2016/ Big Data Track - Real Time Analytics with Druid - Guill...

Data Con LA

Druid @ branch

Biswajit Das

Programmatic Bidding Data Streams & Druid

Charles Allen

Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1FQYcP0. Gian Merlino presents the advantages, challenges, and best practices to deploying and maintaining lambda architectures in the real world, using the infrastructure at Metamarkets as a case study. Filmed at qconsf.com. Gian Merlino is a senior software engineer at Metamarkets, responsible for the infrastructure behind its data ingestion pipelines and is a committer on the Druid project.

Lambda Architectures in Practice

C4Media

Destaque (20)

Scalable Real-time analytics using Druid

Aggregated queries with Druid on terrabytes and petabytes of data

Real-time analytics with Druid at Appsflyer

Druid realtime indexing

Case Study: Realtime Analytics with Druid

Data Analytics with Druid

Real-time Analytics with Apache Flink and Druid

Druid at SF Big Analytics 2015-12-01

Druid at Hadoop Ecosystem

Open Source Lambda Architecture with Hadoop, Kafka, Samza and Druid

July 2014 HUG : Pushing the limits of Realtime Analytics using Druid

Pulsar: Real-time Analytics at Scale with Kafka, Kylin and Druid

Ncrafts.io - Refactor your software architecture

Gregorry Letribot - Druid at Criteo - NoSQL matters 2015

Monitoring @ scale over diverse data sources @ PayPal - Druid, TSDB, Hadoop

Using druid for interactive count distinct queries at scale @ nmc

Big Data Day LA 2016/ Big Data Track - Real Time Analytics with Druid - Guill...

Druid @ branch

Programmatic Bidding Data Streams & Druid

Lambda Architectures in Practice

Semelhante a Interactive analytics at scale with druid

In this session, learn how Bonobos, an online retailer for men's clothing and accessories, powers their personalized customer experiences on top of AWS. We start by exploring the foundational elements required to build an effective retail data platform as well as the building blocks provided by AWS to deliver these experiences. Learn how Bonobos leverages Segment in their architecture, and hear from Bonobos and Segment on the objectives, challenges, and outcomes realized by Bonobos through their journey in constructing and deploying their personalization platform.

Real-Time Personalized Customer Experiences at Bonobos (RET203) - AWS re:Inve...

Amazon Web Services

MongoDB World 2014 - BillRun, Billing on top of MongoDB

Ofer Cohen

Discover Data That Matters- Deep dive into WSO2 Analytics

Sriskandarajah Suhothayan

TDC2016POA | Trilha Banco de Dados - RavenDB: um banco de dados NoSQL de segu...

tdc-globalcode

Learn how you can enjoy the developer productivity, low TCO, and unlimited scale of MongoDB as a tick database for capturing, analyzing, and taking advantage of opportunities in tick data. This presentation will illustrates how MongoDB can easily and quickly store variable data formats, like top and depth of book, multiple asset classes, and even news and social networking feeds. It will explore aggregating and analyzing tick data in real-time for automated trading or in batch for research and analysis and how auto-sharding enables MongoDB to scale with commodity hardware to satisfy unlimited storage and performance requirements.

Using MongoDB As a Tick Database

MongoDB

Designing Capital One's iPhone and iPad App

Sabrina Ngai

Data Modeling for Performance

Michael Dwan

Modeling for Performance

MongoDB

Audit¢rio 09 mercado envios - novas funcionalidades - bruno elia

fsolari

During this session we will cover the best practices for implementing a real-time inventory with MongoDB. This includes properly model quantities and stores to avoid large numbers of documents being indexed, how to efficiently use geo-indexing to find the closest store with a specific item available and how to run aggregation to gather interesting inventory stats. We will also cover operational considerations, like how to make inventory queries and updates from anywhere be low-latency and resilient to network partitions via tag-aware sharding.

