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9A0-386 - Adobe Analytics Architect - BrainDump Information

Vendor Name : Adobe
Exam Code : 9A0-386
Exam Name : Adobe Analytics Architect
Questions and Answers : 60 Q & A
Updated On : March 19, 2019
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9A0-386 exam Dumps Source : Adobe Analytics Architect

Test Code : 9A0-386
Test Name : Adobe Analytics Architect
Vendor Name : Adobe
Q&A : 60 Real Questions

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Adobe Adobe Analytics Architect

Spark on Scala: Adobe Analytics Reference structure | killexams.com Real Questions and Pass4sure dumps

building construction-ready, true-world records applications with Spark and Scala by mixing the appropriate amounts of FP and OOP

Adobe Analytics procedures billions of transactions a day across important net and mobile homes to vigour the Adobe event Cloud. right through contemporary years, we have began to modernize our records processing stack, adopting open supply expertise like Hadoop MapReduce (MR), Storm, and Spark, to identify a number of.

My group has been using Spark and Scala for roughly four years now. We began with a refactoring venture for our Video Analytics product that turned into at the beginning developed using MR and Kafka as building blocks. That labored smartly for a while, however we stored pushing MR to acquire lower end-to-conclusion latency. At one aspect we were running it in a tight one-minute loop throughout tens of millions of activities. Our jobs were stateful and shortly we needed to add some features that could have intended two or extra MR jobs that essential orchestration with anything like Oozie.

We took this chance to trust Spark for an immense refactoring, inspired via past prototypes and relying on right here elements:

  • Spark permits you to define arbitrarily advanced processing pipelines with out the need for external coordination
  • It also has support for stateful streaming aggregations and we could reduce our latency the usage of micro-batches of seconds as a substitute of minutes
  • ultimately, the high-level APIs would imply multiplied developer productivity.
  • related to the remaining aspect, we also made the reasonably brave (on the time) choice of adopting Scala because the core language for the refactoring. back then, our team become primarily developing backend techniques the usage of Java, so our decision seemed quite dangerous. besides the fact that children, we were encouraged to are trying it out. Most of our developers on the group had already achieved the purposeful Programming in Scala song on Coursera, and we had been curious to place it to make use of.

    at the conclusion of the day, we felt that the added productiveness we would get from using Spark with native Scala (as a substitute of the extra clunky Java APIs) become price the inherent possibility of adopting a brand new language.

    considering the fact that that preliminary effort, our group has developed many projects using Scala and Spark, covering a wide array of use situations: batch, streaming, stateful aggregations and analytics, and ETL jobs, just to identify a couple of.

    The rest of this weblog submit will provide an overview of our training learned over the years of using Spark and Scala. First, I’ll focus on one of the vital shortcomings of Spark that we found after establishing the usage of the framework. Then i will introduce our reference structure we constructed to tackle these barriers and clarify how it additionally gives different advantages. within the relaxation of the blog publish, we are able to illustrate the ideas with code examples given to help make the structure concrete. within the end you’ll see how these adjustments have positively helped us, and what we plan to tweak moving forward.

    true existence with Spark: pros and cons

    Spark is a customary engine for distributed statistics processing, with APIs for Scala, Java, and Python. that you may follow it to a wide spectrum of data processing issues, from ETL to analytics, to ML and graph processing. Writing courses that you simply scale out comfortably with the aid of including greater employee nodes is commonly a breeze.

    as an example, listed here are one of the crucial apps that our group has built:

  • records processing applications, frequently ETL and analytics
  • Batch and streaming ingestion and processing
  • Stateless and stateful aggregations
  • …and their high-stage requirements:

  • eat information from Kafka, persist to HBase, HDFS, and Kafka
  • interact in true time with external services (S3, relaxation by the use of http)
  • Deployed on Mesos/Docker throughout AWS and Azure
  • regardless of its huge applicability, Spark isn't a well-known dispensed computing framework.

    many of the APIs are excessive level and anchored in statistics processing. in case you look on the anatomy of a Spark app, it's tough to assert (without event) what code gets performed on the motive force and what code gets accomplished on the executors, the way it’s serialized, and what you’re taking pictures in the closures¹.

    As plenty as we all love practical programming with pure services and free of side effects, real programs need issues like error managing, lifeless letter queues, database connection pools and persistence, arbitrary initialization and shutdown hooks, and so forth. Spark lacks API help for issues like:

  • Lifecycle pursuits round beginning and developing executors (e.g., instantiate a DB connection pool on remote executor)
  • Async processing of activities (e.g., HTTP non-blockading calls on the hot course)
  • handle circulate in case of unhealthy issues going on on far off nodes (e.g., pause processing or controlled shutdown if one node can’t attain an external provider)
  • This frequently potential that you simply deserve to get inventive and locate the correct APIs in the facts processing pipeline to accomplish these operations, devoid of permitting things like serialization or uncontrolled aid advent to become a efficiency bottleneck.

    The “reference structure”

    the entire issues above led us to create an easy template for establishing records processing apps, leveraging Spark’s strengths and dealing around its limitations. We tried to observe the right combination of OOP and purposeful programming, whereas decoupling as much as viable from the Spark APIs in our company code.

    We additionally adopted these high-degree design desires for all our apps:

  • Scalable: horizontally by using adding worker nodes
  • reliable: as a minimum once processing, no facts loss
  • Maintainable: convenient to understand, trade, eradicate code
  • Testable: handy to write down unit and integration checks
  • effortless to configure: installation in a containerized ambiance
  • transportable: to different processing frameworks like Akka Streams or Kafka Streams
  • in the remainder of this article, we can describe how the entire pieces fit collectively within the context of an easy ETL app (let’s call it Ingest) that does right here:

  • Load from persistent queue (Kafka)
  • Unpack and validate protobuf facets
  • Add consumer and processing-linked metadata (from config carrier)
  • Persist to records save (HBase)
  • Emit for downstream processing (Kafka)
  • here is an easy block diagram with the a number of add-ons that make up our architecture:

    Let’s take them one at a time and notice what their main intention is in the structure and how they assist us bring on the brought up design goals.

    be aware: The Scala code samples will miss a considerable number of implementation or production-readiness particulars, however are in any other case copied from our creation apps and very illustrative of the ideas I’m highlighting.

    The main entry factor

    here's simply your normal leading function from any language and we’re without difficulty starting the processing after loading the configuration and building the dependency tree: instantiating SparkContext, the precise streaming source from Kafka, database connections, and so forth.

    You may additionally notice that we’re now not using any libraries for dependency injection and are with no trouble counting on new and passing dependencies round as constructor or feature parameters. here's by way of design, as Spark’s dispensed nature and code serialization mechanics impose strict constraints on the place² and the way³ to create new objects.

