Big Query - Utilizing Google Data Warehouse for Media Analytics
•
0 likes•193 views
This topic will cover the intermediate understanding of Google Big Query and how Media Prima Digital utilizing Big Query as data warehouse for production.
Report
Share
Report
Share
Download to read offline
Recommended
Google BigQuery Best Practices
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
Google BigQuery for Everyday Developer
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.
Big Query Basics
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.
BigQuery for Beginners
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
Owning Your Own (Data) Lake House
Data Con LA 2020
Description
In this session, I introduce the Amazon Redshift lake house architecture which enables you to query data across your data warehouse, data lake, and operational databases to gain faster and deeper insights. With a lake house architecture, you can store data in open file formats in your Amazon S3 data lake.
Speaker
Antje Barth, Amazon Web Services, Sr. Developer Advocate, AI and Machine Learning
Getting Started with Databricks SQL Analytics
It has long been said that business intelligence needs a relational warehouse, but that view is changing. With the Lakehouse architecture being shouted from the rooftops, Databricks have released SQL Analytics, an alternative workspace for SQL-savvy users to interact with an analytics-tuned cluster. But how does it work? Where do you start? What does a typical Data Analyst’s user journey look like with the tool?
This session will introduce the new workspace and walk through the various key features – how you set up a SQL Endpoint, the query workspace, creating rich dashboards and connecting up BI tools such as Microsoft Power BI.
If you’re truly trying to create a Lakehouse experience that satisfies your SQL-loving Data Analysts, this is a tool you’ll need to be familiar with and include in your design patterns, and this session will set you on the right path.
Google BigQuery - Features & Benefits
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
Recommended
Google BigQuery Best Practices
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
Google BigQuery for Everyday Developer
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.
Big Query Basics
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.
BigQuery for Beginners
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
Owning Your Own (Data) Lake House
Data Con LA 2020
Description
In this session, I introduce the Amazon Redshift lake house architecture which enables you to query data across your data warehouse, data lake, and operational databases to gain faster and deeper insights. With a lake house architecture, you can store data in open file formats in your Amazon S3 data lake.
Speaker
Antje Barth, Amazon Web Services, Sr. Developer Advocate, AI and Machine Learning
Getting Started with Databricks SQL Analytics
It has long been said that business intelligence needs a relational warehouse, but that view is changing. With the Lakehouse architecture being shouted from the rooftops, Databricks have released SQL Analytics, an alternative workspace for SQL-savvy users to interact with an analytics-tuned cluster. But how does it work? Where do you start? What does a typical Data Analyst’s user journey look like with the tool?
This session will introduce the new workspace and walk through the various key features – how you set up a SQL Endpoint, the query workspace, creating rich dashboards and connecting up BI tools such as Microsoft Power BI.
If you’re truly trying to create a Lakehouse experience that satisfies your SQL-loving Data Analysts, this is a tool you’ll need to be familiar with and include in your design patterns, and this session will set you on the right path.
Google BigQuery - Features & Benefits
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
Big Data Platform at Pinterest
Presented by Mao Ye at the AWS Loft meetup on Thursday, September 17th, 2015
Learn to Use Databricks for Data Science
Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.
Summary introduction to data engineering
Introduction to data engineering. Data engineering 101. All you need to know.
QuerySurge - the automated Data Testing solution
QuerySurge is the leading Data Testing solution built specifically to automate the testing of Data Warehouses & Big Data. QuerySurge ensures that the data extracted from data sources remains intact in the target data store by analyzing and pinpointing any differences quickly.
And QuerySurge makes it easy for both novice and experienced team members to validate their organization's data quickly through Query Wizards while still allowing power users the flexibility they need.
All with deep dive reporting and data health dashboards that quickly provides you with a holistic view of your project’s data.
Types of Automated Data Testing
--------------------------------------------
QuerySurge provides data testing solutions for all of your automated data testing needs
- Data Warehouse testing & ETL testing
- Big Data (Hadoop, NoSQL) testing
- Data Interface testing
- Data Migration testing
- Database Upgrade testing
FREE TRIAL
www.QuerySurge.com
Hive, Presto, and Spark on TPC-DS benchmark
I've run a few SQL-on-Hadoop systems, Hive (LLAP), Presto, and Spark (Whole Stage Code Generation), using TPC-DS benchmark.
Presto: SQL-on-anything
Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes. Presto was designed and written from the ground up for interactive analytics and approaches the speed of commercial data warehouses while scaling to the size of organizations like Facebook. One key feature in Presto is the ability to query data where it lives via a uniform ANSI SQL interface. Presto’s connector architecture creates an abstraction layer for anything that can be expressed in a row-like format, such as HDFS, Amazon S3, Azure Storage, NoSQL stores, relational databases, Kafka streams and even proprietary data stores. Furthermore, a single Presto query can combine data from multiple sources, allowing for analytics across your entire organization.
This talk will be co-presented by Facebook and Teradata, the two largest contributors to Presto. The talk will focus on Presto’s ability to query virtually any data source via it’s connector interface. Facebook and Teradata will present some of their use cases of Presto querying various data sources, discuss the existing connectors in Presto, and describe the anatomy of a connector.
Building a Data Science as a Service Platform in Azure with Databricks
Machine learning in the enterprise is rarely delivered by a single team. In order to enable Machine Learning across an organisation you need to target a variety of different skills, processes, technologies, and maturities. To do this is incredibly hard and requires a composite of different techniques to deliver a single platform which empowers all users to build and deploy machine learning models.
In this session we discuss how Azure & Databricks enables a Data Science as a Service platform. We look at how a DSaaS platform is empowering users of all abilities to build models, deploy models and enabling organisations to realise and return on investment earlier.
Retail Analytics and BI with Looker, BigQuery, GCP & Leigha Jarett
Leigha Jarett of GCP explains how to bring Cloud "superpowers" to your Data and modernize your Business Intelligence with Looker, BigQuery and Google Cloud services on an example of Cymbal Direct - one of Google Cloud's demo brands. The meetup recording with TOC for easy navigation is at https://youtu.be/BpzJU_S40ic.
P.S. For more interactive lectures like this, go to http://youtube.serverlesstoronto.org/ or sign up for our upcoming live events at https://www.meetup.com/Serverless-Toronto/events/
Democratizing Data Quality Through a Centralized Platform
Bad data leads to bad decisions and broken customer experiences. Organizations depend on complete and accurate data to power their business, maintain efficiency, and uphold customer trust. With thousands of datasets and pipelines running, how do we ensure that all data meets quality standards, and that expectations are clear between producers and consumers? Investing in shared, flexible components and practices for monitoring data health is crucial for a complex data organization to rapidly and effectively scale.
