Présentation de l'événement FME World Tour 2017 qui a eu lieu le 13 avril 2017 à Québec. Découvrez les nouveautés de FME 2017 et FME Server 2017. Voyez les trucs et astuces pour optimiser la performance de vos workbench, une solution pour comparer des workspaces ensemble, un portail de chargement et téléchargement de données avec FME Server ainsi que des outils de validation et correction topologique.
Best Practices for Migrating Your Data Warehouse to Amazon RedshiftAmazon Web Services
by Darin Briskman, Technical Evangelist, AWS
You can gain substantially more business insights and save costs by migrating your existing data warehouse to Amazon Redshift. This session will cover the key benefits of migrating to Amazon Redshift, migration strategies, and tools and resources that can help you in the process. We’ll learn about AWS Database Migration Service and AWS Schema Migration Tool, which were recently enhanced to import data from six common data warehouse platforms. Level: 200
Streaming Auto-scaling in Google Cloud DataflowC4Media
This document discusses auto-scaling in Google Cloud Dataflow. It describes how Dataflow pipelines can automatically adjust parallelism levels based on throughput, backlog growth, and CPU utilization signals. The scaling policy aims to keep pipelines keeping up with input rates and reducing backlogs quickly. The mechanism for changing parallelism involves splitting computation ranges across additional machines or migrating ranges between machines. Future work may include finer-grained range splitting and approximate throughput modeling.
DynamoDB and Schema Design
The document discusses DynamoDB schema design and core concepts. It covers how to model different types of relationships in DynamoDB, using primary keys, secondary indexes, and global secondary indexes. It also discusses techniques for optimizing performance and minimizing costs, such as using projections, sparse indexes, and sharding indexes. The document provides an overview of DynamoDB components and new transactional APIs.
DynamoDB is a fully managed NoSQL database that provides single-digit millisecond performance, durable storage, automatic multi-region and multi-master replication, with built-in security and backup capabilities. It supports key-value and document data models, and can scale to handle internet-scale applications. DynamoDB offers features like on-demand capacity, auto scaling of throughput, secondary indexes, transactions, backup and restore functionality, integration with DAX for caching, and streams for integration with Lambda functions. Security features include encryption at rest, IAM policies, and VPC networking capabilities.
The document provides an overview of new transformers in FME that help optimize GIS workflows by simplifying attribute and data validation tasks. The AttributeManager allows consolidated handling of attribute tasks like creation, renaming, copying, and validation. The AttributeValidator performs validation tests on attributes and outputs validation messages. The FeatureWriter enables writing features in workflows to avoid chaining workspaces and support post-processing like notifications and automation.
AWS July Webinar Series: Amazon Redshift Optimizing PerformanceAmazon Web Services
This document provides an overview and best practices for optimizing performance on Amazon Redshift. It discusses topics like data distribution, sort keys, compression, loading data efficiently, vacuum operations, and query processing. The webinar agenda covers architecture, distribution styles, sort keys, compression, workload management and more. Examples are provided to demonstrate how different techniques can significantly improve query performance. Administrative scripts and views are also recommended as helpful tools.
Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse service that makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools. You can start small for just $0.25 per hour with no commitment or upfront costs and scale to a petabyte or more for $1,000 per terabyte per year, less than a tenth of most other data warehousing solutions.
See a recording of the webinar based on this presentation here on YouTube: https://youtu.be/GgLKodmL5xE
Masterclass series webinars, including on-demand access to all of this years recorded webinars: http://aws.amazon.com/campaigns/emea/masterclass/
Journey Through the Cloud webinar series, including on-demand access to all webinars so far this year: http://aws.amazon.com/campaigns/emea/journey/
Redshift is Amazon's cloud data warehousing service that allows users to interact with S3 storage and EC2 compute. It uses a columnar data structure and zone maps to optimize analytic queries. Data is distributed across nodes using either an even or keyed approach. Sort keys and queries are optimized using statistics from ANALYZE operations while VACUUM reclaims space. Security, monitoring, and backups are managed natively with Redshift.
Redshift is Amazon's petabyte-scale data warehouse service that allows Monetate to generate analytics data for a day in around 2 hours. It provides fully managed, scalable performance for analyzing billions of rows of data from Monetate. The author discusses their experience migrating from Hive to Redshift due to Redshift's superior performance, scalability, integration with other AWS services, and fully managed operations.
