Modern Thinking área digital MSKM 21/09/2017
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El documento presenta una introducción a las soluciones de Big Data de Google Cloud, enfocándose en su plataforma, servicios y casos de uso en el análisis y procesamiento de datos. Se destacan diferentes productos como BigQuery, Cloud Dataflow y herramientas de aprendizaje automático, junto con ejemplos de aplicación en empresas como Traveloka y WePay. Además, se discuten las etapas del ciclo de vida de los datos, desde la ingestión hasta la visualización.
Modern Thinking área digital MSKM 21/09/2017
- 1. Introducción a las soluciones Big Data de Google Ismael Yuste Strategic Cloud Engineer Google Cloud MSMK Madrid, 21 de Septiembre de 2017
- 2. Agenda ● Google Cloud Platform ● BigData ● Machine Learning ● Use Cases
- 4. Google Data Centers Los centros de datos de Google son la base de toda la plataforma de Google Cloud. Ofrecen poder computación, almacenamiento, memoria, GPUs para nuestras aplicaciones. Además, alberga el corazón de aplicaciones como Gmail, Youtube, Search... ● Rapidez ● Baja latencia ● Eficiencia de operaciones ● Eficiencia Energética ● Uso de Energías Renovables ● Cercanía al usuario ● Seguridad de la Información
- 5. Google Datacenters - Cloud Regions
- 6. Big Data Soluciones de Big Data integradas de principio a fin, que permite capturar los datos, procesarlos y almacenarlos en una plataforma integrada. Combina servicios nativos en la nube y herramientas Open Source gestionadas, tanto en tiempo real como por lotes. Big Data BigQuery Cloud Dataflow Cloud Dataproc Cloud Datalab Cloud Pub/Sub Genomics
- 7. Big Data - Big Query Tu almacén de datos corporativo, rápido, económico y completamente gestionado para análisis de grandes grupos de datos ● Ingestión de datos flexible. ● Disponibilidad global. ● Seguridad y permisos integrados. ● Control de coste. ● Altamente disponible. ● Completamente integrado. ● Conecta con otros productos de Google.
- 8. Big Data - Cloud Dataflow Servicio completamente gestionado y modelo de programación para el proceso de Big Data ● Gestión de Recursos integrado. ● A demanda. ● Ejecución de los trabajos inteligente. ● Auto escalado. ● Modelo de programación unificado. ● Open Source. ● Monitorizaje. ● Integración. ● Procesado confiable y consistente.
- 9. Big Data - Cloud Dataproc Servicio gestionado Spark y Hadoop ● Gestión de Cluster integrado. ● Cluster dimensionables. ● Integración. ● Versionado. ● Herramientas de Gestión. ● Acciones de inicialización. ● Gestión manual o automática. ● Máquinas Virtuales flexibles.
- 10. Big Data Datalab. Herramienta de exploración, análisis y visualización de Big Data. Pub/Sub. Servicio global en tiempo real para gestión de mensajes y streaming de datos.
- 11. Big Data Dataprep. Servicio de datos inteligente que permite explorar, limpiar y preparar datos estructurados o no para su posterior análisis. Data Studio. Convierte tus datos en informes y cuadros de mando que son sencillos de crear, de compartir, y totalmente personalizables, desde fuentes de datos como Bigquery, Analytics o Youtube.
- 12. Data Lifecycle Steps Ingest The first stage is to pull in the raw data, such as streaming data from devices, on-premises batch data, application logs, or mobile-app user events and analytics. Store After the data has been retrieved, it needs to be stored in a format that is durable and can be easily accessed. Process & Analyze In this stage, the data is transformed from raw form into actionable information. Explore & Visualize The final stage is to convert the results of the analysis into a format that is easy to draw insights from and to share with colleagues and peers.
