Technical Introduction to Celebrus Technologies


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An introduction to Celebrus Technologies, example use cases, key concepts and deployment architectures

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Technical Introduction to Celebrus Technologies

  1. 1. ©2016CELEBRUSTECHNOLOGIESLTD Technical Introduction to Celebrus Celebrus
  2. 2. Increasing Data Value Raw Event Data Event Collection. Business context e.g. Clicks, views, searches & historical context e.g. Last 10 products browsed. Real-Time Business Event Modeling. Scenarios & compound events added e.g. Check out, abandoned quotation. Document Databases Data Warehouses Real-time Decisioning Discovery Platforms Contact Centre Videos Emails Social Media Adverts Actions Triggered & Decisions Enabled Across Channels HighSpeedDataLoading DataVisualisation Reporting&BI Increasing Data Value Individuals SEO Mobile Web Content Push by Celebrus • Real-time website personalisation • Personalised triggered emails • Campaign & lifecycle attribution • Single customer view creation • Messaging development • Offline personalisation • Hot leads into call centre • Social & video optimisation Creating Business Value ©2016CELEBRUSTECHNOLOGIESLTD
  3. 3. ©2016CELEBRUSTECHNOLOGIESLTD Touchpoints
  4. 4. The Insert ▪ Data collection is enabled through a single static insert on your website: <script type=“text/javascript” src=“CelebrusInsert.js”></script> ▪ Simple to deploy with a minimal footprint ▪ Typically added at the bottom of the web page for best performance ▪ The JavaScript insert is often called the collection agent ©2016CELEBRUSTECHNOLOGIESLTD
  5. 5. ▪ Unified collection from multiple channels: ▪ Set top boxes ▪ Games consoles ▪ Web applications ▪ Mobile devices ▪ Bespoke applications ▪ Native applications have their own agent: ▪ iOS ▪ Windows Phone ▪ Android ▪ Flash/Flex ©2016CELEBRUSTECHNOLOGIESLTD The Insert
  6. 6. ©2016CELEBRUSTECHNOLOGIESLTD Event Stream Click User interactions including images, links, buttons, offers and promotions Content Content such as JavaScript variables, DOM values, meta tags etc Errors Technical errors identified on the page (JavaScript) Field Field interactions on text boxes, lists, check boxes, sliders, pickers etc. Celebrus can collect data from any HTML control. Forms Form submit and attempted submit – includes field values Media Embedded media including name, type, length, current status etc Mouse Mouse events including type of interaction and the target object Page Events related to the page (for example, page loaded) Facebook Facebook events such as adding a comment, like, send, user status etc Twitter Twitter events such as follow, tweet, retweet and user status LinkedIn LinkedIn events such as share and user status Google Plus Google Plus events such as share and user status User-defined Customer specific data collected using JSON Data API
  7. 7. ©2016CELEBRUSTECHNOLOGIESLTD Business Events Basket Events covering basket activities such as checkout, add, amend, and abandoned Behavior Captures any well-defined behavior - for example a customer who has asked for more product information or who has searched for terms and conditions Campaign Data concerning referrers, affiliates and search engines Error Business errors which stops a user completing a task Goal Defines an interaction a visitor has with your web site, for example, clicking on an image Offer Personalized offers, selection, display, interactions and click through tracking Profile Identifies individuals who use your web site, either when they explicitly identify themselves (login) or implicitly through cookies Promotion Family of events related to selecting, displaying and tracking promotions Search Captures search related activity, the search terms which customers used, advanced search options Transaction Everything to do with purchases and transactions including abandonment – a checkout process can further be refined into a series of transaction steps Wishlist Similar to baskets and caters for web sites offering wishlists User defined Extends Celebrus to include any kind of user-defined business event
  8. 8. ©2016CELEBRUSTECHNOLOGIESLTD Basket Visit a product view page followed by a click on “Add To Shopping Cart” Product information (SKU, value and quantity) extracted from page content Behaviour Form submit (for example, registration form, complaint form, follow-up enquiry) Campaign First page in a session combined with a referrer or a query string parameter Error HTML elements of a specific class (red box!) appearing on any page Sometimes combined with a form attempt Goal Click identified by href (for example, a click on “Contact Us” link) Profile Form submit (login form) followed by re-direction to home page Validation by checking cookie values on home page or HTML elements in page Promotion Click on a link or image Promotion details extracted from query string Search Form submit followed by re-direct to a results page Advanced search options extracted from additional form fields Transaction Sequence of: pages, form submits, or button clicks The sequence is important because users often bookmark confirmation pages! Configuration
