The 'macro view' on Big Query:
We started with an overview, some typical uses and moved to project hierarchy, access control and security.
In the end we touch about tools and demos.
An short introduction on Big Query. With this presentation you'll quickly discover :
How load data in BigQuery
How to build dashboard using BigQuery
How to work with BigQuery
and, at last but not least, we've added some best practices
We hope you'll enjoy this presentation and that it will help you to start exploring this wonderful solution. Don't hesitate to send us your feedbacks or questions
Introduction to Google BigQuery. Slides used at the first GDG Cloud meetup in Brussels, about big data on Google Cloud Platform. (http://www.meetup.com/GDG-Cloud-Belgium/events/228206131)
Basic concepts, best practices, pricing of using BigQuery the analytic data platform at petabyte scale from Google Cloud Platform. There is a lot things to learn about this tool and its features such as BI engine and AI Platform.
In this webinar you'll learn about the best practices for Google BigQuery—and how Matillion ETL makes loading your data faster and easier. Find out from our experts how to leverage one of the largest, fastest, and most capable cloud data warehouses to improve your business and save money.
In this webinar:
- Discover how to work fast and efficiently with Google BigQuery
- Find out the best ways to monitor and control costs
- Learn to leverage Matillion ETL and optimize Google BigQuery
- Get tips and tricks for better performance
An short introduction on Big Query. With this presentation you'll quickly discover :
How load data in BigQuery
How to build dashboard using BigQuery
How to work with BigQuery
and, at last but not least, we've added some best practices
We hope you'll enjoy this presentation and that it will help you to start exploring this wonderful solution. Don't hesitate to send us your feedbacks or questions
Introduction to Google BigQuery. Slides used at the first GDG Cloud meetup in Brussels, about big data on Google Cloud Platform. (http://www.meetup.com/GDG-Cloud-Belgium/events/228206131)
Basic concepts, best practices, pricing of using BigQuery the analytic data platform at petabyte scale from Google Cloud Platform. There is a lot things to learn about this tool and its features such as BI engine and AI Platform.
In this webinar you'll learn about the best practices for Google BigQuery—and how Matillion ETL makes loading your data faster and easier. Find out from our experts how to leverage one of the largest, fastest, and most capable cloud data warehouses to improve your business and save money.
In this webinar:
- Discover how to work fast and efficiently with Google BigQuery
- Find out the best ways to monitor and control costs
- Learn to leverage Matillion ETL and optimize Google BigQuery
- Get tips and tricks for better performance
Introduction to our Datawarehouse solutions called BigQuery.
The Google Cloud Platform products are based on our internal systems which are powering Google AdWords, Search, YouTube and our leading research in the field of real-time data analysis.
You can get access ($300 for 60 days) to our free trial through google.com/cloud
Google BigQuery for Everyday DeveloperMárton Kodok
IV. IT&C Innovation Conference - October 2016 - Sovata, Romania
A. Every scientist who needs big data analytics to save millions of lives should have that power
Legacy systems don’t provide the power.
B. The simple fact is that you are brilliant but your brilliant ideas require complex analytics.
Traditional solutions are not applicable.
The Plan: have oversight over developments as they happen.
Goal: Store everything accessible by SQL immediately.
What is BigQuery?
Analytics-as-a-Service - Data Warehouse in the Cloud
Fully-Managed by Google (US or EU zone)
Scales into Petabytes
Ridiculously fast
Decent pricing (queries $5/TB, storage: $20/TB) *October 2016 pricing
100.000 rows / sec Streaming API
Open Interfaces (Web UI, BQ command line tool, REST, ODBC)
Familiar DB Structure (table, views, record, nested, JSON)
Convenience of SQL + Javascript UDF (User Defined Functions)
Integrates with Google Sheets + Google Cloud Storage + Pub/Sub connectors
Client libraries available in YFL (your favorite languages)
Our benefits
no provisioning/deploy
no running out of resources
no more focus on large scale execution plan
no need to re-implement tricky concepts
(time windows / join streams)
pay only the columns we have in your queries
run raw ad-hoc queries (either by analysts/sales or Devs)
no more throwing away-, expiring-, aggregating old data.