Retail Reference Architecture Part 2: Real-Time, Geo Distributed Inventory

MongoDB

Mongo db world 2014 billrun

MongoDB

Scaling Growth and UA in mobile gaming based on Peaksel example

GameCamp

How AdWords UI maps into adwords api

supergigas

In this presentation, learn how an end-to-end smart application can be built in the AWS cloud. We will demonstrate how to use Amazon Machine Learning (Amazon ML) to create machine learning models, deploy them to production, and obtain predictions in real-time. We will then demonstrate how to build a complete smart application using Amazon ML, Amazon Kinesis, and AWS Lambda. We will walk you through the process flow and architecture, demonstrate outcomes, and then dive into the code for implementation. In this session, you will learn how to use Amazon ML as well as how to integrate Amazon ML into your applications to take advantage of predictive analysis in the cloud. Presented by: Guy Ernest, Principal Business Development Manager, Amazon Web Services Customer Guest: Pim Vernooij, Partner, Lab Digital

Amazon Machine Learning

Amazon Web Services

A presentation given to the Sydney Users of R forum about an open source R package I developed for querying Google Analytics data. For instructions on getting started with ganalytics please refer to the readme file here: https://github.com/jdeboer/ganalytics/blob/master/README.md It would be great to hear any feedback or questions you have about the ganalytics package or the presentation. Any difficulties you might encounter with installing or using the ganalytics package, please let me know so that it can be made easier for everyone. Submit issues here: https://github.com/jdeboer/ganalytics/issues/ Contributions to the package are welcome: - Package documentation - Adding examples and demos - Testing and finding bugs to fix - Ideas for improvements or new features Thanks for your interest. Feel free to reach out to me via twitter: @johannux

Digital analytics with R - Sydney Users of R Forum - May 2015

Johann de Boer

IBM Db2 JSON 11.5

Phil Downey

At Heap, we lean on PostgreSQL for all our backend heavy lifting. We support an expressive set of queries — conversion funnels with filtering and grouping, retention analysis, and behavioral cohorting to name a few — across billions of users and tens of billions of events. Results need to come back in a matter of seconds and reflect up-to-the-minute data. This talk will discuss these challenges, with a particular focus on: - Using CitusDB for interactive analysis across 50 terabytes of data and counting. - PostgreSQL and Kafka: two great tastes that taste great together. - UDFs in C and PL/pgSQL, partial indexes for pre-aggregation, and other tricks up our sleeves.

Powering Heap With PostgreSQL And CitusDB (PGConf Silicon Valley 2015)

Dan Robinson

Pazarlama Dahileri 2016 - Haliç Üniversitesi

Gorkem Unel

Gov APIs: The Notorious Case of Official Statistics

Xavier Badosa

Remarketing with Google Analytics

Luigi Reggiani

Semelhante a Interactive analytics at scale with druid (20)

Real-Time Personalized Customer Experiences at Bonobos (RET203) - AWS re:Inve...

MongoDB World 2014 - BillRun, Billing on top of MongoDB

Discover Data That Matters- Deep dive into WSO2 Analytics

TDC2016POA | Trilha Banco de Dados - RavenDB: um banco de dados NoSQL de segu...

Using MongoDB As a Tick Database

Designing Capital One's iPhone and iPad App

Data Modeling for Performance

Modeling for Performance

Audit¢rio 09 mercado envios - novas funcionalidades - bruno elia

Retail Reference Architecture Part 2: Real-Time, Geo Distributed Inventory

Mongo db world 2014 billrun

Scaling Growth and UA in mobile gaming based on Peaksel example

How AdWords UI maps into adwords api

Amazon Machine Learning

Digital analytics with R - Sydney Users of R Forum - May 2015

IBM Db2 JSON 11.5

Powering Heap With PostgreSQL And CitusDB (PGConf Silicon Valley 2015)

Pazarlama Dahileri 2016 - Haliç Üniversitesi

Gov APIs: The Notorious Case of Official Statistics

Remarketing with Google Analytics

Último

"I see eyes in my soup": How Delivery Hero implemented the safety system for ...

