Create a Created Date Schema in Bigquery
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Real-Time Backend Architecture Using Node.Js, Express and Google Cloud
Anh Vu Real-time backend architecture using Node.js, Express and Google Cloud Platform Metropolia University of Applied Sciences Bachelor of Information Technology Information Technology Bachelor’s Thesis 5 January 2021 Abstract Anh Vu Author Real-time backend using NodeJS, Express and Google Cloud Title Platform NumBer of Pages 47 pages Date 5 January 2021 Degree Bachelor of Engineering Degree Programme Information Technology Professional Major MoBile Solutions Instructors Petri Vesikivi, Head of MoBile Solutions Real-time applications, which assure the latency within the defined time limit, are becoming more popular due to the growth of Software as a service trend. Before the evolution of cloud computing, the only solution was to use native WebSockets which are difficult to set up and develop. Recently, Google Cloud Platform provides a developer-friendly, fast and responsive platform to make the process of developing real-time applications seamless. The purpose of the thesis was to demonstrate and Build a scalaBle, high-available and reliaBle Backend architecture using Node.js and Google Cloud Platform. The thesis consists of a theoretical background including Node.js, monolithic and microservices architecture, serverless architecture and real-time dataBase, which provide basic understanding of different architectures and technical solutions. The advantages and disadvantages of the architecture were also clearly analyzed and evaluated. Furthermore, a minimum viable product for a taxi booking app was created to demonstrate the architecture usage in a real use case. To summarize, the thesis aimed to provide the insights of real-time Backend architecture using Node.js and Google Cloud Platform. Moreover, the benefits of using this technology stacks were carefully examined in a case study. -
Google Cloud Platform Integration
Solidatus FACTSHEET Google Cloud Platform Integration The Solidatus Google Cloud Platform (GCP) integration suite helps to discover data structures and lineage in GCP and automatically create and maintain Solidatus models describing these assets when they are added to GCP and when they are changed. As of January 2019, the GCP integration supports the following scenarios: • Through the Solidatus UI: – Load BigQuery dataset schemas as Solidatus objects on-demand. • Automatically using a Solidatus Agent: – Detect new BigQuery schemas and add to a Solidatus model. – Detect changes to BigQuery schemas and update a Solidatus model. – Detect new files in Google Cloud Storage (GCS) and add to a Solidatus model. – Automatically detect changes to files in GCS and update a Solidatus model. • Automatically at build time: – Extract structure and lineage from a Google Cloud Dataflow and create or update a Solidatus model. FEATURES BigQuery Loader Apache Beam (GCP Dataflow) Lineage A user can import a BigQuery table definition, directly Mapper from Google, as an object into a Solidatus model. A developer can visualise their Apache Beam job’s The import supports both nested and flat structures, pipeline in a Solidatus model. The model helps both and also includes meta data about the table and developers and analysts to see that data from sources dataset. Objects created via the BigQuery Loader is correctly mapped through transforms to their sinks, can be easily updated by a right-clicking on an providing a data lineage model of the pipeline. object in Solidatus. Updating models using this Generating the models can be ad-hoc (on-demand by feature provides the ability to visualise differences in the developer) or built into a CI/CD process. -
Google's Mission
& Big Data & Rocket Fuel Dr Raj Subramani, HSBC Reza Rokni, Google Cloud, Solutions Architect Adrian Poole, Google Cloud, Google’s Mission Organize the world’s information and make it universally accessible and useful Eight cloud products with ONE BILLION Users Increasing Marginal Cost of Change $ Traditional Architectures Prohibitively Expensive change Marginal cost of 18 years of Google R&D / Investment Google Cloud Native Architectures (GCP) Increasing complexity of systems and processes Containers at Google Number of running jobs Enabled Google to grow our fleet over 10x faster than we grew our ops team Core Ops Team 2004 2016 4 Google’s innovation in data Millwheel F1 Spanner TensorFlow MapReduce Dremel Flume GFS Bigtable Colossus Megastore Pub/Sub Dataflow 2002 2004 2006 2008 2010 2012 2013 2016 Proprietary + Confidential5 Google’s innovation in data Dataflow Spanner NoSQL Spanner Cloud ML Dataproc BigQuery Dataflow GCS Bigtable GCS Datastore Pub/Sub Dataflow 2002 2004 2006 2008 2010 2012 2013 2016 Proprietary + Confidential6 Now available on Google Cloud Platform Compute Storage & Databases App Engine Container Compute Storage Bigtable Spanner Cloud SQL Datastore Engine Engine Big Data Machine Learning BigQuery Pub/Sub Dataflow Dataproc Datalab Vision API Machine Speech API Translate API Learning Lesson of the last 10 years... ● Democratise ML ● Big datasets beat fancy algorithms ● Good Models ● Lots of compute Google BigQuery BigQuery is Google's fully managed, petabyte scale, low cost enterprise data warehouse for analytics. BigQuery is serverless. There is no infrastructure to manage and you don't need a database administrator, so you can focus on analyzing data to find meaningful insights using familiar SQL. -