ADV Slides: Strategies for Fitting a Data Lake into a Modern Data ArchitectureDATAVERSITY
Whether to take data ingestion cycles off the ETL tool and the Data Warehouse or to facilitate competitive Data Science and building algorithms in the organization, the Data Lake — a place for unmodeled and vast data — will be provisioned widely in 2019.
Though it doesn’t have to be complicated, the Data Lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the Data Swamp, but not the Data Lake! The tool ecosystem is building up around the Data Lake and soon many will have a robust Lake and Data Warehouse. We will discuss policy to keep them straight, send “horses to courses,” and keep up users’ confidence in the Data Platforms.
As for platform, although Hadoop received the early majority of Data Lakes, organizations are now weighing in that the Data Lake will be built in Cloud object storage. We’ll discuss these options as well.
Get this data point for your Data Lake journey.
Orchestrating workflows Apache Airflow on GCP & AWSDerrick Qin
Working in a cloud or on-premises environment, we all somehow move data from A to B on-demand or on schedule. It is essential to have a tool that can automate recurring workflows. This can be anything from an ETL(Extract, Transform, and Load) job for a regular analytics report all the way to automatically re-training a machine learning model.
In this talk, we will introduce Apache Airflow and how it can help orchestrate your workflows. We will cover key concepts, features, and use cases of Apache Airflow, as well as how you can enjoy Apache Airflow on GCP and AWS by demo-ing a few practical workflows.
in this presentation we go through the differences and similarities between Redshift and BigQuery. It was presented during the Athens Big Data meetup May 2017.
My Talk at GCPUG-Taiwan on 2015/5/8.
You use BigQuery with SQL, but the internal work of BigQuery is very different from traditional Relational Database systems you may familiar with.
One of the way to understand how BigQuery works is to see it from the cost you pay for BigQuery. Knowing how to save money while using BigQuery is to know how BigQuery works to some extent.
In this session, let’s talk about practical knowledge (saving money) and exciting technology (how BigQuery works)!
* If you see the screen is not good condition, downloading please. *
Introduction to MariaDB
- mariadb oracle mysql comparison
- mariadb install step by step
- mariadb basic query
All about Zookeeper and ClickHouse Keeper.pdfAltinity Ltd
ClickHouse clusters depend on ZooKeeper to handle replication and distributed DDL commands. In this Altinity webinar, we’ll explain why ZooKeeper is necessary, how it works, and introduce the new built-in replacement named ClickHouse Keeper. You’ll learn practical tips to care for ZooKeeper in sickness and health. You’ll also learn how/when to use ClickHouse Keeper. We will share our recommendations for keeping that happy as well.
The Parquet Format and Performance Optimization OpportunitiesDatabricks
The Parquet format is one of the most widely used columnar storage formats in the Spark ecosystem. Given that I/O is expensive and that the storage layer is the entry point for any query execution, understanding the intricacies of your storage format is important for optimizing your workloads.
As an introduction, we will provide context around the format, covering the basics of structured data formats and the underlying physical data storage model alternatives (row-wise, columnar and hybrid). Given this context, we will dive deeper into specifics of the Parquet format: representation on disk, physical data organization (row-groups, column-chunks and pages) and encoding schemes. Now equipped with sufficient background knowledge, we will discuss several performance optimization opportunities with respect to the format: dictionary encoding, page compression, predicate pushdown (min/max skipping), dictionary filtering and partitioning schemes. We will learn how to combat the evil that is ‘many small files’, and will discuss the open-source Delta Lake format in relation to this and Parquet in general.
This talk serves both as an approachable refresher on columnar storage as well as a guide on how to leverage the Parquet format for speeding up analytical workloads in Spark using tangible tips and tricks.
[Webinar] Getting Started with BigQuery: Basics, Its Appilcations & Use CasesTatvic Analytics
This webinar aims to provide the BigQuery product walkthrough right from the basics. Our core focus will be on the use cases and applications that help to gain additional customer insights from the data integrated within BigQuery.
BigQuery is equipped with the ability to crunch TBs of data in seconds while ensuring scalability and speed. It also enables us to perform advanced statistical analysis by providing unsampled raw hit level analytics data.
Introduction to our Datawarehouse solutions called BigQuery.
