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
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.
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.
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.
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.
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
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
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.
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.
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.
Snowflake: Your Data. No Limits (Session sponsored by Snowflake) - AWS Summit...Amazon Web Services
Struggling to keep up with an ever-increasing demand for data at your organisation? Do you spend hours tinkering with your streaming data pipelines? Does that one data scientist with direct EDW access keep you up at night? Introducing Snowflake, a brand new SQL data warehouse built for the cloud. We’ve designed and implemented a unique cloud-based architecture that addresses the most common shortcomings of existing data solutions. With Snowflake, you can unlock unlimited concurrency, enable instant scalability, and take advantage of built-in tuning and optimisation. Join us and find out what Netflix, Adobe, and Nike all have in common.
Google Cloud Dataproc - Easier, faster, more cost-effective Spark and Hadoophuguk
At Google Cloud Platform, we're combining the Apache Spark and Hadoop ecosystem with our software and hardware innovations. We want to make these awesome tools easier, faster, and more cost-effective, from 3 to 30,000 cores. This presentation will showcase how Google Cloud Platform is innovating with the goal of bringing the Hadoop ecosystem to everyone.
Bio: "I love data because it surrounds us - everything is data. I also love open source software, because it shows what is possible when people come together to solve common problems with technology. While they are awesome on their own, I am passionate about combining the power of open source software with the potential unlimited uses of data. That's why I joined Google. I am a product manager for Google Cloud Platform and manage Cloud Dataproc and Apache Beam (incubating). I've previously spent time hanging out at Disney and Amazon. Beyond Google, love data, amateur radio, Disneyland, photography, running and Legos."
As part of this session, I will be giving an introduction to Data Engineering and Big Data. It covers up to date trends.
* Introduction to Data Engineering
* Role of Big Data in Data Engineering
* Key Skills related to Data Engineering
* Role of Big Data in Data Engineering
* Overview of Data Engineering Certifications
* Free Content and ITVersity Paid Resources
Don't worry if you miss the video - you can click on the below link to go through the video after the schedule.
https://youtu.be/dj565kgP1Ss
* Upcoming Live Session - Overview of Big Data Certifications (Spark Based) - https://www.meetup.com/itversityin/events/271739702/
Relevant Playlists:
* Apache Spark using Python for Certifications - https://www.youtube.com/playlist?list=PLf0swTFhTI8rMmW7GZv1-z4iu_-TAv3bi
* Free Data Engineering Bootcamp - https://www.youtube.com/playlist?list=PLf0swTFhTI8pBe2Vr2neQV7shh9Rus8rl
* Join our Meetup group - https://www.meetup.com/itversityin/
* Enroll for our labs - https://labs.itversity.com/plans
* Subscribe to our YouTube Channel for Videos - http://youtube.com/itversityin/?sub_confirmation=1
* Access Content via our GitHub - https://github.com/dgadiraju/itversity-books
* Lab and Content Support using Slack
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
MongoDB WiredTiger Internals: Journey To TransactionsMydbops
MongoDB has adapted transaction feature (ACID Properties) in MongoDB 4.0. This talk focuses on the internals of how MongoDB adapted the ACID properties with Weird Tiger Engine. Weird tiger offers more future possibilities for MongoDB. This tech talk was presented at Mydbops Database Meetup on 27-04-2019 by Manosh Malai Senior Devops/NoSQL Consultant with Mydbops and Ranjith Database Administrator with Mydbops.
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)!
Benchmarking is hard. Benchmarking databases, harder. Benchmarking databases that follow different approaches (relational vs document) is even harder.
But the market demands these kinds of benchmarks. Despite the different data models that MongoDB and PostgreSQL expose, many organizations face the challenge of picking either technology. And performance is arguably the main deciding factor.
Join this talk to discover the numbers! After $30K spent on public cloud and months of testing, there are many different scenarios to analyze. Benchmarks on three distinct categories have been performed: OLTP, OLAP and comparing MongoDB 4.0 transaction performance with PostgreSQL's.
