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.
GDG DevFest Ukraine - Powering Interactive Data Analysis with Google BigQueryMárton Kodok
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.
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.
An indepth look at Google BigQuery Architecture by Felipe Hoffa of GoogleData Con LA
Abstract:- Come learn about Google BigQuery and its underlying architecture. Felipe will go over the evolution of BigQuery and explain some of the underlying principles of BigQuery and Dremel. Felipe will also go over some of the latest use cases and will demo a use case of Google BigQuery
Bio:-
Felipe Hoffa moved from Chile to San Francisco to join Google as a Software Engineer. Since 2013 he's been a Developer Advocate on big data - to inspire developers around the world to leverage the Google Cloud Platform tools to analyze and understand their data in ways they could never before. You can find him in several YouTube videos, blog posts, and conferences around the world.
Follow Felipe at https://twitter.com/felipehoffa.
Complex realtime event analytics using BigQuery @Crunch WarmupMárton Kodok
Complex event analytics solutions require massive architecture, and Know-How to build a fast real-time computing system. Google BigQuery solves this problem by enabling super-fast, SQL-like queries against append-only tables, using the processing power of Google’s infrastructure.In this presentation we will see how Bigquery solves our ultimate goal: Store everything accessible by SQL immediately at petabyte-scale. We will discuss some common use cases: funnels, user retention, affiliate metrics.
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.
BigQuery ML - Machine learning at scale using SQLMárton Kodok
With BigQuery ML, you can build machine learning models without leaving the data warehouse environment and training it on massive datasets. We are going to demonstrate how to build, train, eval and predict, your own scalable machine learning models using standard SQL language in Google BigQuery.
We will see how can we use CREATE MODEL sql syntax to build different models such as:
-Linear regression
-Multiclass logistic regression for classification
-K-means clustering
-Import TensorFlow models for prediction in BigQuery
We will see how we can apply these models on tabular data in retail and marketing use cases.
Models are trained and accessed in BigQuery using SQL — a language data analysts know. This enables business decision making through predictive analytics across the organization without leaving the query editor.
GDG DevFest Ukraine - Powering Interactive Data Analysis with Google BigQueryMárton Kodok
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.
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.
An indepth look at Google BigQuery Architecture by Felipe Hoffa of GoogleData Con LA
Abstract:- Come learn about Google BigQuery and its underlying architecture. Felipe will go over the evolution of BigQuery and explain some of the underlying principles of BigQuery and Dremel. Felipe will also go over some of the latest use cases and will demo a use case of Google BigQuery
Bio:-
Felipe Hoffa moved from Chile to San Francisco to join Google as a Software Engineer. Since 2013 he's been a Developer Advocate on big data - to inspire developers around the world to leverage the Google Cloud Platform tools to analyze and understand their data in ways they could never before. You can find him in several YouTube videos, blog posts, and conferences around the world.
Follow Felipe at https://twitter.com/felipehoffa.
Complex realtime event analytics using BigQuery @Crunch WarmupMárton Kodok
Complex event analytics solutions require massive architecture, and Know-How to build a fast real-time computing system. Google BigQuery solves this problem by enabling super-fast, SQL-like queries against append-only tables, using the processing power of Google’s infrastructure.In this presentation we will see how Bigquery solves our ultimate goal: Store everything accessible by SQL immediately at petabyte-scale. We will discuss some common use cases: funnels, user retention, affiliate metrics.
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.
BigQuery ML - Machine learning at scale using SQLMárton Kodok
With BigQuery ML, you can build machine learning models without leaving the data warehouse environment and training it on massive datasets. We are going to demonstrate how to build, train, eval and predict, your own scalable machine learning models using standard SQL language in Google BigQuery.
We will see how can we use CREATE MODEL sql syntax to build different models such as:
-Linear regression
-Multiclass logistic regression for classification
-K-means clustering
-Import TensorFlow models for prediction in BigQuery
We will see how we can apply these models on tabular data in retail and marketing use cases.
Models are trained and accessed in BigQuery using SQL — a language data analysts know. This enables business decision making through predictive analytics across the organization without leaving the query editor.
GDG DevFest Romania - Architecting for the Google Cloud PlatformMárton Kodok
Learn about FaaS, PaaS architectural patterns that make use of Cloud Functions, Pub/Sub, Dataflow, Kubernetes and platforms that hides the management of servers from the user and have changed how we develop and deploy future software.
We discuss the difference between an event-driven approach - this means that you can trigger a function whenever something interesting happens within the cloud environment - and the simpler HTTP approach. Quota and pricing of per invocation, and the advantages and disadvantages of the serverless systems.
GDG Heraklion - Architecting for the Google Cloud PlatformMárton Kodok
Learn about cloud components, architecture overviews to build an app using GCP components.
You will get hands-on information on how to build highly scalable and flexible applications optimized to run in GCP on the same infrastructure that powers Google. We will discuss cloud concepts and highlights various design patterns and best practices.
By the end of the session you will have hands-on experience to build a basic cloud application, it could be a simple web tier, powered by highly distributed database, background tasks executed on a pub/subsystem, and you get information how to go next level with advanced concepts like analytics warehouse, recommendation engines, and ML.
Strata 2017 (San Jose): Building a healthy data ecosystem around Kafka and Ha...Shirshanka Das
So, you finally have a data ecosystem with Kafka and Hadoop both deployed and operating correctly at scale. Congratulations. Are you done? Far from it.
As the birthplace of Kafka and an early adopter of Hadoop, LinkedIn has 13 years of combined experience using Kafka and Hadoop at scale to run a data-driven company. Both Kafka and Hadoop are flexible, scalable infrastructure pieces, but using these technologies without a clear idea of what the higher-level data ecosystem should be is perilous. Shirshanka Das and Yael Garten share best practices around data models and formats, choosing the right level of granularity of Kafka topics and Hadoop tables, and moving data efficiently and correctly between Kafka and Hadoop and explore a data abstraction layer, Dali, that can help you to process data seamlessly across Kafka and Hadoop.
Beyond pure technology, Shirshanka and Yael outline the three components of a great data culture and ecosystem and explain how to create maintainable data contracts between data producers and data consumers (like data scientists and data analysts) and how to standardize data effectively in a growing organization to enable (and not slow down) innovation and agility. They then look to the future, envisioning a world where you can successfully deploy a data abstraction of views on Hadoop data, like a data API as a protective and enabling shield. Along the way, Shirshanka and Yael discuss observations on how to enable teams to be good data citizens in producing, consuming, and owning datasets and offer an overview of LinkedIn’s governance model: the tools, process and teams that ensure that its data ecosystem can handle change and sustain #datasciencehappiness.