This document provides an outline and overview of training convolutional neural networks. It discusses update rules like stochastic gradient descent, momentum, and Adam. It also covers techniques like data augmentation, transfer learning, and monitoring the training process. The goal of training a CNN is to optimize its weights and parameters to correctly classify images from the training set by minimizing output error through backpropagation and updating weights.
DynamoDB is a NoSQL database service built for fast, scalable, consistent performance. This presentation introduces DynamoDB and discusses how to get started, provision throughput, design for the DynamoDB data model, query and scan tables and scale reads and writes without downtime.
SQL to NoSQL Best Practices with Amazon DynamoDB - AWS July 2016 Webinar Se...Amazon Web Services
Applications have traditionally stored data in a relational database management system (RDBMS) and have used a Structured Query Language (SQL) to retrieve and update that data. The growth of “internet scale” apps, such as e-commerce, social media, mobile apps, and the rise of big data have increased data throughput demands beyond the range of traditional relational databases. Non-relational (NoSQL) databases enables your application to scale more cost effectively, even for extraordinarily high demand. Amazon DynamoDB is a fully managed NoSQL database service that lets you focus on your app so you don’t have to worry about hardware acquisition or database management and lets you scale down your costs for off-peak periods. In this webinar, we’ll describe common database tasks, then compare and contrast SQL with equivalent DynamoDB operations.
Learning Objectives:
• Why consider the switch from SQL to NoSQL?
• Benefits of Amazon’s NoSQL database service
• Common SQL database operations and their DynamoDB equivalents
6. Apache Kylin Roadmap and Community - Apache Kylin Meetup @ShanghaiLuke Han
The document outlines the roadmap and community growth of Apache Kylin, an open source analytics platform. It discusses Kylin's evolution from an initial prototype in 2013 to adding features like streaming and real-time capabilities. The roadmap also details future plans for advanced OLAP functions, in-memory analysis, and more. Additionally, the document summarizes Kylin's expanding community through meetups, conferences, and new committers from various companies. It concludes by encouraging collaboration to advance the project.
Best Practices for Migrating your Data Warehouse to Amazon RedshiftAmazon Web Services
You can gain substantially more business insights and save costs by migrating your existing data warehouse to Amazon Redshift. This session will cover the key benefits of migrating to Amazon Redshift, migration strategies, and tools and resources that can help you in the process. We’ll learn about AWS Database Migration Service and AWS Schema Migration Tool, which were recently enhanced to import data from six common data warehouse platforms.
Best practices for Data warehousing with Amazon Redshift - AWS PS Summit Canb...Amazon Web Services
Get a look under the hood: Understand how to take advantage of Amazon Redshift's columnar technology and parallel processing capabilities to improve your delivery of queries and improve overall database performance. You’ll also hear about how the University of Technology Sydney (UTS) are using Redshift. The University of Technology Sydney will describe how utilizing Amazon Redshift enabled agility in dealing with Data Quality, a capacity to scale when required, and optimizing development processes through rapid provisioning of Data Warehouse environments.
Speaker: Ganesh Raja, Solutions Architect, Amazon Web Services with Susan Gibson, Manager, Data and Business Intelligence, UTS
Level: 300
In 2001 Ian Painter led the team responsible for OS MasterMap. This ground-breaking project took Ordnance Survey’s Land-Line product and created the world’s first database of ‘real world objects’. Now some 10 years on, this talk will look back at the original premise of its creation and how its been used over the years.
First and foremost OSMM was designed as seamless data and a big hope for the product was that unlike LandLine it would no longer be used as a backdrop map. Many organisations use OSMM as backdrop map but by doing so they’re missing a huge amount of its value. It’s now time to put the map aside and use the data. Welcome to Big Data.
Focusing on Big Data concepts for analysis and query, the second half of this talk will introduce Big Data concepts and how Big Data platform scales on commodity hardware, makes extensive use of parallel processing and works with GI data in a manner totally different from anything we’ve seen before. Not just that, but how Big Data offers all this at a fraction of the cost of traditional GIS and relational databases. Big Data will finally realise the original vision of OSMM – and when that happens OS will need to change the name!