  10. 10. ©2016CELEBRUSTECHNOLOGIESLTD Event Store ▪ Celebrus data collection and processing is extremely agile ▪ Data collection policies can be dynamically changed, taking effect immediately ▪ Customers typically collect everything with just a few exclusions (passwords) ▪ Business meaning is attached to the data as it is collected in real-time ▪ So what happens if something is not configured correctly? ▪ An agency deploys a campaign with the wrong identifiers? ▪ Warehouse data won’t have the correct campaign attributions ▪ Celebrus can re-process data using a revised configuration ▪ Original data must have been stored in an event store ▪ Files in the event store are either Avro or Parquet formats ▪ Re-processing generates a new set of output data for a given time period ▪ All visitor sessions that started in the time period are re-processing ▪ The output data format is exactly the same as before ▪ Either flat files (CSV, JSON, XML) or loaded into your warehouse
  11. 11. ©2016CELEBRUSTECHNOLOGIESLTD Profiles ▪ Celebrus provides a profile store called the ABI Server ▪ Separate database application which runs on MySQL or Oracle ▪ Stores a representative subset of an individual’s data: ▪ Profile information ▪ Product interactions ▪ Campaign attribution and visits ▪ Site search terms ▪ Content actions ▪ Behaviours ▪ User-defined attributes ▪ ABI also implements profile stitching / identity matching ▪ Allows an individual to be reconciled on multiple devices ▪ Can also track individuals across multiple domains ▪ Enables anonymous sessions to be attributed to individuals ▪ On web browsers this uses a persistent first party cookie ▪ Profile data is primarily used in two places in Celebrus: ▪ Real-time rules evaluate against the session and profile data ▪ Web service available which provides HTTP/SOAP query API
  12. 12. ©2016CELEBRUSTECHNOLOGIESLTD Data Layers ▪ Data layers collect valuable user interaction data from web pages ▪ Implemented as JavaScript objects because of the ubiquity of JavaScript ▪ Not part of the HTML content and so does not affect rendering performance ▪ Straightforward to extend JavaScript insert to collect data layer objects ▪ Support for CORS ensures large payloads are transmitted in POST requests ▪ Data layer objects can be shredded and processed using data enrichment plugins ▪ Dynamic collections like require bespoke JavaScript
  13. 13. Scenarios ©2016CELEBRUSTECHNOLOGIESLTD
  14. 14. ©2016CELEBRUSTECHNOLOGIESLTD Scenarios  Celebrus is configured to identify and create business events  Often this is something simple like a customer arriving on a particular page  It may be a more sophisticated business process described by several steps  Configuration for a business event is described using a scenario  Imagine a use case where we target people for car insurance  This segmentation might be for personalisation or for analytics  We might decide that interested in car insurance means:  Viewing more than 10 pages which have the words “Car Insurance” in the title  Or completing the “Insurance Quote” process in the last week  Or clicking on a link in the email “Spring Car Insurance Deals”  Or by viewing the “Insurance Tab” in the web application  Or clicked on the “More Information” in the car insurance web page  Or the customer has an existing policy due for renewal in the next 60 days  Earlier versions of Celebrus used a forms based approach  Works well for simple scenarios like home page analysis  Not rich enough to describe these more sophisticated scenarios
  15. 15. ©2016CELEBRUSTECHNOLOGIESLTD Defining Scenarios  Celebrus has a declarative approach to defining business events  Scenarios proceed by matching events as they arrive from the visitor  When the scenario reaches the end it emits a completed business event  Attributes in the output event populated as the scenario proceeds  Form values  Query strings  Web variables  HTML content  JSON data API  Literal values
  16. 16. ©2016CELEBRUSTECHNOLOGIESLTD Test Cases  Tooling has built in support for test, debug and regression testing  Events can be captured from a web site and used to replay through graphs  Run all test cases on captured events before promoting to production  Link is provided from the event to the configuration which generated it
  17. 17. ©2016CELEBRUSTECHNOLOGIESLTD Managing Configuration  Configuration rules are securely stored in the Celebrus repository  Users are assigned permissions to run the configuration UI, read/write etc  Audit facility tracks who made changes, and what they changed  Changes can be rolled back and rolled forward through the audit UI  Straightforward to build a core set of scenarios for an enterprise  The scenarios can be customised for sub-sites or associated brands  Re-using configuration in this way reduces time and cost from deployment  Scenarios and user-defined events can be imported from test systems