The Google Cloud Platform products are based on our internal systems which are powering Google AdWords, Search, YouTube and our leading research in the field of real-time data analysis.
You can get access ($300 for 60 days) to our free trial through google.com/cloud
Google BigQuery for Everyday DeveloperMárton Kodok
IV. IT&C Innovation Conference - October 2016 - Sovata, Romania
A. Every scientist who needs big data analytics to save millions of lives should have that power
Legacy systems don’t provide the power.
B. The simple fact is that you are brilliant but your brilliant ideas require complex analytics.
Traditional solutions are not applicable.
The Plan: have oversight over developments as they happen.
Goal: Store everything accessible by SQL immediately.
What is BigQuery?
Analytics-as-a-Service - Data Warehouse in the Cloud
Fully-Managed by Google (US or EU zone)
Scales into Petabytes
Ridiculously fast
Decent pricing (queries $5/TB, storage: $20/TB) *October 2016 pricing
100.000 rows / sec Streaming API
Open Interfaces (Web UI, BQ command line tool, REST, ODBC)
Familiar DB Structure (table, views, record, nested, JSON)
Convenience of SQL + Javascript UDF (User Defined Functions)
Integrates with Google Sheets + Google Cloud Storage + Pub/Sub connectors
Client libraries available in YFL (your favorite languages)
Our benefits
no provisioning/deploy
no running out of resources
no more focus on large scale execution plan
no need to re-implement tricky concepts
(time windows / join streams)
pay only the columns we have in your queries
run raw ad-hoc queries (either by analysts/sales or Devs)
no more throwing away-, expiring-, aggregating old data.
ADV Slides: Strategies for Fitting a Data Lake into a Modern Data ArchitectureDATAVERSITY
Whether to take data ingestion cycles off the ETL tool and the Data Warehouse or to facilitate competitive Data Science and building algorithms in the organization, the Data Lake — a place for unmodeled and vast data — will be provisioned widely in 2019.
Though it doesn’t have to be complicated, the Data Lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the Data Swamp, but not the Data Lake! The tool ecosystem is building up around the Data Lake and soon many will have a robust Lake and Data Warehouse. We will discuss policy to keep them straight, send “horses to courses,” and keep up users’ confidence in the Data Platforms.
As for platform, although Hadoop received the early majority of Data Lakes, organizations are now weighing in that the Data Lake will be built in Cloud object storage. We’ll discuss these options as well.
Get this data point for your Data Lake journey.
Orchestrating workflows Apache Airflow on GCP & AWSDerrick Qin
Working in a cloud or on-premises environment, we all somehow move data from A to B on-demand or on schedule. It is essential to have a tool that can automate recurring workflows. This can be anything from an ETL(Extract, Transform, and Load) job for a regular analytics report all the way to automatically re-training a machine learning model.
In this talk, we will introduce Apache Airflow and how it can help orchestrate your workflows. We will cover key concepts, features, and use cases of Apache Airflow, as well as how you can enjoy Apache Airflow on GCP and AWS by demo-ing a few practical workflows.
in this presentation we go through the differences and similarities between Redshift and BigQuery. It was presented during the Athens Big Data meetup May 2017.
My Talk at GCPUG-Taiwan on 2015/5/8.
You use BigQuery with SQL, but the internal work of BigQuery is very different from traditional Relational Database systems you may familiar with.
One of the way to understand how BigQuery works is to see it from the cost you pay for BigQuery. Knowing how to save money while using BigQuery is to know how BigQuery works to some extent.
In this session, let’s talk about practical knowledge (saving money) and exciting technology (how BigQuery works)!
* If you see the screen is not good condition, downloading please. *
Introduction to MariaDB
- mariadb oracle mysql comparison
- mariadb install step by step
- mariadb basic query
All about Zookeeper and ClickHouse Keeper.pdfAltinity Ltd
ClickHouse clusters depend on ZooKeeper to handle replication and distributed DDL commands. In this Altinity webinar, we’ll explain why ZooKeeper is necessary, how it works, and introduce the new built-in replacement named ClickHouse Keeper. You’ll learn practical tips to care for ZooKeeper in sickness and health. You’ll also learn how/when to use ClickHouse Keeper. We will share our recommendations for keeping that happy as well.
