ETL Options Across Cloud Simplifying the Data Preparation, Data Transformation & Data Exploration for Augmented Analytics X-Clouds
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Amazon Connect Data Lake Best Practices AWS Whitepaper Amazon Connect Data Lake Best Practices AWS Whitepaper
Amazon Connect Data Lake Best Practices AWS Whitepaper Amazon Connect Data Lake Best Practices AWS Whitepaper Amazon Connect Data Lake Best Practices : AWS Whitepaper Copyright © Amazon Web Services, Inc. and/or its affiliates. All rights reserved. Amazon's trademarks and trade dress may not be used in connection with any product or service that is not Amazon's, in any manner that is likely to cause confusion among customers, or in any manner that disparages or discredits Amazon. All other trademarks not owned by Amazon are the property of their respective owners, who may or may not be affiliated with, connected to, or sponsored by Amazon. Amazon Connect Data Lake Best Practices AWS Whitepaper Table of Contents Abstract and introduction .................................................................................................................... i Abstract .................................................................................................................................... 1 Are you Well-Architected? ........................................................................................................... 1 Introduction .............................................................................................................................. 1 Amazon Connect ................................................................................................................................ 4 Data lake design principles ................................................................................................................. -
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. -
Building Big Data Storage Solutions (Data Lakes) for Maximum Flexibility
Building Big Data Storage Solutions (Data Lakes) for Maximum Flexibility July 2017 © 2017, Amazon Web Services, Inc. or its affiliates. All rights reserved. Notices This document is provided for informational purposes only. It represents AWS’s current product offerings and practices as of the date of issue of this document, which are subject to change without notice. Customers are responsible for making their own independent assessment of the information in this document and any use of AWS’s products or services, each of which is provided “as is” without warranty of any kind, whether express or implied. This document does not create any warranties, representations, contractual commitments, conditions or assurances from AWS, its affiliates, suppliers or licensors. The responsibilities and liabilities of AWS to its customers are controlled by AWS agreements, and this document is not part of, nor does it modify, any agreement between AWS and its customers. Contents Introduction 1 Amazon S3 as the Data Lake Storage Platform 2 Data Ingestion Methods 3 Amazon Kinesis Firehose 4 AWS Snowball 5 AWS Storage Gateway 5 Data Cataloging 6 Comprehensive Data Catalog 6 HCatalog with AWS Glue 7 Securing, Protecting, and Managing Data 8 Access Policy Options and AWS IAM 9 Data Encryption with Amazon S3 and AWS KMS 10 Protecting Data with Amazon S3 11 Managing Data with Object Tagging 12 Monitoring and Optimizing the Data Lake Environment 13 Data Lake Monitoring 13 Data Lake Optimization 15 Transforming Data Assets 18 In-Place Querying 19 Amazon Athena 20 Amazon Redshift Spectrum 20 The Broader Analytics Portfolio 21 Amazon EMR 21 Amazon Machine Learning 22 Amazon QuickSight 22 Amazon Rekognition 23 Future Proofing the Data Lake 23 Contributors 24 Document Revisions 24 Abstract Organizations are collecting and analyzing increasing amounts of data making it difficult for traditional on-premises solutions for data storage, data management, and analytics to keep pace. -
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. -
Cost Modeling Data Lakes for Beginners How to Start Your Journey Into Data Analytics
Cost Modeling Data Lakes for Beginners How to start your journey into data analytics November 2020 Notices Customers are responsible for making their own independent assessment of the information in this document. This document: (a) is for informational purposes only, (b) represents current AWS product offerings and practices, which are subject to change without notice, and (c) does not create any commitments or assurances from AWS and its affiliates, suppliers or licensors. AWS products or services are provided “as is” without warranties, representations, or conditions of any kind, whether express or implied. The responsibilities and liabilities of AWS to its customers are controlled by AWS agreements, and this document is not part of, nor does it modify, any agreement between AWS and its customers. © 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved. Contents Introduction .......................................................................................................................... 1 What should the business team focus on?...................................................................... 2 Defining the approach to cost modeling data lakes ........................................................... 2 Measuring business value ................................................................................................... 4 Establishing an agile delivery process ................................................................................ 5 Building data lakes ............................................................................................................. -
A Comprehensive Study of Recent Metadata Models for Data Lake