At Zillow, we built a centralized platform to meet our data quality needs across stakeholders. The platform is accessible to engineers, scientists, and analysts, and seamlessly integrates with existing data pipelines and data discovery tools. In this presentation, we will provide an overview of our platform’s capabilities, including:
Giving producers and consumers the ability to define and view data quality expectations using a self-service onboarding portal
Performing data quality validations using libraries built to work with spark
Dynamically generating pipelines that can be abstracted away from users
Flagging data that doesn’t meet quality standards at the earliest stage and giving producers the opportunity to resolve issues before use by downstream consumers
Exposing data quality metrics alongside each dataset to provide producers and consumers with a comprehensive picture of health over time
Azure data platform overview
Want to see a high-level overview of the products in the Microsoft data platform portfolio in Azure? I’ll cover products in the categories of OLTP, OLAP, data warehouse, storage, data transport, data prep, data lake, IaaS, PaaS, SMP/MPP, NoSQL, Hadoop, open source, reporting, machine learning, and AI. It’s a lot to digest but I’ll categorize the products and discuss their use cases to help you narrow down the best products for the solution you want to build.
Redshift VS BigQuery
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.
Neo4j 4.1 overview
Review the latest features released in Neo4j version 4.1 including Cypher, database drivers, clustering, security, and extension libraries like APOC and Spring Data Neo4j!
Building Modern Data Platform with Microsoft Azure
This presentation will cover Cloud history and Microsoft Azure Data Analytics capabilities. Moreover, it has a real-world example of DW modernization. Finally, we will check the alternative solution on Azure using Snowflake and Matillion ETL.
Optimizing the Supply Chain with Knowledge Graphs, IoT and Digital Twins_Moor...
With the world’s supply chain system in crisis, it’s clear that better solutions are needed. Digital twins built on knowledge graph technology allow you to achieve an end-to-end view of the process, supporting real-time monitoring of critical assets.
What’s New with Databricks Machine Learning
In this session, the Databricks product team provides a deeper dive into the machine learning announcements. Join us for a detailed demo that gives you insights into the latest innovations that simplify the ML lifecycle — from preparing data, discovering features, and training and managing models in production.
Near Real-Time Netflix Recommendations Using Apache Spark Streaming with Nit...
As a data driven company, we use Machine learning based algos and A/B tests to drive all of the content recommendations for our members. Traditionally, these recommendations are precomputed in a batch processing fashion, but such a model cannot react quickly based on member interactions, title interests, popularity etc. With an ever-growing Netflix catalog, finding the right content for our audience in near real-time would provide the best personalized experience.
We’ll take a deep dive into our realtime Spark Streaming ecosystem at Netflix. Both it’s infrastructure and business use cases. On the infrastructure front, we will delve into scale challenges, state management, data persistence, resiliency considerations, metrics, operations and auto-remediation. We will talk about a few use cases that leverage real-time data for model training, such as providing the right personalized videos in a member’s Billboard and choosing the right personalized image soon after the launch of the show. We will also reflect on the lessons learnt while building such high volume infrastructure.
Power to the People: A Stack to Empower Every User to Make Data-Driven Decisions
Infectious Media runs on data. But, as an ad-tech company that records hundreds of thousands of web events per second, they have have to deal with data at a scale not seen by most companies. You can not make decisions with data when people need to write manual SQL only for queries take 10-20 minutes to return. Infectious Media made the switch to Google BigQuery and Looker and now every member of every team can get the data they need in seconds.
Infectious Media shares:
- Why they chose their current stack
- Why faster data means happier customers
- Advantages and practical implications of storing and processing that much data
Check out the recording at https://info.looker.com/h/i/308848878-power-to-the-people-a-stack-to-empower-every-user-to-make-data-driven-decisions
Using ClickHouse for Experimentation
ClickHouse is fast OLAP database, in this presentation, we are going to learn why and how we use it.
More Related Content
What's hot
Big Data Platform at Pinterest
Presented by Mao Ye at the AWS Loft meetup on Thursday, September 17th, 2015
Learn to Use Databricks for Data Science
Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.
Summary introduction to data engineering
Introduction to data engineering. Data engineering 101. All you need to know.
QuerySurge - the automated Data Testing solution
QuerySurge is the leading Data Testing solution built specifically to automate the testing of Data Warehouses & Big Data. QuerySurge ensures that the data extracted from data sources remains intact in the target data store by analyzing and pinpointing any differences quickly.
And QuerySurge makes it easy for both novice and experienced team members to validate their organization's data quickly through Query Wizards while still allowing power users the flexibility they need.
All with deep dive reporting and data health dashboards that quickly provides you with a holistic view of your project’s data.
Types of Automated Data Testing
--------------------------------------------
QuerySurge provides data testing solutions for all of your automated data testing needs
- Data Warehouse testing & ETL testing
- Big Data (Hadoop, NoSQL) testing
- Data Interface testing
- Data Migration testing
- Database Upgrade testing
FREE TRIAL
www.QuerySurge.com
Hive, Presto, and Spark on TPC-DS benchmark
I've run a few SQL-on-Hadoop systems, Hive (LLAP), Presto, and Spark (Whole Stage Code Generation), using TPC-DS benchmark.
Presto: SQL-on-anything
Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes. Presto was designed and written from the ground up for interactive analytics and approaches the speed of commercial data warehouses while scaling to the size of organizations like Facebook. One key feature in Presto is the ability to query data where it lives via a uniform ANSI SQL interface. Presto’s connector architecture creates an abstraction layer for anything that can be expressed in a row-like format, such as HDFS, Amazon S3, Azure Storage, NoSQL stores, relational databases, Kafka streams and even proprietary data stores. Furthermore, a single Presto query can combine data from multiple sources, allowing for analytics across your entire organization.
This talk will be co-presented by Facebook and Teradata, the two largest contributors to Presto. The talk will focus on Presto’s ability to query virtually any data source via it’s connector interface. Facebook and Teradata will present some of their use cases of Presto querying various data sources, discuss the existing connectors in Presto, and describe the anatomy of a connector.
Building a Data Science as a Service Platform in Azure with Databricks
Machine learning in the enterprise is rarely delivered by a single team. In order to enable Machine Learning across an organisation you need to target a variety of different skills, processes, technologies, and maturities. To do this is incredibly hard and requires a composite of different techniques to deliver a single platform which empowers all users to build and deploy machine learning models.
