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Qlik Design Blog

44 Posts authored by: Michael Tarallo

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Hey guys! - I'm very excited to introduce you to our latest release of Qlik Sense, June 2017. Yep - no version number or dots in this naming convention. With the announcement of our continuous release process discussed here, Qlik will begin releasing Qlik Sense every 10 weeks!  Since each release will be similar in scope, we are also replacing our number-based naming convention with releases identified by the month and year they were distributed.  This means that instead of Qlik Sense 4.0, our June release is simply called Qlik Sense June 2017.  (Most other Qlik products will also follow a similar cadence going forward as well.)

 

 

Currently customers and partners are invited to Join the Technical Preview and can get the latest software here. If you are not a customer or partner, our freemium product, Qlik Sense Desktop - will be available later in the month - however you can still experience some of these new features immediately (such as the new data preparation features) without downloading any software by registering with Qlik Sense Cloud.

 

There is so much goodness in this release that I don't want to create a giant text wall telling you about it. I created a brief summary video for your enjoyment as well as longer videos detailing and demonstrating the new features. You can view them at the video index below which include video files and samples. Please note that additional information on our Advanced Analytics integration is available in this community group Advanced Analytics Extensions and requires permission. Please send an email to mailto:insight@qlik.com to gain access. I am in the middle of working on a brief summary and "Hello World"-type video to demonstrate its concepts.

 

We want to hear from you, so please your comments and questions below!

 

Qlik Sense June 2017 - Highlights

 

The main video index for the Qlik Sense June 2017 Release and video .mp4 files and samples can be found here: Qlik Sense June 2017 - What's New

 

NOTE: To increase resolution or size of the video, select the YouTube logo at the bottom right of the player. You will be brought directly to YouTube where you can increase the resolution and size of the player window. Look for the 'settings' gears icon in the lower right of the player once at YouTube.

 

Regards,

Michael Tarallo (@mtarallo) | Twitter
Qlik

In this edition of the Qlik Design Blog, our Emerging Technology Evangelist, David Freriks is back discussing integration between Qlik and Kudu.

 

 

Navigating the analytics labyrinth with integration of Kudu, Impala, and Qlik

 


apachekudu_logo_0716_345px.pngUsing Hadoop for Big Data analytics is nothing new, but a new entity has entered the stale file format conversation with the backing of Cloudera – you might have heard of it, it’s called Kudu.

 

What is Kudu?

 

Let’s first take a step back and think about the dullest topic in the universe, file system storage formats. Flat files, AVRO, Parquet, ORC, etc. have been around for a while and all provide various advantages and strategies for data access optimizations in an HDFS construct. However, they all suffer from the same issue… static data that can only be appended to – unlike a real database.

 

So, enter Kudu – defined by Apache: “Kudu provides a combination of fast inserts/updates and efficient columnar scans to enable multiple real-time analytic workloads across a single storage layer.” Deconstructing that message – Kudu acts as a columnar database that allows real database operations that aren’t possible in HDFS file formats. It is now possible to interact with your Hadoop data where INSERTS, UPDATES, DELETES, ALTERS, etc. are now available as data operations. This means not just read/write capabilities for Hadoop , but also interactive operations without having to move to Hbase or other systems. IoT use cases, interactive applications, write-back, and traditional data warehousing are now possible without adding layer upon layer of additional technologies.

 

Now that we have a general understanding of what Kudu can do, how does this benefit Qlik? Kudu is fast, columnar, and designed for analytics – but with the ability to manipulate and transform the data to power new use cases.

 

Let’s start simple by showing how easy it is to move some data from an Impala table on Parquet into Kudu.

 

Starting in Hue we need to do some basic database-like work. To put data into a table, one needs to first create a table, so we’ll start there.

 

Kudu uses standard database syntax for the most part, but you’ll notice that Kudu is less specific and rigid about data types than your typical relational database – and that’s awesome. Not sure if your data is a varchar(20), or if it is smaller or larger?  No worries, with Kudu –  just declare it as a basic string.

 

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Numerical data are basic as well, there a just few types to choose from based on the length of the number.   This makes creating columns and designing a schema very, very straightforward and easy to setup.  It also reduces data type problems when loading data.

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Having a general understanding of table creation, we will go ahead and create a table we are going to copy from Parquet.  It’s worth noting there are some differences here versus creating a Parquet table in Hue.

 

•    First:  A Kudu table needs to have at least 1 primary key to be created.

•    Second:  A Kudu table needs a partition method to distribute those primary keys

 

Referencing the schema design guide, we are going to use a HASH partition and use the number 3 (since we have 3 worker nodes).

 

In summary, we have a bunch of strings, a few integers, and some floating decimals to represent prices and profit. We’ve identified our keys and specified our partitions – let’s roll!