This document summarizes the new features in FME 2017 including over 30 new data formats that can be read and written, more than 10 new transformers, updates to existing transformers, and improvements to usability such as additional options for inspecting data and managing transformer parameters. It also describes enhancements to FME Server including new instance types for running workspaces in the cloud and lower pricing for most users. The goal of FME is to allow data to flow freely between systems and formats while simplifying complex data integration tasks.
1Spatial Australia: Introduction and getting started with fme 20171Spatial
This document introduces new features in FME 2017 including over 20 new data formats that can be read and written, more than 10 new transformers, updates to existing transformers, improved user interface features for workflows, expanded web services and file system capabilities, an updated data inspector, and new automation capabilities for running workflows on demand or on a schedule. The overall goal of FME is to allow data to flow freely between systems and applications while enabling users to spend more time making decisions rather than struggling with data integration tasks.
1Spatial: Cardiff FME World Tour: Getting started with FME1Spatial
This document provides an overview and introduction to new features in FME 2017. It describes how FME can connect more systems, perform powerful transformations, and create workflows quickly and easily. New formats have been added, as have new and polished transformers. The Workbench interface has been improved with features like quick add, inspectors, and handling of rejected features. FME Data Inspector allows inspection of any data. Automation capabilities have been expanded with FME Server updates including new security, deployment, and job control features.
fmewt19 - Around the world stories master deckConsortech
The document contains summaries from various organizations describing how they use FME to solve geospatial data challenges. The summaries are:
1) Suncor Energy automated geospatial data integration to reduce time spent on data tasks from hours to minutes and remove human error.
2) Buccleuch Estates automated farm inspection reports by compiling photos and notes into standardized PDF reports faster than manual methods.
3) Valeron Enviro Consulting visualized windfield data models to create a marketable product for architects by analyzing and calculating the data in FME.
Join Don and Dale for a preview of what’s coming in FME 2020.0. See how you can build Enterprise Integration Patterns using the upgraded FME Server Automations interface, plus learn about new deployment and licensing options that will help you get the most out of the FME Platform. Experience styled connection lines in Workbench, impressive new 3D rendering capabilities, unparalleled Revit support, and new machine learning connections. We’ll cover all of the exciting new formats, transformers, performance tune-ups, and interface upgrades!
The document provides an agenda for a one-day event. It includes times for introductions, presentations, lunch, activities, and the end of the event. It also includes information about the company hosting the event, including their expertise in ETL processes, certifications, projects completed, and values of excellence, responsibility, integrity, and collaboration. Attendees of the event are also listed.
How to Deploy and Maintain several IBM products in a large environment. This presentation is about Saxion University and was made for Engage.ug in Gent Belgium on 31 March 2015.
Introduction and Getting Started with FME 2017Safe Software
This document provides an overview of new features in FME 2017 including more formats supported for reading and writing data, new and updated transformers for performing powerful transformations, improvements to the data inspector for inspecting data, enhancements for automating workflows on FME Server, and updates to FME Cloud including new pricing and instance types. The document demonstrates turning raw satellite imagery into a 3D model and highlights time-saving features for everyday use of FME like adding formats quickly and copying transformer parameters.
AWS Partner Webcast - Analyze Big Data for Consumer Applications with Looker ...Amazon Web Services
Analyze Big Data for Consumer Applications with Looker BI and Amazon Redshift Customizing the customer experience based on user behavior is a constant challenge for today’s consumer apps. Business intelligence helps analyze and model large amounts of data. Looker offers a modern approach to BI leveraging AWS that’s fast, agile, and easy to manage. Join this webinar to learn how MessageMe, which provides emotionally engaging messaging apps to consumers, leverages Looker business intelligence software and the Amazon Redshift data warehouse service to analyze billions of rows of customer data in seconds.
Webinar topics include:
• How MessageMe turns billions of rows of customer data stored in Amazon Redshift into actionable insights
• How Looker connects directly to Amazon Redshift in just a few clicks, enabling MessageMe to build a modern, big data analytics in the cloud. Who should attend
• Information or Solution Architects, Data Analysts, BI Directors, DBAs, Development Leads, Developers, or Technical IT Leaders.
Presenters:
• Justin Rosenthal, CTO, MessageMe
• Keenan Rice, VP, Marketing & Alliances, Looker
• Tina Adams, Senior Product Manager, AWS
From Data to Maps to Docs: Turn Days into Minutes with Automated IntegrationSafe Software
Report generation used to take the Valuations Office in Ireland 20 tedious, long hours per report. Now, it's down to just 3 minutes: saving roughly 42,000 hours annually.