DevFest Romania 2020 Keynote: Bringing the Cloud to you.Márton Kodok
Next OnAir 20 in review,
Real-time AI solutions
like anomaly detection, pattern recognition, and predictive forecasting
2. Recommendations AI rich experience to personalized product recommendations
3. Media Translation API real-time speech translation from streaming audio
4. Lending DocAI solution powered by Document AI for mortgage industry
5. Contact Center AI support over chat/voice calls by identifying intent and providing assistance
Confidential VMs are a breakthrough technology that allow customers to encrypt their most sensitive data in the cloud while it's being processed
Cloud Run: - Minimum idle instances
- Allocate 4 vCPUs and 4GiB memory
- Requests up to 60 minutes
- Server-side HTTP + gRPC streaming
- VPC access support
- External Load Balancing
Serverless orchestration and automation with Cloud Workflows (beta)
- Steps defined in YAML
- Built-in decision and conditional exec
- Subworkflows
- Support for external API calls
- Custom predicate for retries
Predict, recommend and forecast with BigQuery ML
CREATE MODEL syntax in BigQuery to run Machine Learning tasks
Supported models:
- K-means clustering for data segmentation
- Recommend with Matrix Factorization
- Perform time-series forecast
- Import TensorFlow models
Single interface for multiple services with API Gateway
Find Your Topic and Skill Level
Qwiklabs + New Tutorials Center
Cloud computing is shaping the new normal , revolutionizing modern digital businesses.
In the words of Evgeny Morozov "Cloud computing is a great euphemism for centralization of computer services under one server".
In order to familiarize you about how Google cloud works and the various resources offered by cloud, GDSC MH have organized a session on 30 Days of Google Cloud.
Crunching Data with Google BigQuery. JORDAN TIGANI at Big Data Spain 2012Big Data Spain
Session presented at Big Data Spain 2012 Conference
16th Nov 2012
ETSI Telecomunicacion UPM Madrid
www.bigdataspain.org
More info: http://www.bigdataspain.org/es-2012/conference/crunching-data-with-google-bigquery/jordan-tigani
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
Data Ingestion in Big Data and IoT platformsGuido Schmutz
Many of the Big Data and IoT use cases are based on combining data from multiple data sources and to make them available on a Big Data platform for analysis. The data sources are often very heterogeneous, from simple files, databases to high-volume event streams from sensors (IoT devices). It’s important to retrieve this data in a secure and reliable manner and integrate it with the Big Data platform so that it is available for analysis in real-time (stream processing) as well as in batch (typical big data processing). In past some new tools have emerged, which are especially capable of handling the process of integrating data from outside, often called Data Ingestion. From an outside perspective, they are very similar to a traditional Enterprise Service Bus infrastructures, which in larger organization are often in use to handle message-driven and service-oriented systems. But there are also important differences, they are typically easier to scale in a horizontal fashion, offer a more distributed setup, are capable of handling high-volumes of data/messages, provide a very detailed monitoring on message level and integrate very well with the Hadoop ecosystem. This session will present and compare Apache NiFi, StreamSets and the Kafka Ecosystem and show how they handle the data ingestion in a Big Data solution architecture.
The MongoDB Spark Connector integrates MongoDB and Apache Spark, providing users with the ability to process data in MongoDB with the massive parallelism of Spark. The connector gives users access to Spark's streaming capabilities, machine learning libraries, and interactive processing through the Spark shell, Dataframes and Datasets. We'll take a tour of the connector with a focus on practical use of the connector, and run a demo using both Spark and MongoDB for data processing.
MongoDB and Hadoop: Driving Business InsightsMongoDB
MongoDB and Hadoop can work together to solve big data problems facing today's enterprises. We will take an in-depth look at how the two technologies complement and enrich each other with complex analyses and greater intelligence. We will take a deep dive into the MongoDB Connector for Hadoop and how it can be applied to enable new business insights with MapReduce, Pig, and Hive, and demo a Spark application to drive product recommendations.
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.
GDG DevFest Romania - Architecting for the Google Cloud PlatformMárton Kodok
Learn about FaaS, PaaS architectural patterns that make use of Cloud Functions, Pub/Sub, Dataflow, Kubernetes and platforms that hides the management of servers from the user and have changed how we develop and deploy future software.
We discuss the difference between an event-driven approach - this means that you can trigger a function whenever something interesting happens within the cloud environment - and the simpler HTTP approach. Quota and pricing of per invocation, and the advantages and disadvantages of the serverless systems.
GDG Heraklion - Architecting for the Google Cloud PlatformMárton Kodok
Learn about cloud components, architecture overviews to build an app using GCP components.
You will get hands-on information on how to build highly scalable and flexible applications optimized to run in GCP on the same infrastructure that powers Google. We will discuss cloud concepts and highlights various design patterns and best practices.
By the end of the session you will have hands-on experience to build a basic cloud application, it could be a simple web tier, powered by highly distributed database, background tasks executed on a pub/subsystem, and you get information how to go next level with advanced concepts like analytics warehouse, recommendation engines, and ML.
Strata 2017 (San Jose): Building a healthy data ecosystem around Kafka and Ha...Shirshanka Das
So, you finally have a data ecosystem with Kafka and Hadoop both deployed and operating correctly at scale. Congratulations. Are you done? Far from it.
As the birthplace of Kafka and an early adopter of Hadoop, LinkedIn has 13 years of combined experience using Kafka and Hadoop at scale to run a data-driven company. Both Kafka and Hadoop are flexible, scalable infrastructure pieces, but using these technologies without a clear idea of what the higher-level data ecosystem should be is perilous. Shirshanka Das and Yael Garten share best practices around data models and formats, choosing the right level of granularity of Kafka topics and Hadoop tables, and moving data efficiently and correctly between Kafka and Hadoop and explore a data abstraction layer, Dali, that can help you to process data seamlessly across Kafka and Hadoop.
Beyond pure technology, Shirshanka and Yael outline the three components of a great data culture and ecosystem and explain how to create maintainable data contracts between data producers and data consumers (like data scientists and data analysts) and how to standardize data effectively in a growing organization to enable (and not slow down) innovation and agility. They then look to the future, envisioning a world where you can successfully deploy a data abstraction of views on Hadoop data, like a data API as a protective and enabling shield. Along the way, Shirshanka and Yael discuss observations on how to enable teams to be good data citizens in producing, consuming, and owning datasets and offer an overview of LinkedIn’s governance model: the tools, process and teams that ensure that its data ecosystem can handle change and sustain #datasciencehappiness.
