Data Lake allows an organisation to store all of their data, structured and unstructured, in one, centralised repository. Since data can be stored as-is, there is no need to convert it to a predefined schema and you no longer need to know what questions you want to ask of your data beforehand. In this session we will explore the architecture of a Data Lake on AWS and cover topics such as storage, processing and security.
Azure Data Factory is a cloud-based data integration service that orchestrates and automates the movement and transformation of data. In this session we will learn how to create data integration solutions using the Data Factory service and ingest data from various data stores, transform/process the data, and publish the result data to the data stores.
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
Learn about the new AWS Database Migration Service, which helps you migrate databases with minimal downtime from on-premises and Amazon EC2 environments to Amazon RDS, Amazon Redshift, Amazon Aurora and EC2 databases. We discuss homogeneous (e.g. Oracle-to-Oracle, PostgreSQL-to-PostgreSQL, etc.) and heterogeneous (e.g. Oracle to Aurora, SQL Server to MariaDB) database migrations. We also talk about the new AWS Schema Conversion Tool that saves you development time when migrating your Oracle and SQL Server database schemas, including PL/SQL and T-SQL procedural code, to their MySQL, MariaDB and Aurora equivalents.
This is Part 4 of the GoldenGate series on Data Mesh - a series of webinars helping customers understand how to move off of old-fashioned monolithic data integration architecture and get ready for more agile, cost-effective, event-driven solutions. The Data Mesh is a kind of Data Fabric that emphasizes business-led data products running on event-driven streaming architectures, serverless, and microservices based platforms. These emerging solutions are essential for enterprises that run data-driven services on multi-cloud, multi-vendor ecosystems.
Join this session to get a fresh look at Data Mesh; we'll start with core architecture principles (vendor agnostic) and transition into detailed examples of how Oracle's GoldenGate platform is providing capabilities today. We will discuss essential technical characteristics of a Data Mesh solution, and the benefits that business owners can expect by moving IT in this direction. For more background on Data Mesh, Part 1, 2, and 3 are on the GoldenGate YouTube channel: https://www.youtube.com/playlist?list=PLbqmhpwYrlZJ-583p3KQGDAd6038i1ywe
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
Mr. Pollock is an expert technology leader for data platforms, big data, data integration and governance. Jeff has been CTO at California startups and a senior exec at Fortune 100 tech vendors. He is currently Oracle VP of Products and Cloud Services for Data Replication, Streaming Data and Database Migrations. While at IBM, he was head of all Information Integration, Replication and Governance products, and previously Jeff was an independent architect for US Defense Department, VP of Technology at Cerebra and CTO of Modulant – he has been engineering artificial intelligence based data platforms since 2001. As a business consultant, Mr. Pollock was a Head Architect at Ernst & Young’s Center for Technology Enablement. Jeff is also the author of “Semantic Web for Dummies” and "Adaptive Information,” a frequent keynote at industry conferences, author for books and industry journals, formerly a contributing member of W3C and OASIS, and an engineering instructor with UC Berkeley’s Extension for object-oriented systems, software development process and enterprise architecture.
Databricks CEO Ali Ghodsi introduces Databricks Delta, a new data management system that combines the scale and cost-efficiency of a data lake, the performance and reliability of a data warehouse, and the low latency of streaming.
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.
Azure Data Factory is a cloud-based data integration service that orchestrates and automates the movement and transformation of data. In this session we will learn how to create data integration solutions using the Data Factory service and ingest data from various data stores, transform/process the data, and publish the result data to the data stores.
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
Learn about the new AWS Database Migration Service, which helps you migrate databases with minimal downtime from on-premises and Amazon EC2 environments to Amazon RDS, Amazon Redshift, Amazon Aurora and EC2 databases. We discuss homogeneous (e.g. Oracle-to-Oracle, PostgreSQL-to-PostgreSQL, etc.) and heterogeneous (e.g. Oracle to Aurora, SQL Server to MariaDB) database migrations. We also talk about the new AWS Schema Conversion Tool that saves you development time when migrating your Oracle and SQL Server database schemas, including PL/SQL and T-SQL procedural code, to their MySQL, MariaDB and Aurora equivalents.
This is Part 4 of the GoldenGate series on Data Mesh - a series of webinars helping customers understand how to move off of old-fashioned monolithic data integration architecture and get ready for more agile, cost-effective, event-driven solutions. The Data Mesh is a kind of Data Fabric that emphasizes business-led data products running on event-driven streaming architectures, serverless, and microservices based platforms. These emerging solutions are essential for enterprises that run data-driven services on multi-cloud, multi-vendor ecosystems.