  18. 18. Data Sources ©2016CELEBRUSTECHNOLOGIESLTD
  19. 19. ©2016CELEBRUSTECHNOLOGIESLTD Data Sources  Enables third party applications to send event data  Real-Time Server surfaces an HTTP JSON endpoint for delivery  Can have many endpoints where different data is delivered  Effectively allows data to be collected from new environments  Sending application can decide how often to send the data  For example, collect all the data for a session and then transfer  Data format for the JSON payload is configurable  Simple flat JSON object which maps directly into an evenr  Standard Celebrus format which includes event type and group  Bespoke JSON format which is processed by JavaScript  Data sources create custom events for the data  Processed by the event stream like any other event
  20. 20. ©2016CELEBRUSTECHNOLOGIESLTD Data Enrichment  Enables additional data to be retrieved during processing  Typically from external systems: databases, web services, files etc  Data is added to the event stream through one or more events  It is not possible to add data to an existing event (breaks integrity)  Celebrus provides a JavaScript data enrichment plugin  Configure which events it should process (typically relatively few!)  Real-Time Server calls handleEvent function for each event  The following data is available to JavaScript plugins:  The event which triggered the current interaction  Session context containing session and historic profile  Other services available to JavaScript include:  Log information to the Real-Time Server  Standard set of log levels: trace, debug, info etc  Store information in session to be used later on  Create events which are added to the event stream  JavaScript runtime is closely integrate with the JVM  Enables Java APIs to be called from JavaScript code
  21. 21. ©2016CELEBRUSTECHNOLOGIESLTD Data Enrichment Examples  Product includes playbook for data enrichment plugins  Shows how to call the OpenWeatherMap web service API  API provides current weather, forecast, UV index and more  Register with the site to get an API key (required)  Weather service is free for up to 60 calls per minute  HTTP GET requests use XMLHttpRequest object  Generally only synchronous requests useful in event handling  Creates a custom event which receives the weather data  Summary, humidity, temperature, wind speed and much more DemographicsLongitude -73.9851 DemographicsLatitude 40.7589 Description Light rain Temperature 20.96 WindDirection 90.0 WindSpeed 4.1 HumidityPercent 93.0 Sunrise 05:44
  22. 22. ©2016CELEBRUSTECHNOLOGIESLTD Data Enrichment API Description Geo-demographics Population breakdown by race, age, and household income, additional data about the location includes business activity, terrain features, and crime statistics Financial Exchange rates, currency information, credit reports, order status, payment information, blockchain queries, taxation rates, equity prices, market data Social Data mining for social networks: Twitter, Facebook, LinkedIn, GooglePlus Security Email verification, fraud detection, proxy detection (TOR exit nodes), breached website disclosure search, mobile call authentication, gamer ID checks Events Events, contacts, organisation, registration, CRM data, concerts, sports, political rallies, venues, ticket availability, nearby restaurants, travel times, itineries Real-time Travel time tables and arrivals, flight information, Twitter streaming, Uber, ElasticSearch, live scores, beacons, IoT devices, sensors, traffic monitoring eCommerce Product information, price comparison, merchant offers, reviews, coupons, delivery tracking, availability, specifications, loyalty information And many more examples from government, search, payments, storage, advertising et al
  23. 23. Data Loader ©2016CELEBRUSTECHNOLOGIESLTD
  24. 24. ©2016CELEBRUSTECHNOLOGIESLTD Data Loader ▪The process by which event data is delivered to target systems ▪ The target can be a traditional data warehouse for reporting and analytics ▪ Equally, the target data system might be a discovery platform like Aster ▪ More recently, distributed processing clusters like Hadoop have become popular ▪Wide variety of relational and non-relational systems supported ▪ Teradata, Oracle, MySQL, SQL Server, Hadoop and MongoDB ▪ Data can also be written out as files – Avro, CSV, JSON, Parquet and XML ▪ Makes it easy to extend to other target data systems like Aster ▪Real-Time Servers let you capture and act on data within seconds ▪ The data loader however works more slowly to maximise throughput ▪ This means we deliver the data within 60 seconds of it happening ▪ Data can be batched up into larger and less frequent loads if required