What would be faster, MongoDB or PostgreSQL?
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
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
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
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.
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.
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.
Snowflake: Your Data. No Limits (Session sponsored by Snowflake) - AWS Summit...Amazon Web Services
Struggling to keep up with an ever-increasing demand for data at your organisation? Do you spend hours tinkering with your streaming data pipelines? Does that one data scientist with direct EDW access keep you up at night? Introducing Snowflake, a brand new SQL data warehouse built for the cloud. We’ve designed and implemented a unique cloud-based architecture that addresses the most common shortcomings of existing data solutions. With Snowflake, you can unlock unlimited concurrency, enable instant scalability, and take advantage of built-in tuning and optimisation. Join us and find out what Netflix, Adobe, and Nike all have in common.
Google Cloud Dataproc - Easier, faster, more cost-effective Spark and Hadoophuguk
At Google Cloud Platform, we're combining the Apache Spark and Hadoop ecosystem with our software and hardware innovations. We want to make these awesome tools easier, faster, and more cost-effective, from 3 to 30,000 cores. This presentation will showcase how Google Cloud Platform is innovating with the goal of bringing the Hadoop ecosystem to everyone.
Bio: "I love data because it surrounds us - everything is data. I also love open source software, because it shows what is possible when people come together to solve common problems with technology. While they are awesome on their own, I am passionate about combining the power of open source software with the potential unlimited uses of data. That's why I joined Google. I am a product manager for Google Cloud Platform and manage Cloud Dataproc and Apache Beam (incubating). I've previously spent time hanging out at Disney and Amazon. Beyond Google, love data, amateur radio, Disneyland, photography, running and Legos."
As part of this session, I will be giving an introduction to Data Engineering and Big Data. It covers up to date trends.
* Introduction to Data Engineering
* Role of Big Data in Data Engineering
* Key Skills related to Data Engineering
* Role of Big Data in Data Engineering
* Overview of Data Engineering Certifications
* Free Content and ITVersity Paid Resources
Don't worry if you miss the video - you can click on the below link to go through the video after the schedule.
https://youtu.be/dj565kgP1Ss
* Upcoming Live Session - Overview of Big Data Certifications (Spark Based) - https://www.meetup.com/itversityin/events/271739702/
Relevant Playlists:
* Apache Spark using Python for Certifications - https://www.youtube.com/playlist?list=PLf0swTFhTI8rMmW7GZv1-z4iu_-TAv3bi
* Free Data Engineering Bootcamp - https://www.youtube.com/playlist?list=PLf0swTFhTI8pBe2Vr2neQV7shh9Rus8rl
* Join our Meetup group - https://www.meetup.com/itversityin/
* Enroll for our labs - https://labs.itversity.com/plans
* Subscribe to our YouTube Channel for Videos - http://youtube.com/itversityin/?sub_confirmation=1
* Access Content via our GitHub - https://github.com/dgadiraju/itversity-books
* Lab and Content Support using Slack
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
MongoDB WiredTiger Internals: Journey To TransactionsMydbops
MongoDB has adapted transaction feature (ACID Properties) in MongoDB 4.0. This talk focuses on the internals of how MongoDB adapted the ACID properties with Weird Tiger Engine. Weird tiger offers more future possibilities for MongoDB. This tech talk was presented at Mydbops Database Meetup on 27-04-2019 by Manosh Malai Senior Devops/NoSQL Consultant with Mydbops and Ranjith Database Administrator with Mydbops.
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)!
Benchmarking is hard. Benchmarking databases, harder. Benchmarking databases that follow different approaches (relational vs document) is even harder.
But the market demands these kinds of benchmarks. Despite the different data models that MongoDB and PostgreSQL expose, many organizations face the challenge of picking either technology. And performance is arguably the main deciding factor.
Join this talk to discover the numbers! After $30K spent on public cloud and months of testing, there are many different scenarios to analyze. Benchmarks on three distinct categories have been performed: OLTP, OLAP and comparing MongoDB 4.0 transaction performance with PostgreSQL's.