DevFest Romania 2020 Keynote: Bringing the Cloud to you.Márton Kodok
Next OnAir 20 in review,
Real-time AI solutions
like anomaly detection, pattern recognition, and predictive forecasting
2. Recommendations AI rich experience to personalized product recommendations
3. Media Translation API real-time speech translation from streaming audio
4. Lending DocAI solution powered by Document AI for mortgage industry
5. Contact Center AI support over chat/voice calls by identifying intent and providing assistance
Confidential VMs are a breakthrough technology that allow customers to encrypt their most sensitive data in the cloud while it's being processed
Cloud Run: - Minimum idle instances
- Allocate 4 vCPUs and 4GiB memory
- Requests up to 60 minutes
- Server-side HTTP + gRPC streaming
- VPC access support
- External Load Balancing
Serverless orchestration and automation with Cloud Workflows (beta)
- Steps defined in YAML
- Built-in decision and conditional exec
- Subworkflows
- Support for external API calls
- Custom predicate for retries
Predict, recommend and forecast with BigQuery ML
CREATE MODEL syntax in BigQuery to run Machine Learning tasks
Supported models:
- K-means clustering for data segmentation
- Recommend with Matrix Factorization
- Perform time-series forecast
- Import TensorFlow models
Single interface for multiple services with API Gateway
Find Your Topic and Skill Level
Qwiklabs + New Tutorials Center
Cloud computing is shaping the new normal , revolutionizing modern digital businesses.
In the words of Evgeny Morozov "Cloud computing is a great euphemism for centralization of computer services under one server".
In order to familiarize you about how Google cloud works and the various resources offered by cloud, GDSC MH have organized a session on 30 Days of Google Cloud.
Crunching Data with Google BigQuery. JORDAN TIGANI at Big Data Spain 2012Big Data Spain
Session presented at Big Data Spain 2012 Conference
16th Nov 2012
ETSI Telecomunicacion UPM Madrid
www.bigdataspain.org
More info: http://www.bigdataspain.org/es-2012/conference/crunching-data-with-google-bigquery/jordan-tigani
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
Data Ingestion in Big Data and IoT platformsGuido Schmutz
Many of the Big Data and IoT use cases are based on combining data from multiple data sources and to make them available on a Big Data platform for analysis. The data sources are often very heterogeneous, from simple files, databases to high-volume event streams from sensors (IoT devices). It’s important to retrieve this data in a secure and reliable manner and integrate it with the Big Data platform so that it is available for analysis in real-time (stream processing) as well as in batch (typical big data processing). In past some new tools have emerged, which are especially capable of handling the process of integrating data from outside, often called Data Ingestion. From an outside perspective, they are very similar to a traditional Enterprise Service Bus infrastructures, which in larger organization are often in use to handle message-driven and service-oriented systems. But there are also important differences, they are typically easier to scale in a horizontal fashion, offer a more distributed setup, are capable of handling high-volumes of data/messages, provide a very detailed monitoring on message level and integrate very well with the Hadoop ecosystem. This session will present and compare Apache NiFi, StreamSets and the Kafka Ecosystem and show how they handle the data ingestion in a Big Data solution architecture.
The MongoDB Spark Connector integrates MongoDB and Apache Spark, providing users with the ability to process data in MongoDB with the massive parallelism of Spark. The connector gives users access to Spark's streaming capabilities, machine learning libraries, and interactive processing through the Spark shell, Dataframes and Datasets. We'll take a tour of the connector with a focus on practical use of the connector, and run a demo using both Spark and MongoDB for data processing.
MongoDB and Hadoop: Driving Business InsightsMongoDB
MongoDB and Hadoop can work together to solve big data problems facing today's enterprises. We will take an in-depth look at how the two technologies complement and enrich each other with complex analyses and greater intelligence. We will take a deep dive into the MongoDB Connector for Hadoop and how it can be applied to enable new business insights with MapReduce, Pig, and Hive, and demo a Spark application to drive product recommendations.
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.
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.
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
Google Developer Group - Cloud Singapore BigQuery WebinarRasel Rana
Today I had a webinar at #Google #DeveloperGroup #CloudSingapore on #BigQuery.
From this session, you will get four insights
1. From zero to business impact
2. Cut off analysis time by up to 90% with BigQuery & Data analysis tools
3. Advanced visualization and reporting with third-party tools
4. Few best practices
#GDG #BigQuery #Analytics
Google Cloud Platform Solutions for DevOps EngineersMárton Kodok
learn the DevOps essentials about cloud components, FaaS, PaaS architectural patterns that make use of Cloud Functions, Pub/Sub, Dataflow, Kubernetes and how we develop and deploy cloud software. You will get hands on information how to build, run, monitor highly scalable and flexible applications optimized to run on GCP. We will discuss cloud concepts and highlights various design patterns and best practices.
New enhancements for security and usability in EDB 13EDB
EDB 13 is here and it enhances our flagship database server and tools. This webinar will explore its security, usability, and portability updates. Join us to learn how EDB 13 can help you improve your PostgreSQL productivity and data protection.
Webinar highlights include:
- New security features such as SCRAM and the encryption of database passwords and traffic between Failover Manager agents
- Usability updates that automate partitioning, verify backup integrity, and streamline the management of failover and backups
- Portability improvements that simplify running PostgreSQL across on-premise and cloud environments
30-45-min tech talk given at user groups or technical conferences to introducing developers to integrating with Google APIs from Python .
ABSTRACT
Want to integrate Google technologies into the web+mobile apps that you build? Google has various open source libraries & developer tools that help you do exactly that. Users who have run into roadblocks like authentication or found our APIs confusing/challenging, are welcome to come and make these non-issues moving forward. Learn how to leverage the power of Google technologies in the next apps you build!!
Solving enterprise challenges through scale out storage & big compute finalAvere Systems
Google Cloud Platform, Avere Systems, and Cycle Computing experts will share best practices for advancing solutions to big challenges faced by enterprises with growing compute and storage needs. In this “best practices” webinar, you’ll hear how these companies are working to improve results that drive businesses forward through scalability, performance, and ease of management.
The slides were from a webinar presented January 24, 2017. The audience learned:
- How enterprises are using Google Cloud Platform to gain compute and storage capacity on-demand
- Best practices for efficient use of cloud compute and storage resources
- Overcoming the need for file systems within a hybrid cloud environment
- Understand how to eliminate latency between cloud and data center architectures
- Learn how to best manage simulation, analytics, and big data workloads in dynamic environments
- Look at market dynamics drawing companies to new storage models over the next several years
Presenters communicated a foundation to build infrastructure to support ongoing demand growth.
The journey of Moving from AWS ELK to GCP Data PipelineRandy Huang
This is a real case from VMfive to shifting ELK architecture from AWS. Currently GCP Data Pipeline provide us more efficiency and stable environment for running our service.
How a distributed graph analytics platform uses Apache Kafka for data ingesti...HostedbyConfluent
Using Kafka to stream data into TigerGraph, a distributed graph database, is a common pattern in our customers’ data architecture. In the TigerGraph database, Kafka Connect framework was used to build the native S3 data loader. In TigerGraph Cloud, we will be building native integration with many data sources such as Azure Blob Storage and Google Cloud Storage using Kafka as an integrated component for the Cloud Portal.
In this session, we will be discussing both architectures: 1. built-in Kafka Connect framework within TigerGraph database; 2. using Kafka cluster for cloud native integration with other popular data sources. Demo will be provided for both data streaming processes.
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.