  25. 25. ©2016CELEBRUSTECHNOLOGIESLTD Database Support Database Minimum Supported Version JDBC Notes Aster 6.00.01 Aster JDBC driver: noarch-aster-jdbc-driver.jar MongoDB 2.6 No separate download is required, the MongoDB Java driver is integrated with the Real-Time Server MariaDB 5.5 MariaDB Connector/J driver For example: mariadb-java-client-1.3.7.jar MySQL 5.6 MySQL Connector/J driver For example: mysql-connector-java-5.1.30-bin.jar Oracle 11g Oracle JDBC driver, for example: ojdbc6.jar NOTE a different JDBC driver is required if you are using Oracle OCI (Oracle Call Interface). The Data Loader user interface distinguishes between Oracle and Oracle OCI because they have different JDBC connection strings (as well as JDBC JAR files) SQL Server 2014 JTDS Microsoft SQL Server JDBC driver For example: jtds-1.3.1.jar NOTE the Data Loader does not support the Microsoft JDBC driver. The open source jTDS driver is widely considered to be more stable and perform better Teradata 13 Teradata JDBC driver For example: tdgssconfig.jar and terajdbc4.jar
  26. 26. ©2016CELEBRUSTECHNOLOGIESLTD Design Considerations Events Celebrus generates a wide range of events from high volume and low level events such as clicks and field interactions, through to high value business events such as transactions and user identification. The choice of events has considerable impact on the storage requirements. Schema driven Traditional databases such as Teradata, Oracle, MySQL, and SQL Server have a structure which is well defined, enforced from an integrity perspective, and (mostly) normalised. Document databases generally do not enforce a schema and are therefore more flexible in what they store in any given collection. Document oriented An event is conceptually one document and therefore fits well with a document database approach. Equally, the data in an event is straightforward to shred into a more normalised set of tables. How will the data be used? Last but certainly not least – it is very important to consider what the data will be used for. Is everything in an event required or are there key attributes which will be required for a report? This guides a range of considerations such as sharding, clustering, indexing and more.
  27. 27. ©2016CELEBRUSTECHNOLOGIESLTD ▪The product ships with a full featured configuration for the Data Loader ▪ This includes three templates to cover online, offline and recovered activity ▪ Includes 75+ tables and many hundreds of event attributes ▪ Covers wide range of activity including products, individuals, baskets and much more! ▪ Easy to customise and extend for customer specific deployments Out-of-the-Box Schema
  28. 28. ©2016CELEBRUSTECHNOLOGIESLTD Documentation ▪The Data Loader schema provides extensive documentation ▪ Entity relationship diagram for all the tables in the physical data model ▪ Foreign keys, partitioning, default values are provided where appropriate ▪ MySQL Workbench file provides an easy way to work with the data model ▪ Generated DDL adds comments to the tables and columns in the database ▪Query playbook details 50+ common queries using the schema ▪ For example: attribution, customer journey, and cross device reconciliation
  29. 29. ©2016CELEBRUSTECHNOLOGIESLTD Hadoop ▪ The economics of Hadoop has propelled it forwards at great pace ▪ Variety of data tools provides a rich environment (Hive, Impala, Pig et al) ▪ Hadoop is an excellent platform for processing Celebrus digital data ▪ Data partitions very efficiently by individual, session, timestamp etc ▪ Data Loader supports loading event data directly into HDFS ▪ Uses the popular open source Avro and Parquet file formats ▪ Easy to consume files directly in Hadoop data tools through HDFS ▪ Requires webhdfs to upload files to HDFS over HTTP/s ▪ Supports Apache BigTop 0.6+ Cloudera 5.3+ Hortonworks 2.2+
  30. 30. ©2016CELEBRUSTECHNOLOGIESLTD Hadoop ▪ Avro files can be loaded into Hive/Impala after tables are defined ▪ The LOAD DATA INPATH moves files into the Hive warehouse directory ▪ This operation is very efficient and requires no copying of the data in HDFS ▪ Schedule Hadoop tools to INSERT/SELECT and avoid small files problem ▪ Avro files can also contain structured data not just flat rows ▪ Similar in concept to structured content in JSON files (maps and arrays)
  31. 31. ©2016CELEBRUSTECHNOLOGIESLTD Reporting Views ▪Celebrus data can be used for many different purposes ▪ Marketing attribution, purchasing propensity, behavioural analytics and more ▪ Everything links to an individual and so can be rolled up if required ▪ Which makes Celebrus data very good for web analytics style reporting ▪Example Tableau and Qlik projects help get started quickly ▪ Provides structure for joining data from other channels into the digital data ▪ Eases report creation and ensures table joins are done correctly
  32. 32. ©2016CELEBRUSTECHNOLOGIESLTD Reporting Views
  33. 33. ©2016CELEBRUSTECHNOLOGIESLTD Reporting Views