Zilliz

Following the popularity of "Cloud Revolution: Exploring the New Wave of Serverless Spatial Data," we're thrilled to announce this much-anticipated encore webinar. In this sequel, we'll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR. Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios. Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects. Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you're building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Safe Software

Artificial Intelligence Chap.5 : Uncertainty

Khushali Kathiriya

Whatsapp Number Escorts Call girls 8617370543 Available 24x7 Navi Mumbai Call Girls Service Offer Genuine VIP Model Escorts Call Girls in Your Budget. Navi Mumbai Call Girls Service Provide Real Call Girls Number. Make Your Sexual Pleasure Memorable with Our Navi Mumbai Call Girls at Affordable Price. Top VIP Escorts Call Girls, High Profile Independent Escorts Call Girls, Housewife Women Escorts Call Girl, College Girls Escorts Call Girls, Russian Escorts Call girls Service in Your Budget.

Navi Mumbai Call Girls 🥰 8617370543 Service Offer VIP Hot Model

Deepika Singh

Axa Assurance Maroc - Insurer Innovation Award 2024

The Digital Insurer

Manulife - Insurer Transformation Award 2024

The Digital Insurer

Corporate and higher education. Two industries that, in the past, have had a clear divide with very little crossover. The difference in goals, learning styles and objectives paved the way for differing learning technologies platforms to evolve. Now, those stark lines are blurring as both sides are discovering they have content that’s relevant to the other. Join Tammy Rutherford as she walks through the pros and cons of corporate and higher ed collaborating. And the challenges of these different technology platforms working together for a brighter future.

Corporate and higher education May webinar.pptx

Rustici Software

Scaling API-first – The story of a global engineering organization Ian Reasor, Senior Computer Scientist - Adobe Radu Cotescu, Senior Computer Scientist - Adobe Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

apidays

Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood

Juan lago vázquez

The Good, the Bad and the Governed - Why is governance a dirty word? David O'Neill, Chief Operating Officer - APIContext Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...

apidays

MINDCTI Revenue Release Quarter One 2024

MIND CTI

ICT role in 21st century education and its challenges

rafiqahmad00786416

Effective data discovery is crucial for maintaining compliance and mitigating risks in today's rapidly evolving privacy landscape. However, traditional manual approaches often struggle to keep pace with the growing volume and complexity of data. Join us for an insightful webinar where industry leaders from TrustArc and Privya will share their expertise on leveraging AI-powered solutions to revolutionize data discovery. You'll learn how to: - Effortlessly maintain a comprehensive, up-to-date data inventory - Harness code scanning insights to gain complete visibility into data flows leveraging the advantages of code scanning over DB scanning - Simplify compliance by leveraging Privya's integration with TrustArc - Implement proven strategies to mitigate third-party risks Our panel of experts will discuss real-world case studies and share practical strategies for overcoming common data discovery challenges. They'll also explore the latest trends and innovations in AI-driven data management, and how these technologies can help organizations stay ahead of the curve in an ever-changing privacy landscape.

TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery

TrustArc

This presentations targets students or working professionals. You may know Google for search, YouTube, Android, Chrome, and Gmail, but did you know Google has many developer tools, platforms & APIs? This comprehensive yet still high-level overview outlines the most impactful tools for where to run your code, store & analyze your data. It will also inspire you as to what's possible. This talk is 50 minutes in length.

Powerful Google developer tools for immediate impact! (2023-24 C)

wesley chun

MS Copilot expands with MS Graph connectors

Nanddeep Nachan

Webinar Recording: https://www.panagenda.com/webinars/why-teams-call-analytics-is-critical-to-your-entire-business Nothing is as frustrating and noticeable as being in an important call and being unable to see or hear the other person. Not surprising then, that issues with Teams calls are among the most common problems users call their helpdesk for. Having in depth insight into everything relevant going on at the user’s device, local network, ISP and Microsoft itself during the call is crucial for good Microsoft Teams Call quality support. To ensure a quick and adequate solution and to ensure your users get the most out of their Microsoft 365. But did you know that ‘bad calls’ are also an excellent indicator of other problems arising? Precisely because it is so noticeable!? Like the canary in the mine, bad calls can be early indicators of problems. Problems that might otherwise not have been noticed for a while but can have a big impact on productivity and satisfaction. Join this session by Christoph Adler to learn how true Microsoft Teams call quality analytics helped other organizations troubleshoot bad calls and identify and fix problems that impacted Teams calls or the use of Microsoft365 in general. See what it can do to keep your users happy and productive! In this session we will cover - Why CQD data alone is not enough to troubleshoot call problems - The importance of attributing call problems to the right call participant - What call quality analytics can do to help you quickly find, fix-, and prevent problems - Why having retrospective detailed insights matters - Real life examples of how others have used Microsoft Teams call quality monitoring to problem shoot problems with their ISP, network, device health and more.