MongoDB SoCal 2020: Migrate Anything* to MongoDB AtlasMongoDB
During this talk we'll navigate through a customer's journey as they migrate an existing MongoDB deployment to MongoDB Atlas. While the migration itself can be as simple as a few clicks, the prep/post effort requires due diligence to ensure a smooth transfer. We'll cover these steps in detail and provide best practices. In addition, we’ll provide an overview of what to consider when migrating other cloud data stores, traditional databases and MongoDB imitations to MongoDB Atlas.
Gen Apps on Google Cloud PaLM2 and Codey APIs in ActionMárton Kodok
Build applications with generative AI on Google Cloud! We are going to see in action what Gen App Builder is for developers to build and deploy AI-driven applications. We will explore Model Garden powered experiences, then we are going to learn more about the integration of these generative AI APIs. Vertex AI includes a suite of models that work with code. Together these code models are referred to as the PaLM and Codey APIs. The Vertex AI Codey APIs include the code generation API which supports generating code using a natural language description. We will show strategies for creating prompts that work with the model to generate code. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative AI industry trends.
DevBCN Vertex AI - Pipelines for your MLOps workflowsMárton Kodok
In recent years, one of the biggest trends in applications development has been the rise of Machine Learning solutions, tools, and managed platforms. Vertex AI is a managed unified ML platform for all your AI workloads. On the MLOps side, Vertex AI Pipelines solutions let you adopt experiment pipelining beyond the classic build, train, eval, and deploy a model. It is engineered for data scientists and data engineers, and it’s a tremendous help for those teams who don’t have DevOps or sysadmin engineers, as infrastructure management overhead has been almost completely eliminated. Based on practical examples we will demonstrate how Vertex AI Pipelines scores high in terms of developer experience, how fits custom ML needs, and analyze results. It’s a toolset for a fully-fledged machine learning workflow, a sequence of steps in the model development, a deployment cycle, such as data preparation/validation, model training, hyperparameter tuning, model validation, and model deployment. Vertex AI comes with all classic resources plus an ML metadata store, a fully managed feature store, and a fully managed pipelines runner. Vertex AI Pipelines is a managed serverless toolkit, which means you don't have to fiddle with infrastructure or back-end resources to run workflows.
Discover BigQuery ML, build your own CREATE MODEL statementMárton Kodok
With BigQuery ML, you can build machine learning models without leaving the database environment and training it on massive datasets. In this demo session we are going to demonstrate common marketing Machine Learning use cases of how to build, train, eval, and predict, your own scalable machine learning models using SQL language in Google BigQuery and to address the following use cases: - Customer Segmentation + Product cross sale recommendation - Conversion/Purchase prediction - Inference with other in-built >20 models The audience will get first-hand experience with how to write CREATE MODEL sql syntax to build machine learning models such as: - Multiclass logistic regression for classification - K-means clustering - Matrix factorization - ARIMA time series predictions ... and more Models are trained and accessed in BigQuery using SQL — a language data analysts know. This enables business decision-making through predictive analytics across the organization without leaving the query editor. In the end, the audience will learn how everyday developers can build/train/run their own machine-learning models straight from the database query editor, by issuing CREATE MODEL statements
Cloud Run - the rise of serverless and containerizationMárton Kodok
Two of the biggest trends in applications development in recent years have been the rise of serverless and containerization. And Cloud Run has become a defacto container runtime service to production in seconds. Based on practical examples we will demonstrate how Cloud Run scores high in terms of developer experience. It differs from functions runtime as You can bring your own container, your own code, a folder, or binarys and it pairs great with the container ecosystem: Cloud Build, Cloud Code, Artifact Registry, and Docker. Each Cloud Run service gets an out-of-the-box stable HTTPS endpoint, with TLS termination handled for you. Map your services to your own domains and use either for web sites, backend APIs, workflows, invoke and connect services with the newest protocols of HTTP/2, WebSockets or gRPC (unary and streaming). Cloud Run is serverless containers, which means you don't have to fiddle with infrastructure or back-end resources to run applications.
BigQuery best practices and recommendations to reduce costs with BI Engine, S...Márton Kodok
best practices and recommendations for tuning BI Engine for your existing BigQuery workloads for cheaper and faster queries. Learn how we at REEA are orchestrating BI Engine reservations, on a 5TB dataset, considered small for BigQuery but with big cost savings and accelerated queries. We are seeing many presentations for big enterprises, but now we are showcasing how our queries perform better with lower costs. We are going to address the top considerations when to turn on BI Engine, how to use cloud orchestration for making this an automatic process, and combined with BigQuery and Datastudio query complexity that might save precious development time, lower bills, faster queries.
Vertex AI - Unified ML Platform for the entire AI workflow on Google CloudMárton Kodok
Vertex AI is a managed ML platform for practitioners to accelerate experiments and deploy AI models.
Enhanced developer experience
- Build with the groundbreaking ML tools that power Google
- Approachable from the non-ML developer perspective (AutoML, managed models, training)
- Ease the life of a data scientist/ML (has feature store, managed datasets, endpoints, notebooks)
- Infrastructure management overhead have been almost completely eliminated
- Unified UI for the entire ML workflow
- End-to-end integration for data and AI with build pipelines that outperform and solve complex ML tasks
- Explainable AI and TensorBoard to visualize and track ML experiments
Vertex AI: Pipelines for your MLOps workflowsMárton Kodok
In recent years, one of the biggest trends in applications development has been the rise of Machine Learning solutions, tools, and managed platforms. Vertex AI is a managed unified ML platform for all your AI workloads. On the MLOps side, Vertex AI Pipelines solutions let you adopt experiment pipelining beyond the classic build, train, eval, and deploy a model. It is engineered for data scientists and data engineers, and it’s a tremendous help for those teams who don’t have DevOps or sysadmin engineers, as infrastructure management overhead has been almost completely eliminated.
Based on practical examples we will demonstrate how Vertex AI Pipelines scores high in terms of developer experience, how fits custom ML needs, and analyze results. It’s a toolset for a fully-fledged machine learning workflow, a sequence of steps in the model development, a deployment cycle, such as data preparation/validation, model training, hyperparameter tuning, model validation, and model deployment. Vertex AI comes with all standard resources plus an ML metadata store, a fully managed feature store, and a fully managed pipelines runner.
Vertex AI Pipelines is a managed serverless toolkit, which means you don't have to fiddle with infrastructure or back-end resources to run workflows.
Cloud Workflows What's new in serverless orchestration and automationMárton Kodok
understand how Cloud Workflows resolves challenges in connecting services, HTTP based service orchestration and automation. We are going to dive deep how serverless HTTP service automation works to automate step engines. Based on practical examples we will demonstrate the newest features that lets you automate the cloud and integration with any Google Cloud product without worrying about authentication
Serverless orchestration and automation with Cloud WorkflowsMárton Kodok
Join this session to understand how Cloud Workflows resolves challenges in connecting services, HTTP based service orchestration and automation. We are going to dive deep how serverless HTTP service automation works to automate step engines. Based on practical examples we will demonstrate the built-in decision and conditional executions, subworkflows, support for external built-in API calls, and integration with any Google Cloud product without worrying about authentication. We are going to cover Marketing, Retail, Industrial and Developer possibilities, such as event driven marketing workflow execution, or inventory chain operations, generating and automatic state machines, or orchestrate DevOps workflows and automating the Cloud.