In this first of two parts webinar series, join us and special guest Philip Jacob to learn how they made this possible, and how you can capture time savings like this too by converting data using the Mapnik Rasterizer and FME.
To follow Philips' story, Scenario Specialist Dmitri Bagh will walk you through the basics of understanding and getting started with the Mapnik Rasterizer. You'll learn how to generate beautiful raster imagery and save time with FME’s ability to integrate data from 500+ sources.
You’ll also see how to optimize data before it reaches Mapnik -- including the ability to perform a wide range of geometry transformations. And with the MapnikRasterizer transformer, all of this is done with scalable and automated workflows at your fingertips -- no Python, XML or CSS needed.
P.S. Stay tuned in the New Year for the announcement of a second Data Democratization webinar to complete this series: creating Microsoft Word documents with FME.
This document provides an overview of new features in FME 2017 including improved connectivity to more formats and web services, more powerful transformations for working with dates and times, enhancements to the data inspector and FME Server, and updates to FME Cloud pricing and capabilities. Key highlights include Active Directory support for FME Server user management, new options for handling failed jobs, and increased compute units and RAM for all FME Cloud instance types.
The document provides an agenda for a day-long event. The morning includes introductory presentations from 8:45am to 12pm, with a coffee break scheduled from 10:30-11am. The afternoon runs from 1:15pm to 3:30pm and includes additional presentations as well as an interactive activity from 3:00-3:30pm.
We describe an application of CEP using a microservice-based streaming architecture. We use Drools business rule engine to apply rules in real time to an event stream from IoT traffic sensor data.
FME World Tour 2015 - Around the World - Ken BraggIMGS
The document discusses how Pelmorex leverages FME Cloud to generate over 880,000 web map tiles from meteorological data every 12 hours in near real-time, using AWS services like S3, SQS, and Lambda to dynamically provision compute capacity and process the large volume of data more cost effectively than maintaining their own on-premises servers. FME Cloud allows Pelmorex to generate the time-sensitive map tiles much faster and at a lower annual cost than maintaining their own hardware infrastructure.
O Amazon Redshift é um data warehouse rápido, gerenciado e em escala de petabytes que torna mais simples e econômica a análise de todos os seus dados usando as ferramentas de inteligência de negócios de que você já dispõe. Comece aos poucos, por apenas 0,25 USD por hora, sem compromissos, e aumente a escala até petabytes por 1.000 USD por terabyte por ano, menos de um décimo do custo das soluções tradicionais. Normalmente, os clientes relatam uma compactação de 3x, que reduz seus custos para 333 USD por terabyte não compactado por ano.
The document summarizes using FME for several mapping and geospatial data projects. It describes using FME to generate web map tiles from meteorological forecast models for Pelmorex in near real-time. It also discusses using FME Cloud to dynamically provision compute capacity for tile generation every 12 hours to process large amounts of data. Finally, it mentions that the approach costs $80 per run compared to $300,000 if done on-premises.
Building Your First Digital File Submission Safe Software
Creating a digital file submission using FME can allow you to automate routine tasks that may otherwise take you hours, if not days, to complete.
During this webinar, our team of Support Specialists will show you how to add time back in your day and money back in your pocket using this automation tactic. They’ll take you through how to get started with your first digital file submission and give you an inside glimpse into what happens to the file after it has been built into an automation.
They’ll also share a real life story of what a local government team made possible with their own digital submission plan using FME and some tips and tricks you can use along the way.
Don’t miss out on the chance to learn how you can save your city time and money. We hope to see you there!
Who is Mercator, and how did he change navigation forever? If you’ve ever had to analyze a map or GPS navigation data, chances are you’ve encountered and even worked with the world of coordinate systems. Coordinate systems are becoming more and more important now that we have historical data that is continually updating and moving through GPS systems. But, not everyone knows why we have coordinate systems, or how to deal with them in a way that will maximize the value of their data.
During this webinar, you’ll learn exactly what coordinate systems are and how you can use FME to transform your coordinate system data in an easy to understand way, accurately represented in the geographical space that it exists within. Our team of Support Specialists will walk you through:
- An overview of coordinate systems
- Why we need datums and projections, and units between coordinate systems
- How FME handles coordinate systems
- A brief summary of the 3 main reprojectors
- Issues with datums and why we are moving towards a time based datum
- Where we are heading with FME on coordinate systems
Don’t miss the opportunity to improve the value you receive from your coordinate system data, ultimately allowing you to streamline your data analysis and maximize your time. See you there!