Are3na Crabbé Et Al
ARe3NA Crabbé et al. (2014) AAA for Data and Services (D1.1.2 & D1.2.2): Analysing Standards &Technologies for AAA ISA Action 1.17: A Reusable INSPIRE Reference Platform (ARE3NA) Authentication, Authorization & Accounting for Data and Services in EU Public Administrations D1.1.2 & D1.2.2– Analysing standards and technologies for AAA Ann Crabbé Danny Vandenbroucke Andreas Matheus Dirk Frigne Frank Maes Reijer Copier 0 ARe3NA Crabbé et al. (2014) AAA for Data and Services (D1.1.2 & D1.2.2): Analysing Standards &Technologies for AAA This publication is a Deliverable of Action 1.17 of the Interoperability Solutions for European Public Admin- istrations (ISA) Programme of the European Union, A Reusable INSPIRE Reference Platform (ARE3NA), managed by the Joint Research Centre, the European Commission’s in-house science service. Disclaimer The scientific output expressed does not imply a policy position of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use which might be made of this publication. Copyright notice © European Union, 2014. Reuse is authorised, provided the source is acknowledged. The reuse policy of the European Commission is implemented by the Decision on the reuse of Commission documents of 12 December 2011. Bibliographic Information: Ann Crabbé, Danny Vandenbroucke, Andreas Matheus, Dirk Frigne, Frank Maes and Reijer Copier Authenti- cation, Authorization and Accounting for Data and Services in EU Public Administrations: D1.1.2 & D1.2.2 – Analysing standards and technologies for AAA. European Commission; 2014. JRC92555 1 ARe3NA Crabbé et al. (2014) AAA for Data and Services (D1.1.2 & D1.2.2): Analysing Standards &Technologies for AAA Contents 1. -
Economic and Social Impacts of Google Cloud September 2018 Economic and Social Impacts of Google Cloud |
Economic and social impacts of Google Cloud September 2018 Economic and social impacts of Google Cloud | Contents Executive Summary 03 Introduction 10 Productivity impacts 15 Social and other impacts 29 Barriers to Cloud adoption and use 38 Policy actions to support Cloud adoption 42 Appendix 1. Country Sections 48 Appendix 2. Methodology 105 This final report (the “Final Report”) has been prepared by Deloitte Financial Advisory, S.L.U. (“Deloitte”) for Google in accordance with the contract with them dated 23rd February 2018 (“the Contract”) and on the basis of the scope and limitations set out below. The Final Report has been prepared solely for the purposes of assessment of the economic and social impacts of Google Cloud as set out in the Contract. It should not be used for any other purposes or in any other context, and Deloitte accepts no responsibility for its use in either regard. The Final Report is provided exclusively for Google’s use under the terms of the Contract. No party other than Google is entitled to rely on the Final Report for any purpose whatsoever and Deloitte accepts no responsibility or liability or duty of care to any party other than Google in respect of the Final Report and any of its contents. As set out in the Contract, the scope of our work has been limited by the time, information and explanations made available to us. The information contained in the Final Report has been obtained from Google and third party sources that are clearly referenced in the appropriate sections of the Final Report. -
Google Cloud Dataflow – an Insight
International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 Index Copernicus Value (2015): 78.96 | Impact Factor (2015): 6.391 Google Cloud Dataflow – An Insight Eashani Deorukhkar1 Department of Information Technology, RamraoAdik Institute of Technology, Mumbai, India Abstract: The massive explosion of data and the need for its processing has become an area of interest with many companies vying to occupy a significant share of this market. The processing of streaming data i.e. data generated by multiple sources simultaneously and continuously is an ability that some widely used data processing technologies lack. This paper discusses the “Cloud Data Flow” technology offered by Google as a possible solution to this problem and also a few of its overall pros and cons. Keywords: Data processing, streaming data, Google, Cloud Dataflow 1. Introduction To examine a real-time stream of events for significant [2] patterns and activities The processing of large datasets to generate new insights and To implement advanced, multi-step processing pipelines to relationships from it is quite a common activity in numerous extract deep insight from datasets of any size[2] companies today. However, this process is quite difficult and resource intensive even for experts[1]. Traditionally, this 3. Technical aspects processing has been performed on static datasets wherein the information is stored for a while before being processed. 3.1 Overview This method was not scalable when it came to processing of data streams. The huge amounts of data being generated by This model is specifically intended to make data processing streams like real-time data from sensors or from social on a large scale easier. -