Serverless orchestration and automation with Cloud WorkflowsMárton Kodok
Join this session to understand how Cloud Workflows resolves challenges in connecting services, HTTP based service orchestration and automation. We are going to dive deep how serverless HTTP service automation works to automate step engines. Based on practical examples we will demonstrate the built-in decision and conditional executions, subworkflows, support for external built-in API calls, and integration with any Google Cloud product without worrying about authentication. We are going to cover Marketing, Retail, Industrial and Developer possibilities, such as event driven marketing workflow execution, or inventory chain operations, generating and automatic state machines, or orchestrate DevOps workflows and automating the Cloud.
Serverless orchestration and automation with Cloud WorkflowsMárton Kodok
Join this session to understand how Cloud Workflows resolves challenges in connecting services, HTTP based service orchestration and automation. We are going to dive deep how serverless HTTP service automation works to automate step engines. Based on practical examples we will demonstrate the built-in decision and conditional executions, subworkflows, support for external built-in API calls, and integration with any Google Cloud product without worrying about authentication. We are going to cover Marketing, Retail, Industrial and Developer possibilities, such as event driven marketing workflow execution, or inventory chain operations, generating and automatic state machines, or orchestrate DevOps workflows and automating the Cloud.
BigdataConference Europe - BigQuery MLMárton Kodok
One of the hottest topics in database land these days is BigQuery ML. A new way to use machine learning on top of tabular data straight on your tables without leaving the query editor.
With BigQuery ML, you can build machine learning models without leaving the database environment and training it on massive datasets.
In this demo session, we are going to demonstrate common marketing Machine Learning use cases how to build, train, eval and predict, your own scalable machine learning models using SQL language.
The audience will get first hand experience how to write CREATE MODEL sql syntax to build machine learning models such as:
– Multiclass logistic regression for classification
– K-means clustering
– Matrix factorization
– ARIMA time series predictions
– Import TensorFlow models for prediction in BigQuery
Models are trained and accessed in BigQuery using SQL — a language data analysts know. This enables business decision making through predictive analytics across the organization without leaving the query editor.
Applying BigQuery ML on e-commerce data analyticsMárton Kodok
With BigQuery ML, you can build machine learning models without leaving the database environment and training it on massive datasets. We are going to demonstrate common marketing Machine Learning use cases we do at REEA.net to build, train, eval and predict, your own scalable machine learning models using SQL language in Google BigQuery and to address the following use cases:
Customer Segmentation
Customer Lifetime Value (LTV) prediction
Conversion/Purchase prediction
The audience will get first hand experience how to write CREATE MODEL sql syntax to build machine learning models such as:
Multiclass logistic regression for classification
K-means clustering
Import TensorFlow models for prediction in BigQuery
Models are trained and accessed in BigQuery using SQL — a language data analysts know. This enables business decision making through predictive analytics across the organization without leaving the query editor
Vibe Koli 2019 - Utazás az egyetem padjaitól a Google Developer ExpertigMárton Kodok
VIBE Koli 2019 - Vibe Garázs - Gokart.
Kodok Márton, miután elvégezte tanulmányait a Sapientián, IT-s karriert épített ki magának, ma pedig már tagja a Google Developer Expert (GDE) csapatának, így az ország kiemelkedő szakemberei közé tartozik. A VIBE Kolin abban segít neked, hogy megtaláld a saját utad. Bebizonyítja, csak akaraterő kell ahhoz, hogy egy társadhoz képest mást, többet csinálj.
BigQuery ML - Machine learning at scale using SQLMárton Kodok
With BigQuery ML, you can build machine learning models without leaving the data warehouse environment and training it on massive datasets. We are going to demonstrate how to build, train, eval and predict, your own scalable machine learning models using standard SQL language in Google BigQuery.
We will see how can we use CREATE MODEL sql syntax to build different models such as:
Linear regression
Multiclass logistic regression for classification
K-means clustering
Import TensorFlow models for prediction in BigQuery
We will see how we can apply these models on tabular data in retail and marketing use cases.
Models are trained and accessed in BigQuery using SQL — a language data analysts know. This enables business decision making through predictive analytics across the organization without leaving the query editor.
6. DISZ - Webalkalmazások skálázhatósága a Google Cloud PlatformonMárton Kodok
Az előadás témája hogyan építhető fel egy rugalmas, jól skálázható szolgáltatás a felhőszolgáltatók platformjain. Hogyan lehet megoldani, hogy a szolgáltatás, amelynek induláskor legfeljebb néhány tíz vagy száz felhasználót kell kiszolgálnia, akár több ezer vagy nagyságrendekkel több felhasználót is képes legyen kiszolgálni rugalmasan? Hátradőlni és csodálni az autoscaling funkciót a Black Friday napján. Beszélni fogunk virtualizációról, platformszintű virtualizációről, szuperkönnyű alkalmazáskonténerekről, a munkaterhek közel valósidejű “pakolgatásával”. Bemutatásra kerül a Google Cloud Platform számos komponense. Bankok, biztosítók, webshopok és így tovább mind a cloudban látják a kitörési pontot.
Globus Connect Server Deep Dive - GlobusWorld 2024Globus
We explore the Globus Connect Server (GCS) architecture and experiment with advanced configuration options and use cases. This content is targeted at system administrators who are familiar with GCS and currently operate—or are planning to operate—broader deployments at their institution.
Why React Native as a Strategic Advantage for Startup Innovation.pdfayushiqss
Do you know that React Native is being increasingly adopted by startups as well as big companies in the mobile app development industry? Big names like Facebook, Instagram, and Pinterest have already integrated this robust open-source framework.
In fact, according to a report by Statista, the number of React Native developers has been steadily increasing over the years, reaching an estimated 1.9 million by the end of 2024. This means that the demand for this framework in the job market has been growing making it a valuable skill.
But what makes React Native so popular for mobile application development? It offers excellent cross-platform capabilities among other benefits. This way, with React Native, developers can write code once and run it on both iOS and Android devices thus saving time and resources leading to shorter development cycles hence faster time-to-market for your app.
Let’s take the example of a startup, which wanted to release their app on both iOS and Android at once. Through the use of React Native they managed to create an app and bring it into the market within a very short period. This helped them gain an advantage over their competitors because they had access to a large user base who were able to generate revenue quickly for them.
First Steps with Globus Compute Multi-User EndpointsGlobus
In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.
Modern design is crucial in today's digital environment, and this is especially true for SharePoint intranets. The design of these digital hubs is critical to user engagement and productivity enhancement. They are the cornerstone of internal collaboration and interaction within enterprises.