In this session we discuss how Azure & Databricks enables a Data Science as a Service platform. We look at how a DSaaS platform is empowering users of all abilities to build models, deploy models and enabling organisations to realise and return on investment earlier.
Retail Analytics and BI with Looker, BigQuery, GCP & Leigha Jarett
Leigha Jarett of GCP explains how to bring Cloud "superpowers" to your Data and modernize your Business Intelligence with Looker, BigQuery and Google Cloud services on an example of Cymbal Direct - one of Google Cloud's demo brands. The meetup recording with TOC for easy navigation is at https://youtu.be/BpzJU_S40ic.
P.S. For more interactive lectures like this, go to http://youtube.serverlesstoronto.org/ or sign up for our upcoming live events at https://www.meetup.com/Serverless-Toronto/events/
Democratizing Data Quality Through a Centralized Platform
Bad data leads to bad decisions and broken customer experiences. Organizations depend on complete and accurate data to power their business, maintain efficiency, and uphold customer trust. With thousands of datasets and pipelines running, how do we ensure that all data meets quality standards, and that expectations are clear between producers and consumers? Investing in shared, flexible components and practices for monitoring data health is crucial for a complex data organization to rapidly and effectively scale.
At Zillow, we built a centralized platform to meet our data quality needs across stakeholders. The platform is accessible to engineers, scientists, and analysts, and seamlessly integrates with existing data pipelines and data discovery tools. In this presentation, we will provide an overview of our platform’s capabilities, including:
Giving producers and consumers the ability to define and view data quality expectations using a self-service onboarding portal
Performing data quality validations using libraries built to work with spark
Dynamically generating pipelines that can be abstracted away from users
Flagging data that doesn’t meet quality standards at the earliest stage and giving producers the opportunity to resolve issues before use by downstream consumers
Exposing data quality metrics alongside each dataset to provide producers and consumers with a comprehensive picture of health over time
Azure data platform overview
Want to see a high-level overview of the products in the Microsoft data platform portfolio in Azure? I’ll cover products in the categories of OLTP, OLAP, data warehouse, storage, data transport, data prep, data lake, IaaS, PaaS, SMP/MPP, NoSQL, Hadoop, open source, reporting, machine learning, and AI. It’s a lot to digest but I’ll categorize the products and discuss their use cases to help you narrow down the best products for the solution you want to build.
Redshift VS BigQuery
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.
Neo4j 4.1 overview
Review the latest features released in Neo4j version 4.1 including Cypher, database drivers, clustering, security, and extension libraries like APOC and Spring Data Neo4j!
Building Modern Data Platform with Microsoft Azure
This presentation will cover Cloud history and Microsoft Azure Data Analytics capabilities. Moreover, it has a real-world example of DW modernization. Finally, we will check the alternative solution on Azure using Snowflake and Matillion ETL.
Optimizing the Supply Chain with Knowledge Graphs, IoT and Digital Twins_Moor...
With the world’s supply chain system in crisis, it’s clear that better solutions are needed. Digital twins built on knowledge graph technology allow you to achieve an end-to-end view of the process, supporting real-time monitoring of critical assets.
What’s New with Databricks Machine Learning
In this session, the Databricks product team provides a deeper dive into the machine learning announcements. Join us for a detailed demo that gives you insights into the latest innovations that simplify the ML lifecycle — from preparing data, discovering features, and training and managing models in production.
Near Real-Time Netflix Recommendations Using Apache Spark Streaming with Nit...
As a data driven company, we use Machine learning based algos and A/B tests to drive all of the content recommendations for our members. Traditionally, these recommendations are precomputed in a batch processing fashion, but such a model cannot react quickly based on member interactions, title interests, popularity etc. With an ever-growing Netflix catalog, finding the right content for our audience in near real-time would provide the best personalized experience.
We’ll take a deep dive into our realtime Spark Streaming ecosystem at Netflix. Both it’s infrastructure and business use cases. On the infrastructure front, we will delve into scale challenges, state management, data persistence, resiliency considerations, metrics, operations and auto-remediation. We will talk about a few use cases that leverage real-time data for model training, such as providing the right personalized videos in a member’s Billboard and choosing the right personalized image soon after the launch of the show. We will also reflect on the lessons learnt while building such high volume infrastructure.
What's hot (20)
Building a Data Science as a Service Platform in Azure with Databricks
Building a Data Science as a Service Platform in Azure with Databricks
Retail Analytics and BI with Looker, BigQuery, GCP & Leigha Jarett
Retail Analytics and BI with Looker, BigQuery, GCP & Leigha Jarett
Gartner 2021 Magic Quadrant for Cloud Database Management Systems.pdf
Gartner 2021 Magic Quadrant for Cloud Database Management Systems.pdf
Democratizing Data Quality Through a Centralized Platform
Democratizing Data Quality Through a Centralized Platform
Building Modern Data Platform with Microsoft Azure
Building Modern Data Platform with Microsoft Azure
Optimizing the Supply Chain with Knowledge Graphs, IoT and Digital Twins_Moor...
Optimizing the Supply Chain with Knowledge Graphs, IoT and Digital Twins_Moor...
Near Real-Time Netflix Recommendations Using Apache Spark Streaming with Nit...
Near Real-Time Netflix Recommendations Using Apache Spark Streaming with Nit...
Similar to Big Query - Utilizing Google Data Warehouse for Media Analytics
Power to the People: A Stack to Empower Every User to Make Data-Driven Decisions
Infectious Media runs on data. But, as an ad-tech company that records hundreds of thousands of web events per second, they have have to deal with data at a scale not seen by most companies. You can not make decisions with data when people need to write manual SQL only for queries take 10-20 minutes to return. Infectious Media made the switch to Google BigQuery and Looker and now every member of every team can get the data they need in seconds.
Infectious Media shares:
- Why they chose their current stack
- Why faster data means happier customers
- Advantages and practical implications of storing and processing that much data
Check out the recording at https://info.looker.com/h/i/308848878-power-to-the-people-a-stack-to-empower-every-user-to-make-data-driven-decisions
Using ClickHouse for Experimentation
ClickHouse is fast OLAP database, in this presentation, we are going to learn why and how we use it.