Join this session to get a fresh look at Data Mesh; we'll start with core architecture principles (vendor agnostic) and transition into detailed examples of how Oracle's GoldenGate platform is providing capabilities today. We will discuss essential technical characteristics of a Data Mesh solution, and the benefits that business owners can expect by moving IT in this direction. For more background on Data Mesh, Part 1, 2, and 3 are on the GoldenGate YouTube channel: https://www.youtube.com/playlist?list=PLbqmhpwYrlZJ-583p3KQGDAd6038i1ywe
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
Mr. Pollock is an expert technology leader for data platforms, big data, data integration and governance. Jeff has been CTO at California startups and a senior exec at Fortune 100 tech vendors. He is currently Oracle VP of Products and Cloud Services for Data Replication, Streaming Data and Database Migrations. While at IBM, he was head of all Information Integration, Replication and Governance products, and previously Jeff was an independent architect for US Defense Department, VP of Technology at Cerebra and CTO of Modulant – he has been engineering artificial intelligence based data platforms since 2001. As a business consultant, Mr. Pollock was a Head Architect at Ernst & Young’s Center for Technology Enablement. Jeff is also the author of “Semantic Web for Dummies” and "Adaptive Information,” a frequent keynote at industry conferences, author for books and industry journals, formerly a contributing member of W3C and OASIS, and an engineering instructor with UC Berkeley’s Extension for object-oriented systems, software development process and enterprise architecture.
Databricks CEO Ali Ghodsi introduces Databricks Delta, a new data management system that combines the scale and cost-efficiency of a data lake, the performance and reliability of a data warehouse, and the low latency of streaming.
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.
The pathway to the cloud has many different options and levers that customers can pull. This webinar walks customers through actual steps from creating a cloud adoption vision to actually building a migration roadmap with actionable guidance. We’ll go through proven migration patterns, methods and tooling that AWS has leveraged successfully with hundreds of Enterprise customers around the globe. Learn what challenges customers face when planning the migrations to cloud, and how they overcome them to minimize risk and accelerate the adoption.
AWS offers a variety of data migration services and tools to help you easily and rapidly move everything from gigabytes to petabytes of data. We can provide guidance and methodologies to help you find the right service or tool to fit your requirements, and we share examples of customers who have used these options in their cloud journey.
Cloud Migration Cookbook: A Guide To Moving Your Apps To The CloudNew Relic
The process of building new apps or migrating existing apps to a cloud-based platform is complex. There are hundreds of paths you can take and only a few will make sense for you and your business. Get a step-by-step guide on how to plan for a successful app migration.
Analyze key aspects to be considered before embarking on your cloud journey. The presentation outlines the strategies, approach, and choices that need to be made, to ensure a smooth transition to the cloud.
The new Microsoft Azure SQL Data Warehouse (SQL DW) is an elastic data warehouse-as-a-service and is a Massively Parallel Processing (MPP) solution for "big data" with true enterprise class features. The SQL DW service is built for data warehouse workloads from a few hundred gigabytes to petabytes of data with truly unique features like disaggregated compute and storage allowing for customers to be able to utilize the service to match their needs. In this presentation, we take an in-depth look at implementing a SQL DW, elastic scale (grow, shrink, and pause), and hybrid data clouds with Hadoop integration via Polybase allowing for a true SQL experience across structured and unstructured data.
AWS Glue is a fully managed, serverless extract, transform, and load (ETL) service that makes it easy to move data between data stores. AWS Glue simplifies and automates the difficult and time consuming tasks of data discovery, conversion mapping, and job scheduling so you can focus more of your time querying and analyzing your data using Amazon Redshift Spectrum and Amazon Athena. In this session, we introduce AWS Glue, provide an overview of its components, and share how you can use AWS Glue to automate discovering your data, cataloging it, and preparing it for analysis.
Data Lakehouse, Data Mesh, and Data Fabric (r2)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a modern data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. They all may sound great in theory, but I'll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I'll discuss Microsoft version of the data mesh.
Amazon Web Services gives you fast access to flexible and low cost IT resources, so you can rapidly scale and build virtually any big data and analytics application including data warehousing, clickstream analytics, fraud detection, recommendation engines, event-driven ETL, serverless computing, and internet-of-things processing regardless of volume, velocity, and variety of data.
In this one-hour webinar, we will look at the portfolio of AWS Big Data services and how they can be used to build a modern data architecture.
We will cover:
Using different SQL engines to analyze large amounts of structured data
Analysing streaming data in near-real time
Architectures for batch processing
Best practices for Data Lake architectures
This session is suited for:
Solution and enterprise architects
Data architects/ Data warehouse owners
IT & Innovation team members
Migrate an Existing Application to Microsoft AzureChris Dufour
First we will talk about what Microsoft Azure is and why you would want to use Microsoft’s cloud services.
Then we will take an existing on premise line of business (LOB) application with a SQL Server backend and walk through the process of moving the site to Microsoft Azure.
Driving Business Outcomes with a Modern Data Architecture - Level 100Amazon Web Services
Your business data contains critical information about customer behaviors, operational decisions, and many factors that have financial impact on your organisation. Increasingly though, this data is too big, too fast, and too complex for existing systems to handle. AWS Data and Analytics services are designed to ingest, store, analyse, and consume information at record-breaking scale. In this session you will learn how these services work together to deliver business automation, enhance customer engagement and intelligence.