  34. 34. Real-Time Offers ©2016CELEBRUSTECHNOLOGIESLTD
  35. 35. ©2016CELEBRUSTECHNOLOGIESLTD Real-Time Offers ▪Most personalisation systems are either rule driven, or model driven ▪Rule driven systems typically segment their visitors (the audience) ▪ Visitors are targeted with appropriate offers based on their segment ▪ Segmentation is based on a set of pre-defined; deterministic; rules ▪Model driven solutions are often based on pre-built analytical models ▪ These models target visitors based on their profile or contact history ▪ Often seen in retail environments to make product recommendations ▪ Successful where mathematical models can predict next best actions ▪Celebrus is a flexible platform for real-time personalisation ▪ Celebrus provides a powerful rules system for offers and messages ▪ Model driven systems are supported through data streams ▪ Pega, SAS and Teradata (CIM/RTIM) data streams are available out-of-the-box ▪Combine Celebrus rules and third party decisioning systems ▪ Enables a crawl/walk/run approach to add incremental value ▪ Celebrus rules for default marketing messages to anonymous visitors
  36. 36. ©2016CELEBRUSTECHNOLOGIESLTD Real-Time Rules ▪Rules allow personalised content to be displayed to visitors ▪ Visitors are targeted with appropriate content based on their segment ▪ Options allow for control groups, testing (by IP address) and date ranges ▪Rules in Celebrus require the following configuration: Who Segmentation is based on a set of pre-defined deterministic rules – the configuration for one segment is called a trigger in Celebrus. What A rule must decide what content is to be displayed. The Real-Time Server provides a content library where images, HTML, Javascript and other resources can be stored. The Real-Time Server hosts the content through its built-in web server (which must therefore be accessible to the browser). When A trigger must decide when to evaluate, generally this is whenever something relevant occurs. For example, a trigger might have a condition based on the pages someone has visited. The trigger would be automatically evaluated each time a visitor navigates to another page. Where A trigger specifies what actions to perform when it evaluates true. Actions display content in the visitor’s web page. Actions specify the web pages which are relevant to them (page URL, title, name etc), and where in those pages the content should be displayed (often an HTML id or name).
  37. 37. ©2016CELEBRUSTECHNOLOGIESLTD Segmentation ▪Visitors can be selected based on a wide variety of conditions ▪ Both historic behaviours and conditions related to the current session
  38. 38. ©2016CELEBRUSTECHNOLOGIESLTD Data Streams ▪Out-of-the-box support for decision management systems ▪ The decision management system selects offers appropriate to the visitor ▪ The offers selected are often based on information fed from Celebrus ▪ For example: behaviours, products viewed, purchases etc ▪ The offers are delivered and presented to the visitor by Celebrus ▪Tracking updates sent to the decision management system ▪ Notification when an offer is displayed avoids offer fatigue ▪ Confirmations are provided when a visitor clicks on an offer ▪Data streams support content decisions for personalisation  Decisions are represented as content IDs such as Sport and Travel ▪Data streams integrate with decision management systems  Teradata RTIM for real-time customer marketing  Teradata CIM for marketing automation  SAS ESP for fraud and threat detection  Pega for multi channel profiling and decisioning  HTTP JSON for custom third party applications
  39. 39. ©2016CELEBRUSTECHNOLOGIESLTD Deployment ▪Data streams from Real-Time Server to decisioning system ▪ Integrates with content management systems where required ▪ Ideally all systems are co-located for optimum performance ▪ Increasingly common to integrate with cloud decisioning systems
  40. 40. ©2016CELEBRUSTECHNOLOGIESLTD Pega  Pega data stream provides an out-of-the-box integration  Events are streamed to Pega in real-time (HTTP JSON)  Data model includes a starter set of business events  Goals, purchases, forms, site searches and visits  Click through and display notifications  Anonymous and logged in visitor tracking  Easy to extend as customer requirements change  Combines with a pre-built blueprint which is deployed to Pega  Supports content decisioning requests for personalisation  Uses latest Pega technology: decision data stores and data flows
  41. 41. ©2016CELEBRUSTECHNOLOGIESLTD SAS ESP Event StreamProcessor  Integrates with the SAS Event Stream Processor (ESP)  Well suited for threat detection and fraud analysis  One-way delivery of data – no content decisions  Data transported using RabbitMQ messaging  Out-of-the-box data model focused on interaction events  These require no site specific semantic configuration  Pages, forms, clicks, field interactions, geolocation et al  Additional real-time feed from Celebrus Security Agent
