Migrate from Oracle to Amazon Aurora using AWS Schema Conversion Tool & AWS D...Amazon Web Services
• Understand the issues with commercial database pricing and licensing.
• Learn about the benefits of Amazon Aurora for improving performance and decreasing costs.
• See how AWS Database Migration Service helps with your migration.
• See how AWS Schema Conversion Tool makes conversions simple and quick.
If you’re looking to improve application performance and availability and decrease database costs, it’s time to replace your expensive Oracle databases with an open-source compatible solution. Amazon Aurora is a MySQL-compatible relational database that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. You'll learn how to use the AWS Database Migration Service to migrate your data with minimal downtime, and how the AWS Schema Conversion Tool converts your Oracle schemas and procedural code into Amazon Aurora. We’ll follow with a quick demo of the entire process.
First Steps with Apache Kafka on Google Cloud Platformconfluent
Speakers: Jay Smith, Cloud Customer Engineer, Google Cloud + Gwen Shapira, Product Manager, Confluent
Curious about Apache Kafka®? Find out why you would want to use the de facto standard for real-time streaming, the easiest way to get started and how to leverage the extensive Apache Kafka ecosystem. In this chat, we'll talk about three common use cases, review stream processing patterns and discuss integration with important GCP services such as BigQuery. We'll also demo how to implement real-time clickstream analytics on Confluent Cloud, fully managed Apache Kafka as a service.
AWS provides a broad platform of managed services to help you build, secure, and seamlessly scale end-to-end Big Data applications quickly and with ease. Want to get ramped up on how to use Amazon's big data web services? Learn when to use which service? Want to write your first big data application on AWS? Join us in this session as we discuss reference architecture, design patterns, and best practices for pulling together various AWS services to meet your big data challenges.
AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics. You can create and run an ETL job with a few clicks in the AWS Management Console. You simply point AWS Glue to your data stored on AWS, and AWS Glue discovers your data and stores the associated metadata (e.g. table definition and schema) in the AWS Glue Data Catalog. Once cataloged, your data is immediately searchable, queryable, and available for ETL. AWS Glue generates the code to execute your data transformations and data loading processes.
Level: Intermediate
Speakers:
Ryan Malecky - Solutions Architect, EdTech, AWS
Rajakumar Sampathkumar - Sr. Technical Account Manager, AWS
Migrate from Oracle to Amazon Aurora using AWS Schema Conversion Tool & AWS D...Amazon Web Services
• Understand the issues with commercial database pricing and licensing.
• Learn about the benefits of Amazon Aurora for improving performance and decreasing costs.
• See how AWS Database Migration Service helps with your migration.
• See how AWS Schema Conversion Tool makes conversions simple and quick.
If you’re looking to improve application performance and availability and decrease database costs, it’s time to replace your expensive Oracle databases with an open-source compatible solution. Amazon Aurora is a MySQL-compatible relational database that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. You'll learn how to use the AWS Database Migration Service to migrate your data with minimal downtime, and how the AWS Schema Conversion Tool converts your Oracle schemas and procedural code into Amazon Aurora. We’ll follow with a quick demo of the entire process.
First Steps with Apache Kafka on Google Cloud Platformconfluent
Speakers: Jay Smith, Cloud Customer Engineer, Google Cloud + Gwen Shapira, Product Manager, Confluent
Curious about Apache Kafka®? Find out why you would want to use the de facto standard for real-time streaming, the easiest way to get started and how to leverage the extensive Apache Kafka ecosystem. In this chat, we'll talk about three common use cases, review stream processing patterns and discuss integration with important GCP services such as BigQuery. We'll also demo how to implement real-time clickstream analytics on Confluent Cloud, fully managed Apache Kafka as a service.
AWS provides a broad platform of managed services to help you build, secure, and seamlessly scale end-to-end Big Data applications quickly and with ease. Want to get ramped up on how to use Amazon's big data web services? Learn when to use which service? Want to write your first big data application on AWS? Join us in this session as we discuss reference architecture, design patterns, and best practices for pulling together various AWS services to meet your big data challenges.
AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics. You can create and run an ETL job with a few clicks in the AWS Management Console. You simply point AWS Glue to your data stored on AWS, and AWS Glue discovers your data and stores the associated metadata (e.g. table definition and schema) in the AWS Glue Data Catalog. Once cataloged, your data is immediately searchable, queryable, and available for ETL. AWS Glue generates the code to execute your data transformations and data loading processes.