Why Teams call analytics are critical to your entire business

panagenda

In this session, we will delve into strategic approaches for optimizing knowledge management within Microsoft 365, amidst the evolving landscape of Copilot. From leveraging automatic metadata classification and permission governance with SharePoint Premium, to unlocking Viva Engage for the cultivation of knowledge and communities, you will gain actionable insights to bolster your organization's knowledge-sharing initiatives. In this session, we will also explore how to facilitate solutions to enable your employees to find answers and expertise within Microsoft 365. You will leave equipped with practical techniques and a deeper understanding of how there is more to effective knowledge management than just enabling Copilot, but building actual solutions to prepare the knowledge that Copilot and your employees can use.

Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...

Drew Madelung

Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows. We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases. This video focuses on the deployment of external web forms using Jotform for Bonterra Impact Management. This solution can be customized to your organization’s needs and deployed to support the common use cases below: - Intake and consent - Assessments - Surveys - Applications - Program registration Interested in deploying web form automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.

Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...

Jeffrey Haguewood

ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke

Product Anonymous

Scalable LLM APIs for AI and Generative AI Application Development Ettikan Karuppiah, Director/Technologist - NVIDIA Apidays Singapore 2024: Connecting Customers, Business and Technology (April 17 & 18, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays Singapore 2024 - Scalable LLM APIs for AI and Generative AI Applicati...

apidays

Interactive analytics at scale with druid

10.

11.

12.

13.

14.

15.

16.

17.

18.

19.

20.

21.

22.

23. By advertiser… By country… … …

24.

25.

26.

27.

28.

29.

30. Device Publisher Cost Iphone Google 0.1€ Android Yahoo 0.2€ Dimension Cost Iphone 0.1€ Android 0.2€ Google 0.1€ Yahoo 0.1€ Iphone, Google 0.1€ Android, Yahoo 0,2€

31.

32. Device Publisher Product Cost Iphone Google Computer 0.1€ Android Yahoo Cloth 0.2€ Dimension Cost Iphone 0.1€ Android 0.2€ Google 0.1€ Yahoo 0.1€ Computer 0.1€ Cloth 0.2€ Iphone, Google 0.1€ Iphone, Computer 0.1€ Google, Computer 0.1€ Android, Yahoo 0.2€ Android, Cloth 0.2€ Yahoo, Cloth 0.2€ Iphone, Google, Computer 0.1€ Android, Yahoo, Cloth 0.2€

33.

34.

35.

36.

37.

38.

39.

40.

41.

42.

43.

44. SELECT sum(revenue) AS “Revenue", sum(sales) AS “Sales" FROM customer-insights WHERE client_id = 2255 AND date BETWEEN "2014-08-01" AND "2014-08-08" GROUP BY day(date)

45. { queryType: "Timeseries", dataSource: "customer-insights", granularity: "day", aggregations: [ { type: "longSum", fieldName: "revenue", name: "Revenue" }, { type: "longSum ", fieldName: "sales", name: "Sales" } ], filter: { type: "selector", dimension: "client_id", value: 2255 }, intervals: ["2014-08-01T00:00/2014-08-08T00"] }

46. { queryType: "Timeseries", dataSource: "customer-insights", granularity: "day", aggregations: [ { type: "longSum", fieldName: "revenue", name: "Revenue" }, { type: "longSum ", fieldName: "sales", name: "Sales" } ], filter: { type: "selector", dimension: "client_id", value: 2255 }, intervals: ["2014-08-01T00:00/2014-08-08T00"] }