The Parquet Format and Performance Optimization OpportunitiesDatabricks
The Parquet format is one of the most widely used columnar storage formats in the Spark ecosystem. Given that I/O is expensive and that the storage layer is the entry point for any query execution, understanding the intricacies of your storage format is important for optimizing your workloads.
As an introduction, we will provide context around the format, covering the basics of structured data formats and the underlying physical data storage model alternatives (row-wise, columnar and hybrid). Given this context, we will dive deeper into specifics of the Parquet format: representation on disk, physical data organization (row-groups, column-chunks and pages) and encoding schemes. Now equipped with sufficient background knowledge, we will discuss several performance optimization opportunities with respect to the format: dictionary encoding, page compression, predicate pushdown (min/max skipping), dictionary filtering and partitioning schemes. We will learn how to combat the evil that is ‘many small files’, and will discuss the open-source Delta Lake format in relation to this and Parquet in general.
This talk serves both as an approachable refresher on columnar storage as well as a guide on how to leverage the Parquet format for speeding up analytical workloads in Spark using tangible tips and tricks.
[Webinar] Getting Started with BigQuery: Basics, Its Appilcations & Use CasesTatvic Analytics
This webinar aims to provide the BigQuery product walkthrough right from the basics. Our core focus will be on the use cases and applications that help to gain additional customer insights from the data integrated within BigQuery.
BigQuery is equipped with the ability to crunch TBs of data in seconds while ensuring scalability and speed. It also enables us to perform advanced statistical analysis by providing unsampled raw hit level analytics data.
Supercharge your data analytics with BigQueryMárton Kodok
Powering interactive data analysis require massive architecture, and Know-How to build a fast real-time computing system. BigQuery solves this problem by enabling super-fast, SQL-like queries against petabytes of data using the processing power of Google’s infrastructure. We will cover its core features, creating tables, columns, views, working with partitions, clustering for cost optimizations, streaming inserts, User Defined Functions, and several use cases for everydaay developer: funnel analytics, behavioral analytics, exploring unstructured data.
The other part will be about BigQuery ML, which enables users to create and execute machine learning models in BigQuery using standard SQL queries. BigQuery ML democratizes machine learning by enabling SQL practitioners to build models using existing SQL tools and skills. BigQuery ML increases development speed by eliminating the need to move data.
Quick Intro to Google Cloud TechnologiesChris Schalk
This is the "Lightning Presentation" given at DreamForce 2011 on Google's Cloud Technologies. It covers, App Engine, Google Storage and BigQuery. #df11
[Webinar] Interacting with BigQuery and Working with Advanced QueriesTatvic Analytics
In this webinar, we will cover advanced concepts and some complex queries. We will give a demo of how to fetch data from BigQuery into tools like Excel, R and python so we can continue further analysis. Along With Hands-on exercise, we will demonstrate how to automate queries using Apps Script and Command Line tool.
CodeCamp Iasi - Creating serverless data analytics system on GCP using BigQueryMárton Kodok
Teaser: provide developers a new way of understanding advanced analytics and choosing the right cloud architecture
The new buzzword is #serverless, as there are many great services that helps us abstract away the complexity associated with managing servers. In this session we will see how serverless helps on large data analytics backends.
We will see how to architect for Cloud and implement into an existing project components that will take us into the #serverless architecture that will ingest our streaming data, run advanced analytics on petabytes of data using BigQuery on Google Cloud Platform - all this next to an existing stack, without being forced to reengineer our app.
BigQuery enables super-fast, SQL/Javascript queries against petabytes of data using the processing power of Google’s infrastructure. We will cover its core features, SQL 2011 standard, working with streaming inserts, User Defined Functions written in Javascript, reference external JS libraries, and several use cases for everyday backend developer: funnel analytics, email heatmap, custom data processing, building dashboards, extracting data using JS functions, emitting rows based on business logic.