Big Data Developer Career Path: Job & Interview Preparation
Youtube link : https://www.youtube.com/watch?v=iggl879a0s8
Intellipaat Big Data Hadoop Training: https://intellipaat.com/big-data-hadoop-training/
Read complete Big Data Hadoop tutorial here: https://intellipaat.com/blog/tutorial/hadoop-tutorial/
Self-Service Analytics with Guard Rails
Watch this webinar in full here: https://buff.ly/2MVTKqL
Self-Service BI promises to remove the bottleneck that exists between IT and business users. The truth is, if data is handed over to a wide range of data consumers without proper guardrails in place, it can result in data anarchy.
Attend this session to learn why data virtualization:
• Is a must for implementing the right self-service BI
• Makes self-service BI useful for every business user
• Accelerates any self-service BI initiative
Big Data Analytics with Microsoft
Slides from a recent Big Data Warehousing Meetup titled, Big Data Analytics with Microsoft.
See Power Pivot/ Power Query/ Power View/ Power Maps and Azure Machine Learning be used to analyze Big Data.
One challenge of dealing with Big Data project is to acquire both structured and instructed information in order to find the right correlation. During the event, we explained all the steps to build your model and enhance your existing data through Microsoft's Power BI.
We had an in-depth discussion about the innovations built into the latest stack of Microsoft Business Intelligence, and practical tips from Technology Specialist’s from Microsoft.
The session also featured demos to help you see the technology as an end-to-end solution.
For more information, visit www.casertaconcepts.com
Connecta Event: Big Query och dataanalys med Google Cloud Platform
Avancerad dataanalys och ”big data” har under de senaste åren klättrat på trendlistorna och är nu ett av de mest prioriterade områdena i utvecklingen av nya tjänster och produkter för ledarföretag i det digitala landskapet.
Informationen som byggs upp i systemen när kundmötena digitaliseras har visat sig vara guld värt. Här finns allt vi behöver veta för att göra våra affärer mer effektiva.
Sedan sommaren 2013 har Connecta tillsammans med Google ett etablerat samarbete för att hjälpa våra kunder med övergången till moln-tjänster för bland annat avancerad dataanalys. För att göra oss själva redo att hjälpa våra kunder har vi under ett antal år utvecklat såväl kunskaper som skaffat oss erfarenheter kring Googles olika moln-produkter, som exempelvis ”Big Query”.
Big Query är ett molnbaserat analysverktyg och en del av Google Cloud Platform. Big Query gör det möjligt att ställa snabba frågor mot enorma dataset på bara någon sekund. Big Query och Google Cloud Platform erbjuder färdiga lösningar för att sätta upp och underhålla en infrastruktur som med enkla medel gör allt detta möjligt.
På Connecta Digital Consultings tredje event för våren introducerade vi våra kunder och partners i koncepten dataanalys och Big Query.
Under eventet berördes följande punkter:
- Big Data och Business Intelligence (BI)
- “The Google Big Data tools” – framgångsfaktorer och hur man kommer igång
- Google Cloud Platform och hur man genomför en framgångsrik molnsatsning
Vi presenterade case och berättade om viktiga lärdomar vi dragit i samarbetet med Google och våra kunder.
Snowflake: The Good, the Bad, and the Ugly
Learn how to solve the top 3 challenges Snowflake customers face, and what you can do to ensure high-performance, intelligent analytics at any scale. Ideal for those currently using Snowflake and those considering it. Learn more at: https://kyligence.io/
Big Data Pipeline for Analytics at Scale @ FIT CVUT 2014
The recent boom in big data processing and democratization of the big data space has been enabled by the fact that most of the concepts originated in the research labs of companies such as Google, Amazon, Yahoo and Facebook are now available as open source. Technologies such as Hadoop, Cassandra let businesses around the world to become more data driven and tap into their massive data feeds to mine valuable insights.
At the same time, we are still at a certain stage of the maturity curve of these new big data technologies and of the entire big data technology stack. Many of the technologies originated from a particular use case and attempts to apply them in a more generic fashion are hitting the limits of their technological foundations. In some areas, there are several competing technologies for the same set of use cases, which increases risks and costs of big data implementations.
We will show how GoodData solves the entire big data pipeline today, starting from raw data feeds all the way up to actionable business insights. All this provided as a hosted multi-tenant environment letting its customers to solve their particular analytical use case or many analytical use cases for thousands of their customers all using the same platform and tools while abstracting them away from the technological details of the big data stack.
Architecting Snowflake for High Concurrency and High Performance
Cloud Data Warehousing juggernaut Snowflake has raced out ahead of the pack to deliver a data management platform from which a wealth of new analytics can be run. Using Snowflake as a traditional data warehouse has some obvious cost advantages over a hardware solution. But the real value of Snowflake as a data platform lies in its ability to support a high-concurrency analytics platform using Kyligence Cloud, powered by Apache Kylin.
In this presentation, Senior Solutions Architect Robert Hardaway will describe a modern data service architecture using precomputation and distributed indexes to provide interactive analytics to hundreds or even thousands of users running against very large Snowflake datasets (TBs to PBs).
The New Frontier: Optimizing Big Data Exploration
The Briefing Room with Dr. Robin Bloor and Cirro
Live Webcast on February 11, 2014
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=0ec1fa381886313cc06d841015c65898
As information ecosystems continue to expand, businesses are searching for ways to combine traditional analytics with a new source of insight: Big Data. But with data flooding in from all kinds of sources, fast access and performance at scale can easily become an issue. One effective approach for solving this challenge is data federation, a method that involves taking the analytical processing to the data, allowing streamlined access to multiple data sources without the expensive ETL overhead or building of semantic layers.
Register for this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor as he explains how the prevalence of distributed data calls for a new approach to Big Data. He will be briefed by Mark Theissen of Cirro, who will tout his company’s Data Hub, a data federation solution that provides a single point of access to all enterprise data assets without excessive data movements, preprocessing or staging. He will discuss how data federation differs from virtualization and ETL approaches, and demonstrate how a Cirro deployment solves the analytics challenge of integrating data silos across the data center – and the cloud – using the BI tools you already have on your desktop for real-time distributed analytics.
Visit InsideAnlaysis.com for more information.
[Webinar] Getting Started with BigQuery: Basics, Its Appilcations & Use Cases
This webinar aims to provide the BigQuery product walkthrough right from the basics. Our core focus will be on the use cases and applications that help to gain additional customer insights from the data integrated within BigQuery.
BigQuery is equipped with the ability to crunch TBs of data in seconds while ensuring scalability and speed. It also enables us to perform advanced statistical analysis by providing unsampled raw hit level analytics data.