Level: Intermediate
Speakers:
Ryan Malecky - Solutions Architect, EdTech, AWS
Rajakumar Sampathkumar - Sr. Technical Account Manager, AWS
Amazon Elastic Compute Cloud (Amazon EC2) provides a broad selection of instance types to accommodate a diverse mix of workloads. In this technical session, we provide an overview of the Amazon EC2 instance platform, key platform features, and the concept of instance generations. We dive into the current-generation design choices of the different instance families, including the General Purpose, Compute Optimized, Storage Optimized, Memory Optimized, and GPU instance families. We also detail best practices and share performance tips for getting the most out of your Amazon EC2 instances.
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
Training for AWS Solutions Architect at http://zekelabs.com/courses/amazon-web-services-training-bangalore/.This slide describes about cloud trail key concepts, workflow and event history
___________________________________________________
zekeLabs is a Technology training platform. We provide instructor led corporate training and classroom training on Industry relevant Cutting Edge Technologies like Big Data, Machine Learning, Natural Language Processing, Artificial Intelligence, Data Science, Amazon Web Services, DevOps, Cloud Computing and Frameworks like Django,Spring, Ruby on Rails, Angular 2 and many more to Professionals.
Reach out to us at www.zekelabs.com or call us at +91 8095465880 or drop a mail at info@zekelabs.com
Best Practices for Building a Data Lake with Amazon S3 - August 2016 Monthly ...Amazon Web Services
Uncovering new, valuable insights from big data requires organizations to collect, store, and analyze increasing volumes of data from multiple, often disparate sources at disparate points in time. This makes it difficult to handle big data with data warehouses or relational database management systems alone. A Data Lake allows you to store massive amounts of data in its original form, without the need to enforce a predefined schema, enabling a far more agile and flexible architecture, which makes it easier to gain new types of analytical insights from your data.
Learning Objectives:
• Introduce key architectural concepts to build a Data Lake using Amazon S3 as the storage layer
• Explore storage options and best practices to build your Data Lake on AWS
• Learn how AWS can help enable a Data Lake architecture
• Understand some of the key architectural considerations when building a Data Lake
• Hear some important Data Lake implementation considerations when using Amazon S3 as your Data Lake
In this session we’ll take a high-level overview of AWS Lambda, a serverless compute platform that has changed the way that developers around the world build applications. We’ll explore how Lambda works under the hood, the capabilities it has, and how it is used. By the end of this talk you’ll know how to create Lambda based applications and deploy and manage them easily.
Speaker: Chris Munns - Principal Developer Advocate, AWS Serverless Applications, AWS
AWS Storage and Database Architecture Best Practices (DAT203) | AWS re:Invent...Amazon Web Services
Learn about architecture best practices for combining AWS storage and database technologies. We outline AWS storage options (Amazon EBS, Amazon EC2 Instance Storage, Amazon S3 and Amazon Glacier) along with AWS database options including Amazon ElastiCache (in-memory data store), Amazon RDS (SQL database), Amazon DynamoDB (NoSQL database), Amazon CloudSearch (search), Amazon EMR (hadoop) and Amazon Redshift (data warehouse). Then we discuss how to architect your database tier by using the right database and storage technologies to achieve the required functionality, performance, availability, and durability—at the right cost.
Azure SQL Database (SQL DB) is a database-as-a-service (DBaaS) that provides nearly full T-SQL compatibility so you can gain tons of benefits for new databases or by moving your existing databases to the cloud. Those benefits include provisioning in minutes, built-in high availability and disaster recovery, predictable performance levels, instant scaling, and reduced overhead. And gone will be the days of getting a call at 3am because of a hardware failure. If you want to make your life easier, this is the presentation for you.
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.
In this introduction to Aws certified solutions architect, we answer the key question “What is the Aws cloud computing architect?” With a solid, standards based approach and examples from the real word.
Amazon Elastic Compute Cloud (Amazon EC2) provides a broad selection of instance types to accommodate a diverse mix of workloads. In this technical session, we provide an overview of the Amazon EC2 instance platform, key platform features, and the concept of instance generations. We dive into the current-generation design choices of the different instance families, including the General Purpose, Compute Optimized, Storage Optimized, Memory Optimized, and GPU instance families. We also detail best practices and share performance tips for getting the most out of your Amazon EC2 instances.