Getting the Most Out of Oracle's EPM Cloud ServicesAlithya
Ranzal implemented Oracle EPM Cloud services including PBCS, FCCS, EDMCS, and FDMEE for Baha Mar to support daily operational reporting, budgeting and forecasting, monthly financial close, and data management. Key aspects included automating the monthly close in FCCS, daily driver-based forecasting in PBCS, leveraging EDMCS for shared hierarchies, and using FDMEE to integrate source system data. The implementation went live successfully and provided benefits like improved accuracy, a single source of financial data, and automated reporting. Lessons learned included thorough requirements, early data gathering, and ensuring client resource availability. The next steps involve implementing account reconciliation in ARCS.
Similar to Présentation du FME World Tour du 13 avril 2017 à Quebec (20)
Airline Satisfaction Project using Azure
This presentation is created as a foundation of understanding and comparing data science/machine learning solutions made in Python notebooks locally and on Azure cloud, as a part of Course DP-100 - Designing and Implementing a Data Science Solution on Azure.
Amazon Aurora 클러스터를 초당 수백만 건의 쓰기 트랜잭션으로 확장하고 페타바이트 규모의 데이터를 관리할 수 있으며, 사용자 지정 애플리케이션 로직을 생성하거나 여러 데이터베이스를 관리할 필요 없이 Aurora에서 관계형 데이터베이스 워크로드를 단일 Aurora 라이터 인스턴스의 한도 이상으로 확장할 수 있는 Amazon Aurora Limitless Database를 소개합니다.
How We Added Replication to QuestDB - JonTheBeachjavier ramirez
Building a database that can beat industry benchmarks is hard work, and we had to use every trick in the book to keep as close to the hardware as possible. In doing so, we initially decided QuestDB would scale only vertically, on a single instance.
A few years later, data replication —for horizontally scaling reads and for high availability— became one of the most demanded features, especially for enterprise and cloud environments. So, we rolled up our sleeves and made it happen.
Today, QuestDB supports an unbounded number of geographically distributed read-replicas without slowing down reads on the primary node, which can ingest data at over 4 million rows per second.
In this talk, I will tell you about the technical decisions we made, and their trade offs. You'll learn how we had to revamp the whole ingestion layer, and how we actually made the primary faster than before when we added multi-threaded Write Ahead Logs to deal with data replication. I'll also discuss how we are leveraging object storage as a central part of the process. And of course, I'll show you a live demo of high-performance multi-region replication in action.
2. Programme
(avant-midi)
8h45 Mot de bienvenue
9h00 Survol des nouveautés de FME
2017
9h15 Trucs et astuces pour optimiser la
performance de vos workbench
dans FME 2017
10h00 Pause-café
10h30 Time Machines and Attribute
Alchemy
11h00 Capsules et astuces de Consortech
• Comparateur de Workspaces
• Chargement et téléchargement
de données avec FME Server
• Validation et correction
topologique
11h45 Dîner
3. Programme
(après-midi)
13h00 Quoi de neuf dans FME Server 2017
• Automatisation des données
corporatives (Enterprise
Automation)
13h30 Actions automatisées en temps reel
avec FME Server
14h15 Jeu interactif
14h45 Conclusion
6. Mission
Vous permettre
d’accéder à une
information de qualité
afin de prendre des
décisions éclairées
pouvant bénéficier à
l’ensemble de la
collectivité.
11. Vos défis
Productivité :
• Automatiser les tâches
redondantes
• Accélérer le traitement
des données provenant
de sources multiples
Qualité :
• Assurer la conformité
des données selon des
normes
12. Vos défis
Coûts :
• Réduire les coûts liés à
l’extraction, la validation,
la publication et
l’intégration de vos
données
organisationnelles
18. Why we do what we do:
Data should be free to use wherever, whenever
and however it’s needed.
Information should flow between
systems, applications, and formats.
You should spend your time making decisions,
not fighting with data.