PCM18 (Big Data Analytics)
PCM18 (Big Data Analytics) slides of Pentaho Community Meetup in Bologna about Big Data OLAP using Pentaho, Vertica and Kylin
Before vs After: Redesigning a Website to be Useful and Informative for Devel...
There are so many fun challenges in creating a useful website for a developer audience today: you’ve got to empathize with your audience, nail the voice, understand the “jobs” your site’s visitors are trying to accomplish, make sure you anticipate (and answer!) the questions people are likely to have. In this quick lightning talk, I’ll share some before vs. after pics of a recent Citus Data site redesign—and will share some of the best practices we used, based on my years as a developer, software engineering manager, product manager, and now, as a marketer.
Data Science and Enterprise Engineering with Michael Finger and Chris Robison
How Data Scientists and Engineers work in tandem to achieve real-time personalization at Overstock
Personalizing online experiences for users is nothing new, but real-time personalization requires sub-second speed and close collaboration between data scientists and enterprise engineers.
Like the hands on a clock, data scientists and enterprise engineers have shifted their focus from hour- hand quickness to minute-hand speeds with a craving to take advantage of each tick of the second hand and personalize in real-time. Previously, daily activities were consumed on improving customers’ experiences tomorrow. Workflows ran overnight when on perm resources were not being tasked. The focus was on the-day-before jobs, always inching forward 24-hours behind.
Since then, we have shifted to hourly jobs and even to tasks that run every five minutes. Finally, we have been personalizing user experiences within the same day and even during the same session. But could we personalize these experiences instantly, immediately, and in real-time? What would that require? What does it look like? Michael Finger and Chris Robinson explore how data scientists and engineers are working in tandem to achieve real-time personalization at Overstock.com
Building Enterprise OLAP on Hadoop for FSI
Building Enterprise OLAP on Hadoop for Finance Services Industry, and following a use case of CPIC (fortune 500 insurance company) about how to replace legacy IBM Cognos OLAP with Kyligence platform
Webinar: NoSQL as the New Normal
What started as a way for web giants to solve problems of serious scale has become the default way all enterprises manage Big Data. Despite having a catchy, if inaccurate title, there really isn't a coherent "NoSQL" category, nor is there a simple future for the range of NoSQL databases. In this presentation, Matt Asay will outline the reasons for NoSQL's existence and persistence, how the different NoSQL technologies help enterprises get control of Big Data, and will identify the trends that point to a bright future for post-relational databases.
Playing to Win: Turbocharged Tableau with a GPU Database
Gene Lee - SVP, Chief Analytics Officer at Caesars Entertainment Corporation.
Tableau Conference 2017 in Las Vegas.
Assessing New Database Capabilities – Multi-Model
Today’s enterprises have an unprecedented variety of data store choices to meet the needs of the varied workloads of an enterprise because there is no one-size-fits-all when it comes to data stores. Putting in place data stores to support a modern enterprise that is now reliant on data can lead to confusion and chaos.
Enterprises have many needs for databases, including for cache, operational, data warehouse, master data, ERP, analytical, graph data, data lake, time series data, and numerous other specific needs.
Today’s enterprises have an unprecedented variety of data store choices to meet the needs of the varied workloads of an enterprise because there is no one-size-fits-all when it comes to data stores. Putting in place data stores to support a modern enterprise that is now reliant on data can lead to confusion and chaos.
Enterprises have many needs for databases, including for cache, operational, data warehouse, master data, ERP, analytical, graph data, data lake, time series data, and numerous other specific needs.
While vendor offerings have exploded in recent years, in due time frameworks will integrate components into what amounts to, for practical purposes, a single offering for multiple workloads, perhaps even for the enterprise.
A multi-model database is a database that can store, manage, and query data in multiple models, such as relational, document-oriented, key-value, graph (triplestore), and column store.
An enterprise will find reduced overhead and other synergies from choosing a single vendor for these workloads.
This session will explore the multi-model option and some criteria that decision makers should evaluate when choosing a multi-model solution.
Similar to Big Query - Utilizing Google Data Warehouse for Media Analytics (20)
Power to the People: A Stack to Empower Every User to Make Data-Driven Decisions
Power to the People: A Stack to Empower Every User to Make Data-Driven Decisions
Big Data Developer Career Path: Job & Interview Preparation
Big Data Developer Career Path: Job & Interview Preparation
Connecta Event: Big Query och dataanalys med Google Cloud Platform
Connecta Event: Big Query och dataanalys med Google Cloud Platform
Big Data Pipeline for Analytics at Scale @ FIT CVUT 2014
Big Data Pipeline for Analytics at Scale @ FIT CVUT 2014
Architecting Snowflake for High Concurrency and High Performance
Architecting Snowflake for High Concurrency and High Performance
Hybrid Data Architecture: Integrating Hadoop with a Data Warehouse
Hybrid Data Architecture: Integrating Hadoop with a Data Warehouse
[Webinar] Getting Started with BigQuery: Basics, Its Appilcations & Use Cases
[Webinar] Getting Started with BigQuery: Basics, Its Appilcations & Use Cases
Before vs After: Redesigning a Website to be Useful and Informative for Devel...
Before vs After: Redesigning a Website to be Useful and Informative for Devel...