DATA ANALYTICS 2020 : The Ninth International Conference on Data Analytics A Comprehensive Study of Recent Metadata Models for Data Lake Redha Benaissa∗yz, Omar Boussaidy, Aicha Mokhtariz, and Farid Benhammadix ∗x DBE Laboratory, Ecole Militaire Polytechnique, Bordj el Bahri, Algiers, Algeria y ERIC, Universite de Lyon, Lyon 2, France z RIIMA Laboratory, USTHB University, Algiers, Algeria Email: ∗[email protected], [email protected], [email protected], [email protected] Data Source Abstract—In the era of Big Data, an un amount of 1 precedented Data Discovery Data Source heterogeneous and unstructured data is generated every day, 2 which needs to be stored, managed, and processed to create new services and applications. This has brought new concepts in data Data Source n Construction of Data Storage management such as Data Lakes (DL) where the raw data is Catalog stored without any transformation. Successful DL systems deploy efficient metadata techniques in order to organize the DL. This paper presents a comprehensive study of recent metadata models for Data Lake that points out their rationales, strengths, and Query Tools weaknesses. More precisely, we provide a layered taxonomy of recent metadata models and their specifications. This is followed Figure 1. Data management process in a Data Lake. by a survey of recent works dealing with metadata management in DL, which can be categorized into level, typology, and content extraction of the right metadata to build the catalog remains a metadata. Based on such a study, an in-depth analysis of key challenging issue. features, strengths, and missing points is conducted. This, in turn, allowed to find the gap in the literature and identify open research This paper presents a comprehensive study of recent meta- issues that require the attention of the community. -
Harness the Power of Your Data
Harness the power of your data: Why Financial Services institutions are building data lakes on AWS What is a data lake? A data lake is a centralized repository that allows you to store all structured and unstructured data at scale and run flexible analytics Financial institutions are collecting such as dashboards, visualizations, big data processing, real-time analytics, and machine learning, to guide better decisions. and storing massive amounts of data Machine The Financial Services industry has relied on traditional data infrastructures Learning Analytics for decades, but traditional data solutions can’t keep up with the volumes and variety of data financial institutions are collecting today. A cloud-based data lake helps financial institutions store all of their data in one central repository, making it easy to support compliance priorities, realize cost efficiencies, perform forecasts, execute risk assessments, better understand customer behavior, and drive innovation. AWS delivers an integrated suite of services that provides the capabilities needed to quickly build and manage a secure data lake that is ready for analysis and the application of machine learning. In this overview, learn how financial institutions are unlocking the value of their data by building data lakes on AWS. On-Premises Real-Time Data Movement Data Movement © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. | 2 There are many benefits to building adata lake on AWS Compliance & Security Scalability Agility Innovation Cost effective Encrypt highly sensitive Amazon S3 data lakes allow any Perform ad-hoc and Aggregated and normalized Pay-as-you-go pricing data and enable controls type of data to be stored at any cost-effective analytics on a data sets provide a foundation for compute, storage, for data access, auditability, scale, making it easy to meet per-query basis without moving for advanced analytics and and analytics. -
Lake Data Warehouse Architecture for Big Data Solutions
(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 11, No. 8, 2020 Lake Data Warehouse Architecture for Big Data Solutions Emad Saddad1 Hoda M. O. Mokhtar3 Climate Change Information Center and Renewable Energy Faculty of Computers and Artificial Intelligence and Expert System Cairo University Agricultural Research Center (ARC) Giza, Egypt Giza, Egypt 4 2 Maryam Hazman Ali El-Bastawissy Climate Change Information Center and Renewable Energy Faculty of Computer Science and Expert System MSA University Agricultural Research Center (ARC), Giza, Egypt Giza, Egypt Abstract—Traditional Data Warehouse is a multidimensional [2], [3]. To achieve this, we propose a new DW architecture repository. It is nonvolatile, subject-oriented, integrated, time- called Lake Data Warehouse Architecture. Lake Data variant, and non-operational data. It is gathered from Warehouse Architecture is a hybrid system that preserves the multiple heterogeneous data sources. We need to adapt traditional DW features. It adds additional features and traditional Data Warehouse architecture to deal with the capabilities that facilitate working with big data technologies new challenges imposed by the abundance of data and the and tools (Hadoop, Data Lake, Delta Lake, and Apache Spark) current big data characteristics, containing volume, value, in a complementary way to support and enhance existing variety, validity, volatility, visualization, variability, and venue. architecture. The new architecture also needs to handle existing drawbacks, including availability, scalability, and consequently query Our proposed contribution solve several issues that face performance. This paper introduces a novel Data Warehouse integrating data from big data repositories such as: architecture, named Lake Data Warehouse Architecture, to provide the traditional Data Warehouse with the capabilities Integrating traditional DW technique, Hadoop to overcome the challenges. -
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