Into the Box Keynote Day 2: Unveiling amazing updates and announcements for modern CFML developers! Get ready for exciting releases and updates on Ortus tools and products. Stay tuned for cutting-edge innovations designed to boost your productivity.
Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...informapgpstrackings
Keep tabs on your field staff effortlessly with Informap Technology Centre LLC. Real-time tracking, task assignment, and smart features for efficient management. Request a live demo today!
For more details, visit us : https://informapuae.com/field-staff-tracking/
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
Prosigns: Transforming Business with Tailored Technology SolutionsProsigns
Unlocking Business Potential: Tailored Technology Solutions by Prosigns
Discover how Prosigns, a leading technology solutions provider, partners with businesses to drive innovation and success. Our presentation showcases our comprehensive range of services, including custom software development, web and mobile app development, AI & ML solutions, blockchain integration, DevOps services, and Microsoft Dynamics 365 support.
Custom Software Development: Prosigns specializes in creating bespoke software solutions that cater to your unique business needs. Our team of experts works closely with you to understand your requirements and deliver tailor-made software that enhances efficiency and drives growth.
Web and Mobile App Development: From responsive websites to intuitive mobile applications, Prosigns develops cutting-edge solutions that engage users and deliver seamless experiences across devices.
AI & ML Solutions: Harnessing the power of Artificial Intelligence and Machine Learning, Prosigns provides smart solutions that automate processes, provide valuable insights, and drive informed decision-making.
Blockchain Integration: Prosigns offers comprehensive blockchain solutions, including development, integration, and consulting services, enabling businesses to leverage blockchain technology for enhanced security, transparency, and efficiency.
DevOps Services: Prosigns' DevOps services streamline development and operations processes, ensuring faster and more reliable software delivery through automation and continuous integration.
Microsoft Dynamics 365 Support: Prosigns provides comprehensive support and maintenance services for Microsoft Dynamics 365, ensuring your system is always up-to-date, secure, and running smoothly.
Learn how our collaborative approach and dedication to excellence help businesses achieve their goals and stay ahead in today's digital landscape. From concept to deployment, Prosigns is your trusted partner for transforming ideas into reality and unlocking the full potential of your business.
Join us on a journey of innovation and growth. Let's partner for success with Prosigns.
Strategies for Successful Data Migration Tools.pptxvarshanayak241
Data migration is a complex but essential task for organizations aiming to modernize their IT infrastructure and leverage new technologies. By understanding common challenges and implementing these strategies, businesses can achieve a successful migration with minimal disruption. Data Migration Tool like Ask On Data play a pivotal role in this journey, offering features that streamline the process, ensure data integrity, and maintain security. With the right approach and tools, organizations can turn the challenge of data migration into an opportunity for growth and innovation.
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
Large Language Models and the End of ProgrammingMatt Welsh
Talk by Matt Welsh at Craft Conference 2024 on the impact that Large Language Models will have on the future of software development. In this talk, I discuss the ways in which LLMs will impact the software industry, from replacing human software developers with AI, to replacing conventional software with models that perform reasoning, computation, and problem-solving.
Your Digital Assistant.
Making complex approach simple. Straightforward process saves time. No more waiting to connect with people that matter to you. Safety first is not a cliché - Securely protect information in cloud storage to prevent any third party from accessing data.
Would you rather make your visitors feel burdened by making them wait? Or choose VizMan for a stress-free experience? VizMan is an automated visitor management system that works for any industries not limited to factories, societies, government institutes, and warehouses. A new age contactless way of logging information of visitors, employees, packages, and vehicles. VizMan is a digital logbook so it deters unnecessary use of paper or space since there is no requirement of bundles of registers that is left to collect dust in a corner of a room. Visitor’s essential details, helps in scheduling meetings for visitors and employees, and assists in supervising the attendance of the employees. With VizMan, visitors don’t need to wait for hours in long queues. VizMan handles visitors with the value they deserve because we know time is important to you.
Feasible Features
One Subscription, Four Modules – Admin, Employee, Receptionist, and Gatekeeper ensures confidentiality and prevents data from being manipulated
User Friendly – can be easily used on Android, iOS, and Web Interface
Multiple Accessibility – Log in through any device from any place at any time
One app for all industries – a Visitor Management System that works for any organisation.
Stress-free Sign-up
Visitor is registered and checked-in by the Receptionist
Host gets a notification, where they opt to Approve the meeting
Host notifies the Receptionist of the end of the meeting
Visitor is checked-out by the Receptionist
Host enters notes and remarks of the meeting
Customizable Components
Scheduling Meetings – Host can invite visitors for meetings and also approve, reject and reschedule meetings
Single/Bulk invites – Invitations can be sent individually to a visitor or collectively to many visitors
VIP Visitors – Additional security of data for VIP visitors to avoid misuse of information
Courier Management – Keeps a check on deliveries like commodities being delivered in and out of establishments
Alerts & Notifications – Get notified on SMS, email, and application
Parking Management – Manage availability of parking space
Individual log-in – Every user has their own log-in id
Visitor/Meeting Analytics – Evaluate notes and remarks of the meeting stored in the system
Visitor Management System is a secure and user friendly database manager that records, filters, tracks the visitors to your organization.
"Secure Your Premises with VizMan (VMS) – Get It Now"
Gamify Your Mind; The Secret Sauce to Delivering Success, Continuously Improv...Shahin Sheidaei
Games are powerful teaching tools, fostering hands-on engagement and fun. But they require careful consideration to succeed. Join me to explore factors in running and selecting games, ensuring they serve as effective teaching tools. Learn to maintain focus on learning objectives while playing, and how to measure the ROI of gaming in education. Discover strategies for pitching gaming to leadership. This session offers insights, tips, and examples for coaches, team leads, and enterprise leaders seeking to teach from simple to complex concepts.
Accelerate Enterprise Software Engineering with PlatformlessWSO2
Key takeaways:
Challenges of building platforms and the benefits of platformless.
Key principles of platformless, including API-first, cloud-native middleware, platform engineering, and developer experience.
How Choreo enables the platformless experience.
How key concepts like application architecture, domain-driven design, zero trust, and cell-based architecture are inherently a part of Choreo.
Demo of an end-to-end app built and deployed on Choreo.
In software engineering, the right architecture is essential for robust, scalable platforms. Wix has undergone a pivotal shift from event sourcing to a CRUD-based model for its microservices. This talk will chart the course of this pivotal journey.
Event sourcing, which records state changes as immutable events, provided robust auditing and "time travel" debugging for Wix Stores' microservices. Despite its benefits, the complexity it introduced in state management slowed development. Wix responded by adopting a simpler, unified CRUD model. This talk will explore the challenges of event sourcing and the advantages of Wix's new "CRUD on steroids" approach, which streamlines API integration and domain event management while preserving data integrity and system resilience.