    The utility

    The “software” has a important position in our architecture, because it glues together functions with their dependencies to create an genuine facts processing app. it is implemented as a Scala trait and models its dependencies as summary methods. this manner, we facilitate integration testing, by way of not counting on concrete Kafka queues, HBase connections, etc.

    it's additionally the simplest location (study: best file) in the codebase that makes use of Spark APIs: DStream, RDD, radically change, and many others. This makes it possible for us to treat Spark greater like a runtime for disbursed statistics processing and opens up a transparent migration path to a different facts processing framework like Akka Streams or Kafka Streams.

    moreover, here's the place we’re dealing with Spark complexities in order that the business capabilities don’t need to:

  • Caching, broadcasting variables, controlling facet effects
  • transport code and stateful objects (e.g. DB connection) to executors
  • features

    The capabilities make up the vast majority of our company common sense, which is fully decoupled from concerns like Spark, Kafka, configuration, and many others. in the onion architectural model⁴, this often is the API layer (as adverse to infrastructure, which is what we covered thus far).

    this is the place we beginning to lean heavily on the practical side of Scala. more specifically, features are carried out as Scala traits with the following self-imposed constraints:

  • Collections of pure functions grouped logically
  • the entire components (e.g. DB connection) are provided as normal characteristic parameters in the calling website, averting dependency injection and serialization concerns
  • most of the aspect effects are pushed to the outer layers.
  • just a brief example to make clear the final bullet. Let’s anticipate that, as a part of the validation, we’re losing messages and would like to raise a counter. If we did this silently inner the carrier, checking out would become harder as you deserve to look at various the absence of whatever in the output and additionally mock the metrics registry.

    an easier approach is to make use of a validation facts class⁷ like either and return Left(validationError) or appropriate(experience), ignoring metrics altogether. This makes unit testing trivial, gives us a chance to address dangerous events (log to a separate queue for later inspection) and we will push metrics assortment (a aspect effect) to the outer layers.

    Above we have an easy example with the contract for a basic ingest provider that does here:

  • Deserialization, validation
  • Annotate with customer metadata (the use of a customer config repository)
  • Persist to HBase by way of event repository.
  • quick techniques on repositories and different stateful objects

    As i mentioned within the introduction, not every thing in the real world will also be implemented with pure capabilities and free of aspect effects. The simplest instance is persisting the valid hobbies to a database. as the DB connection pool needs to be existing on the executors (that’s the place the statistics is and we don’t want to collect it on the motive force), the query is — how can we instantiate these DB connection objects? many of the time they are not even serializable.

    In Spark streaming, this would typically be performed on the executors by using the foreachRDD/ foreachPartition APIs². within the simplest implementation, you may create it, use it to retailer the statistics, then destroy it. here's a satisfactory strategy for primary objects with little overhead (e.g. HTTP customer), nevertheless it’s not feasible for more costly valued clientele like Kafka and HBase (their respective doctors point out that remember to create and reuse a long-working example per JVM).

    as a way to solve this situation, we now have adopted the ExecutorSingleton strategy described by means of Nicola Ferraro on his blog³. here's only 1 of the factors for doing “guide” injection of substances as basic function arguments in the entire features.

    purposeful area modeling

    As we get decrease in the stack, we’ve reached the core of our structure: the various entities that make up our domain mannequin. this is the place Scala truly shines, because the expressive type equipment encourages developers to create granular types and finished classification hierarchies, with out a great deal of the ceremony essential in Java to create even the easiest POJOs.

    For this, we're certainly the use of certainly one of my favorite features from Scala: case classes geared up in sealed trait hierarchies (also called ADTs⁵).

  • All entities are immutable and serializable
  • Default good implementations for equals and hashCode
  • pattern matching and exhaustiveness exams from the compiler
  • When coupling this with another effective method for imposing invariants in datatypes⁶, we are able to simplify a lot of the code with the aid of counting on the compiler to do the complicated work:

  • smart constructors (manufacturing unit methods on the associate object) are used to implement invariants at advent time.
  • shielding tests are eradicated as data is guaranteed to be legitimate.
  • as an example, right here’s an easy DataSource. notice so that you can’t create an invalid example and that we're modeling “special” facts sources as case objects in its place of magic numbers / Intconstants:

    Closing suggestions

    We lined lots of ground during this submit and that i’ll resist the temptation of mentioning all of the cool tricks we’ve learned over the years :).

    The architecture presented here has proven its strengths in fairly a number of teams within the Adobe Analytics ecosystem. Some teams have even ported it to Java eight and are reaping most of the advantages even though they should engage with legacy APIs and current Java domain models.

    looking ahead, there are nonetheless some issues that we might cherish to do:

  • Adapt it to new Spark fashions like SQL and Structured Streaming; here, Spark takes on a extra vital function and bleeds into the provider layer. As adoption of the new APIs grows, we’ll need to locate a chic approach to integrate them.
  • Extract some of the common utilities in a separate library.
  • Create a template assignment the use of anything like giter8 to accelerate adoption and enhance productivity; when we do this we are able to publish it on public GitHub.

  • Adobe Redefines client event at Adobe Summit 2017 | killexams.com Real Questions and Pass4sure dumps

    LAS VEGAS--(enterprise WIRE)--nowadays, Adobe (Nasdaq:ADBE) kicked off Summit 2017 with a record-breaking 12,000 attendees. Adobe unveiled event Cloud, a complete set of cloud features designed to provide companies everything they need to deliver incredible client experiences. constituted of Adobe advertising and marketing Cloud, Adobe promoting Cloud and Adobe Analytics Cloud, adventure Cloud is constructed on the Adobe Cloud Platform, Adobe’s underlying, cross-cloud architecture that unifies content and statistics. The business additionally announced new developer equipment and companion integrations as well as new intelligence facets for businesses powered by way of Adobe Sensei. finally, Adobe and Microsoft introduced the availability of their first set of joint options focused on remodeling customer experiences.

    besides showcasing Adobe's latest know-how breakthroughs, the convention will feature Saturday evening are living’s Kate McKinnon and tremendous Bowl MVP Peyton Manning, who will share how digital has impacted their careers. Executives from facebook, Microsoft, national Geographic, the NBA and others will be a part of Adobe executives on leading stage.

    Introducing Adobe event Cloud

    Digital has disrupted every trade and forward-searching businesses remember that a very good consumer experience is the differentiator that separates market leaders from market laggards. an outstanding adventure is the most important step in an extended-term relationship with a consumer and requires deep intelligence and spectacular design. in accordance with these challenges, Adobe these days brought Adobe journey Cloud, a comprehensive set of cloud capabilities designed to provide companies every little thing they need to convey notable customer experiences. made out of Adobe advertising Cloud, Adobe promoting Cloud (see separate press unlock) and Adobe Analytics Cloud, event Cloud is constructed on the Adobe Cloud Platform. and since it’s from Adobe, event Cloud integrates with Adobe artistic Cloud and doc Cloud (see separate press unlock).

    massive Enhancements to Adobe Cloud Platform and Adobe Sensei

    Adobe added new enhancements to its Adobe Cloud Platform, the underlying, cross-cloud architecture that unifies content and statistics. Key updates consist of new Sensei capabilities for business valued clientele in addition to new equipment and associate integrations obtainable through Adobe I/O, Adobe’s go-cloud developer portal. in addition, AppDynamics, Clicktale, Dun & Bradstreet, ForeSee, Mastercard, Ooyala, SapientRazorfish and others introduced new Adobe experience Cloud integrations, now attainable on Adobe exchange.