Data Science and Enterprise Engineering with Michael Finger and Chris Robison
Data Science and Enterprise Engineering with Michael Finger and Chris Robison
Playing to Win: Turbocharged Tableau with a GPU Database
Playing to Win: Turbocharged Tableau with a GPU Database
Recently uploaded
一比一原版(QU毕业证)皇后大学毕业证成绩单
QU毕业证【微信95270640】办理皇后大学毕业证原版一模一样、QU毕业证制作【Q微信95270640】《皇后大学毕业证购买流程》《QU成绩单制作》皇后大学毕业证书QU毕业证文凭皇后大学
本科毕业证书,学历学位认证如何办理【留学国外学位学历认证、毕业证、成绩单、大学Offer、雅思托福代考、语言证书、学生卡、高仿教育部认证等一切高仿或者真实可查认证服务】代办国外（海外）英国、加拿大、美国、新西兰、澳大利亚、新西兰等国外各大学毕业证、文凭学历证书、成绩单、学历学位认证真实可查。
1:1完美还原海外各大学毕业材料上的工艺：水印阴影底纹钢印LOGO烫金烫银LOGO烫金烫银复合重叠。文字图案浮雕激光镭射紫外荧光温感复印防伪。
可办理以下真实皇后大学存档留学生信息存档认证：
1皇后大学真实留信网认证（网上可查永久存档无风险百分百成功入库）；
2真实教育部认证（留服）等一切高仿或者真实可查认证服务（暂时不可办理）；
3购买英美真实学籍（不用正常就读直接出学历）；
4英美一年硕士保毕业证项目（保录取学校挂名不用正常就读保毕业）
留学本科/硕士毕业证书成绩单制作流程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询皇后大学皇后大学硕士毕业证成绩单）；
2开始安排制作皇后大学毕业证成绩单电子图；
3皇后大学毕业证成绩单电子版做好以后发送给您确认；
4皇后大学毕业证成绩单电子版您确认信息无误之后安排制作成品；
5皇后大学成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
— — — — — — — — — — — 《文凭顾问Q/微：95270640》很感动很无奈房东的儿子小伍笨手笨脚的不会说普通话满口粤语态度十分傲慢一副盛气凌人的样子山娃试图接近他跟他交友与城里人交友但他俩就好像是两个世界里的人根本拢不到一块儿不知不觉山娃倒跟周围出租屋里的几个小伙伴成了好朋友因为他们也是从乡下进城过暑假的小学生快乐的日子总是过得飞快山娃尚未完全认清那几位小朋友时他们却一个接一个地回家了山娃这时才恍然发现二个月的暑假已转到了尽头他的城市生活也将划上一个不很圆义
一比一原版(YU毕业证)约克大学毕业证成绩单
YU毕业证【微信95270640】（约克大学毕业证高仿学位证书((+《Q微信95270640》))）购买YU毕业证修改YU成绩单购买约克大学毕业证办YU文凭办高仿毕业证约克大学毕业证购买修改成绩单挂科退学如何进行学历认证留学退学办毕业证书/ 出国留学无法毕业买毕业证留学被劝退买毕业证（非正常毕业教育部认证咨询） York University
办理国外约克大学毕业证书 #成绩单改成绩 #教育部学历学位认证 #毕业证认证 #留服认证 #使馆认证（留学回国人员证明） #（证）等
真实教育部认证教育部存档中国教育部留学服务中心认证（即教育部留服认证）网站100%可查.
真实使馆认证（即留学人员回国证明）使馆存档可通过大使馆查询确认.
留信网认证国家专业人才认证中心颁发入库证书留信网永久存档可查.
约克大学约克大学毕业证学历书毕业证 #成绩单等全套材料从防伪到印刷从水印到钢印烫金跟学校原版100%相同.
国际留学归国服务中心：实体公司注册经营行业标杆精益求精！
国外毕业证学位证成绩单办理流程：
1客户提供办理约克大学约克大学毕业证学历书信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作约克大学毕业证成绩单电子图；
3约克大学毕业证成绩单电子版做好以后发送给您确认；
4约克大学毕业证成绩单电子版您确认信息无误之后安排制作成品；
5约克大学成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快递邮寄约克大学约克大学毕业证学历书）。心温柔地对待我将渐渐老去的父母体谅他们以反哺之心奉敬父母以感恩之心孝顺父母哪怕只为父母换洗衣服为父母喂饭送汤按摩酸痛的腰背握着父母的手扶着他们一步一步地慢慢散步.让我们的父母幸福快乐地度过一生挽着清风芒耀似金的骄阳如将之绽放的花蕾一般静静的从远方的山峦间缓缓升起这一片寂静的城市默默的等待着它的第一缕光芒将之唤醒那飘散在它前方的几层薄云像是新娘的婚纱一般为它的光芒添上了几分淡淡的浮晕在悄无声息间这怕
一比一原版(IIT毕业证)伊利诺伊理工大学毕业证成绩单
IIT毕业证【微信95270640】购买（伊利诺伊理工大学毕业证成绩单硕士学历）Q微信95270640代办IIT学历认证留信网伪造伊利诺伊理工大学学位证书精仿伊利诺伊理工大学本科/硕士文凭证书补办伊利诺伊理工大学 diplomaoffer,Transcript购买伊利诺伊理工大学毕业证成绩单购买IIT假毕业证学位证书购买伪造伊利诺伊理工大学文凭证书学位证书,专业办理雅思、托福成绩单，学生ID卡，在读证明，海外各大学offer录取通知书，毕业证书，成绩单，文凭等材料:1:1完美还原毕业证、offer录取通知书、学生卡等各种在读或毕业材料的防伪工艺（包括 烫金、烫银、钢印、底纹、凹凸版、水印、防伪光标、热敏防伪、文字图案浮雕，激光镭射，紫外荧光，温感光标）学校原版上有的工艺我们一样不会少，不论是老版本还是最新版本，都能保证最高程度还原，力争完美以求让所有同学都能享受到完美的品质服务。
#一整套伊利诺伊理工大学文凭证件办理#—包含伊利诺伊理工大学伊利诺伊理工大学毕业证假文凭学历认证|使馆认证|归国人员证明|教育部认证|留信网认证永远存档教育部学历学位认证查询办理国外文凭国外学历学位认证#我们提供全套办理服务。
一整套留学文凭证件服务：
一：伊利诺伊理工大学伊利诺伊理工大学毕业证假文凭毕业证 #成绩单等全套材料从防伪到印刷水印底纹到钢印烫金
二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站永久可查
四：留信认证留学生信息网站永久可查
国外毕业证学位证成绩单办理方法：
1客户提供办理伊利诺伊理工大学伊利诺伊理工大学毕业证假文凭信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
教育部文凭学历认证认证的用途：
如果您计划在国内发展那么办理国内教育部认证是必不可少的。事业性用人单位如银行国企公务员在您应聘时都会需要您提供这个认证。其他私营 #外企企业无需提供！办理教育部认证所需资料众多且烦琐所有材料您都必须提供原件我们凭借丰富的经验帮您快速整合材料让您少走弯路。
实体公司专业为您服务如有需要请联系我: 微信95270640声和哐咣的关门声待山娃醒来时父亲早已上班去了床头总搁着山娃最爱吃的馒头和肉包还有白花花的豆浆父亲中午留在工地吃饭和午休山娃的中饭是对面快餐店送来的不用山娃付钱父亲早跟老板谈妥了钱到时一起结父亲给山娃配了台手机二手货诺基亚的父亲说有什么事只管给他挂电话能拥有自己的手机山娃很高兴除了玩游戏发短信除了挂电话给爷爷奶奶和母亲山娃还给班主任邱老师连挂了二个电话并给同学阿强和阿昌家挂山娃兴奋地向他们诉说城市一
一比一原版(RUG毕业证)格罗宁根大学毕业证成绩单
RUG毕业证【微信95270640】办文凭{格罗宁根大学毕业证}Q微Q微信95270640RUG毕业证书成绩单/学历认证RUG Diploma未毕业、挂科怎么办？+QQ微信：Q微信95270640-大学Offer（申请大学）、成绩单（申请考研）、语言证书、在读证明、使馆公证、办真实留信网认证、真实大使馆认证、学历认证
办理国外格罗宁根大学毕业证书 #成绩单改成绩 #教育部学历学位认证 #毕业证认证 #留服认证 #使馆认证（留学回国人员证明） #（证）等
真实教育部认证教育部存档中国教育部留学服务中心认证（即教育部留服认证）网站100%可查.