Speaker: Craig Stires, APAC Business Development - Big Data & Analytics, Amazon Web Services
Driving Business Insights with a Modern Data Architecture AWS Summit SG 2017Amazon Web Services
Your customers probably want a better experience with your brand. Your different business teams want and need better insights in their decision making. Almost certainly, your finance and operations teams require this to happen at a fraction of the cost of traditional on-premises options. Modern data architectures on AWS help many of our best customers realize all of those goals. Your business data contains critical information about customer behaviors, operational decisions, and many factors that have financial impact on your organization. Increasingly, this data sits beyond your transactional systems, and is too big, too fast, and too complex for existing systems to handle. AWS Data and Analytics services are designed from our customers' requirements to ingest, store, analyze, and consume information at record-breaking scale. In this session you will learn how these services work together to deliver business automation, enhance customer engagement and intelligence.
The pathway to the cloud has many different options and levers that customers can pull. This webinar walks customers through actual steps from creating a cloud adoption vision to actually building a migration roadmap with actionable guidance. We’ll go through proven migration patterns, methods and tooling that AWS has leveraged successfully with hundreds of Enterprise customers around the globe. Learn what challenges customers face when planning the migrations to cloud, and how they overcome them to minimize risk and accelerate the adoption.
AWS offers a variety of data migration services and tools to help you easily and rapidly move everything from gigabytes to petabytes of data. We can provide guidance and methodologies to help you find the right service or tool to fit your requirements, and we share examples of customers who have used these options in their cloud journey.
Cloud Migration Cookbook: A Guide To Moving Your Apps To The CloudNew Relic
The process of building new apps or migrating existing apps to a cloud-based platform is complex. There are hundreds of paths you can take and only a few will make sense for you and your business. Get a step-by-step guide on how to plan for a successful app migration.
Analyze key aspects to be considered before embarking on your cloud journey. The presentation outlines the strategies, approach, and choices that need to be made, to ensure a smooth transition to the cloud.
The new Microsoft Azure SQL Data Warehouse (SQL DW) is an elastic data warehouse-as-a-service and is a Massively Parallel Processing (MPP) solution for "big data" with true enterprise class features. The SQL DW service is built for data warehouse workloads from a few hundred gigabytes to petabytes of data with truly unique features like disaggregated compute and storage allowing for customers to be able to utilize the service to match their needs. In this presentation, we take an in-depth look at implementing a SQL DW, elastic scale (grow, shrink, and pause), and hybrid data clouds with Hadoop integration via Polybase allowing for a true SQL experience across structured and unstructured data.
AWS Glue is a fully managed, serverless extract, transform, and load (ETL) service that makes it easy to move data between data stores. AWS Glue simplifies and automates the difficult and time consuming tasks of data discovery, conversion mapping, and job scheduling so you can focus more of your time querying and analyzing your data using Amazon Redshift Spectrum and Amazon Athena. In this session, we introduce AWS Glue, provide an overview of its components, and share how you can use AWS Glue to automate discovering your data, cataloging it, and preparing it for analysis.
Data Lakehouse, Data Mesh, and Data Fabric (r2)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a modern data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. They all may sound great in theory, but I'll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I'll discuss Microsoft version of the data mesh.
Amazon Web Services gives you fast access to flexible and low cost IT resources, so you can rapidly scale and build virtually any big data and analytics application including data warehousing, clickstream analytics, fraud detection, recommendation engines, event-driven ETL, serverless computing, and internet-of-things processing regardless of volume, velocity, and variety of data.
In this one-hour webinar, we will look at the portfolio of AWS Big Data services and how they can be used to build a modern data architecture.
We will cover:
Using different SQL engines to analyze large amounts of structured data
Analysing streaming data in near-real time
Architectures for batch processing
Best practices for Data Lake architectures
This session is suited for:
Solution and enterprise architects
Data architects/ Data warehouse owners
IT & Innovation team members
Migrate an Existing Application to Microsoft AzureChris Dufour
First we will talk about what Microsoft Azure is and why you would want to use Microsoft’s cloud services.
Then we will take an existing on premise line of business (LOB) application with a SQL Server backend and walk through the process of moving the site to Microsoft Azure.
Driving Business Outcomes with a Modern Data Architecture - Level 100Amazon Web Services
Your business data contains critical information about customer behaviors, operational decisions, and many factors that have financial impact on your organisation. Increasingly though, this data is too big, too fast, and too complex for existing systems to handle. AWS Data and Analytics services are designed to ingest, store, analyse, and consume information at record-breaking scale. In this session you will learn how these services work together to deliver business automation, enhance customer engagement and intelligence.
Speaker: Craig Stires, APAC Business Development - Big Data & Analytics, Amazon Web Services
Driving Business Insights with a Modern Data Architecture AWS Summit SG 2017Amazon Web Services
Your customers probably want a better experience with your brand. Your different business teams want and need better insights in their decision making. Almost certainly, your finance and operations teams require this to happen at a fraction of the cost of traditional on-premises options. Modern data architectures on AWS help many of our best customers realize all of those goals. Your business data contains critical information about customer behaviors, operational decisions, and many factors that have financial impact on your organization. Increasingly, this data sits beyond your transactional systems, and is too big, too fast, and too complex for existing systems to handle. AWS Data and Analytics services are designed from our customers' requirements to ingest, store, analyze, and consume information at record-breaking scale. In this session you will learn how these services work together to deliver business automation, enhance customer engagement and intelligence.
This overview presentation discusses big data challenges and provides an overview of the AWS Big Data Platform by covering:
- How AWS customers leverage the platform to manage massive volumes of data from a variety of sources while containing costs.