20. FME 2017
✔ Connect more systems
✔ Perform powerful transformations
✔ Create workflows quickly and easily
✔ Inspect any data, any time
✔ Automate anything
21. Who has the Magic Ticket?
One Lizard Wizard USB drive
holds a free pass to the
FME International User Conference 2017
May 24-26 in Vancouver, BC
+ 5 Other Chances to Win FME Prize Packs
fme.ly/magicticket
27. New formats
• Autodesk FBX (R/W)
• BIM Collaboration Format (BCF) (R/W)
• Cesium 3D Tiles (W)
• glTF (W)
• HTML Table (R)
• IBM dashDB (R/W)
• MapBox MBTiles (R/W)
• MapInfo Extended TAB (R)
• OGC Geopackage Raster Tiles (R/W)
• OpenStreetMap PBF (R)
• Planet (R)
• QlikMaps (R/W)
• Sentinel-2 on AWS (R)
• STL (R/W)
• New background map: Japan GSI Maps
• New on OS X: Tableau (W), Collada (R/W)
• New on Linux: Tableau (W)
28. FME 2017
✔ Connect more systems
✔ Perform powerful transformations
✔ Create workflows quickly and easily
✔ Inspect any data, any time
✔ Automate anything
37. FME 2017
✔ Connect more systems
✔ Perform powerful transformations
✔ Create workflows quickly and easily
✔ Inspect any data, any time
✔ Automate anything
55. FME 2017
✔ Connect more systems
✔ Perform powerful transformations
✔ Create workflows quickly and easily
✔ Inspect any data, any time
✔ Automate anything
56. Reveal the Prince or
Princess in your data
Frog
(no kissing necessary!)
59. FME 2017
✔ Connect more systems
✔ Perform powerful transformations
✔ Create workflows quickly and easily
✔ Inspect any data, any time
✔ Automate anything
69. FME 2017
✔ Connect more systems
✔ Perform powerful transformations
✔ Create workflows quickly and easily
✔ Inspect any data, any time
✔ Automate anything
75. A Quick List
● Remodeled CSV Reading
● Improved Attribute Management
● Better List Handling
● Enhanced Polygon Dissolving
● Faster FeatureReader
Workflows
● Empowered Reading
of Data From Web Sources
78. Steps
1. Read a Geographical Names CSV Dataset
2. Filter the Geographical Names Data for a List of Cities Only
3. Read the Current List of FME World Tour Events
4. Merge the FMEWT Event with its Country Code
5. Match the FMEWT Event to its Host City
6. Ensure the Correct Location is chosen for each Event
81. Summary
Performance in Creation
• Attributes to Read
• List Element Selection
• HTML Table Reader
• Select Source from Web
• Modeless Parameter Dialogs
• Quick Add Readers/Writers
• User Parameter Copy/Paste
• Open Containing Folder
Performance in Execution
• Attributes to Read
• List Element Selection
• Feature Table Formats
• Feature Table Transformers
• Faster Polygon Dissolving
• Faster FeatureReader
87. RCMP E Division
Heidi Lee | Robert Shultz
Goal: Load GPS records into ArcGIS.
Problems:
➔ Inconsistent date formats
➔ Time zones
➔ Daylight saving
Dates and times are
complicated.
88. Formatting?
● YYMMDD, HHMMSS, UTC
● Jun 2016
● ‘on Saturday, Jan 9th 2016, 01:00 am’ & ‘+0530’
● 2016-12-07 12:20:07.785403-05
● 20160313020000.000 (March 13 - Daylight Saving)
● <d v="2016-12-13T00:00:00"/> (Excel)
● YYYY-MM-DD hh:mm:ss[.nnnnnnn] (SQL Server
‘datetime2’ value)
Calculations?
● Date2-Date1 = How many days?
96. Southern Company
Jeff DeWitt
HOK Inc.
David Baldacchino
Goals:
➔ Test for patterns in attribute values
➔ Extract substrings from attribute values
➔ Validate strings
2. Finding patterns
NGI Belgium
Jan Beyen
RCMP E Div.
Heidi Lee
97. Southern Company
Problem: Attribute value cleanup
- MONTANA * or Sales/Other (1)
HOK Inc.
Problem: Extract Sheet numbers from file names
- MyProject - Sheet - A512 - PARTITION TYPES & …
- G001 - GENERAL NOTES, ABBREVIATIONS, SYMBOLS, ...