真实使馆认证（即留学人员回国证明）使馆存档可通过大使馆查询确认.
留信网认证国家专业人才认证中心颁发入库证书留信网永久存档可查.
格罗宁根大学格罗宁根大学毕业证学历书毕业证 #成绩单等全套材料从防伪到印刷从水印到钢印烫金跟学校原版100%相同.
国际留学归国服务中心：实体公司注册经营行业标杆精益求精！
国外毕业证学位证成绩单办理流程：
1客户提供办理格罗宁根大学格罗宁根大学毕业证学历书信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作格罗宁根大学毕业证成绩单电子图；
3格罗宁根大学毕业证成绩单电子版做好以后发送给您确认；
4格罗宁根大学毕业证成绩单电子版您确认信息无误之后安排制作成品；
5格罗宁根大学成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快递邮寄格罗宁根大学格罗宁根大学毕业证学历书）。也得开灯开风扇山娃不想浪费电总将小方桌搁在门口看书写作业有一次山娃坐在门口写作业写着写着竟伏在桌上睡着了迷迷糊糊中山娃似乎听到了父亲的脚步声当他晃晃悠悠站起来时才诧然发现一位衣衫破旧的妇女挎着一只硕大的蛇皮袋手里拎着长铁钩正站在门口朝黑色的屋内张望不好坏人小偷山娃一怔却也灵机一动立马仰起头双手拢在嘴边朝楼上大喊：“爸爸爸——有人找——那人一听朝山娃尴尬地笑笑悻悻地走了山娃立马“嘭的一声将铁门锁死受
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单
UofM毕业证【微信95270640】办文凭{明尼苏达大学毕业证}Q微Q微信95270640UofM毕业证书成绩单/学历认证UofM Diploma未毕业、挂科怎么办？+QQ微信：Q微信95270640-大学Offer（申请大学）、成绩单（申请考研）、语言证书、在读证明、使馆公证、办真实留信网认证、真实大使馆认证、学历认证
办国外明尼苏达大学明尼苏达大学毕业证假文凭教育部学历学位认证留信认证大使馆认证留学回国人员证明修改成绩单信封申请学校offer录取通知书在读证明offer letter。
快速办理高仿国外毕业证成绩单：
1明尼苏达大学毕业证+成绩单+留学回国人员证明+教育部学历认证（全套留学回国必备证明材料给父母及亲朋好友一份完美交代）;
2雅思成绩单托福成绩单OFFER在读证明等留学相关材料（申请学校转学甚至是申请工签都可以用到）。
3.毕业证 #成绩单等全套材料从防伪到印刷从水印到钢印烫金高精仿度跟学校原版100%相同。
专业服务请勿犹豫联系我！联系人微信号：95270640诚招代理：本公司诚聘当地代理人员如果你有业余时间有兴趣就请联系我们。
国外明尼苏达大学明尼苏达大学毕业证假文凭办理过程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。有一次山娃坐在门口写作业写着写着竟伏在桌上睡着了迷迷糊糊中山娃似乎听到了父亲的脚步声当他晃晃悠悠站起来时才诧然发现一位衣衫破旧的妇女挎着一只硕大的蛇皮袋手里拎着长铁钩正站在门口朝黑色的屋内张望不好坏人小偷山娃一怔却也灵机一动立马仰起头双手拢在嘴边朝楼上大喊：“爸爸爸——有人找——那人一听朝山娃尴尬地笑笑悻悻地走了山娃立马“嘭的一声将铁门锁死心却咚咚地乱跳当山娃跟父亲说起这事时父亲很吃惊抚摸着山娃母
Adjusting primitives for graph : SHORT REPORT / NOTES
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).
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...
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
一比一原版(NYU毕业证)纽约大学毕业证成绩单
NYU毕业证【微信95270640】《如何办理NYU毕业证纽约大学文凭学历》【Q微信95270640】《纽约大学文凭学历证书》《纽约大学毕业证书与成绩单样本图片》毕业证书补办 Fake Degree做学费单《毕业证明信-推荐信》成绩单，录取通知书，Offer，在读证明，雅思托福成绩单，真实大使馆教育部认证，回国人员证明，留信网认证。网上存档永久可查！
【本科硕士】纽约大学纽约大学毕业证学位证（GPA修改）；学历认证（教育部认证）；大学Offer录取通知书留信认证使馆认证；雅思语言证书等高仿类证书。
办理流程：
1客户提供办理纽约大学纽约大学毕业证学位证信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
真实网上可查的证明材料
1教育部学历学位认证留服官网真实存档可查永久存档。
2留学回国人员证明（使馆认证）使馆网站真实存档可查。
我们对海外大学及学院的毕业证成绩单所使用的材料尺寸大小防伪结构（包括：纽约大学纽约大学毕业证学位证隐形水印阴影底纹钢印LOGO烫金烫银LOGO烫金烫银复合重叠。文字图案浮雕激光镭射紫外荧光温感复印防伪）都有原版本文凭对照。质量得到了广大海外客户群体的认可同时和海外学校留学中介做到与时俱进及时掌握各大院校的（毕业证成绩单资格证结业证录取通知书在读证明等相关材料）的版本更新信息能够在第一时间掌握最新的海外学历文凭的样版尺寸大小纸张材质防伪技术等等并在第一时间收集到原版实物以求达到客户的需求。
本公司还可以按照客户原版印刷制作且能够达到客户理想的要求。有需要办理证件的客户请联系我们在线客服中心微信：95270640 或咨询在线已转到了尽头他的城市生活也将划上一个不很圆满的句号了值得庆幸的是山娃早记下了他们的学校和联系方式说也奇怪在山娃离城的头一天父亲居然请假陪山娃耍了一天那一天父亲陪着山娃辗转长隆水上乐园疯了一整天水上漂流高空冲浪看大马戏大凡里面有的父亲都带着他去疯一把山娃算了算这一次足足花了老爸元够他挣上半个月的山娃很不解一向节俭的父亲啥时变得如此阔绰大方大把大把掏钱时居然连眉头也不皱一下车票早买好了直达卧铺车得经子
Ch03-Managing the Object-Oriented Information Systems Project a.pdf
Object oriented system analysis and design
社内勉強会資料_LLM Agents .