VoxxedDays Bucharest 2017 - Powering interactive data analysis with Google Bi...Márton Kodok
Every scientist who needs big data analytics to save millions of lives should have that power. Complex interactive Big Data analytics solutions require massive architecture, and Know-How to build a fast real-time computing system.BigQuery solves this problem by enabling super-fast, SQL-like queries against petabytes of data using the processing power of Google’s infrastructure. We will cover its core features, working with BigQuery, streaming inserts, User Defined Functions in Javascript, and several use cases for everyday developer: funnel analytics, behavioral analytics, exploring unstructured data.
An overview of the different Google Cloud Technologies. Includes coverage of Google App Engine, Google Storage, Google Prediction Api, and BigQuery.
This presentation was given to the San Diego GTUG on Aug 26th, 2011.
Introduction to Google's Cloud TechnologiesChris Schalk
An overview of the different Cloud technologies available from Google including App Engine, Google Storage, Google Prediction API, and BigQuery.
This presentation was given to the San Diego GTUG on Aug 26th, 2011.
Building Apps on Google Cloud TechnologiesChris Schalk
This is a presentation on how to use the different Google Cloud technologies to build applications.
It was delivered in Mexico City at the "EstoEsGoogle" aka Devfest Mexico event on Aug 9th, 2011 in Mexico City by Google Developer Advocate Chris Schalk.
Data Provision API with BigQuery - Google Cloud Summit Jakarta 18Imre Nagi
This talk presented how Traveloka uses Google Cloud BigQuery to build Data Provisioning API which enables the microservices in Traveloka to consume data from our BigQuery.
A Big (Query) Frog in a Small Pond, Jakub Motyl, BuffPanelData Science Club
How does one process 200GB of streaming raw data, daily? Where dedicated servers and home-made solutions fail, BigQuery comes out the victor. We will talk about the big data architecture with over 110 million players total on record, how we managed to implement it, and how is it possible that we keep daily operational costs under $50.
In the beginning we will explain what kinds of data sources a top-selling game has to integrate and analyze and how to pre-process the data to avoid ramping up costs in disaster scenarios. Part of the talk is also dedicated to all the components that are involved in the many transformations the data undergoes and we will show you how the output from the entire pipeline looks.
Implementing google big query automation using google analytics dataCountants
The increasing value of big data analytics for business presents a lot of use cases for BigQuery technology. Through Google Analytics to BigQuery automation, data analysts can save time as well as extract deeper insights from the latest Google Analytics data.
Voxxed Days Cluj - Powering interactive data analysis with Google BigQueryMárton Kodok
Every company,
no matter how far from the tech they are,
is evolving into a software company,
and by extension a data company.
For a small company it’s important
to have access to modern BigData tools
without running a dedicated team for it.
Building Data Products with BigQuery for PPC and SEO (SMX 2022)Christopher Gutknecht
In this data management session, Christopher describes how to build robust and reliable data products in BigQuery and dbt, for PPC and SEO use cases. After an introduction to the modern data stack, six principles of reliable data products are presented, followed by the following use cases:
- Google Ads Conversion upload
- SEO sitemap efficiency report
- Google Shopping product rating sync
- Large-Scale link checker with advertools
- Inventory-based PPC campaigns with dbt
Here is the referenced selection of gists on github: https://gist.github.com/ChrisGutknecht
Building Integrated Applications on Google's Cloud TechnologiesChris Schalk
This is the presentation "Building Integrated Applications on Google's Cloud Technologies" that was given at GDD 2011 #gdd11 in Sao Paulo and Buenos Aires by Google Developer Advocate Chris Schalk @cschalk.
How to get things done - Lessons from Yahoo, Google, Netflix and Meta Ido Green
How can you make your software teams better?
What are the values and processes that you wish to embrace?
In these slides, we will share some stories from leading companies (e.g., Google, Meta, and Netflix), and we will see what is working for them.
What is a blockchain?
Why is cryptocurrency the future?
It's a deck I was preparing for a lighting talk at ESGgo.
Since I got some excellent feedback on it - I decided to open-source it :)
Hopefully, you will find it valuable.
The Future of Continuous Software Updates Is HereIdo Green
DevOps and “Liquid Software” release practices are rapidly becoming the standard. But, as software shapes digital transformation, DevOps teams are feeling challenged to manage their growing influence on corporations’ success or failure.