NGI
Problem: Validate address strings
- Rue Achille Masset 52A
98. Sheet number extraction
MyProject - Sheet - A512 - PARTITION TYPES & …
G001 - GENERAL NOTES, ABBREVIATIONS, SYMBOLS, ...
^[Ss]*?[-]?[ ]?([A-Z0-9]{1,5})[ ]+[-]+[Ss]*$
Address validation
Rue Achille Masset 52A
^((([a-zA-Z]+) )+)([0-9]+)([a-zA-Z]*)$
Wave the wand of regex
101. Regex vs. String Functions Example
Code ABD3705337067
Regular Expression: ([A-Z]{3})([0-9]+)
String Functions:
Attribute String Function
alpha @Left(@Value(Code),3)
beta @Substring(@Value(Code),3,-1)
102. TRC Inc.
Peter Veensta
Goals:
➔ Compare current and previous
Excel rows.
➔ Sum attribute values with the
previous row.
3. Time-travelling
attributes.
FPInnovations
Matt Kurowski
106. Summary
1. DateTime transformers and Text Editor
functions help with:
○ Date/time formatting
○ Calculations
○ Time zones
2. Regex and string functions help with
patterns.
3. Work with current and previous
attribute values in the AttributeManager.
111. Achieving Automation
• Define schema to use for import to database (52+ attributes, one
feature type)
• AttributeFilter: Separate data streams for each company
• FeatureReader: Reads the actual data
• AttributeManager: Convert to common schema
• Can be run at scheduled intervals when a file arrives
112. Achieving Quality
Custom transformers to improve and split data, reused on multiple files:
• SplittFornavnEtternavn: Separate firstname and lastname into 2 different
attributes.
• SplitTelefonOgMobiltlf: Decide if number is a cellular or landline and
create 2 different attributes.
• SplitStreetNameNumberLetter: You have one attribute in which contains
streetname, housenumber, houseletter. Output is 3 different attributes.
113. Achieving Quality
Use existing services and databases to look up and verify values:
• CheckAIDToOwner: Checks if this is the official owner of that property.
• NorkartGeocoder: API to check the validity of an address, handles
misspellings, validates postal number, municipality number, etc. Fresh data
every day!
114. Achieving a Common Schema
Translate each customer’s
schema to the common
schema:
AttributeManager
One separate
AttributeManager for each
company.
133. 1- Réception des
paramètres
3- Vérification
Anti-Virus
2- Envoi du courriel
de réception
4- Traitement du
Workspace “Child”
5- Envoi du courriel
des résultats
Aperçu du Workspace
136. Objectifs
• Faciliter l’intégration de
données provenant de
diverses sources dans
une base de données
spatiales.
• Identifier et corriger les
erreurs topologiques
des intrants avant
l’intégration finale.
150. Enterprise-Level Charms
★ Security - a new level with new charms
★ Deployment - move solutions easily
★ Automate the enterprise
★ Control over jobs
152. Any number of Active Directory domains
Any number of users per domain
A hybrid model - mix server and AD users
Active Directory
Functionality based on feedback and
experience from users! Thank you!
156. ✓ Users can share with other users or groups.
✓ Sharing defined for everything except
“shared resources”.
✓ Users are isolated from each other.
✓ Administration load is low.
Familiar Sharing Model (Google, Dropbox, etc.)
Modern Sharing Model
157. User-Controlled Security
✓ Users have control of all FME Server components
✓ Similar to state-of-the-art services like Google Drive.
159. Connection Based 3rd Party Integration
Why save connections?
● Encapsulate user credentials in a secure manner.
● Define for Databases and Web Applications
● Use across multiple workspaces.
● Share without revealing credentials.
160. Connection Based 3rd
Party Integration
Many web connections.
● Amazon Web Services
● OAuth 2.0 (tens of
thousands of services)
● Token
● HTTP Authentication
All FME-supported databases.
165. On Premises vs FME Cloud
On Premises:
● You secure your infrastructure
● You configure FME Server security
FME Cloud:
● You configure FME Server security
166. FME Cloud Security
We manage data, application and transmission security.
● HTTPS only
● Annual security audit
● Automated security patching
● Leverage AWS Compliance - Industry leaders
● Control firewall rules (protocols & ports)
169. Your FME Server solutions usually
consist of many components.