    Adobe Sensei, the intelligence layer in the Adobe Cloud Platform, offers consumers a unified AI and machine researching framework in addition to intelligent functions. nowadays, Adobe released new Sensei capabilities for the enterprise, including Fluid Experiences, Auto-target, greater Anomaly Detection and Contribution evaluation (see separate press liberate).

    Adobe and Microsoft Announce Availability of Joint choices to radically change consumer Experiences

    Adobe and Microsoft announced availability of their first set of joint options designed to aid corporations seriously change their customer experiences. purchasable today, these options will seriously change cross-channel experiences and crusade orchestration the usage of Adobe journey Cloud and Microsoft Cloud. moreover, the agencies announced they're collaborating on the primary open business ordinary to define and unify the language for advertising and marketing, income and services facts mandatory to deliver digital experiences continually at scale (see separate press release).

    Adobe Sneaks New Tech

    At “Summit Sneaks,” Adobe will preview unannounced expertise in areas including AI, VR and IoT. Sneaks offer the viewers an extraordinary – and pleasing – seem to be into the long run, showing brands how they can reimagine their present consumer experiences and company processes. Hosted by way of SNL’s Kate McKinnon, attendees will get a sneak peek on the newest innovation coming out of Adobe’s R&D groups. Sneaks is a perennial spotlight of Summit and viewers favorites often develop into part of future product offerings.

    helpful hyperlinks:

    About Adobe

    Adobe is altering the realm via digital experiences. For greater tips, seek advice from www.adobe.com.

    © 2017 Adobe programs integrated. All rights reserved. Adobe and the Adobe logo are both registered trademarks or logos of Adobe techniques included within the u.s. and/or other nations. All different trademarks are the property of their respective owners.


    Brad Rencher Exits Adobe, Key advertising Cloud leader | killexams.com Real Questions and Pass4sure dumps

    Adobe advertising Cloud’s top govt, Brad Rencher, is leaving the company after essentially a decade.

    Rencher, who as EVP and GM Digital experience set the vision and approach for the utility enormous’s digital advertising platform initiatives, first joined Adobe in 2009 in the course of the acquisition of Omniture, which later grew to become Adobe Analytics, a key part of the Adobe advertising Cloud.

    Adobe describes Rencher’s departure as an “organizational change,” but it’s uncertain precisely what precipitated it – and there’s no alternative for his role as of yet.

    based on Adobe, company CEO Shantanu Narayen goes to create a brand new “wide” organization that includes product, earnings, advertising, functions and assist with Narayen at the helm unless Adobe can discover a new grownup to run it.

    Adobe did not immediately respond to a request for comment.

    A seasoned Adobe vet like Rencher would seem just like the foremost candidate to steer the company group. instead of grabbing the reins, notwithstanding, Rencher will serve as a brief “special adviser” whereas Adobe hunts for someone everlasting.

    Rencher is exiting at a time of untamed boom for the Adobe experience Cloud, which serves as an umbrella for Adobe’s advertising and marketing, Analytics and promoting products. event Cloud doesn't include Adobe’s creative or doc management equipment.

    all the way through his tenure at Adobe, Rencher turned into a predominant architect of the business’s digital advertising method and at once accountable for helping construct and combine the Adobe experience Cloud.

    Rencher changed into there in 2011 when Adobe obtained its demand-side platform Demdex; in 2013 when Adobe scooped up go-channel advertising automation enterprise Neolane; three years later for the buy programmatic video platform TubeMogul; and right through the heady latter half of 2018 when Adobe spent pretty much $6.5 billion mixed to purchase ecommerce platform Magento and B2B advertising platform Marketo in short succession.

    “It’s not an exaggeration to say that he’s played an incredible half in offering continuity, management and imaginative and prescient for creating one of the vital a hit advertising and marketing expertise carriers where so many others have struggled,” said Joe Stanhope, a VP and a fundamental analyst at Forrester. “He leaves the community deploy for fulfillment.”

    The reorg and the direct involvement of CEO Narayen additionally “shows us how severely Adobe takes their advertising and marketing cloud division and the way critical it is to the future of the business,” Stanhope said.

    “they are gearing up to pass the subsequent primary divide, which is bringing advertising Cloud and a lot of different offerings right into a large, aggressive and compelling client journey providing for companies,” he pointed out.


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    The Best User Experience (UX) tools and services in 2018 (updated) | killexams.com real questions and Pass4sure dumps

    Have you ever looked at your web statistics and found that your bounce rates are really high but your conversion rates are really low? You know that this isn’t a good sign, but you have no idea why it’s happening or how to fix it. So what now? In this blog, we will take a closer look at some of the best User Experience (UX) tools.

    User experience might be something to consider if you want to see improvements in this area. Heard of it but not entirely sure of the specifics? User experience includes all aspects of user interaction with your company, your services and/or products. Perhaps you think that your company’s website works well, but do your customers agree? Not able to answer this question? Then it’s definitely a critical time to start focusing on the user experience of your website or mobile app.

    The design process of a good website is now more complex than ever. The success of your company depends on the needs of your customers. Fortunately, there are countless user experience tools that facilitate the entire process. That is why we have made an overview of some great tools to help you determine which of these are best for your business:

    Note:We have added licence costs of the tools in the overview (where possible). Many of these company websites (not surprisingly) use A/B testing for pricing so the price tends to vary quite a bit, and business models change over time. Therefore the price figures for each of these tools will not be updated regularly. They were confirmed the date of this blog post’s publication.

    UX analytics and event-tracking tools

    UX analytics and event-tracking tools enable user researchers to understand what their website visitors are doing on their website. This knowledge can be very useful in that you can quickly determine if a particular design change will be effective before you implement it. On the other hand, these tools aren’t able to give you an explanation, or tell you “why” a user does something. One of the most well-known tools that falls under this category is Google Analytics. But if you’re looking for more specialised tools in regards to click and event analysis, might be good to consider these:

    1. Clicky

    Clicky - UX analytics and event tracking tools

    Clicky - UX analytics and event tracking tools

    Clicky registers and reports various ‘on-site’ activities, such as page views and downloads. However it also shows data such as Twitter statistics. Many analytics tools update once a day, but not Clicky. This tool shows real-time statistics. The dashboard is very user-friendly, also for beginners. If you choose a Premium account, you have access to a lot of other extras such as heatmaps, campaign tracking and conversions. The price? It varies from free to $19.99 a month and there is also a custom option.