In this talk, Ido Green looks into the growing pains that most enterprises (many of them JFrog customers) face when adopting and consolidating DevOps at scale, and how these challenges are being mitigated with end-to-end platform solutions. We’ll wrap up with some DevOps best practices - from the trenches - that will help you address emerging trends that your bosses’ bosses really care about.
Open Source & DevOps Market trends - Open Core SummitIdo Green
Open Source developers are pushing the world of technology forward. At JFrog, from day one, we worked closely with developers (we’re developers too!) to make sure we solved actual problems.
We’re not a developer-first company.
We’re a developer company.
But this means that, like you, we’ve had plenty of “learning moments.” In this talk, we’ll share some key insights so other project owners can avoid falling into the same holes we’ve fallen into. Further, we’ll share some interesting statistics about the DevOps market that will help you gain insight into your own domain, and how you can practically address larger market movements that the bosses’ bosses’ bosses are really caring about.
“If we have data, let’s look at data. If all we have are opinions, let’s go with mine.”
This talk is going to be based on data and the real world.
No theoretical stories just data and action items on how to make your company better/faster/more secure in shipping software.
Data Driven DevOps & Technologies (swampUP 2019 keynote)Ido Green
The world (of DevOps) has many buzzwords that people like to use.
Some are more relevant to the real world and some aren't.
In this talk, we covered what is going on in the real world and what is just hype at the moment.
You can read more: https://greenido.wordpress.com/2019/06/19/market-trends-talk-swampup-2019/
Create An Amazing Apps For The Google Assistant!Ido Green
The Google Assistant is available on many devices (eg. Google mini/home/max, Android, iPhones and more).
Actions on Google lets developers extend the Google Assistant to create your own conversational assistant apps.
In this talk, Ido Green will describe the key components of actions on Google. He will show you how to easily build your first assistant app using tools such as Dialogflow, and explore voice user interface (VUI) best practices in order to design compelling conversational experiences that delight users.
We are all experts at human-to-human conversation. But conversing only seems easy because it’s familiar, you’ve been doing it since you were born.
The key to building a good voice interface is to not fall into the trap of simply converting a GUI into a VUI.
In these slides we will cover the best practices to design our Action on Google (and any other Voice UI).
At Google, we believe the future is AI first, and we’re investing heavily in the fields of machine learning, speech recognition and language understanding. These technologies come together in the Google Assistant, which allows you to have a conversation with Google that helps you get things done.
Developers can build apps for the Google Assistant using Actions on Google and in these slides we will show you how you can do it and why you wish to be in this new platform.
The Google Assistant - Macro View (October 2017)Ido Green
The past few years, the buzz about conversational experiences and digital assistants has increased dramatically. According to a recently issued report by eMarketer, 87% of B2C marketers in the US believe that chatbots and digital assistants will play a significant role in marketing before 2021.
In these slides we will cover the Google Assistant and learn why you wish to build an action for it.
At Google, we believe the future is AI first.
We have been investing heavily in the areas of: Machine learning, Speech recognition & Language understanding.
These things come together in the Google Assistant. In these slides we will go over what is exciting about this new platform and how you can build you Assistant apps.
Which Allows you to have a conversation with Google, that helps you get things done.
Because of these investments in AI, the conversation can be completely natural.
Use your voice, ask in a natural way, and the Assistant helps you.
As you can see - it’s everywhere.
Building conversational experiences with Actions on GoogleIdo Green
The Google Assistant is Google’s conversational software for helping you get things done in your world. It is the culmination of all of Google’s research in AI, ML, NLP, etc.
It runs on various devices, including the Google Home which launched in 2017, as well as many Android and iOS devices. Actions on Google is the third-party platform for the Google Assistant, allowing you, the developer. to manage a conversation between your service and the user.
In these slides you will see how/why you can leverage this new platform for your service.
What are the ways that startups can leverage the benefits that progressive web apps allow these days?
In this talk, I covered some of the startups best practices and how entrepreneurs can take advantage from the capabilities that PWAs give them.
Earn More Revenue With Firebase and AdMobIdo Green
In these slides we will see how to take advantage of firebase and AdMob in order to increase your revenue stream. We will explore the major ways to monetize your apps with AdMob.
In these slides we will see how to use Firebase Analytics in order to grow your user base. We will see how to effectively use insights from both paid and organic channels in order to create growth.