Workspaces Subscriptions Schedules
Publications Resources Connections
Custom Formats Custom Transformers Topics
171. FME Server Projects
Portable way to move, distribute, and share FME Server solutions.
Benefit #1 - Move a project from one machine to another.
Development StagingTest Production
172. Projects.
Benefit #2 - Backup (export) a project any time for archiving.
FME Server Projects
173. Projects.
Benefit #3 - Share your projects.
First - Export your project!
Projects.
Project Export
FME Server Projects
174. Projects.
Benefit #3 - Share your projects
Next - Send to those with whom you want to share it.
Projects.
Project Export
FME Server Projects
175. Projects.
Benefit #3 - Share your projects
Finally - Receivers simply import the project.
Projects.
Project Export
FME Server Projects
176. Projects.
Benefit #4 - Publish Projects to FME Hub. (Coming Soon)
Share FME Server solutions with the FME
community.
Projects.
Project Export
FME Server Projects
180. Goal: Ensure FME Engines don’t get stuck on a job.
Workflows are often dependent on 3rd Party applications/services.
An engine may hang or take too long waiting on responses.
181. For any job you now have a new setting (Schedules and run Workspace)
Max elapsed time job will run.
If timer expires job is killed!
Charm: Specify maximum time a job can run.
182. Charm: Specify maximum time a job can run.
If job takes longer than allocated then the job is cancelled.
183. Summary - FME Server 2017 Charms
✓ Enterprise Level Security: Enable fine-tuned control with
the new security model.
✓ Enterprise Deployment Model: Move server solutions
between machines using Projects.
✓ Enterprise Automation: Automate everything.*
✓ Enterprise Level Job Control: Define max job time so the
server doesn’t hang.
186. Objectifs
• Faciliter l’intégration de
données provenant de
diverses sources
(internes et externes).
• Ajouter une
composante spatiale
aux données de
propriété immobilière.
• Mettre à jour des
services web.
191. Infractions – Matières
résiduelles
Photos prises sur le terrain
à partir de l’application
CityView.
Chargement des photos
directement sur le serveur.
Problème de taille des
photos.
194. FME Server Notification Service lets you act
on events as they happen, and send
information as it becomes available.
Publications and Subscriptions let you publish
to and monitor different systems and data.
196. Notifications
What they are for
A brief message, usually to
trigger an action.
What they are not for
x Transmitting large amounts of
spatial data.
197. Notifications
What they are for
A brief message, usually to
trigger an action.
Triggering an FME Server
response to an event that
happened outside of FME.
What they are not for
x Transmitting large amounts of
spatial data.
x Triggering an FME Server
response to a continuous
series of messages (many
per second).
198. Notifications
What they are for
A brief message, usually to
trigger an action.
Triggering an FME Server
response to an event that
happened outside of FME.
Sending a message about
something that happened in
FME.
What they are not for
x Transmitting large amounts of
spatial data.
x Triggering an FME Server
response to a continuous
series of messages (many
per second).
x Sending more than one
message per second about
what’s happening in FME.
199. FME Server Notification Service
See the list of what built-in publications and subscribers can be: http://fme.ly/protocols
212. Pros and Cons
Caching whole datasets
Easy to set up and detect
changes.
x Need to store entire
dataset - processing time?
Caching timestamps
No storage needed.
x Harder to set up -
variables, datetimes.
Consider: Does my dataset have a timestamp? Can I rely on the timestamp to indicate change?
222. Webhooks
● i.e. Give systems/services
a URL that will respond.
● Send the notification to
FME Server.
● Queue the received
notifications and process
the requests on a
separate thread.
223. Pros and Cons
Database Triggers
Pushes right to FME
Server topic.
Can push lots of changes
to a holding table.
Perfect for real-time
database changes.
x Database permissions.
Webhooks
No need to waste effort
polling.
Truly real time.
x Complex to set up.
x Not all systems provide or
accept webhooks.
229. Poll vs. Push Considerations
● Simplicity vs. “real-time”ness
● Can the system push to FME Server?
● Are jobs too long for polling?
● API restrictions for polling?
230. Tip: Use FME Server for
internal systems and FME Cloud
for external systems.
236. Summary
• Polling workflow:
o Change detection + scheduling
• Pushing options:
o Database triggers, webhooks
o FME Server: URL or REST API
• Updating:
o Operations in writer parameters
o SQLExecutor
o HTTPCaller