    Website: www.clicky.com

    2. Chartbeat

    ChartBeat - UX analytics and event tracking tools

    ChartBeat - UX analytics and event tracking tools

    Chartbeat focuses on content intelligence and analytics, giving the user insight into reader engagement. With this tool, you can determine where your best content and traffic sources are. Filter users by location, devise or visitor frequency and uncover which pages get the most traffic. All is possible with Chartbeat! Starting from $10 a month you can easily discover what all of your visitors are up to on your website.

    Website: www.chartbeat.com

    3. Mixpanel

    Mixpanel - UX analytics and event tracking tools

    Mixpanel - UX analytics and event tracking tools

    Mixpanel really focuses on the user interactions with both website and mobile applications. This tool provides targeted communication with visitors (including e-mails and smart notifications), in-app A/B tests and user survey forms. Additionally, you can visualise the data collected in less than 10 minutes. Another benefit? Mixpanel offers a ‘freemium’. However, if you want more access with additional services, they have a custom pricing model all the way up to the enterprise level.

    Website: www.mixpanel.com

    4. Woopra

    Woopra - UX analytics and event tracking tools

    Woopra - UX analytics and event tracking tools

    Woopra also offers real-time data-driven analytics, enabling you to easily analyse different segments and funnels on your website. The tool creates customer segments that automatically update as user behaviour changes. Using these segments and profiles, users can create funnel reports, custom reports and retention reports. Pricing for this tool is catered to both small businesses (free and up to $1,199.95/month) and enterprises (custom pricing).

    Website: www.woopra.com

    5. Appsee

    Appsee - UX analytics and event tracking tools

    Appsee - UX analytics and event tracking tools

    Appsee is a platform that focuses on analytics in mobile apps. This tool makes it possible to follow all user interactions in real-time. The statistics appear in an easy-to-use dashboard, where you can visualise, among others, heatmaps, conversion funnels and crash-recordings. Appsee offers both premium pricing and custom enterprise pricing.

    Website: www.appsee.com

    6. Keen

    Keen - UX analytics and event tracking tools

    Keen - UX analytics and event tracking tools

    Bring together all events from your website, app, device or webstore and save it on Keen. This tool is a third-party application that shows and shares data in self-service reports and dashboards. Keen makes customer analyses much easier thanks to APIs that collect only the data that you want. In this way, you will find the right answers, which are saved in the Cloud. Keen uses ‘transparent pricing’, meaning that you pay after use. The more you use Keen, the more you pay.

    Website: www.keen.io

    7. Piwik

    Piwik - UX analytics and event tracking tools

    Piwik - UX analytics and event tracking tools

    Piwik is a self-hosted or cloud-based web analytics tool, which among other things, tracks what your visitors are doing on your website. This tool is a bit like Google Analytics, but not entirely the same: You host and manage your own data with Piwik, rather than Google’s servers. The data is available in real-time. Piwik is free. Would you like the hosted variant? Then you will pay (based on pageviews) between $29 and $49 a month. There is also an enterprise option.

    Website: www.piwik.org

    Session recording and heatmapping tools

    This is also a form of quantitative research. On one hand, you can track entire sessions, including full recordings of how the users moves about on your website or app (session recording). On the other hand, you can recognise patterns using heatmapping at page level to see how users interact with the page (for example, where the mouse moves, mouse activity or where they click).

    1. MouseStats

    MouseStat - Session recording and heatmapping tools

    MouseStat - Session recording and heatmapping tools

    With MouseStats you know exactly what your visitors are doing. View all mouse movements, scrolls, keystrokes and clicks as a movie. Thanks to interactive heatmaps, you can see exactly which parts of the page attract attention as well as where they don’t. The price of MouseStats ranges between $29 to $299 a month, depending the number of recordings.

    Website: www.mousestats.com

    2. Crazy Egg

    Crazy Egg - Session recording and heatmapping tools

    Crazy Egg - Session recording and heatmapping tools

    Crazy Egg offers several types of heatmaps that enable the user to analyse visitor behaviour. Where do they click? Do they scroll to the bottom of the page? What are they looking at? These are questions that Crazy Egg answers. Implementation of this tool is easy and the reporting features are even easier. An advantage: you can use Crazy Egg for free for the first thirty days. After the thirty days are up, the tool can cost between $9 and $99 per month. There are also packages for premium plans that range between $189 to $2499 a month.

    Website: www.crazyegg.com

    3. Smartlook

    SmartLook - Session recording and heatmapping tools

    SmartLook - Session recording and heatmapping tools

    Smartlook is a tool that records everything your visitors do on your website. It enables you to see where your visitors have clicked, how long they stay on a page and what information they’ve filled in (e.g. on an order form). Thanks to SmartLook you can easily determine why visitors are leaving the website, which user problems they are experiencing and why the customer ended up purchasing. The price of this tool depends on the amount of recordings collected and stored. They offer a freemium and packages up to €80 as well as custom packages.

    Website: www.smartlook.com

    4. Clicktale

    ClickTale - Session recording and heatmapping tools

    ClickTale - Session recording and heatmapping tools

    Clicktale is a website analytics software tool that is meant to analyse visitor behaviour. With this tool, you can see the drop off rate per input field in the form, how far a visitor has scrolled down on a page and you can track each page for page errors. All your data can be linked easily to a testing tool such as VWO (read more about VWO under ‘A/B and split testing tools’) but also, for example MailChimp. The price depends on what you would like to achieve with ClickTale.

    Website: www.clicktale.com

    5. Inspectlet

    Inspectlet - Session recording and heatmapping tools

    Inspectlet - Session recording and heatmapping tools

    By way of videos, this tool ensures that you know exactly what your visitors are doing on your website. Each mouse movement, scroll, click and keypress is recorded. With this tool, you can determine what attracts attention, what causes confusion and when visitors leave the website whilst it the conversion funnel. The price ranges between free and $299 a month.

    Website: www.inspectlet.com

    6. Heat Map Tracker

    Heat Map Tracker - Session recording and heatmapping tools

    Heat Map Tracker - Session recording and heatmapping tools

    Heat Map Tracker is a software tool that records and shows what every visitors is doing on the website in real-time. Through heatmaps, user behaviour and conversion optimisation, you can practically look over your visitor’s shoulder. The result? Better user experience, customer loyalty and sales. To use Heat Map Tracker, you must pay a one-time fee of $97 and thereafter, $19 a month (for a personal license) and $49 a month (for an agency license).

