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Introduction to Google Cloud Platform for Big Data - Trusted Conf

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Overview and advantages of the products available in GCP (Google Cloud Platform) for big data storage, processing, and analysis. Big Data and GCP

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Introduction to Google Cloud Platform for Big Data - Trusted Conf

  1. 1. INTRODUCTION TO GOOGLE CLOUD PLATFORM FOR BIG DATA
  2. 2. ” FIRST THINGS FIRST... ● Who Am I? ● What I'm Going to Talk About? 2
  3. 3. 3 ● Brazilian Data Analyst ● Databases Management Student ● Google fan ● Mom of 1 / Pet Mom of 8 ● Plant Based Geek ● Crazy about Nature
  4. 4. 4 WHAT I'M GOING TO TALK ABOUT? ■ Big Data Beyond the Hype [ What Is | The 5 Vs ] ■ What is the Google Cloud Platform? [ What Is | The Ecosystem ] ■ GCP Products for Big Data [ Example of Big Data Lifecycle | Ingesting | Storing | Processing | Analysing ] ■ GCP Big Data Solutions to IMWT's Portfolio [ Challenges | Example | Steps to Success ]
  5. 5. 5 Big Data Beyond the Hype
  6. 6. 6 High-volume, high-velocity and high-variety information assets that demand cost- effective, innovative forms of information processing for enhanced insight and decision making. WHAT IS BIG DATA? Source: Gartner IT Glossary
  7. 7. 7 BIG DATA Source: Adapted from Michael Walker (2012) THE 5 Vs Terabytes to Exabytes of existing data to process Milliseconds to Seconds to process VOLUME Data at Rest VALUE Data Into Money VERACITY Data In Doubt VARIETY Data In Many Forms VELOCITY Data In Motion Structured, unstructured, text, multimedia...Uncertainty due to data inconsistency, incompleteness, Ambiguities, model approximations... Business models can be associated to the data
  8. 8. 8 What Is Google Cloud Platform?
  9. 9. 9 A suite of cloud computing services that runs on the same infrastructure that Google uses internally for its end-user products. WHAT IS GOOGLE CLOUD PLATFORM? Source: GCP Website (2018)
  10. 10. 10 GCP ECOSYSTEM Source: Google Cloud Platform (2018)
  11. 11. 11 GCP ECOSYSTEM
  12. 12. 12 GCP Products to Big Data
  13. 13. 13 EXAMPLE OF BIG DATA LIFECYCLE Source: GCP Website(2018)
  14. 14. 14 INGESTION Source: GCP Website(2018) Serverless, fully managed, scalable and pay- for-use platform for apps and beckends. Save money while focus on code rather than infrastructure Integrated, open and global real-time event stream ingestion, delivery and analysis platform. Fast reporting, targeting and optimization in advertising and media
  15. 15. 15 PROCESSING Source: GCP Website(2018) Simple, automated and reliable stream and batch data processing platform. Fast, easy-to-use and fully managed cloud service for running Apache Spark and Hadoop cluster. Minimize latency and maximize utilization. Low costs. Focus on the data, not on the cluster.
  16. 16. 16 STORAGE Source: GCP Website(2018) In memory, relational, non-relational, object and warehouse cloud storage solutions. Secure, cost-effective and easily access storage for every need.
  17. 17. 17 EXPLORATION Source: GCP Website(2018) Easy-to-use and interactive tool for data exploration, analysis, visualization and machine learning. Fast, scalable, cost-effective and fully managed cloud data warehouse for analytics. Set of integrated data-and- marketing analysis products. Free. May incur compute, storage and other cloud services. Serverless and built-in Machine Learning.
  18. 18. 18 ANALYTICS Source: GCP Website(2018) Fast, large scale and easy-to- use AI products and services. Easy-to-use deep learning models to speech-to-text / image-to-JSON conversion and dynamic translation. Pre trained models. No advanced ML skill required. Better training performance compared to other deep learning systems.
  19. 19. 19 GCP Big Data Solutions to IMWT's Portfolio
  20. 20. 20 Source: Adapted from Nasser T, Tariq RS (2015) Big Data Challenges. J Comput Eng Inf Technol 4:3 CHALLENGES
  21. 21. STORAGE 21 EXAMPLE INGESTION PROCESSING EXPLORATION ANALYSIS Web Crawler Solution Simplified Architecture APP ENGINE DATAFLOW DATAPROC SQL DATAPREP DATALAB MACHINE LEARNING DATA STUDIO
  22. 22. 22 Source: Adapted from IBM (2014) STEPS TO SUCCESS Identify high-value opportunities Establish the right architecture and funding model Prove value to business through pilot programs Scale by expanding to additional use cases Transform to a data-driven culture
  23. 23. ” Thank You!

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