  42. 42. ©2016CELEBRUSTECHNOLOGIESLTD Teradata CIM Customer Interaction Manager  Teradata CIM is a marketing automation application  CIM analysis based on a subset of the Celebrus data model  Data pulled from Teradata EDW using an overnight batch process  Marketing messages are queried through Teradata CIS  Teradata Channel Integration Services (TCIS)  Data stream used for content decisions and follow on tracking
  43. 43. ©2016CELEBRUSTECHNOLOGIESLTD Teradata RTIM Real-Time Interaction Manager  Teradata RTIM provides real-time interaction management  Events are fed to RTIM in real-time so it can build a customer profile  Content decisions converted into display content using templates  Visitor interactions fed back to RTIM (display, click through etc)  Includes the following events in a standard data model:  Basket  Behaviours  Forms  Goals  Media  Pages  Profile  Sessions  Site search  Transaction  User identification
  44. 44. ©2016CELEBRUSTECHNOLOGIESLTD JavaScript  Data streams can be customised with JavaScript  Enables adhoc and bespoke processing to be implemented  For example, sorting or filtering of the content decisions  JavaScript is essential when configuring Teradata CIS  Response messages are almost entirely customer specific  Scripts can customise all of these interactions:  The event data sent to a data stream  Whether a content decision is sent or cancelled  The URL for events and content requests  Content decisions displayed to the visitor  The following data is available to JavaScript:  The event which triggered the current interaction  Session context containing session and historic profile  Other services available to JavaScript include:  Log information to the Real-Time Server  Standard set of log levels: trace, debug, info etc  Raise application errors which generate status alerts  These are visible in the Cluster Management Console
  45. 45. Deployment ©2016CELEBRUSTECHNOLOGIESLTD
  46. 46. ©2016CELEBRUSTECHNOLOGIESLTD Who Does What? ▪Collection Servers are the point of contact for client applications ▪ Collect events as they arrive from the browsers and applications ▪ Writes the events into the events database and sends them to Real-Time Servers ▪Configuration Server stores versioned system configuration ▪ Business event configuration stored along with security information ▪ Role based security provides authentication and authorisation ▪Real-Time Servers process the event stream in real-time ▪ Responsible for handling personalisation and real-time rules execution ▪ Real-Time export of events in a variety of formats (CSV, XML and JSON) ▪ Interfaces to external decision management systems (RTIM, Pega etc) ▪ General purpose plugins call out to HTTP, SOAP and JMS applications ▪ABI Server stores information about each individual who visits ▪ Stateless server which manages profile information for each visitor ▪ Also responsible for implicitly identifying visitors based on cookies
  47. 47. ©2016CELEBRUSTECHNOLOGIESLTD Clusters ▪A Celebrus deployment cluster is typically split into two parts: ▪ One or more Collection Servers which are located in the DMZ ▪ Traffic should be load balanced between the Collection Servers ▪ Sessions should be sticky to keep traffic on the same Collection Server ▪ Configuration and Real-Time Servers deployed behind the inner firewall ▪ Real-Time Servers can be used to provide personalised content to browsers ▪ Alternatively a separate web server or CMS can take this role ▪Security is an important consideration for any deployment ▪ All network connections can be secured using SSL ▪ Port numbers for all communications are configurable ▪ Includes connections between the Celebrus applications ▪ Complete flexibility on what is collected and what isn’t ▪ Addresses privacy and regulatory concerns ▪Servers can be added and removed to clusters as required ▪ Cluster Management Console provides complete administrative application ▪ Management status alerts easily plugged into enterprise monitoring tools
  49. 49. ©2016CELEBRUSTECHNOLOGIESLTD Summary ▪ Celebrus collects and loads event data into your target systems ▪ The target can be a traditional data warehouse for reporting and analytics ▪ Equally, the target data system might be a discovery platform like Aster ▪ More recently, distributed processing clusters like Hadoop have become popular ▪ Wide range of target systems supported for maximum flexibility ▪ Aster, Teradata, Oracle, MySQL, SQL Server, Hadoop and MongoDB ▪ Data can also be written out as files – Avro, CSV, JSON, Parquet and XML ▪ An out-of-the-box schema provides a full featured configuration ▪ Data Loader Schema Guide has many example queries to help you get started! ▪ Wide set of analytics use cases supported (for example, customer journeys) ▪ Built-in re-processing supports the full data collection lifecycle ▪ File based event store provides efficient storage for collected events