Participants will gain valuable insights into Wix's strategies for ensuring atomicity in database updates and event production, as well as caching, materialization, and performance optimization techniques within a distributed system.
Join us to discover how Wix has mastered the art of balancing simplicity and extensibility, and learn how the re-adoption of the modest CRUD has turbocharged their development velocity, resilience, and scalability in a high-growth environment.
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
How Recreation Management Software Can Streamline Your Operations.pptxwottaspaceseo
Recreation management software streamlines operations by automating key tasks such as scheduling, registration, and payment processing, reducing manual workload and errors. It provides centralized management of facilities, classes, and events, ensuring efficient resource allocation and facility usage. The software offers user-friendly online portals for easy access to bookings and program information, enhancing customer experience. Real-time reporting and data analytics deliver insights into attendance and preferences, aiding in strategic decision-making. Additionally, effective communication tools keep participants and staff informed with timely updates. Overall, recreation management software enhances efficiency, improves service delivery, and boosts customer satisfaction.
How Recreation Management Software Can Streamline Your Operations.pptx
CodeCamp Iasi - Creating serverless data analytics system on GCP using BigQuery
1. Creating #serverless data analytics
system on GCP using BigQuery
Márton Kodok / @martonkodok
Google Developer Expert at REEA.net
March 2018 - Tirgu Mures, Romania
2. ● Geek. Hiker. Do-er.
● Among the Top3 romanians on Stackoverflow 120k reputation
● Google Developer Expert on Cloud technologies
● Crafting Web/Mobile backends at REEA.net
● BigQuery/Redis and database engine expert
● Active in mentoring and IT community
Twitter: @martonkodok
StackOverflow: pentium10
Slideshare: martonkodok
GitHub: pentium10
Creating #serverless data analytics system on GCP using BigQuery @martonkodok
About me
4. Google Cloud Platform (GCP)
Compute Big Data
BigQuery
Cloud
Dataflow
Cloud
Dataproc
Cloud
Datalab
Cloud
Pub/Sub
Genomics
Storage & Databases
Cloud
Storage
Cloud
Bigtable
Cloud
Datastore
Cloud SQL
Cloud
Spanner
Persistent
Disk
Machine Learning
Cloud Machine
Learning
Cloud
Vision API
Cloud
Speech API
Cloud Natural
Language API
Cloud
Translation
API
Cloud
Jobs API
Data
Studio
Cloud
Dataprep
Cloud Video
Intelligence
API
Advanced
Solutions Lab
Compute
Engine
App
Engine
Kubernetes
Engine
GPU
Cloud
Functions
Container-
Optimized OS
Identity & Security
Cloud IAM
Cloud Resource
Manager
Cloud Security
Scanner
Key
Management
Service
BeyondCorp
Data Loss
Prevention API
Identity-Aware
Proxy
Security Key
Enforcement
Internet of Things
Cloud IoT
Core
Transfer
Appliance
5. Google Cloud Platform (GCP)
Developer Tools
Cloud SDK
Cloud
Deployment
Manager
Cloud Source
Repositories
Cloud
Tools for
Android Studio
Cloud Tools
for IntelliJ
Cloud
Tools for
PowerShell
Cloud
Tools for
Visual Studio
Container
Registry
Google Plug-in
for Eclipse
Cloud Test
Lab
Networking
Virtual
Private Cloud
Cloud Load
Balancing
Cloud
CDN
Cloud
Interconnect
Cloud DNS
Cloud
Network
Cloud
External IP
Addresses
Cloud
Firewall Rules
Cloud
Routes
Cloud VPN
Management Tools
Stackdriver Monitoring Logging
Error
Reporting
Trace
Debugger
Cloud
Deployment
Manager
Cloud
Endpoints
Cloud
Console
Cloud
Shell
Cloud Mobile
App
Cloud
Billing API
Cloud
APIs
Cloud
Router
Dedicated
Interconnect
Container
Builder
7. Meet Serverless
serverless data center depicted
Creating #serverless data analytics system on GCP using BigQuery @martonkodok
8. Event-driven serverless compute platform
Cloud
Services
Changes in data state
Business logic events
Integrations
Event Router
Gateway
HTTPS
Event Source
Multiple Platforms
Data Warehouse
Pub/Sub
Cloud Functions
Streaming
Business Value
Application
Task
Analysis
9. Serverless is about maximizing elasticity, cost
savings, and agility of cloud computing.
@martonkodok
Creating #serverless data analytics system on GCP using BigQuery @martonkodok
10. Crafting a solution for building high-performance,
petabyte scale data analytics, serverless
reporting system on Google Cloud Platform
Goal today
Creating #serverless data analytics system on GCP using BigQuery @martonkodok
11. Legacy Reporting System
App
Cloud Load
Balancing
NGINX
Compute Engine
10GB PD
2 1
Database Service (Master/Slave)
Compute Engine
10GB PD
4 1
Compute Engine
10GB PD
4 1
Compute Engine
10GB PD
4 1
Report & Share
Business Analysis
Scheduled
Tasks
Batch Processing
Compute Engine
Multiple Instances
Creating #serverless data analytics system on GCP using BigQuery @martonkodok
12. Serverless Reporting System
App
Cloud Load
Balancing
NGINX
Compute Engine
10GB PD
2 1
Database Service (Master/Slave)
Compute Engine
10GB PD
4 1
Compute Engine
10GB PD
4 1
Compute Engine
10GB PD
4 1
Report & Share
Business Analysis
Scheduled
Tasks
Batch Processing
Compute Engine
Multiple Instances
BigQuery Data Studio
Report & Share
Business Analysis
Creating #serverless data analytics system on GCP using BigQuery @martonkodok
14. Analytics-as-a-Service - Data Warehouse in the Cloud
Scales into Petabytes on Managed Google Infrastructure (US or EU zone)
SQL 2011 + Javascript UDF (User Defined Functions)
Familiar DB Structure (table, views, struct, nested, JSON)
Integrates with Google Sheets + Cloud Storage + Pub/Sub connectors
Decent pricing (queries $5/TB, storage: $20/TB cold: $10/TB) *Mar 2018
Open Interfaces (Web UI, BQ command line tool, REST, ODBC)
What is BigQuery?
Creating #serverless data analytics system on GCP using BigQuery @martonkodok
15. Columnar storage (max 10 000 columns in table)
Large files for loading: 5TB (CSV or JSON)
UDF in Javascript or SQL
Rich SQL 2011: JSON,IP,Math,RegExp,Geocode,Window functions
Modern data types: Record, Nested, Struct, Array.
Append-only tables prefered (DML syntax available)
Day column partitioned tables (select * from t where day=’2018-01-01’)
BigQuery: Convenience of SQL
Creating #serverless data analytics system on GCP using BigQuery @martonkodok
16. Architecting for The Cloud
BigQuery
On-Premises Servers
Pipelines
ETL
Engine
Event Sourcing
Frontend
Platform Services
Metrics / Logs/
Streaming
Creating #serverless data analytics system on GCP using BigQuery @martonkodok
17. “ Our project generates many/big files.