An overview of Accelerated Mobile Pages Project. See how you can leverage this important open source project today in production and improve your sites' performance and the happiness of your users.
AMP is coming to improve the mobile web. Big time.
There are many aspect to a great user experience on sites.
In order to improve the speed of the media websites on mobile and the monetization, we needed few things:
1. Fast pages. Fast to load, fast to display, saving bandwidth when possible.
2. Easy for the developers and companies to create. Only based on known and widely used technologies.
3. Mobile Friendly: they should respect a standard and thanks to this standard, pages would be automatically optimized for mobile devices
4. Embrace the open web: non-proprietary technology, open source, available to anyone to use and improve. It should not only help for search engines, but for everyone.
In these slides, we will cover AMP and what it can do for you.
Let's focus on the Mobile Web and talk about the keys to a building a great mobile experience.
From AMP (=Accelerated Mobile Pages) as a starting point up to PWA (=Progressive Web Apps). I will cover the steps through some of the key features we see as core to the modern web experience. As a bonus, we will close with new APIs that expending the web even farther.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
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 notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
3. BigQuery Basics
Topics we cover in this lesson
●
●
●
●
●
●
●
BigQuery Overview
Typical Uses
Project Hierarchy
Access Control and Security
Datasets and Tables
Tools
Demos
4. BigQuery Basics
How does BigQuery fit in the analytics landscape?
● MapReduce based analysis can be slow for ad-hoc queries
● Managing data centers and tuning software takes time & money
● Analytics tools should be services
5. BigQuery Basics
Why BigQuery?
● Generate big data reports require expensive servers
and skilled database administrators
● Interacting with big data has been expensive, slow and
inefficient
● BigQuery changes all that
○ Reducing time and expense to query data
6. BigQuery Basics
What's BigQuery?
● Service for interactive analysis of massive datasets (TBs)
○ Query billions of rows: seconds to write, seconds to return
○ Uses a SQL-style query syntax
○ It's a service, accessed by a RESTful API
● Reliable and secure
○ Replicated across multiple sites
○ Secured through Access Control Lists
● Scalable
○ Store hundreds of terabytes
○ Pay only for what you use
● Fast (really)
○ Run ad hoc queries on multi-terabyte data sets in seconds
10. BigQuery Basics
Typical Uses
Another way to analyze query results with Google Spreadsheets
○
greenido.wordpress.com/2013/12/16/big-query-and-google-spreadsheet-intergration/
○
greenido.wordpress.com/2013/07/24/big-query-power-with-javascript/
11. BigQuery Basics
BigQuery Use Cases
● Log Analysis. Making sense of computer generated records
● Retailer. Using data to forecast product sales
● Ads Targeting. Targeting proper customer sections
● Sensor Data. Collect and visualize ambient data
● Data Mashup. Query terabytes of heterogeneous data
12. BigQuery Basics
Some Customer Case Studies
Uses BigQuery to hone ad targeting
and gain insights into their business
Dashboards using BigQuery to
analyze booking and inventory data
Use BigQuery to provide their
customers ways to expand game
engagement and find new channels for
monetization
Used BigQuery, App Engine and the
Visualizaton API to build a business
intelligence solution
14. BigQuery Basics
Project Hierarchy
● Project. All data in BigQuery belongs inside a project
○ Set of users, APIs, authentication, billing information
● Dataset. Holds one or more tables
○ Lowest access control unit (to which ACLs are applied)
● Table. Row-column structure that contains actual data
● Job. Used to start potentially long running queries
15. BigQuery Basics
Datasets and Tables
Table name is represented as
follows:
● Current Project
<dataset>.<table
name>
● Different Project
<project>:<dataset>.<table>
e.g. publicdata:samples.wikipedia
17. BigQuery Basics
Data Types
●
●
●
●
●
String
○ UTF-8 encoded, <64kB
Integer
○ 64 bit signed
Float
Boolean
○ "true" or "false", case insensitive
Timestamp
○ String format
■ YYYY-MM-DD HH:MM:SS[.sssss] [+/-][HH:MM]
○ Numeric format (seconds from UNIX epoch)
■ 1234567890, 1.234567890123456E9
(*) Max row size: 64kB
Date type is supported as timestamp
18. BigQuery Basics
Data Format
BigQuery supports the following format for loading data:
1. Comma Separated Values (CSV)
2. JSON
a. BigQuery can load data faster,
embedded newlines.