    Website: www.heatmaptracker.com

    A/B and split testing tools

    A/B testing (or multivariate testing) and split testing are user testing techniques used to determine which variations of a web page have the highest success rates with your visitors. With A/B testing, you are able to compare two versions of a web page, content or an element on the page. This form of testing enables you to determine which version is consistent with the goals you have set. The goal can be anything: increasing sales, reducing the bounce rate or increasing the conversion rate. A/B testing offers real-time, quantitative data about your users and helps you make the right decisions.

    1. Visual Website Optimizer (VWO)

    VWO - A/B and split testing tools

    VWO - A/B and split testing tools

    VWO is one of the easiest A/B Testing tools. It gives you the ability to easily alter headlines, buttons, pictures and other elements for testing. It also offers a visual editor that can be used to edit and design different variations of a page. With VWO you can also identify sales, signups, clicks and other conversion goals, by using statistical data and results. VWO can cost somewhere between $49 (Standard) and $999 (Enterprise) a month, depending on the amount of traffic you have on your website.

    Website: www.vwo.com

    2. Optimizely

    Optimizely - A/B and split testing tools

    Optimizely - A/B and split testing tools

    Optimizely is a popular testing tools that works quite simple: with just one line of code on your website, it’s up and running. It also has A/B and split testing features similar to those of VWO. The tool can link automatically to among others, Google Analytics, so you can simulate test sessions. Ideal for funnel tests, for example! You can also use this tool to customise colours, graphics and text. Optimizely uses a “Pay as you go” model, charging $49 for every 1,000 monthly unique visitors that you test.

    Website: www.optimizely.com

    3. Kissmetrics

    KissMetrics - A/B and split testing tools

    KissMetrics - A/B and split testing tools

    Kissmetrics is an analytics platform that helps increase customer loyalty and retention rates. The Kissmetrics database has a feature that helps you with your A/B test. You can also use this tool with your pre-existing test code, or you can integrate Kissmetrics with another A/B Testing Platform. The nice thing about Kissmetrics is that you can test two different page designs. Kissmetrics Kissmetrics costs between $220 (self-service) to $700 a month.

    Website: www.kissmetrics.com

    4. Oracle Maxymiser

    Oracle Maxymiser - A/B and split testing tools

    Oracle Maxymiser - A/B and split testing tools

    Oracle Maxymiser is an enterprise application of Oracle, which is part of Digital Marketing Suite Oracle. With Oracle Maxymiser you can conduct simple A/B tests as well as complex, multi-variable tests. Track all visitor behaviour on digital channels and analyse this data. That way you can display personalised content on the website. Oracle Maxymiser comes at a somewhat higher price: the cheapest version costs $2,000 per month.

    Website: www.oracle.com

    5. Adobe Target

    Adobe Target - A/B and split testing tools

    Adobe Target - A/B and split testing tools

    Adobe Target is part of Adobe Marketing cloud and offers an intuitive user interface that lets you create personalised web experiences, easy A/B testing and is an easy way of targeting content based on customer profiles (personalisation). Customise the interface with a large number of filtering options. This tool also works well on your smartphone. The price of the tool is unknown.

    Website: www.adobe.com

    6. UsabilityHub

    Usability Hub - A/B and split testing tools

    Usability Hub - A/B and split testing tools

    This tool is a platform where developers and designers can perform simple usability tests. You can choose your own testers or testers who have registered at UsabilityHub. Anyone over the age of 18 can become a tester. Using this tool, you can get insight into how your users experience your website page. Price can range anywhere between free to $396 a month, depending on the amount of users.

    Website: www.usabilityhub.com

    7. Convert

    Convert - A/B and split testing tools

    Convert - A/B and split testing tools

    Convert provides multi-domain A/B and multi-variable testing and tracking tools for both JavaScript, CSS and jQuery. With just a small piece of JavaScript code, you can create and edit your tests immediately as well as test your e-commerce products and category pages. Convert is very similar to Optimizely and VWO in terms of features and functionality, apart from its pricing structure. The price of Convert varies quite a bit from $449 to $6,999 a month (Enterprise). This tool, in particular, caters more to the enterprise option.

    Website: www.convert.com

    Visual/Usability Feedback tools

    Visual feedback tools are also often referred to as usability feedback tools. These types of tools are used by marketeers and other user experience specialists to improve page usability by capturing input from their users as it comes in. Using these tools, you can point to specific page elements (including forms, images, buttons or paragraphs) and provide feedback on those page elements. Once an element is selected, the tool saves the screenshot with the user’s feedback. These tools are great for new websites and apps where the bugs or any unclear content needs to be identified quickly.

    1. Verify

    Verify - Visual/Usability Feedback Tools

    Verify - Visual/Usability Feedback Tools

    With Verify you can collect and analyse visitor feedback from screens or test versions (mockups). You collect feedback, see what people remember about your design and what they think of it. Verify is a nice app for testing ideas before they are implemented. You can get a basic count for $19 a month.

    Website: www.verifyapp.com

    2. UserSnap

    UserSnap - Visual/Usability Feedback Tools

    UserSnap - Visual/Usability Feedback Tools

    UserSnap makes its easier to check for bugs and other issues. It also contributes to fast communication between designers, customers and employees. At the click of a button, visitors and designers can submit a bug report from the website. You can also create in-browser screenshots with this tool, so you can see exactly what your visitor sees without needing a plug-in. Pricing for Small to Medium businesses (SMB) range anywhere between €69 to €329 a month and there is custom pricing for enterprises.

    Website: www.usersnap.com

    3. Trackduck

    TrackDuck - Visual/Usability Feedback Tools

    TrackDuck - Visual/Usability Feedback Tools

    Trackduck is a tool that allows you to highlight visual feedback on specific pages in real-time. Bug reports are sent directly to the dashboard. You can also chat via the dashboard with other testers and designers. The price is anywhere between $9 and $49 a month.

    Website: www.trackduck.com

    4. Bugherd

    BugHerd - Visual/Usability Feedback Tools

    BugHerd - Visual/Usability Feedback Tools

    Bugherd is a web-based issue tracking and project management software system for developers and designers. Like all of the other tools, it also has an in-page feedback option, allowing customers to report issues directly from the website. This enables you to see exactly which parts have been reported. Additionally, it includes project management tools, based on scrum. The software helps you convert customer feedback data into actionable tasks. The tool costs anywhere between $29 and $180 a month.

    Website: www.bugherd.com

    5. Marker

    Marker - Visual/Usability Feedback Tools

    Marker - Visual/Usability Feedback Tools

    Marker is a visual feedback and bug reporting tool for web professionals. With this tool you can convert screenshots from any website into a powerful bug report directly into your existing tools. What is unique about this tool is that the feedback is captured via a browser plug-in, rather than on the page itself so it must first be installed in Chrome before it can be used. Some key features are screenshot annotation tools, shareable links and workflow integration. Marker can be integrated with various scrum and project management tools such as Jira, Slack, Trello and Github. A premium account costs anywhere from $19 to $119 a month.