- Reference architectures for popular use cases, including, connected devices (IoT), log streaming, real-time intelligence, and analytics.
- The AWS big data portfolio of services, including, Amazon S3, Kinesis, DynamoDB, Elastic MapReduce (EMR), and Redshift.
- The latest relational database engine, Amazon Aurora— a MySQL-compatible, highly-available relational database engine, which provides up to five times better performance than MySQL at one-tenth the cost of a commercial database.
Created by: Rahul Pathak,
Sr. Manager of Software Development
Building a Data Lake in Amazon S3 & Amazon Glacier (STG401-R1) - AWS re:Inven...Amazon Web Services
Whether you are part of a startup or a global enterprise, using a data lake to store and analyze data can help your business glean insights to evolve service offerings and capitalize on emerging market opportunities. In this workshop, AWS engineers and experts provide hands-on guidance for IT professionals looking to build a data lake for their organization. We provide overviews of Amazon S3, Amazon Glacier, and AWS query-in-place features and services, such as Amazon S3 Select, Amazon Glacier Select, Amazon Athena, and Amazon Redshift Spectrum. Attendees also learn how to use these services with third-party tools to build data lakes and other analytics solutions. Familiarity of with AWS object storage and analytics services is helpful but not required.
The introductory morning session will discuss big data challenges and provide an overview of the AWS Big Data Platform. We will also cover:
• How AWS customers leverage the platform to manage massive volumes of data from a variety of sources while containing costs.
• Reference architectures for popular use cases, including: connected devices (IoT), log streaming, real-time intelligence, and analytics.
• The AWS big data portfolio of services, including Amazon S3, Kinesis, DynamoDB, Elastic MapReduce (EMR) and Redshift.
• The latest relational database engine, Amazon Aurora - a MySQL-compatible, highly-available relational database engine which provides up to five times better performance than MySQL at a price one-tenth the cost of a commercial database.
• Amazon Machine Learning – the latest big data service from AWS provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology.
"Building a Modern Data platform in the Cloud", Alex Casalboni, AWS Dev Day K...Provectus
AWS Dev Day Kyiv 2019
Track: Analytics & Machine Learning
Session: "Building a Modern Data platform in the Cloud"
Speaker: Alex Casalboni, AWS Technical Evangelist
Level: 300
AWS Dev Day is a free, full-day technical event where new developers will learn about some of the hottest topics in cloud computing, and experienced developers can dive deep on newer AWS services.
Provectus has organized AWS Dev Day Kyiv in close collaboration with Amazon Web Services: 800+ participants, 18 sessions, 3 tracks, a really AWSome Day!
Now, together with Zeo Alliance, we're building and nurturing AWS User Group Ukraine — join us on Facebook to stay updated about cloud technologies and AWS services: https://www.facebook.com/groups/AWSUserGroupUkraine
Video: https://youtu.be/HIDnAG9AxZo
Amazon QuickSight is a fast, cloud-powered business intelligence (BI) service that makes it easy to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data. In this session, we demonstrate how you can point Amazon QuickSight to AWS data stores, flat files, or other third-party data sources and begin visualizing your data in minutes. We also introduce you to SPICE - a Super-fast, Parallel, In-memory, Calculation Engine in Amazon QuickSight, which performs advanced calculations and render visualizations rapidly without requiring any additional infrastructure, SQL programming, or dimensional modeling, so you can seamlessly scale to hundreds of thousands of users and petabytes of data. Lastly, you will see how Amazon QuickSight provides you with smart visualizations and graphs that are optimized for your different data types, to ensure the most suitable and appropriate visualization to conduct your analysis, and how to share these visualization stories using the built-in collaboration tools.
Big Data Meets AI - Driving Insights and Adding Intelligence to Your SolutionsAmazon Web Services
Over 90% of today’s data has been generated in the last two years, and growth rates continue to climb. In this session, we’ll step through challenges and best practices with data capturing, how to derive meaningful insights to help predict the future, and common pitfalls in data analysis.
Come discover how integrated solutions involving Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, Amazon Kinesis, and Amazon Machine Learning, and Deep Learning on AWS result in effective data systems for data scientists and business users, alike.
Ben Snively, Principal Solutions Architect, AWS; Kate Werling, Solutions Architect, AWS
AWS Summit 2013 | Singapore - Big Data Analytics, Presented by AWS, Intel and...Amazon Web Services
Learn more about the tools, techniques and technologies for working productively with data at any scale. This session will introduce the family of data analytics tools on AWS which you can use to collect, compute and collaborate around data, from gigabytes to petabytes. We'll discuss Amazon Elastic MapReduce, Hadoop, structured and unstructured data, and the EC2 instance types which enable high performance analytics.
Visualize your data in Data Lake with AWS Athena and AWS Quicksight Hands-on ...Amazon Web Services
Level 200: Visualize Your Data in Data Lake with AWS Athena and AWS Quicksight
Nowadays, enterprises are building Data Lake which store lots of structured and unstructured data for data analysis. But it takes lots of time for building the data modeling and infrastructure that is required. How to make quick data queries without servers and databases is the next big question for every enterprises.