b. Supports nested/repeated data fields
if your data con
19. BigQuery Basics
Repeated and Nested Fields
[
[
Schema
example
{
{
"fields": [
"fields": [
{
{
Loading data with repeated and
nested fields is supported by
JSON data format only
"mode":
"mode":
"name":
"name":
"nullable",
"nullable",
"country",
"country",
"type": "string"
"type": "string"
},
},
{
{
"mode": "nullable",
"mode": "nullable",
"name": "city",
"name": "city",
"type": "string"
"type": "string"
}
}
],
],
"mode": "repeated",
"mode": "repeated",
"name": "location",
"name": "location",
"type": "record"
"type": "record"
},
},
...........
...........
20. BigQuery Basics
Accessing BigQuery
● BigQuery Web browser
○
Imports/exports data, runs
queries
● bq command line tool
○ Performs operations from
the command line
● Service API
○ RESTful API to access
BigQuery programmatically
○
Requires authorization by
OAuth2
○
Google client libraries for
Python, Java, JavaScript,
PHP, ...
○
22. BigQuery Basics
Example of Visualization Tools
Using commercial visualization tools to graph the query results
23. BigQuery Basics
Loading Data Using the Web Browser
●
●
●
●
Upload from local disk or from Cloud Storage
Start the Web browser
Select Dataset
Create table and follow the wizard steps
24. BigQuery Basics
Loading Data Using bq Tool
"bq load" command
Syntax
bq load [--source_format=NEWLINE_DELIMITED_JSON|CSV]
destination_table data_source_uri table_schema
●
●
●
●
If not specified, the default file format is CSV (comma separated values)
The files can also use newline delimited JSON format
Schema
○ Either a filename or a comma-separated list of column_name:datatype
pairs that describe the file format.
Data source may be on local machine or on Cloud Storage
25. BigQuery Basics
Load Limitations
● 1,000 import jobs per table per day
● 10,000 import jobs per project per day
● File size (for both CSV and JSON)
○ 1GB for compressed file
○ 1TB for uncompressed
■ 4GB for uncompressed CSV with newlines in strings
● 10,000 files per import job
● 1TB per import job
26. BigQuery Basics
A Few Best Practices
CSV/JSON must be split into chunks less than 1TB
● "split" command with --line-bytes option
● Split to smaller files
○ Easier error recovery
○ To smaller data unit (day, month instead of year)
● Uploading to Cloud Storage is recommended
Cloud Storage
BigQuery
27. BigQuery Basics
A Few Best Practices
● Split Tables by Dates
○ Minimize cost of data scanned
○ Minimize query time
● Upload Multiple Files to Cloud Storage
○ Allows parallel upload into BigQuery
● Denormalize your data
29. BigQuery Basics
Exercise
Work through Big Query Exercise 1 -- Basics
● Use the BigQuery UI
● Use the bq command line tool
● Upload a dataset
You will query the public sample GSOD (global summary of
day) weather dataset.
You will get and upload earthquake data.
30. BigQuery Basics
Questions
● What are the different ways to load data into
BigQuery?
● What is the maximum size of data in a BigQuery
table?
● How can we import data into BigQuery?
○ What's the limitation?
○ What formats does BigQuery accept?
31. BigQuery Basics
Google I/O Data Sensing
● Start the BigQuery Web browser
● Click on Display Project in the project chooser dialog window
● Enter data-sensing-lab when prompted
● In the dataset data-sensing-lab:io_sensor_data, select the table
moscone_io13
● In the New Query box, enter the following query:
SELECT * FROM [data-sensing-lab:io_sensor_data.moscone_io13] LIMIT 10
● Click Run Query button
● Scroll to see relevant results
32. BigQuery Basics
Data Structure
● Define table schema when creating table
● Data is stored in per-column structure
● Each column is handled separately and only combined when
necessary
Advantage of this data structure:
● No need to set index in advance
● Load only the relevant Columns