    Website: www.marker.io

    6. Feedback.js

    Feedback.js - Visual/Usability Feedback Tools

    Feedback.js - Visual/Usability Feedback Tools

    Feedback.js is an open source project created in JavaScript. This framework enables you to create feedback forms that include a screenshot. This is, for instance, known for a number of applications of Google in which it is used. If you’re mainly looking for the collection of visual feedback, this is an inexpensive alternative to other tools. Feedback.js is free to use but some coding skills in JavaScript are required.

    Design and wireframing tools

    Design and wireframing tools are tools focused on the creation of new designs and developing concepts for funnels, landing pages, etc. These types of tools aid in the communication of ideas during the development process as they provide a visual representation of the design and lead to rapid prototyping.

    1. Adobe XD

    adobe xd - Design and wireframing tools

    adobe xd - Design and wireframing tools

    Adobe XD is a vector-based tool developed and published by Adobe Inc for designing and prototyping user experience for web and mobile apps. According to TopTal, the aggressively growing Adobe XD is one of the best product design tools speeding up the product design process.

    Adobe XD handles complex design systems and symbol libraries. It also features intuitive tools for easily connecting screens and creating interactive prototypes that can be utilized in user testing without the need for code.

    Website: https://www.adobe.com/products/xd.html

    2. MockFlow

    MockFlow - Design and WireFraming tools

    MockFlow - Design and WireFraming tools

    MockFlow is an online wireframe tool for software and websites, with an emphasis on collaboration and sharing. The tools has various functions including, drawing up user interface mockups, making design guidelines, creating and publishing websites, annotating images, and more. Pricing ranges anywhere from free (with limited use) to $39 a month (team package).

    Website: www.mockflow.com

    3. Mockplus

    MockPlus - Design and WireFraming tools

    MockPlus - Design and WireFraming tools

    Mockplus is a desktop-based application used for easy and rapid prototyping on platforms such as mobile apps, desktop apps and websites. Users do not need code or technical expertise to use this tool. With Mockplus you can create interactive prototypes in a minimum amount of time. This tool costs $129 a year or you can choose an Unlimited perpetual license for $399.user.

    Website: www.mockplus.com

    4. Pidoco

    Pidoco - Design and WireFraming tools

    Pidoco - Design and WireFraming tools

    Pidoco is a cloud-based software used to create, share and test wireframes, mockups and prototypes. The software includes a library of drag-and-drop interface elements and users can add multiple pages and layers. Pidoco enables you to share prototypes with clients online and has functions to facilitate feedback and back-and-forth discussion. This tool costs anywhere from $12 to $175 a month (depending on the number of active projects.

    Website: www.pidoco.com

    5. Gliffy

    Gliffy - Design and WireFraming tools

    Gliffy - Design and WireFraming tools

    Gliffy is a web-based diagram editor. In this tool, users can drag and drop components and export images. Users can also collaborate online and there is a version tracking function included. It is recommended for personal use and/or smaller businesses. Pricing ranges between $3.99 to $7.99 a month or you can choose the enterprise option (custom pricing).

    Website: www.gliffy.com

    6. Mockingbird

    Mockingbird - Design and WireFraming tools

    Mockingbird - Design and WireFraming tools

    A web-based beta software, Mockingbird enables its users to create, link together, preview and share wireframes from a website or app. It includes features such as drag and drop user interace (UI), smart text resizing and interactive page linking, as well as sharing. Pricing is between $12 to $85 a month (depending on the number of projects).

    Website: www.gomockingbird.com

    7. Lucidchart

    Lucidchart - Design and WireFraming tools

    Lucidchart - Design and WireFraming tools

    Lucidchart is a cloud-based diagramming and visualisation tool. Users can map ideas with clients and colleagues in real-time. This tools is easy to use – for beginners and professionals. Pricing ranges from $4.95 to $20 a month, however it can cost more depending on the number of users. There is also an enterprise option.

    Website: www.lucidchart.com

    Choosing the right tools for your business

    As you can see, there is a lot to choose from, no matter what your need is. Making the right choice all comes down to what you want to achieve and how far along your team or company is in terms of maturity.

    So first things first. Have some goals in mind for what you want to achieve using the tool(s). Then take a look at how these tools fit into your budget. While there are several tools that cater their services more towards enterprise businesses, there are also more cost-efficient options for smaller companies who, for example, might benefit more from a typical SaaS delivery model in which you pay per use (e.g. depending on how many users or website visitors you have). And lastly, don’t forget about the technical limitations. This includes factors such as IT restrictions when it comes to privacy or security (e.g. how and where data is stored). Depending on the possibilities IT-wise, you can choose between a self-hosted, open source solution or an enterprise solution that will satisfy the needs of a larger organisation.

    We hope this overview provides you with some inspiration in finding suitable UX tools.

    Article originally published by Kees Wolters at Mopinion.


    Managing the Threat Above—How AI Can Streamline Cloud Computing with Burgeoning Data Growth | killexams.com real questions and Pass4sure dumps

    In this special guest feature, Tyrone Pike, President & CEO of FileShadow, Inc., believes the number of files transferred daily makes it extremely difficult to manage manually, but AI is improving cloud technology to handle the influx. Applying AI to cloud storage enables users to gather, analyze, catalog and protect more data than ever before. With more than 35 years of experience in the high-tech sector, Tyrone has held numerous top executive positions in software, software systems integration and networking infrastructure companies, including Solid Instance, Sparxent Inc., Bravara Communication, Inc. and InterNAP Network Services Corporate. He serves as the Chairman of the Board of Solid Instance, Inc., and holds an AB in Architecture from Princeton University.

    Tech gurus are not surprised to hear that 2.5 quintillion bytes of data are generated every day. The role of cloud technology in how businesses and prosumers store and manage this data is enormous.

    But because cloud technology has emerged only recently as a vital tool for businesses, and because businesses and individuals have dumped their data into cloud applications without a strategy (or have resisted moving to the cloud because they have no strategy), there are quite a few shortcomings. Many cloud applications have weak search capabilities. Human error, and the sheer amount of searchable data now being produced innately contributes to misplaced files across multiple cloud platforms. It’s easy to forget a filename, or where a file is stored, and PDF documents don’t always show up in a keyword search if there is no keyword in the filename.

    The number of files transferred daily makes it extremely difficult to manage manually, but artificial intelligence (AI) is improving cloud technology to handle the influx. Applying AI to cloud storage enables users to gather, analyze, catalog and protect more data than ever before.

    Here are a few examples of AI solutions in cloud computing:

    Digital Asset Management

    Take the aforementioned example of lost or hard-to-find files. AI is now smart enough to know more information about your files than you could ever find out manually. Technology from Google and IBM scans images to determine key characteristics, including image location and context. For example, you may have pictures of your sailing excursion in Boston, but you didn’t take the time to name or manually tag them. AI can examine each image and recognize pictures with sails or sailboats in them, along with other related characteristics such as ocean, boat, water, etc. It can then tag those images with all of those words for easy searching.