In this workshop, eCloudvalley, the first and only Premier Consulting Partner in GCR, will demonstrate how to use serverless architecture to visualize your data using Amazon Athena and Amazon Quicksight.
You can easily query and visualize the data in your S3, and get business insights with the combination of these two services. Also, you can also build business reports with other tools such as AWS IoT, Amazon Kinesis Firehose.
Reason to Attend:
Learn how to quickly search for thousands of data on S3 via serverless Amazon's Athena
Learn how to use AWS QuickSight to retrieve information from your database quickly and create detailed reports
Using AWS to design and build your data architecture has never been easier to gain insights and uncover new opportunities to scale and grow your business. Join this workshop to learn how you can gain insights at scale with the right big data applications.
ABD202_Best Practices for Building Serverless Big Data ApplicationsAmazon Web Services
Serverless technologies let you build and scale applications and services rapidly without the need to provision or manage servers. In this session, we show you how to incorporate serverless concepts into your big data architectures. We explore the concepts behind and benefits of serverless architectures for big data, looking at design patterns to ingest, store, process, and visualize your data. Along the way, we explain when and how you can use serverless technologies to streamline data processing, minimize infrastructure management, and improve agility and robustness and share a reference architecture using a combination of cloud and open source technologies to solve your big data problems. Topics include: use cases and best practices for serverless big data applications; leveraging AWS technologies such as Amazon DynamoDB, Amazon S3, Amazon Kinesis, AWS Lambda, Amazon Athena, and Amazon EMR; and serverless ETL, event processing, ad hoc analysis, and real-time analytics.
How TrueCar Gains Actionable Insights with Splunk Cloud PPTAmazon Web Services
The vast amount of big data that today’s companies generate makes it difficult to separate the signal from the noise. Organizations need to derive meaningful insights into operations and business to take action. TrueCar needed a better way to manage, search, and analyze their hybrid environment. In this webinar, you’ll learn how TrueCar centralized all of their data in one place using Amazon Kinesis and Splunk Cloud, gaining deep visibility, scalability, and the ability to monitor and troubleshoot operational issues – all while migrating to AWS.
Modern Data Architectures for Business Insights at Scale Amazon Web Services
Using AWS to design and build your data architecture has never been easier to gain insights and uncover new opportunities to scale and grow your business. Join this workshop to learn how you can gain insights at scale with the right big data applications.
Building a real-time analytics solution has never been faster or more cost-efficient. Most organizations are trying to find a way to improve customer experience and respond to business events in real time. Importantly, to do this quickly and at a fraction of the price of traditional approaches. In this session we will look at how to use the AWS services to best meet your real-time analytics needs.
Using AWS to design and build your data architecture has never been easier to gain insights and uncover new opportunities to scale and grow your business. Join this workshop to learn how you can gain insights at scale with the right big data applications.
Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...Amazon Web Services
Il Forecasting è un processo importante per tantissime aziende e viene utilizzato in vari ambiti per cercare di prevedere in modo accurato la crescita e distribuzione di un prodotto, l’utilizzo delle risorse necessarie nelle linee produttive, presentazioni finanziarie e tanto altro. Amazon utilizza delle tecniche avanzate di forecasting, in parte questi servizi sono stati messi a disposizione di tutti i clienti AWS.
In questa sessione illustreremo come pre-processare i dati che contengono una componente temporale e successivamente utilizzare un algoritmo che a partire dal tipo di dato analizzato produce un forecasting accurato.
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...Amazon Web Services
La varietà e la quantità di dati che si crea ogni giorno accelera sempre più velocemente e rappresenta una opportunità irripetibile per innovare e creare nuove startup.
Tuttavia gestire grandi quantità di dati può apparire complesso: creare cluster Big Data su larga scala sembra essere un investimento accessibile solo ad aziende consolidate. Ma l’elasticità del Cloud e, in particolare, i servizi Serverless ci permettono di rompere questi limiti.
Vediamo quindi come è possibile sviluppare applicazioni Big Data rapidamente, senza preoccuparci dell’infrastruttura, ma dedicando tutte le risorse allo sviluppo delle nostre le nostre idee per creare prodotti innovativi.
Ora puoi utilizzare Amazon Elastic Kubernetes Service (EKS) per eseguire pod Kubernetes su AWS Fargate, il motore di elaborazione serverless creato per container su AWS. Questo rende più semplice che mai costruire ed eseguire le tue applicazioni Kubernetes nel cloud AWS.In questa sessione presenteremo le caratteristiche principali del servizio e come distribuire la tua applicazione in pochi passaggi
Vent'anni fa Amazon ha attraversato una trasformazione radicale con l'obiettivo di aumentare il ritmo dell'innovazione. In questo periodo abbiamo imparato come cambiare il nostro approccio allo sviluppo delle applicazioni ci ha permesso di aumentare notevolmente l'agilità, la velocità di rilascio e, in definitiva, ci ha consentito di creare applicazioni più affidabili e scalabili. In questa sessione illustreremo come definiamo le applicazioni moderne e come la creazione di app moderne influisce non solo sull'architettura dell'applicazione, ma sulla struttura organizzativa, sulle pipeline di rilascio dello sviluppo e persino sul modello operativo. Descriveremo anche approcci comuni alla modernizzazione, compreso l'approccio utilizzato dalla stessa Amazon.com.