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
Training for AWS Solutions Architect at http://zekelabs.com/courses/amazon-web-services-training-bangalore/.This slide describes about cloud trail key concepts, workflow and event history
___________________________________________________
zekeLabs is a Technology training platform. We provide instructor led corporate training and classroom training on Industry relevant Cutting Edge Technologies like Big Data, Machine Learning, Natural Language Processing, Artificial Intelligence, Data Science, Amazon Web Services, DevOps, Cloud Computing and Frameworks like Django,Spring, Ruby on Rails, Angular 2 and many more to Professionals.
Reach out to us at www.zekelabs.com or call us at +91 8095465880 or drop a mail at info@zekelabs.com
Best Practices for Building a Data Lake with Amazon S3 - August 2016 Monthly ...Amazon Web Services
Uncovering new, valuable insights from big data requires organizations to collect, store, and analyze increasing volumes of data from multiple, often disparate sources at disparate points in time. This makes it difficult to handle big data with data warehouses or relational database management systems alone. A Data Lake allows you to store massive amounts of data in its original form, without the need to enforce a predefined schema, enabling a far more agile and flexible architecture, which makes it easier to gain new types of analytical insights from your data.
Learning Objectives:
• Introduce key architectural concepts to build a Data Lake using Amazon S3 as the storage layer
• Explore storage options and best practices to build your Data Lake on AWS
• Learn how AWS can help enable a Data Lake architecture
• Understand some of the key architectural considerations when building a Data Lake
• Hear some important Data Lake implementation considerations when using Amazon S3 as your Data Lake
In this session we’ll take a high-level overview of AWS Lambda, a serverless compute platform that has changed the way that developers around the world build applications. We’ll explore how Lambda works under the hood, the capabilities it has, and how it is used. By the end of this talk you’ll know how to create Lambda based applications and deploy and manage them easily.
Speaker: Chris Munns - Principal Developer Advocate, AWS Serverless Applications, AWS
AWS Storage and Database Architecture Best Practices (DAT203) | AWS re:Invent...Amazon Web Services
Learn about architecture best practices for combining AWS storage and database technologies. We outline AWS storage options (Amazon EBS, Amazon EC2 Instance Storage, Amazon S3 and Amazon Glacier) along with AWS database options including Amazon ElastiCache (in-memory data store), Amazon RDS (SQL database), Amazon DynamoDB (NoSQL database), Amazon CloudSearch (search), Amazon EMR (hadoop) and Amazon Redshift (data warehouse). Then we discuss how to architect your database tier by using the right database and storage technologies to achieve the required functionality, performance, availability, and durability—at the right cost.
Azure SQL Database (SQL DB) is a database-as-a-service (DBaaS) that provides nearly full T-SQL compatibility so you can gain tons of benefits for new databases or by moving your existing databases to the cloud. Those benefits include provisioning in minutes, built-in high availability and disaster recovery, predictable performance levels, instant scaling, and reduced overhead. And gone will be the days of getting a call at 3am because of a hardware failure. If you want to make your life easier, this is the presentation for you.
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.
In this introduction to Aws certified solutions architect, we answer the key question “What is the Aws cloud computing architect?” With a solid, standards based approach and examples from the real word.
Streaming Auto-scaling in Google Cloud DataflowC4Media
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1Z2JXhs.
Manuel Fahndrich describes how they tackled one particular resource allocation aspect of Google Cloud Dataflow pipelines, namely, horizontal scaling of worker pools as a function of pipeline input rate. Managing the redistribution of key ranges across new pool sizes and the associated persistent data storage was particularly challenging. Filmed at qconlondon.com.
Manuel Fahndrich earned his Ph.D. in C.S. from UC Berkeley in 1999. He spent the next 15 years as a Research Scientist at Microsoft, working on static and dynamic verification tools for object-oriented programs and system software. After joining Google in 2014 he has been working on data-parallel infrastructure, in particular auto-scaling for batch and streaming pipelines.
Stratio is a Big Data platform based on Spark. It is 100% open source and enterprise - ready. In Stratio we are Pure Spark, since it is the only technology in the market able to combine stored data analyses with real time streaming data, all in the same query.
We are unique in integrating Spark processing with the main NoSql databases: Cassandra, MongoDB, ElasticSearch, ...
Introduction to Google BigQuery. Slides used at the first GDG Cloud meetup in Brussels, about big data on Google Cloud Platform. (http://www.meetup.com/GDG-Cloud-Belgium/events/228206131)
Real Time Analytics: Algorithms and SystemsArun Kejariwal
In this tutorial, an in-depth overview of streaming analytics -- applications, algorithms and platforms -- landscape is presented. We walk through how the field has evolved over the last decade and then discuss the current challenges -- the impact of the other three Vs, viz., Volume, Variety and Veracity, on Big Data streaming analytics.