What would be faster, MongoDB or PostgreSQL?
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
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.
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.
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.
Google BigQuery is the future of Analytics! (Google Developer Conference)Rasel Rana
Google Developer Group (GDG) Sonargaon is a community based focused group for developers on Google and related technologies. I tried to cover a topic on Big Data & BigQuery which is the future of analytics.
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.
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.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
2. SUMMARY
HOW TO LOAD DATA TO BIGQUERY ?
01
PAGE 2
HOW TO WORK WITH BIGQUERY ?
02
PAGE 2
HOW TO BUILD DASHBOARDS USING BIGQUERY ?
03
PAGE 2
SOME BEST PRACTICES
04
PAGE 2
4. BIGQUERY - NON GEEKY DEFINITION
This is a place where you can stock and query your data for 0,02 $ per GB (~256 MP3 audio files) and then create nice (and
free!) dashboards in Google Data Studio.
It is a part of Google Cloud Platform.
You’ll need some basic SQL knowledge to work with it.
(Officially : BigQuery is a Web service from Google that is used for handling or analyzing big data. It is part of the Google
Cloud Platform.)
6. SQL
Language that allows you to communicate with your database.
Example :
SELECT name, job, salary
FROM people
WHERE salary IS NULL
Learn basic SQL with free courses :
https://campus.datacamp.com/courses/intro-to-sql-for-data-science/
https://www.codecademy.com/courses/sql-analyzing-business-metrics/
https://www.w3schools.com/sql/
7. ADVANTAGES OF BIGQUERY
2
1 3
MANAGEMENT SIMPLICITY
Easy Data management
No administration
Fully-managed
(no worries)
DATA PERFORMANCE
No limits
Fast imports
Scans TBs in seconds
GDPR - COMPLIANCE
Data storage in EU/US
Encryption
Access Controls
2−step verification
Data Loss prevention
etc.
Integration with other Google tools…
300$ to test GBQ…
Large community..
Education tools (courses, labs,etc.)
1 TB (125GB) per month is Free
14. WORK WITG GOOGLE BIGQUERY
Into (Before)
Put your data into BigQuery
Out (After)
Extract your prepared data to
your dashboard
Inside
Work with your data in
BigQuery01
02
03
16. BIGQUERY INGESTION - GOOGLE SERVICES
BigQuery Data Transfer Service
Google Analytics 360
Google Play, Google Ads(2.5$ per month per customerID), Youtube (5$ per month per channel)
Cloud Storage (No charges, only for storage
+ Using Google Cloud Dataflow (pipeline to write data to BigQuery)
etc…
18. BIGQUERY INGESTION - LOCAL MACHINE
Load data from a readable data source
- You can load data manually
- You can use API or Client Librairies
(create connectors or use paid connectors)
API is a set of clearly defined methods of communication among
various components - applications, software
BATCH task automation without manual work
19. BIGQUERY INGESTION - STREAMING
Load data immediately without delaying (for real-time reports)
- You can create your custom solution
- Use Google solutions (Google Dataflow)
- Or use paid solutions
Use-cases : mobile application - sending errors events in real time
32. BEST PRACTICES TO WORK WITH GBQ
Control costs (custom quotas)
- Set maximum per query cost limit
- Set per user daily budget
- Set per project daily budget
Use denormalized tables
Use preview
Use Query Validator
NO * SELECT (!)
Use table expiration
Use partition
Use pricing calculator
33. SOURCES AND TOOLS
- Coursera “ From Data to Insights with Google Cloud Platform” from Google Cloud
- Google Cloud Summit Prezentations
- Google BigQuery Documentation
- Qwiklabs
- StitchData / Owox / Fivetran
- dbt (command line tool)
- SinterData
- cron/crontab
34. THANKS !
7 place du Griffon | 69001 Lyon | France
+33 4 28 29 07 52 | contact@better-stronger.com
Khrystyna GRYNKO
khrystyna@better-stronger.com | +33 4 28 29 07 92