47. { queryType: "Timeseries", dataSource: "customer-insights", granularity: "day", aggregations: [ { type: "longSum", fieldName: "revenue", name: "Revenue" }, { type: "longSum ", fieldName: "sales", name: "Sales" } ], filter: { type: "selector", dimension: "client_id", value: 2255 }, intervals: ["2014-08-01T00:00/2014-08-08T00"] }

48. { queryType: "Timeseries", dataSource: "customer-insights", granularity: "day", aggregations: [ { type: "longSum", fieldName: "revenue", name: "Revenue" }, { type: "longSum ", fieldName: "sales", name: "Sales" } ], filter: { type: "selector", dimension: "client_id", value: 2255 }, intervals: ["2014-08-01T00:00/2014-08-08T00"] }

49. { queryType: "Timeseries", dataSource: "customer-insights", granularity: "day", aggregations: [ { type: "longSum", fieldName: "revenue", name: "Revenue" }, { type: "longSum ", fieldName: "sales", name: "Sales" } ], filter: { type: "selector", dimension: "client_id", value: 2255 }, intervals: ["2014-08-01T00:00/2014-08-08T00"] }

50. { queryType: "Timeseries", dataSource: "customer-insights", granularity: "day", aggregations: [ { type: "longSum", fieldName: "revenue", name: "Revenue" }, { type: "longSum ", fieldName: "sales", name: "Sales" } ], filter: { type: "selector", dimension: "client_id", value: 2255 }, intervals: ["2014-08-01T00:00/2014-08-08T00"] }

51. { queryType: "Timeseries", dataSource: "customer-insights", granularity: "day", aggregations: [ { type: "longSum", fieldName: "revenue", name: "Revenue" }, { type: "longSum ", fieldName: "sales", name: "Sales" } ], filter: { type: "selector", dimension: "client_id", value: 2255 }, intervals: ["2014-08-01T00:00/2014-08-08T00"] }

52.

53. { queryType: "groupBy", dataSource: "customer-insights", granularity: "all", dimensions: ["device"], aggregations: [ { type: "longSum", fieldName: "clicks", name: "Clicks" }, { type: "longSum", fieldName: "cost", name: "Cost" } ], filter: { type: "selector", dimension: "client_id", value: 2255 }, intervals: ["2014-08-01T00:00/2014-08-08T00"] }

54.

55.

56.

57.

58. Iphone Google 0.35€08:12:00 Android Yahoo 0.2€08:12:00 Iphone Google 0.1€08:12:37 Android Yahoo 0.2€08:12:38 Iphone Google 0.15€08:12:39 Iphone Google 0.1€08:12:40

59. Iphone Google 0.1€08:12:37 Android Yahoo 0.2€08:12:38 Iphone Google 0.15€08:13:02 Iphone Google 0.1€08:13:08 bob@mail.com joe@mail.com bob@mail.com tony@mail.com

60.

61.

62.

63.

64. Iphone Google Computer 0.1€08:12:37 Android Yahoo Cloth 0.2€08:12:38 Iphone Google Computer 0.1€08:12:37 Android Yahoo Cloth 0.2€08:12:38

65. Iphone Google Computer 0.1€08:12:37 Android Yahoo Cloth 0.2€08:12:38

66. Google Yahoo Microsoft Google Yahoo 1 2 3 1 2

67. Google Yahoo Microsoft Google Yahoo Google 1,0,0,0,1 Yahoo 0,1,0,1,0 Microsoft 0,0,1,0,0

68.

69.

70.

71.

72. Filter Time period Aggregated rows Time Biggest advertiser 6 months 1.5M 225ms Android devices 1 month 42M 750ms Desktop 1 month 110M 910ms RTB networks 7 months 753M 2.4s

Interactive analytics at scale with druid

Recomendados

Recomendados

Mais conteúdo relacionado

Mais procurados

Mais procurados (20)

Destaque

Destaque (20)

Semelhante a Interactive analytics at scale with druid

Semelhante a Interactive analytics at scale with druid (20)

Último

Último (20)

Interactive analytics at scale with druid