Lambda Architecture with Spark, Spark Streaming, Kafka, Cassandra, Akka and S...Helena Edelson
Regardless of the meaning we are searching for over our vast amounts of data, whether we are in science, finance, technology, energy, health care…, we all share the same problems that must be solved: How do we achieve that? What technologies best support the requirements? This talk is about how to leverage fast access to historical data with real time streaming data for predictive modeling for lambda architecture with Spark Streaming, Kafka, Cassandra, Akka and Scala. Efficient Stream Computation, Composable Data Pipelines, Data Locality, Cassandra data model and low latency, Kafka producers and HTTP endpoints as akka actors...
Lambda Architecture with Spark Streaming, Kafka, Cassandra, Akka, ScalaHelena Edelson
Scala Days, Amsterdam, 2015: Lambda Architecture - Batch and Streaming with Spark, Cassandra, Kafka, Akka and Scala; Fault Tolerance, Data Pipelines, Data Flows, Data Locality, Akka Actors, Spark, Spark Cassandra Connector, Big Data, Asynchronous data flows. Time series data, KillrWeather, Scalable Infrastructure, Partition For Scale, Replicate For Resiliency, Parallelism
Isolation, Data Locality, Location Transparency
Introduction to GCP DataFlow PresentationKnoldus Inc.
In this session, we will learn about how Dataflow is a fully managed streaming analytics service that minimizes latency, processing time, and cost through autoscaling and batch processing.
Hourglass: a Library for Incremental Processing on HadoopMatthew Hayes
Hadoop enables processing of large data sets through its relatively easy-to-use semantics. However, jobs are often written inefficiently for tasks that could be computed incrementally due to the burdensome incremental state management for the programmer. This paper introduces Hourglass, a library for developing incremental monoid computations on Hadoop. It runs on unmodified Hadoop and provides an accumulator-based interface for programmers to store and use state across successive runs; the framework ensures that only the necessary subcomputations are performed. It is successfully used at LinkedIn, one of the largest online social networks, for many use cases in dashboarding and machine learning. Hourglass is open source and freely available.
Research Inventy : International Journal of Engineering and Scienceinventy
Research Inventy : International Journal of Engineering and Science
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed
Dear Students
Ingenious techno Solution offers an expertise guidance on you Final Year IEEE & Non- IEEE Projects on the following domain
JAVA
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EMBEDDED SYSTEMS
ROBOTICS
MECHANICAL
MATLAB etc
For further details contact us:
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044-42046028 or 8428302179.
Ingenious Techno Solution
#241/85, 4th floor
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http://www.ingenioustech.in/
This presentation looks at how to build an architecture for big and fast data. It reviews the Kappa & Lambda architectures and looks at the role Hazelcast Jet & IMDG can play in the Kappa architecture. It then proposes an evolution of the Kappa architecture to provide a transactional big data system.
Architecting Analytic Pipelines on GCP - Chicago Cloud Conference 2020Mariano Gonzalez
Modernizing analytics data pipelines to gain the most of your data while optimizing costs can be challenging. However, today cloud providers offer a good set of services that can help with this endeavor. We will do a tour across some GCP services during this hands-on session, using DataFlow (apache beam) as the backbone to architect a modern analytics pipeline to wire them all together.
MAP-REDUCE IMPLEMENTATIONS: SURVEY AND PERFORMANCE COMPARISONijcsit
Map Reduce has gained remarkable significance as a rominent parallel data processing tool in the research community, academia and industry with the spurt in volume of data that is to be analyzed. Map Reduce is used in different applications such as data mining, data analytic where massive data analysis is required, but still it is constantly being explored on different parameters such as performance and efficiency. This survey intends to explore large scale data processing using Map Reduce and its various implementations to facilitate the database, researchers and other communities in developing the technical understanding of the Map Reduce framework. In this survey, different Map Reduce implementations are explored and their inherent features are compared on different parameters. It also addresses the open issues and challenges raised on fully functional DBMS/Data Warehouse on Map Reduce. The comparison of various Map Reduce implementations is done with the most popular implementation Hadoop and other similar implementations using other platforms.
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DreamwebTechnosolution
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Ph: 0431 4050403, 7200021403, 7200021404.