How can I seamlessly ingest them?
Creating #serverless data analytics system on GCP using BigQuery @martonkodok
18. Serverless file ingest
BigQuery
On-Premises Servers
ApplicationEvent Sourcing
Frontend
Platform Services
Metrics / Logs/
Streaming
Cloud
Storage
Cloud
Functions
Triggered Code
Creating #serverless data analytics system on GCP using BigQuery @martonkodok
19. “ Data needs to be processed in
multiple services.
How can we pipe to multiple places?
Creating #serverless data analytics system on GCP using BigQuery @martonkodok
20. Architecting for The Cloud
On-Premises Servers
Event Sourcing
Frontend
Platform Services
Analyze
Metrics / Logs/
Streaming
Cloud Storage
Creating #serverless data analytics system on GCP using BigQuery @martonkodok
Cloud
Dataflow
Process
BigQuery
Cloud SQL
Stream
Batch
Data
Studio
Third-Party
Tools
21. “ We have our app outside of GCP.
How can we use the benefits of BigQuery?
Creating #serverless data analytics system on GCP using BigQuery @martonkodok
22. Data Pipeline Integration at REEA.net
Analytics Backend
BigQuery
On-Premises Servers
Pipelines
FluentD
Event Sourcing
Frontend
Platform Services
Metrics / Logs/
Streaming
Development
Team
Data Analysts
Report & Share
Business Analysis
Tools
Tableau
QlikView
Data Studio
Internal
Dashboard
Database
SQL
Application
ServersServers
Cloud Storage
archive
Load
Export
Replay
Standard
Devices
HTTPS
Creating #serverless data analytics system on GCP using BigQuery @martonkodok
23. The following slides will present a sample Fluentd configuration to:
1. Transform a record
2. Copy event to multiple outputs
3. Store event data in File (for backup/log purposes)
4. Stream to BigQuery (for immediate analyses)
Creating #serverless data analytics system on GCP using BigQuery @martonkodok
24. <filter frontend.user.*>
@type record_transformer
</filter>
<match frontend.user.*>
@type copy
<store>
@type forest
subtype file
</store>
<store>
@type bigquery
</store>
…
</match>
Filter plugin mutates incoming data. Add/modify/delete
event data transform attributes without a code deploy.1
2
3
4
The copy output plugin copies events to multiple outputs.
File(s), multiple databases, DB engines.
Great to ship same event to multiple subsystems.
The Bigquery output plugin on the fly streams the event to
the BigQuery warehouse. No need to write integration.
Data is available immediately for querying.
Whenever needed other output plugins can be wired in:
Kafka, Google Cloud Storage output plugin.
Creating #serverless data analytics system on GCP using BigQuery @martonkodok
25. record_transformer copy file BigQuery
<filter frontend.user.*>
@type record_transformer
enable_ruby
remove_keys host
<record>
bq {"insert_id":"${uid}","host":"${host}",
"created":"${time.to_i}"}
avg ${record["total"] / record["count"]}
</record>
</filter>
syntax: Ruby, easy to use.
Great for:
- date transformation,
- quick normalizations,
- calculating something on the fly,
and store in clear log/analytics db
- renaming without code deploy.
1 2 3 4
Creating #serverless data analytics system on GCP using BigQuery @martonkodok
26. record_transformer copy file BigQuery
<match frontend.user.*>
@type copy
<store>
@type forest
subtype file
<template>
path /tank/storage/${tag}.*.log
time_slice_format %Y%m%d
</template>
</store>
</match>
1 2 3 4
Creating #serverless data analytics system on GCP using BigQuery @martonkodok
27. record_transformer copy file BigQuery
<match frontend.user.*>
@type bigquery
method insert
auth_method json_key
json_key /etc/td-agent/keys/key-31da042be48c.json
time_field timestamp
time_slice_format %Y%m%d
table user$%{time_slice}
ignore_unknown_values
schema_path /etc/td-agent/schema/user_login.json
</match>
1 2 3 4
Connector uses:
- JSON key auth file
- JSON table schema
Pro features:
- streaming to Partitioned tables
- ignore unknown values
(not reflected in schema)
Creating #serverless data analytics system on GCP using BigQuery @martonkodok
28. ● On data that it is difficult to process/analyze using traditional databases
● Not a replacement to traditional DBs, but it compliments the system
● Major strength is handling Large datasets
● Applying Javascript UDF on columnar storage to resolve complex tasks
(eg: JS for natural language processing)
● On streams (forms, IoT, Kafka)
● On exploring unstructured data
Where to use BigQuery?
Creating #serverless data analytics system on GCP using BigQuery @martonkodok
29. ➢ Optimize product pages
➢ Email engagement
➢ Funnel Analysis
Achievements - goal reached by measuring everything
Creating #serverless data analytics system on GCP using BigQuery @martonkodok
31. Funnel analysis: Time on upsell pages
Creating #serverless data analytics system on GCP using BigQuery @martonkodok
32. Example HITS chain:
● article1 -> page2 -> page3 -> page4 -> orderpage1 -> thankyoupage1
● page1 -> article2-> page3 -> orderpage2 -> ...
Attribute credit to first article visited on purchase
Creating #serverless data analytics system on GCP using BigQuery @martonkodok
33. ● Funnel Analysis
● Email URL click heatmap
● Email Health Dashboard (SPAM, ISP deferral, content
A/B split tests, trends or low open rate campaigns)
● Advanced segmentation (all raw data stored)
● Behavioral analytics - engaged users etc...
Achievements Continued
Creating #serverless data analytics system on GCP using BigQuery @martonkodok
34. ● SQL language to run BigData queries
● run raw ad-hoc queries (either by analysts/sales or Devs)
● no more throwing away-, expiring-, aggregating old data
● 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)
Our benefits
Creating #serverless data analytics system on GCP using BigQuery @martonkodok
35. ● No manual sharding
● No capacity guessing
● No idle resources
● No maintenance windows
● No manual scaling
● No file mgmt
BigQuery: Serverless Data Warehouse
Creating #serverless data analytics system on GCP using BigQuery @martonkodok
36. ● No servers to provision or manage
● Abstract away the complexity
● Scales with usage (ready every time for viral spikes or #BlackFriday)
● Availability and fault tolerance built in
● No orchestration in code
● Never pay for idle
● Cost savings (ps: we don’t have the same budget for security like GCP or AWS)
● Decoupled: APIs as contracts
● Monitored: Metrics and logging are a universal right
● Think concurrent, stateless, queue, stream based.
Serverlessmeans
Creating #serverless data analytics system on GCP using BigQuery @martonkodok
37. Easily Build Custom Reports and Dashboards
Creating #serverless data analytics system on GCP using BigQuery @martonkodok
38. Thank you.
Slides available on:
slideshare.net/martonkodok
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