    These same AI tools can also tag a file with the location where it was taken, making it much easier to find photos of “sailing in Boston” than manually scouring through your cloud storage to try to find the photos.

    AI can perform optical character recognition (OCR) on PDF’s, allowing you to find a word in a document that hasn’t been indexed in a regular search engine. For example, you could find a specific term in a contract from the 1980s that referenced work you did with a client. Searching for the company name would yield instances of that company name in documents, even if the company’s name wasn’t included in the title.

    And, these technologies aren’t treating your cloud data in silos. Services can now connect to and search in multiple cloud storage repositories, such as Box, Dropbox, Google Drive, Adobe Creative Cloud, OneDrive or another account.

    Aerial Imagery

    There’s no shortage of potential uses of machine learning and artificial intelligence in data science, namely in aerial imagery as a service. Also, cloud delivery enables easy access to an entire world of aerial captures from any device.

    With high-resolution aerial maps in 2D and 3D, machine algorithms are now able to immediately detect stationary features on the ground without human interaction, such as roadways, buildings, swimming pools, solar panels, patios, parking lots, trees, etc. Applications include drone delivery, smart cities, autonomous driving, along with several engineering, construction and architecture use cases.

    Virtual Assistant Development

    Thanks to cloud computing and advances in machine learning, virtual assistants (VAs) created by Amazon, Google and Apple have quickly become a fixture in consumer homes. These companies are developing AI systems which can learn new words and how to carry on a conversation.

    Cloud computing could be key in achieving this goal by storing the data which the AI accesses to respond to inquiries and learn new things. As AI learns, it can impart this new data back to the cloud, improving future AI as well.

    A significant application of digital assistants is boosting workplace productivity and workflow. Speech-to-text can be used for email dictation and to transcribe meeting notes. Text-to-speech can be used to read written content aloud. Speech recognition can allow for conversational interactions and task management with the VA and “sentiment analysis” can detect overall morale in an enterprise through speech analytics.

    Analytics and Business Intelligence

    Companies can now use machine algorithms to identify insights in large data sets stored in the Cloud in real time so that decision-makers can determine trends, patterns, behaviors, and predict risk and potential outcomes to make recommendations. This has the potential to save time, money and optimize company logistics and resources.

    Business intelligence (BI) dashboards like SAP, NetSuite, Oracle and Microsoft Dynamics-NAV can now process a much wider variety of data than before, so users catch insights that previously were lost in the data.

    This predictive technology shifts the traditional role of BI from reactionary to proactive.

    Cybersecurity

    Last month, IBM Corp. launched IBM Security Connect which, according to the company, is “the first security cloud platform built on open technologies, with AI at its core, to analyze federated security data across previously unconnected tools and environments.”

    IBM Security Connect allows vendors and developers to apply cloud technology, machine learning and AI to cybersecurity products to increase their effectiveness. Through predictive analytics, the platform detects suspicious behavior which will help cybersecurity staff who deal with threats on a daily basis.

    Developments in cloud capabilities and AI will be interesting to watch because one will only be improved with the development of the other, guaranteeing infiltration in both our professional and personal lives.

    Sign up for the free insideBIGDATA newsletter.


    Spark on Scala: Adobe Analytics Reference Architecture | killexams.com real questions and Pass4sure dumps

    Building production-ready, real-world data applications with Spark and Scala by mixing the right amounts of FP and OOP

    Adobe Analytics processes billions of transactions a day across major web and mobile properties to power the Adobe Experience Cloud. During recent years, we have started to modernize our data processing stack, adopting open source technology like Hadoop MapReduce (MR), Storm, and Spark, to name a few.

    My team has been using Spark and Scala for about four years now. We started with a refactoring project for our Video Analytics product that was initially developed using MR and Kafka as building blocks. That worked well for some time, but we kept pushing MR to obtain lower end-to-end latency. At one point we were running it in a tight one-minute loop across millions of events. Our jobs were stateful and soon we needed to add some features that would have meant two or more MR jobs that needed orchestration with something like Oozie.

    We took this opportunity to consider Spark for a major refactoring, encouraged by earlier prototypes and relying on the following features:

  • Spark allows you to define arbitrarily complex processing pipelines without the need for external coordination
  • It also has support for stateful streaming aggregations and we could reduce our latency using micro-batches of seconds instead of minutes
  • Finally, the high-level APIs would mean increased developer productivity.
  • Related to the last point, we also made the rather courageous (at the time) decision of adopting Scala as the core language for the refactoring. Back then, our team was mostly developing backend systems using Java, so our decision seemed rather risky. However, we were encouraged to try it out. Most of our developers on the team had already completed the Functional Programming in Scala track on Coursera, and we were curious to put it to use.

    At the end of the day, we felt that the extra productivity we would get from using Spark with native Scala (instead of the more clunky Java APIs) was worth the inherent risk of adopting a new language.

    Since that initial effort, our team has developed many projects using Scala and Spark, covering a wide range of use cases: batch, streaming, stateful aggregations and analytics, and ETL jobs, just to name a few.

    The rest of this blog post will give an overview of our lessons learned over the years of using Spark and Scala. First, I’ll discuss some of the shortcomings of Spark that we discovered after developing using the framework. Then I will introduce our reference architecture we built to address these limitations and explain how it also provides other benefits. In the rest of the blog post, we will illustrate the concepts with code examples given to help make the architecture concrete. In the end you’ll see how these changes have positively helped us, and what we plan to tweak moving forward.

    Real life with Spark: Pros and cons

    Spark is a general engine for distributed data processing, with APIs for Scala, Java, and Python. You can apply it to a wide spectrum of data processing problems, from ETL to analytics, to ML and graph processing. Writing programs that you scale out simply by adding more worker nodes is generally a breeze.

    For example, here are some of the apps that our team has built:

  • Data processing applications, mostly ETL and analytics
  • Batch and streaming ingestion and processing
  • Stateless and stateful aggregations
  • …and their high-level requirements:

  • Consume data from Kafka, persist to HBase, HDFS, and Kafka
  • Interact in real time with external services (S3, REST via http)
  • Deployed on Mesos/Docker across AWS and Azure
  • Regardless of its wide applicability, Spark is not a generic distributed computing framework.

    Most of the APIs are high level and anchored in data processing. If you look at the anatomy of a Spark app, it is hard to say (without experience) what code gets executed on the driver and what code gets executed on the executors, how it’s serialized, and what you’re capturing in the closures¹.