How to track and improve Customer Experience with LEO CDPTrieu Nguyen
1) Why CX measurement is so important
2) Introduction to key metrics of CX
2.1 Customer Feedback Score (CFS)
2.2 Customer Effort Score (CES)
2.3 Customer Satisfaction Score (CSAT)
2.4 Net Promoter Score (NPS)
3) Using Journey Map to CX Data Management
4) Introduction to LEO CDP and demo
[Notes] Customer 360 Analytics with LEO CDPTrieu Nguyen
Part 1: Why should every business need to deploy a CDP ?
1. Big data is the reality of business today
2. What are technologies to manage customer data ?
3. The rise of first-party data and new technologies for Digital Marketing
4. How to apply USPA mindset to build your CDP for data-driven business
Part 2: How to use LEO CDP for your business
1. Core functions of LEO CDP for marketers and IT managers
2. Data Unification for Customer 360 Analytics
3. Data Segmentation
4. Customer Personalization
5. Customer Data Activation
Part 3: Case study in O2O Retail and Ecommerce
1. How to build customer journey map for ecommerce and retail
2. How to do customer analytics to find ideal customer profiles
The ideal customer profile in a B2B context
The ideal customer profile in a B2C context
3. Manage product catalog for customer personalization
4. Monitoring Data of Customer Experience (CX Analytics)
CX Data Flow
CX Rating plugin is embedded in the website, to collect feedback data
An overview of CX Report
A CX Report in a customer profile
5. Monitoring data with real-time event tracking reports
Event Data Flow
Summary Event Data Report
Event Data Report in a Customer Profile
Part 4: How to setup an instance of LEO CDP for free
1. Technical architecture
2. Server infrastructure
3. Setup middlewares: Nginx, ArangoDB, Redis, Java and Python
Network requirements
Software requirements for new server
ArangoDB
Nginx Proxy
SSL for Nginx Server
Java 8 JVM
Redis
Install Notes for Linux Server
Clone binary code for new server
Set DNS hosts for LEO CDP workers
4. Setup data for testing and system verification
Part 5: Summary all key ideas
Why should you invest in LEO CDP ?
Purpose: Big data and AI democracy for SMEs companies
Problem: Customer Analytics and Customer Personalization
Solutions: CDP + CX + Personalization Engine
Product demo: LEO CDP for Ecommerce and Fintech
Business model: Freemium → Ecosystem → Subscription
Market size: 20 billion USD in 2026 and CAGR 34.6%
Differentiation: cloud-native software
Go-to-market approach: Community → Free → Paid
Team: 1 full-stack dev, 1 data scientist and 12,000 fans of BigDataVietnam.org Community
Need 150,000 USD for scaling business (you get 20% share)
Lộ trình triển khai LEO CDP cho ngành bất động sảnTrieu Nguyen
1) Hiểu bài toán số hoá trải nghiệm khách hàng
2) Nghiên cứu giải pháp LEO CDP
3) Lộ trình triển khai
Phát triển / số hoá điểm chạm khách hàng
Xây dựng bản đồ hành trình khách hàng
Định nghĩa các metrics và KPI quan trọng
Xây dựng web portal và mobile data hub
Xây dựng kế hoạch Digital Marketing
Triển khai CDP và Marketing Automation
Xây dựng đội Analytics để phân tích dữ liệu
From Dataism to Customer Data PlatformTrieu Nguyen
1) How to think in the age of Dataism with LEO CDP ?
2) Why is Dataism for human, business and society ?
3) How should LEO Customer Data Platform (LEO CDP) work ?
4) How to use LEO CDP for your business ?
Data collection, processing & organization with USPA frameworkTrieu Nguyen
1) How to think in the age of Dataism with USPA framework ?
2) How to collect customer data
3) Data Segmentation Processing for flexibility and scalability
4) Data Organization for personalization and business activation
Part 1: Introduction to digital marketing technologyTrieu Nguyen
Outline of this course
1. Digital Media Models in the age of marketing 4.0
2. Strategic Thought as It Relates to Digital Marketing
3. Web: The Center of Digital Marketing Delivery Mix
4. Content Management System (CMS) and headless CMS
5. Search Engine Marketing
6. Email Marketing
7. Social Media and Mobile Marketing
8. Introduction to Advertising Technology (Ad Tech)
9. Introduction to Customer Database and Customer Data Platform (CDP)
10. Legal Issues: Data privacy, Security, and Intellectual Property
11. Case study: IKEA - from business strategy to digital marketing strategy
12. Recommended books for self-study
Transform your marketing and sales capabilities with Big Data and A.I
1) Why is Customer Data Platform (CDP) ?