- 13. © 2017 Google Inc. All rights reserved. Ingestion Storage Process & Analyze Cloud Pub/Sub Stackdriver Logging Cloud Transfer Service Cloud Storage Cloud SQL Cloud Datastore Cloud BigTable BigQuery Cloud Dataflow Cloud Dataproc BigQuery Cloud Console Google Data Studio Google Sheets Cloud Datalab BI/Analytics Partners Cloud Spanner Explore & Visualize Products to Support Data Lifecycle
- 14. Typical Big Data Jobs Programming Resource provisioning Performance tuning Monitoring Reliability Deployment & configuration Handling growing scale Utilization improvements
- 15. Big Data with Google Focus on insights. Not infrastructure. From batch to real-time. Programming Understanding
- 16. Data & Analytics Cloud Dataproc Fully managed Hadoop and Spark with industry-leading performance BigQuery Fully managed data warehouse for large-scale analytics Cloud Dataflow Real-time data pipelines, with open source SDK via Apache Beam
- 17. Separation of Storage and Compute ● Access any storage system from any processing tool ● Keep as much data as you want, economically ● Share data in place, no more FTP and copying Storage Processing BigQuery Storage (tables) BigQuery Analytics Cloud Bigtable (NoSQL) Cloud Dataproc Cloud Storage (files) Cloud Dataflow
- 18. 10+ years of Big Data innovation - Open Source Google Papers 20082002 2004 2006 2010 2012 2014 2015 GFS Map Reduce Flume Java Millwheel Open Source 2005 Google Cloud Products BigQuery Pub/Sub Dataflow Bigtable BigTable Dremel PubSub Tensorflow Dataflow Apache Beam(Incubating)
- 20. Machine Learning Google Cloud ML Platform facilita servicios modernos de machine learning, con modelos pre-entrenados y un servicio para generar tus propios modelos. Machine Learning Cloud Machine Learning Vision API Speech API Natural Language API Translation API Jobs API
- 21. Machine Learning - Cloud ML Machine learning sobre cualquier tipo y volumen de datos ● Predicción a escala. ● Construcción de modelos sencilla. ● Capacidades de Aprendizaje Profundo (Deep Learning). ● Integración. ● HyperTune. ● Servicio gestionado y escalable. ● Modelos portables.
- 22. Machine Learning - APIs Vision API . Analiza imágenes con el poder de Google. Speech API. Convierte conversaciones a texto con el poder de la nube.
- 23. Machine Learning - APIs Natural Language API . Saca conclusiones de texto desestructurado con Cloud ML. Translation API. Traduce sobre la marcha entre miles de pares de lenguas.
- 24. Machine Learning - APIs Jobs API . Gestiona tu portal de empleo con Cloud ML. Cloud Video Intelligence API. Analiza y extrae información de tus videos.
- 25. Referencias para estar al día Google Cloud Platform Blog Google Cloud Platform Web GCP Twitter Google + GCP Community GCP Podcast Google Cloud Platform Canal de Youtube
- 26. Ejemplos de uso When art meets big data: Analyzing 200,000 items from The Met collection in BigQuery Today we’re adding a new public dataset to Google BigQuery: over 200,000 items from The Metropolitan Museum of Art (aka “The Met”), representing all its public domain art from a total of 1.5 million art objects. The Met Museum Public Domain dataset includes metadata about each piece of art, along with an image or images of the artifact. Google and The Met Museum have been close collaborators for years through Google Arts & Culture and we’re incredibly excited to bring the museum's public dataset to BigQuery.
- 27. Ejemplos de uso Traveloka’s journey to stream analytics on Google Cloud Platform Traveloka is a travel technology company based in Jakarta, Indonesia, currently operating in six countries. Founded in 2012 by former Silicon Valley engineers, its goal is to revolutionize human mobility. One of the most strategic parts of our business is a streaming data processing pipeline that powers a number of use cases, including fraud detection, personalization, ads optimization, cross selling, A/B testing, and promotion eligibility. That pipeline is also used by our business analysts for monitoring and understanding business metrics, both for historical analysis and in real time.
- 28. Ejemplos de uso Getting Your Feet Wet in the Data Lake: Analytics 360 in BigQuery Benefits for Data Engineers, Analysts and Marketers As a Big Data platform, BigQuery offers benefits for multiple stages and roles in the Big Data process: For marketers and analysts, you can run ad hoc queries and get the results within minutes or seconds. The elusive quest for understanding online and offline attribution, user funnels, and long-term customer value comes within reach. For data engineers, BigQuery offers a tremendous operational benefit, as outlined in the next section.
- 29. Ejemplos de uso How WePay uses stream analytics for real-time fraud detection using GCP and Apache Kafka When payments platform WePay was founded in 2008, MySQL was our only backend storage. It served its purpose well when data volume and traffic throughput were relatively low, but by 2016, our business was growing rapidly and they were growing along with it. Consequently, we started to see performance degradation to the point where we could no longer run concurrent queries without a negative impact on latency. Clearly, we needed a new stream analytics pipeline for fraud detection that would give us answers to queries in near-real time without affecting our main transactional business system. In this post, I’ll explain how we built and deployed such a pipeline to production using Apache Kafka and Google Cloud Platform (GCP) services like Google Cloud Dataflow and Cloud Bigtable.
- 30. ¿ Preguntas ? Ismael Yuste linkedin.com/in/ismaelyuste/ @IsmaelYuste