Come spendere fino al 90% in meno con i container e le istanze spot Amazon Web Services
L’utilizzo dei container è in continua crescita.
Se correttamente disegnate, le applicazioni basate su Container sono molto spesso stateless e flessibili.
I servizi AWS ECS, EKS e Kubernetes su EC2 possono sfruttare le istanze Spot, portando ad un risparmio medio del 70% rispetto alle istanze On Demand. In questa sessione scopriremo insieme quali sono le caratteristiche delle istanze Spot e come possono essere utilizzate facilmente su AWS. Impareremo inoltre come Spreaker sfrutta le istanze spot per eseguire applicazioni di diverso tipo, in produzione, ad una frazione del costo on-demand!
In recent months, many customers have been asking us the question – how to monetise Open APIs, simplify Fintech integrations and accelerate adoption of various Open Banking business models. Therefore, AWS and FinConecta would like to invite you to Open Finance marketplace presentation on October 20th.
Event Agenda :
Open banking so far (short recap)
• PSD2, OB UK, OB Australia, OB LATAM, OB Israel
Intro to Open Finance marketplace
• Scope
• Features
• Tech overview and Demo
The role of the Cloud
The Future of APIs
• Complying with regulation
• Monetizing data / APIs
• Business models
• Time to market
One platform for all: a Strategic approach
Q&A
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...Amazon Web Services
Per creare valore e costruire una propria offerta differenziante e riconoscibile, le startup di successo sanno come combinare tecnologie consolidate con componenti innovativi creati ad hoc.
AWS fornisce servizi pronti all'utilizzo e, allo stesso tempo, permette di personalizzare e creare gli elementi differenzianti della propria offerta.
Concentrandoci sulle tecnologie di Machine Learning, vedremo come selezionare i servizi di intelligenza artificiale offerti da AWS e, anche attraverso una demo, come costruire modelli di Machine Learning personalizzati utilizzando SageMaker Studio.
OpsWorks Configuration Management: automatizza la gestione e i deployment del...Amazon Web Services
Con l'approccio tradizionale al mondo IT per molti anni è stato difficile implementare tecniche di DevOps, che finora spesso hanno previsto attività manuali portando di tanto in tanto a dei downtime degli applicativi interrompendo l'operatività dell'utente. Con l'avvento del cloud, le tecniche di DevOps sono ormai a portata di tutti a basso costo per qualsiasi genere di workload, garantendo maggiore affidabilità del sistema e risultando in dei significativi miglioramenti della business continuity.
AWS mette a disposizione AWS OpsWork come strumento di Configuration Management che mira ad automatizzare e semplificare la gestione e i deployment delle istanze EC2 per mezzo di workload Chef e Puppet.
Scopri come sfruttare AWS OpsWork a garanzia e affidabilità del tuo applicativo installato su Instanze EC2.
Microsoft Active Directory su AWS per supportare i tuoi Windows WorkloadsAmazon Web Services
Vuoi conoscere le opzioni per eseguire Microsoft Active Directory su AWS? Quando si spostano carichi di lavoro Microsoft in AWS, è importante considerare come distribuire Microsoft Active Directory per supportare la gestione, l'autenticazione e l'autorizzazione dei criteri di gruppo. In questa sessione, discuteremo le opzioni per la distribuzione di Microsoft Active Directory su AWS, incluso AWS Directory Service per Microsoft Active Directory e la distribuzione di Active Directory su Windows su Amazon Elastic Compute Cloud (Amazon EC2). Trattiamo argomenti quali l'integrazione del tuo ambiente Microsoft Active Directory locale nel cloud e l'utilizzo di applicazioni SaaS, come Office 365, con AWS Single Sign-On.
Dal riconoscimento facciale al riconoscimento di frodi o difetti di fabbricazione, l'analisi di immagini e video che sfruttano tecniche di intelligenza artificiale, si stanno evolvendo e raffinando a ritmi elevati. In questo webinar esploreremo le possibilità messe a disposizione dai servizi AWS per applicare lo stato dell'arte delle tecniche di computer vision a scenari reali.
Amazon Web Services e VMware organizzano un evento virtuale gratuito il prossimo mercoledì 14 Ottobre dalle 12:00 alle 13:00 dedicato a VMware Cloud ™ on AWS, il servizio on demand che consente di eseguire applicazioni in ambienti cloud basati su VMware vSphere® e di accedere ad una vasta gamma di servizi AWS, sfruttando a pieno le potenzialità del cloud AWS e tutelando gli investimenti VMware esistenti.
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
Crea la tua prima serverless ledger-based app con QLDB e NodeJSAmazon Web Services
Molte aziende oggi, costruiscono applicazioni con funzionalità di tipo ledger ad esempio per verificare lo storico di accrediti o addebiti nelle transazioni bancarie o ancora per tenere traccia del flusso supply chain dei propri prodotti.
Alla base di queste soluzioni ci sono i database ledger che permettono di avere un log delle transazioni trasparente, immutabile e crittograficamente verificabile, ma sono strumenti complessi e onerosi da gestire.