Case study: Enhancing the revenue of your restaurant with CDP and mobile app marketing
Question: Why can CDP disrupt business model for restaurant industry (B2C) ?
2) How would CDP work in practice ?
Introducing USPA.tech as logical framework for implementing CDP in practice
How Can a Customer Data Platform Enhance Your Account-Based Marketing Strategy (B2B) ?
3) How can we implement CDP for business?
Introducing the CDP as customer-first marketing platform for all industries (my key idea in this slide)
Video Ecosystem and some ideas about video big dataTrieu Nguyen
Introduction to Video Ecosystem Mind Map
Video Streaming Platform
Video Ad Tech Platform
Video Player Platform
Video Content Distribution Platform
Video Analytics Platform
Summary of key ideas
Q & A
Concepts, use cases and principles to build big data systems (1)Trieu Nguyen
1) Introduction to the key Big Data concepts
1.1 The Origins of Big Data
1.2 What is Big Data ?
1.3 Why is Big Data So Important ?
1.4 How Is Big Data Used In Practice ?
2) Introduction to the key principles of Big Data Systems
2.1 How to design Data Pipeline in 6 steps
2.2 Using Lambda Architecture for big data processing
3) Practical case study : Chat bot with Video Recommendation Engine
4) FAQ for student
Apache Hadoop and Spark: Introduction and Use Cases for Data AnalysisTrieu Nguyen
Growth of big datasets
Introduction to Apache Hadoop and Spark for developing applications
Components of Hadoop, HDFS, MapReduce and HBase
Capabilities of Spark and the differences from a typical MapReduce solution
Some Spark use cases for data analysis
Introduction to Recommendation Systems (Vietnam Web Submit)Trieu Nguyen
1) Why do we need recommendation systems ?
2) How can we think with recommendation systems ?
3) How can we implement a recommendation system with open source technologies ?
RFX framework https://github.com/rfxlab
Apache Kafka: https://kafka.apache.org
Apache Spark: https://spark.apache.org
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
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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.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Predicting Product Ad Campaign Performance: A Data Analysis Project Presentation
Google Cloud Dataflow and lightweight Lambda Architecture for Big Data App
1. Google Cloud Dataflow
and lightweight Lambda
Architecture
for Big Data App
New innovative ideas and core concepts for
simple real-time analytic development
Compiled from Internet by
@tantrieuf31
http://nguyentantrieu.info
2. How it works:
an immutable sequence of records is captured and fed into
a batch system and a stream processing system in parallel.
You implement your transformation logic twice, once in the
batch system and once in the stream processing system.
You stitch together the results from both systems at query
time to produce a complete answer.
The Lambda Architecture
3. And the bad…
The problem with the Lambda Architecture is that
maintaining code that needs to produce the same
result in two complex distributed systems is exactly as
painful as it seems like it would be. I don’t think this
problem is fixable.
Programming in distributed frameworks like Storm and
Hadoop is complex. Inevitably, code ends up being
specifically engineered toward the framework it runs on.
The resulting operational complexity of systems
implementing the Lambda Architecture is the one thing that
seems to be universally agreed on by everyone doing it.
4. Too hard to build flexible analytics pipelines
Google is now making a huge point of the fact
that it abandoned MapReduce a long time ago
as it was too hard to build flexible analytics
pipelines. http://java.dzone.com/articles/google-cloud-dataflow-%E2%
80%93-game
6. Google's new Dataflow
Google's new Dataflow architecture, which is
based on FlumeJava and MillWheel? They also
support code sharing.
Cloud Dataflow is a successor to MapReduce,
and is based on Google’s internal technologies
like Flume and MillWheel. This new project in
which Google placed their servers can be
considered the natural evolution of
MapReduce.
9. Lightweight Lambda Architecture
Stream processing system could be improved
to handle the full problem set in its target
domain.
→ Kappa Architecture + Micro-service
10. Ideas
● Use Kafka or some other system that will let you retain
the full log of the data you want to be able to reprocess
and that allows for multiple subscribers. For example, if
you want to reprocess up to 30 days of data, set your
retention in Kafka to 30 days.
● When you want to do the reprocessing, start a second
instance of your stream processing job that starts
processing from the beginning of the retained data,
but direct this output data to a new output table.
● When the second job has caught up, switch the
application to read from the new table.
● Stop the old version of the job, and delete the old output
table.