    As much as we all love functional programming with pure functions and free of side effects, real programs need things like error handling, dead letter queues, database connection pools and persistence, arbitrary initialization and shutdown hooks, etc. Spark lacks API support for things like:

  • Lifecycle events around starting and creating executors (e.g., instantiate a DB connection pool on remote executor)
  • Async processing of events (e.g., HTTP non-blocking calls on the hot path)
  • Control flow in case of bad things happening on remote nodes (e.g., pause processing or controlled shutdown if one node can’t reach an external service)
  • This generally means that you need to get creative and find the right APIs in the data processing pipeline to accomplish these operations, without allowing things like serialization or uncontrolled resource creation to become a performance bottleneck.

    The “reference architecture”

    All the concerns above led us to create a simple template for developing data processing apps, leveraging Spark’s strengths and working around its limitations. We tried to apply the right mix of OOP and functional programming, while decoupling as much as possible from the Spark APIs in our business code.

    We also adopted these high-level design goals for all our apps:

  • Scalable: horizontally by adding worker nodes
  • Reliable: at least once processing, no data loss
  • Maintainable: easy to understand, change, remove code
  • Testable: easy to write unit and integration tests
  • Easy to configure: deploy in a containerized environment
  • Portable: to other processing frameworks like Akka Streams or Kafka Streams
  • In the remainder of this article, we will describe how all the pieces fit together in the context of a simple ETL app (let’s call it Ingest) that does the following:

  • Load from persistent queue (Kafka)
  • Unpack and validate protobuf elements
  • Add customer and processing-related metadata (from config service)
  • Persist to data store (HBase)
  • Emit for downstream processing (Kafka)
  • Here is a simple block diagram with the various components that make up our architecture:

    Let’s take them one by one and see what their main purpose is in the architecture and how they help us deliver on the stated design goals.

    Note: The Scala code samples will leave out various implementation or production-readiness details, but are otherwise copied from our production apps and very illustrative of the concepts I’m highlighting.

    The main entry point

    This is just your typical main function from any language and we’re simply starting the processing after loading the configuration and building the dependency tree: instantiating SparkContext, the actual streaming source from Kafka, database connections, etc.

    You may notice that we’re not using any libraries for dependency injection and are simply relying on new and passing dependencies around as constructor or function parameters. This is by design, as Spark’s distributed nature and code serialization mechanics impose strict constraints on where² and how³ to create new objects.

    The application

    The “application” has a central role in our architecture, as it glues together services with their dependencies to create an actual data processing app. It is implemented as a Scala trait and models its dependencies as abstract methods. This way, we facilitate integration testing, by not relying on concrete Kafka queues, HBase connections, etc.

    It is also the only place (read: only file) in the codebase that makes use of Spark APIs: DStream, RDD, transform, etc. This allows us to treat Spark more like a runtime for distributed data processing and opens up a clear migration path to another data processing framework like Akka Streams or Kafka Streams.

    Moreover, this is where we’re dealing with Spark complexities so that the business services don’t have to:

  • Caching, broadcasting variables, controlling side effects
  • Shipping code and stateful objects (e.g. DB connection) to executors
  • Services

    The services make up the majority of our business logic, which is completely decoupled from concerns like Spark, Kafka, configuration, etc. In the onion architectural model⁴, this would be the API layer (as opposed to infrastructure, which is what we covered so far).

    This is where we start to lean heavily on the functional side of Scala. More specifically, services are implemented as Scala traits with the following self-imposed constraints:

  • Collections of pure functions grouped logically
  • All the resources (e.g. DB connection) are provided as regular function parameters in the calling site, avoiding dependency injection and serialization issues
  • Most of the side effects are pushed to the outer layers.
  • Just a quick example to clarify the last bullet. Let’s assume that, as part of the validation, we’re dropping messages and would like to increase a counter. If we did this silently inside the service, testing would become harder as you need to test the absence of something in the output and also mock the metrics registry.

    A simpler strategy is to use a validation data type⁷ like Either and return Left(validationError) or Right(event), ignoring metrics altogether. This makes unit testing trivial, gives us an opportunity to handle bad events (log to a separate queue for later inspection) and we can push metrics collection (a side effect) to the outer layers.

    Above we have a simple example with the contract for a basic ingest service that does the following:

  • Deserialization, validation
  • Annotate with customer metadata (using a customer config repository)
  • Persist to HBase via event repository.
  • Quick thoughts on repositories and other stateful objects

    As I mentioned in the introduction, not everything in the real world can be implemented with pure functions and free of side effects. The simplest example is persisting the valid events to a database. As the DB connection pool needs to be present on the executors (that’s where the data is and we don’t want to collect it on the driver), the question is — how do we instantiate these DB connection objects? Most of the time they are not even serializable.

    In Spark streaming, this would typically be done on the executors by using the foreachRDD/ foreachPartition APIs². In the simplest implementation, one can create it, use it to save the data, then destroy it. This is a fine strategy for simple objects with little overhead (e.g. HTTP client), but it’s not feasible for more expensive clients like Kafka and HBase (their respective docs indicate that you should create and reuse a long-running instance per JVM).

    In order to solve this issue, we have adopted the ExecutorSingleton strategy described by Nicola Ferraro on his blog³. This is just one of the reasons for doing “manual” injection of resources as simple function arguments in all of the services.

    Functional domain modeling

    As we get lower in the stack, we’ve reached the core of our architecture: the various entities that make up our domain model. This is where Scala truly shines, as the expressive type system encourages developers to create granular types and comprehensive type hierarchies, without much of the ceremony needed in Java to create even the simplest POJOs.

    For this, we are mainly using one of my favorite features from Scala: case classes organized in sealed trait hierarchies (also called ADTs⁵).

  • All entities are immutable and serializable
  • Default sensible implementations for equals and hashCode
  • Pattern matching and exhaustiveness checks from the compiler
  • When coupling this with another useful technique for enforcing invariants in datatypes⁶, we can simplify a lot of the code by relying on the compiler to do the hard work:

  • Smart constructors (factory methods on the companion object) are used to enforce invariants at creation time.
  • Defensive checks are eliminated as data is guaranteed to be valid.
  • As an example, here’s a simple DataSource. Note that you can’t create an invalid instance and that we are modeling “special” data sources as case objects instead of magic numbers / Intconstants:

    Closing thoughts

    We covered a lot of ground in this post and I’ll resist the temptation of mentioning all the cool tricks we’ve learned over the years :).

    The architecture presented here has proven its strengths in quite a few teams in the Adobe Analytics ecosystem. Some teams have even ported it to Java 8 and are reaping many of the benefits even if they have to interact with legacy APIs and existing Java domain models.

    Looking forward, there are still some things that we would like to do:

  • Adapt it to new Spark models like SQL and Structured Streaming; here, Spark takes on a more central role and bleeds into the Service layer. As adoption of the new APIs grows, we’ll have to find an elegant way to integrate them.
  • Extract some of the common utilities in a separate library.
  • Create a template project using something like giter8 to accelerate adoption and increase productivity; when we do this we will post it on public GitHub.


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