単純な応答だけでなく、複雑な課題に対応できる柔軟性を持つLLM Agents。主要コンポーネントのPlanning、Memory、Tool Useについて解説しています。
Empowering Data Analytics Ecosystem.pptx
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.
Tabula.io Cheatsheet: automate your data workflows
Tabula - is a data analytics and automation tool. Here is our a cheatsheet
Q1’2024 Update: MYCI’s Leap Year Rebound
Key things you need to know on consumer confidence, key behavioral, tech, e-wallet & esports trends in Malaysia.
一比一原版(UVic毕业证)维多利亚大学毕业证成绩单
UVic毕业证【微信95270640】（维多利亚大学毕业证成绩单本科学历）Q微信95270640(补办UVic学位文凭证书)维多利亚大学留信网学历认证怎么办理维多利亚大学毕业证成绩单精仿本科学位证书硕士文凭证书认证Seneca College diplomaoffer,Transcript办理硕士学位证书造假维多利亚大学假文凭学位证书制作UVic本科毕业证书硕士学位证书精仿维多利亚大学学历认证成绩单修改制作，办理真实认证、留信认证、使馆公证、购买成绩单，购买假文凭，购买假学位证，制造假国外大学文凭、毕业公证、毕业证明书、录取通知书、Offer、在读证明、雅思托福成绩单、假文凭、假毕业证、请假条、国际驾照、网上存档可查！
【实体公司】办维多利亚大学维多利亚大学毕业证文凭证书学历认证学位证文凭认证办留信网认证办留服认证办教育部认证（网上可查实体公司专业可靠）
— — — 留学归国服务中心 — — -
【主营项目】
一.维多利亚大学毕业证成绩单使馆认证教育部认证成绩单等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
国外毕业证学位证成绩单办理流程：
1客户提供维多利亚大学维多利亚大学毕业证文凭证书办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
专业服务请勿犹豫联系我！本公司是留学创业和海归创业者们的桥梁。一次办理终生受用一步到位高效服务。详情请在线咨询办理,欢迎有诚意办理的客户咨询!洽谈。
招聘代理：本公司诚聘英国加拿大澳洲新西兰美国法国德国新加坡各地代理人员如果你有业余时间有兴趣就请联系我们咨询顾问：+微信:95270640刀劈开抑或用拳头砸开每人抱起一大块就啃啃得满嘴满脸猴屁股般的红艳大家一个劲地指着对方吃吃地笑瓜裂得古怪奇形怪状却丝毫不影响瓜味甜丝丝的满嘴生津遍地都是瓜横七竖八的活像掷满了一地的大石块摘走二三只爷爷是断然发现不了的即便发现爷爷也不恼反而教山娃辨认孰熟孰嫩孰甜孰淡名义上是护瓜往往在瓜棚里坐上一刻饱吃一顿后山娃就领着阿黑漫山遍野地跑阿黑是一条黑色的大猎狗挺机灵的是山娃多年的忠实伙伴平时山娃上学阿黑也静
Recently uploaded (20)
Sample_Global Non-invasive Prenatal Testing (NIPT) Market, 2019-2030.pdf
Sample_Global Non-invasive Prenatal Testing (NIPT) Market, 2019-2030.pdf
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Adjusting primitives for graph : SHORT REPORT / NOTES
Adjusting primitives for graph : SHORT REPORT / NOTES
tapal brand analysis PPT slide for comptetive data
tapal brand analysis PPT slide for comptetive data
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...
Ch03-Managing the Object-Oriented Information Systems Project a.pdf
Ch03-Managing the Object-Oriented Information Systems Project a.pdf
Best best suvichar in gujarati english meaning of this sentence as Silk road ...
Best best suvichar in gujarati english meaning of this sentence as Silk road ...
Tabula.io Cheatsheet: automate your data workflows
Tabula.io Cheatsheet: automate your data workflows
Big Query - Utilizing Google Data Warehouse for Media Analytics
- 1. Big Query – Utilizing Google Data Warehouse for Analytics in Media Industry Hafeez Nazri Head of Data Engineering, Media Prima Digital
- 2. 01 Intro to GCP & Big Data Managed Services on GCP 02 Intro to Big Query 03 How Media Prima utilizing Big Query for Analytics 04 Q &A Agenda
- 3. 01 Intro to GGP & Big Data Managed Services on GCP
- 4. OK, real questions! • How many here has an account on GCP? • How many here is already work on Big Data? • How many uses GCP on production? • How many knows about Big Query or any other GCP managed services? • How many didn’t know what I’m talking about? J
- 6. 163 Zetabytes of Data by 2025 (1 trillion gigabytes) (IDC)
- 12. For questions, contact us at: next18extended@gmail.com | #next18extended
- 20. 02 Intro to Big Query #nextxkl #next18extended
- 21. #nextxkl #next18extended GCP Storage Portfolio
- 22. What is Big Query? • GCP’s Enterprise Data Warehouse for Analytics • Petabyte scale storage and analysis • Real-time analytics on streaming data • Fully-managed and serverless • Support standard SQL, JDBC & ODBC • Power of Big Query: having petabyte scale of data that is fully managed and serverless
- 23. What Big Query Offers? • You can start to query your data right away – without hassle of provisioning your infrastructures or worry about downtime • Support SQL 2011 query –familiar language • Many ways to ingest data into Big Query seamlessly – ETL connectors, transfer service etc. • Importantly – affordable pricing!
- 24. Why our team choose to use Big Query? • Easy to ingest – from GA premium to Big Query • You can verify and query your data right away using familiar SQL • Easy export to our own EDW – AWS infra ;p • Inexpensive pricing • Work with our ETL tools – data ingestion
- 29. 03 How Media Prima Utilizing Big Query for analytics #nextxkl #next18extended
- 31. • Customer360 • DFP Ads Analysis • Recommendation Engine Many more… Current project underway!
- 34. Query Editor Project ID Datasets Query Results