Google Cloud Dataflow a Unified Model for Batch and Streaming Data Processing Jelena Pjesivac-Grbovic
Google Cloud Dataflow A Unified Model for Batch and Streaming Data Processing Jelena Pjesivac-Grbovic STREAM 2015 Agenda 1 Data Shapes 2 Data Processing Tradeoffs 3 Google’s Data Processing Story 4 Google Cloud Dataflow Score points Form teams Geographically distributed Online and Offline mode User and Team statistics in real time Accounting / Reporting Abuse detection https://commons.wikimedia.org/wiki/File:Globe_centered_in_the_Atlantic_Ocean_(green_and_grey_globe_scheme).svg 1 Data Shapes Data... ...can be big... ...really, really big... Thursday Wednesday Tuesday ...maybe even infinitely big... 8:00 1:00 9:00 2:00 10:00 3:00 11:00 4:00 12:00 5:00 13:00 6:00 14:00 7:00 … with unknown delays. 8:00 8:00 8:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 2 Data Processing Tradeoffs Data Processing Tradeoffs 1 + 1 = 2 $$$ Completeness Latency Cost Requirements: Billing Pipeline Important Not Important Completeness Low Latency Low Cost Requirements: Live Cost Estimate Pipeline Important Not Important Completeness Low Latency Low Cost Requirements: Abuse Detection Pipeline Important Not Important Completeness Low Latency Low Cost Requirements: Abuse Detection Backfill Pipeline Important Not Important Completeness Low Latency Low Cost Google’s 3 Data Processing Story Data Processing @ Google Dataflow MapReduce FlumeJava Dremel Spanner GFS Big Table Pregel Colossus MillWheel 2002 2004 2006 2008 2010 2012 2014 2016 Data Processing @ Google Dataflow MapReduce FlumeJava Dremel Spanner GFS Big Table Pregel Colossus MillWheel 2002 2004 2006 2008 2010 -
Google Genomics Documentation Release V1
Google Genomics Documentation Release v1 Google Genomics <https://cloud.google.com/genomics> Mar 07, 2018 Contents 1 Select Genomic Data to work with3 1.1 Discover Published Data.........................................3 2 Process Data on Google Cloud 19 2.1 Run workflows and common tasks in parallel.............................. 19 2.2 Run Galaxy on Compute Engine..................................... 43 2.3 Run NCBI BLAST on Compute Engine................................. 43 2.4 Run Bioconductor on Compute Engine................................. 43 2.5 Run iPython Notebooks on Compute Engine.............................. 45 3 Access Genomic Data using. 47 3.1 Integrative Genomics Viewer (IGV)................................... 47 3.2 Run Picard and GATK tools on Cloud-Resident Genomic Data..................... 49 3.3 Browse Reads with Bioconductor.................................... 54 3.4 Beacon.................................................. 55 3.5 GABrowse................................................ 55 3.6 The R client............................................... 56 3.7 Python.................................................. 56 3.8 Java.................................................... 57 3.9 Go.................................................... 57 4 Analyze Data in Google Genomics 59 4.1 Analyze Reads.............................................. 59 4.2 Analyze Variants............................................. 67 4.3 Annotate Variants............................................ 101 4.4 Perform Quality Control Checks.................................... -
Overview Objectives Exploring the Bigquery Console
CS6502 - APPLIED BIG DATA AND VISUALIZATION (Spring 2020) Lab 3 Overview SQL (Structured Query Language) is a standard language for data operations that allows you to ask questions and get insights from structured datasets. It's commonly used in database management and allows you to perform tasks like transaction record writing into relational databases and petabyte-scale data analysis. This lab serves as an introduction to SQL and is intended to prepare you for the many labs in this course. This lab is divided into two parts: in the first half, you will learn fundamental SQL querying keywords, which you will run in the BigQuery console on a public dataset that contains information on London bikeshares. In the second half, you will learn how to export subsets of the London bikeshare dataset into CSV files, which you will then upload to Cloud SQL. From there you will learn how to use Cloud SQL to create and manage databases and tables. Towards the end, you will get hands-on practice with additional SQL keywords that manipulate and edit data. Objectives In this lab, you will learn how to: ● Distinguish databases from tables and projects. ● Use the SELECT, FROM, and WHERE keywords to construct simple queries. ● Identify the different components and hierarchies within the BigQuery console. ● Load databases and tables into BigQuery. ● Execute simple queries on tables. ● Learn about the COUNT, GROUP BY, AS, and ORDER BY keywords. ● Execute and chain the above commands to pull meaningful data from datasets. ● Export a subset of data into a CSV file and store that file into a new Cloud Storage bucket. -
Data Processing with Apache Beam and Google Cloud Dataflow