Amazon QLDB elimina la necessità di costruire sistemi personalizzati e complessi fornendo un database ledger serverless completamente gestito.
In questa sessione scopriremo come realizzare un'applicazione serverless completa che utilizzi le funzionalità di QLDB.
Con l’ascesa delle architetture di microservizi e delle ricche applicazioni mobili e Web, le API sono più importanti che mai per offrire agli utenti finali una user experience eccezionale. In questa sessione impareremo come affrontare le moderne sfide di progettazione delle API con GraphQL, un linguaggio di query API open source utilizzato da Facebook, Amazon e altro e come utilizzare AWS AppSync, un servizio GraphQL serverless gestito su AWS. Approfondiremo diversi scenari, comprendendo come AppSync può aiutare a risolvere questi casi d’uso creando API moderne con funzionalità di aggiornamento dati in tempo reale e offline.
Inoltre, impareremo come Sky Italia utilizza AWS AppSync per fornire aggiornamenti sportivi in tempo reale agli utenti del proprio portale web.
Database Oracle e VMware Cloud™ on AWS: i miti da sfatareAmazon Web Services
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
In queste slide, gli esperti AWS e VMware presentano semplici e pratici accorgimenti per facilitare e semplificare la migrazione dei carichi di lavoro Oracle accelerando la trasformazione verso il cloud, approfondiranno l’architettura e dimostreranno come sfruttare a pieno le potenzialità di VMware Cloud ™ on AWS.
Amazon Elastic Container Service (Amazon ECS) è un servizio di gestione dei container altamente scalabile, che semplifica la gestione dei contenitori Docker attraverso un layer di orchestrazione per il controllo del deployment e del relativo lifecycle. In questa sessione presenteremo le principali caratteristiche del servizio, le architetture di riferimento per i differenti carichi di lavoro e i semplici passi necessari per poter velocemente migrare uno o più dei tuo container.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
3. Outcome 1 : Modernize and consolidate
• Insights to enhance business applications and create new digital services
Outcome 2 : Innovate for new revenues
• Personalization, demand forecasting, risk analysis
Outcome 3 : Real-time engagement
• Interactive customer experience, event-driven automation, fraud detection
Outcome 4 : Automate for expansive reach
• Automation of business processes and physical infrastructure
Business Outcomes on a Modern Data Architecture
4. Expanding access requirements
Data
scientists
Automation /
events
Business
users
Data
analysts
Engagement
platforms
1. More personas need access to data, through appropriate tools
2. More systems need to link to data for decision and process automation
3. Users need to be able to find information, and access it securely
5. Exponential growth of business data
1. Data must be captured from diverse sources at speed and scale
2. Data needs to be pulled together, breaking down traditional silos
3. Benefits need to far outweigh the costs of collection and analysis
Transactions ERP Connected
devices
Social mediaWeb logs /
cookies
6. Speed (Real-time)
Ingest ServingData
sources
Scale (Batch)
Modern data architecture
Insights to enhance business applications, new digital services
Data analysts
Data scientists
Business users
Engagement platforms
Automation / events
7. Speed (Real-time)
Ingest ServingData
sources
Scale (Batch)
Modern data architecture
Insights to enhance business applications, new digital services
Transactions
Web logs /
cookies
ERP
Data analysts
Data scientists
Business users
Engagement platformsConnected
devices
Social media Automation / events
8. Speed (Real-time)
Ingest ServingData
sources
Scale (Batch)
Modern data architecture
Insights to enhance business applications, new digital services
Data Warehouse
Amazon Redshift
Legacy Apps
Amazon RDS
Data analysts
Data scientists
Business users
Engagement platforms
Schemaless
Amazon ElasticSearch
Direct Query
Amazon Athena
Near-Zero Latency
Amazon DynamoDB
Automation / events
Semi/Unstructured
Amazon EMR
Transactions
Web logs /
cookies
ERP
Connected
devices
Social media
9. Characteristics of a Data Lake
Future
Proof
Flexible
Access
Dive in
Anywhere
Collect
Anything
10. Speed (Real-time)
Ingest ServingData
sources
Scale (Batch)
Modern data architecture
Insights to enhance business applications, new digital services
Data Warehouse
Amazon Redshift
Legacy Apps
Amazon RDS
Data analysts
Data scientists
Business users
Engagement platforms
Schemaless
Amazon ElasticSearch
Direct Query
Amazon Athena
Near-Zero Latency
Amazon DynamoDB
Automation / events
Semi/Unstructured
Amazon EMR
Transactions
Web logs /
cookies
ERP
Connected
devices
Social media
Designed for 11 9s
of durability
Designed for
99.99% availability