Data Processing with Apache Beam (incubating) and Google Cloud Dataflow Jelena Pjesivac-Grbovic Staff software engineer Cloud Big Data In collaboration with Frances Perry, Tayler Akidau, and Dataflow team XLDB’16 - May 2016 Agenda 1 Infinite, Out-of-Order Data Sets 2 What, Where, When, How 3 Apache Beam (incubating) 4 Google Cloud Dataflow 1 Infinite, Out-of-Order Data Sets Data... ...can be big... ...really, really big... Thursday Wednesday Tuesday … maybe infinitely big... 8:00 9:00 10:00 11:00 12:00 13:00 14:00 … with unknown delays. 8:00 8:00 8:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 Element-wise transformations Processing 8:00 9:00 10:00 11:00 12:00 13:00 14:00 Time Aggregating via Processing-Time Windows Processing 8:00 9:00 10:00 11:00 12:00 13:00 14:00 Time Aggregating via Event-Time Windows Input Processing Time 10:00 11:00 12:00 13:00 14:00 15:00 Output Event Time 10:00 11:00 12:00 13:00 14:00 15:00 Formalizing Event-Time Skew Skew Reality Ideal Processing Time Event Time Formalizing Event-Time Skew Watermarks describe event time progress. Skew "No timestamp earlier than the ~Watermark watermark will be seen" Ideal Often heuristic-based. Processing Time Too Slow? Results are delayed. Too Fast? Some data is late. Event Time 2 What, Where, When, How What are you computing? Where in event time? When in processing time? How do refinements relate? What are you computing? Element-Wise Aggregating Composite What Where When How What: Computing Integer Sums // Collection of raw log lines PCollection<String> raw = IO.read(...); // Element-wise transformation into team/score pairs PCollection<KV<String, Integer>> input = raw.apply(ParDo.of(new ParseFn()); // Composite transformation containing an aggregation PCollection<KV<String, Integer>> scores = input.apply(Sum.integersPerKey()); What Where When How What: Computing Integer Sums What Where When How What: Computing Integer Sums What Where When How Where in event time? Windowing divides data into event-time-based finite chunks. -
Tamr on Google Cloud Platform: Walkthrough
Tamr on Google Cloud Platform: Walkthrough Tamr on Google Cloud Platform: Walkthrough Overview Tamr on Google Cloud Platform empowers users to manage and publish data without learning a new SDK or coding in Java. This preview version of Tamr on Google Cloud Platform allows users to move data from Google Cloud Storage to BigQuery via a visual interface for selection and transformation of data. The preview of Tamr on Google Cloud Platform covers: + Attribute selection from CSV files + Joining CSV sources + Transformation of missing values, and + Publishing a table in BigQuery, Google’s fully managed, NoOps, data analytics service Signing Into Tamr & Google Cloud Platform To get started, register with Tamr and sign into Google Cloud Platform (using a Gmail account) by going to gcp-preview.tamr.com + If you don’t have an account with Google Cloud Platform, you can go through the Tamr portion of the offering, but will not be able to push your dataset to BigQuery. + If you don’t have a Google Cloud Platform account but would like to register for one, select the “Free Trial” option at the top of the Google Cloud Platform sign-in page. Selecting Sources Once you have signed in: + Select the project and bucket on Google Cloud Platform from which you would like to pull data into Tamr. Tamr on Google Cloud Platform: Walkthrough Adding / Subtracting Attributes Now that a data source has been added, attributes related to that data source should now appear on the left side of the screen. At this point, you have the option to add all of the attributes to a preview (via ‘Add All’ button) or add some of the attributes of interest to the preview (via click-and-drag functionality). -
For Cloud Professionals, Part of the on Cloud Podcast
Architecting the Cloud at Google Cloud Next 2018_The past, present, and future of BigQuery For Cloud professionals, part of the On Cloud Podcast Mike Kavis, Chief Cloud Architect, Deloitte Consulting LLP File Duration: 0:18:04 Operator: Welcome to Architecting the Cloud, part of the On Cloud Podcast, where we get real about Cloud Technology what works, what doesn't and why. Now here is your host Mike Kavis. Mike Kavis: Welcome to Deloitte’s Architecting the Cloud Podcast, I'm your host Mike Kavis and I am here in San Francisco on day three of the exciting Google Next Conference, and I'm here with Google's Product Manager for BigQuery, Tino Tereshko. And Tino, nice to meet you, I‘ve been following you on Twitter for a while, you may not remember but we chatted back and forth on a couple topics over the years, it was nice to meet you in person here. Tino Tereshko: Yes, nice to meet you as well. Mike Kavis: Yes. 1 Architecting the Cloud at Google Cloud Next 2018_The past, present, and future of BigQuery Tino Tereshko: Thank you for not butchering my last name, you got it. Mike Kavis: Well I practiced it a few times, and I still worry. Tell us a little bit about your background and then tell you what keeps you busy, Google these days? Tino Tereshko: It’s a great question, so my background is in mathematics, spent my time in finance, in my time (0:01:00) had a number of start-up’s before coming to Google.