Durable
Available
Store as much as you need
Scale storage and compute
independently
Scalable
Amazon S3
Amazon Redshift / Spectrum
Amazon EMR
Amazon Athena
Amazon DynamoDB
Integrated
11. Speed (Real-time)
Ingest ServingData
sources
Scale (Batch)
Modern data architecture
Insights to enhance business applications, new digital services
Data Warehouse
Amazon Redshift
Legacy Apps
Amazon RDS
Data analysts
Data scientists
Business users
Engagement platforms
Schemaless
Amazon ElasticSearch
Direct Query
Amazon Athena
Near-Zero Latency
Amazon DynamoDB
Automation / events
Amazon S3
Staged Data
(Data Lake)
Semi/Unstructured
Amazon EMR
Transactions
Web logs /
cookies
ERP
Connected
devices
Social media
13. Important Components of a Data Lake
Catalogue
& Search
Protect
& Secure
Access &
User Interface Ingest & Store
14. Data Ingestion into S3
AWS Direct Connect
AWS SnowballISV Connectors
Amazon Kinesis
Firehose
AWS Storage
Gateway
S3 Transfer
Acceleration
15. Speed (Real-time)
Ingest ServingData
sources
Scale (Batch)
Modern data architecture
Insights to enhance business applications, new digital services
Data Warehouse
Amazon Redshift
Legacy Apps
Amazon RDS
Data analysts
Data scientists
Business users
Engagement platforms
Schemaless
Amazon ElasticSearch
Direct Query
Amazon Athena
Near-Zero Latency
Amazon DynamoDB
Automation / events
Amazon S3
Staged Data
(Data Lake)
Semi/Unstructured
Amazon EMR
Transactions
Web logs /
cookies
ERP
AWS Database
Migration
AWS Direct
Connect
Internet
Interfaces
Amazon
Kinesis
Connected
devices
Social media
16. Building a Data Catalogue
• Aggregated information about your storage & streaming
layer
• Storage service for metadata
Ownership, data lineage
• Data abstraction layer
Customer data = collection of prefixes
• Enabling data discovery
• API for use by entitlements service
18. AWS
Glue
Managed Transform Engine
Job Scheduler
Data Catalog
Built on Apache Spark
Integrated with S3, RDS, Redshift & any
JDBC-compliant data store
19. Security
Identity and Access
Management (IAM) policies
Bucket policies
Access Control Lists (ACLs)
Private VPC endpoints to
Amazon S3
Pre-signed S3 URLs
Encryption
SSL endpoints
Server Side Encryption
(SSE-S3)
S3 Server Side
Encryption with
provided keys (SSE-C,
SSE-KMS)
Client-side Encryption
Audit & Compliance
Buckets access logs
Lifecycle Management
Policies
Versioning & MFA
deletes
Certifications – HIPAA,
PCI, SOC 1/2/3 etc.
Implement the right cloud security controls
20. Speed (Real-time)
Ingest ServingData
sources
Scale (Batch)
Modern data architecture
Insights to enhance business applications, new digital services
Data Warehouse
Amazon Redshift
Legacy Apps
Amazon RDS
Data analysts
Data scientists
Business users
Engagement platforms
Schemaless
Amazon ElasticSearch
Direct Query
Amazon Athena
Near-Zero Latency
Amazon DynamoDB
Automation / events
Amazon S3
Staged Data
(Data Lake)
Semi/Unstructured
Amazon EMR
Transactions
Web logs /
cookies
ERP
AWS Database
Migration
AWS Direct
Connect
Internet
Interfaces
Amazon
Kinesis
Connected
devices
Social media
AWS
Cloud Trail
AWS
IAM
Amazon
CloudWatch
AWS
KMS
21. Speed (Real-time)
Ingest ServingData
sources
Scale (Batch)
Modern data architecture
Insights to enhance business applications, new digital services
Data Warehouse
Amazon Redshift
Legacy Apps
Amazon RDS
Data analysts
Data scientists
Business users
Engagement platforms
Schemaless
Amazon ElasticSearch
Direct Query
Amazon Athena
Near-Zero Latency
Amazon DynamoDB
Automation / events
Amazon S3
Staged Data
(Data Lake)
Semi/Unstructured
Amazon EMR
Transactions
Web logs /
cookies
ERP
AWS Database
Migration
AWS Direct
Connect
Internet
Interfaces
Amazon
Kinesis
Connected
devices
Social media
Amazon S3
Raw Data
Amazon EMR
ETL
Advanced
Analytics
MLlib
Event Capture
Amazon Kinesis
Stream Analysis
Amazon EMR
AWS
Cloud Trail
AWS
IAM
Amazon
CloudWatch
AWS
KMS
28. • Free and easy
• Excellent for initial
• Good learning materials
Google Analytics (GA)
29. • Free and easy
• Excellent for initial
• Good learning materials
Google Analytics (GA)
• free version latency &
accuracy issue
• GA 360 (Premium) +
BigQuery are expensive
• Not flexible enough
30. Our needs
• Large data volume
• Raw data for Machine Learning
• Flexible for further processing
• Low latency
35. Experience on AWS
• Complete and Integrated
• Quick. 2 man weeks for first version
• Easy to scale
• Minimal maintenance cost
36. Future
• More server-less in future
• S3 as datalake
• click event, system log, etc
• raw, processed data (like ML result)
• Hot on disk, cold on s3
• Explore AWS Machine Learning
AWS
Lambda
API
Gateway
Kinesis
Firehose
Redshift
Quicksight
S3
Machine
learning
EMR
SparkML
ML
Hot data
Raw data
Processed data
Cold data
Direct Query
Athena
Redshift
Spectrum
P2
Deep
learning AMI
Visualization
Serverless