 

The query runs for a second and viola – we have our new (albeit empty) table. Next, we need some data. We have an existing table that we would like to copy over into Kudu. We will run another query to move the data and make a little tweak on the keys to match our new table.

 

We had to cast our customer_sk and item_sk columns from string in Parquet to int in Kudu but that’s pretty easy to do as shown in the SQL here.

 

We run the INSERT query and now we have our data moved over into Kudu, and even better – that table is now immediately available to query using Impala!

 

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Enter Qlik

 

With the data loaded into Kudu and exposed via Impala – we can now connect to it with Qlik and start building visualizations.

 

Using the latest Cloudera Impala drivers , we start the process of building a Qlik app by connecting to our new data set.

 

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Opening Qlik Sense, we will create a new connection to our cluster and select our new table.

 

Once we have the table and columns selected – we can modify the load script created by the data manager to directly query Kudu (versus loading the data into memory) to take advantage of the speed and power of Impala on Kudu.(we do this using Direct Discovery - NOTE the Direct Query syntax) This change is accomplished with a slight alteration in the syntax to identify dimensions and measures.

 

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We now have live queries running against Kudu data sets through Impala.

 

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The great part about Kudu is that we’re just getting started with the possibilities of how we can leverage the technology with Qlik. Some things we’re cooking up for the not too distant future involve write-back with Kafka and Qlik Server Side Extension integration – so stayed tuned.

 

Please visit cloudera.qlik.com for more demos and to see the Kudu demo in action.

 

Regards,

David Freriks (@dlfreriks) | Twitter
Emerging Technology Evangelist

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First - a bit on Qonnections


Last week was my 6th Qlik Qonnections, our annual partner and user conference. As usual it was a tremendous event filled with learning, networking and of course "fun and games"...literally this time around for those whom attended.  What was in the past, a partner only event, has grown to include our valued customers for its 2nd year. Our customers, partners and analysts from all over the world came to one awesome place (Gaylord Palms in Kissimmee Florida - my home town!) to share, collaborate, communicate and witness all the great innovation each had to offer...of course including a few things from Qlik. In my humble opinion, each year Qonnections has increased in quality and content....and this one felt like the best one yet, credit goes to our amazing events team, our sponsors and of course our customers and partners! If you want to learn more about all the happenings at Qonnections 2017, I suggest you check our company blog for the daily recaps as well as Cindi Howson's latest blog on the Gartner Blog Network: Qlik Reveals More Roadmap and Vision.

 

The Qlik Analytics Platform Demo

 

While at Qonnections, along with my colleague Josh Good, we had the pleasure of presenting a session that in short, basically highlights everything available in the Qlik Analytics Platform. Qlik has so much growing goodness in one box, that it's becoming almost impossible to cover everything we can do for an organization's various needs in just one meeting. So this presentation was created to quickly show what is possible and is performed using 5 "Acts" that demonstrates our core product capabilities while connecting the full breadth of analytic use cases across a fictitious organization - using one coherent story-line. We originally used this as an internal enablement-type resource, but also realized the value it contains for our customers and partners and decided to publish it.

 

There are 2 videos in this blog, including links out to detailed specifics on each of the use cases. The first video (3 min) is basically a short summary of the 2nd video - introducing you the main concepts, but sacrifices the detailed demonstration. The 2nd video is a longer (23 min) step by step demo flow that dives in deeper into each analytic use case. I hope you find this information useful and please note I am checking on the public availability of the demonstration app used in the videos. Once approved I will post this as an attachment to this post. I am also looking into making all the resources available on our Partner Portal.

 

Enjoy!

 

Michael Tarallo (@mtarallo) | Twitter
Qlik

 

 

 

Qlik Analytics Platform Demo Highlights (short)

 

Qlik Analytics Platform Demo (long)

 

 

For more detailed information on the full range of Qlik Analytical use cases, please view this videos at the following links.

 

 

NOTE: To increase resolution or size of the video, select the YouTube logo at the bottom right of the player. You will be brought directly to YouTube where you can increase the resolution and size of the player window. Look for the 'settings' gears icon in the lower right of the player once at YouTube.

 

NOTE:  Can't see the video? Download the .mp4 to play on your machine or mobile device.

 

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The following is a recording from our latest Tips and Tricks Webinar.

 

 

We want to hear from you. Please leave your comments and questions in the comments section below.

 

NOTE: To increase resolution or size of the video, select the YouTube logo at the bottom right of the player. You will be brought directly to YouTube where you can increase the resolution and size of the player window. Look for the 'settings' gears icon in the lower right of the player once at YouTube.

Happy Tuesday everyone!. You guys are going to love this one. In this edition of the Qlik Design Blog, our Emerging Technology Evangelist, David Freriks is back discussing integration between Qlik and a powerful big data unstructured search platform called Solr. Not only does David discuss an out-of-the-box approach to this integration, he takes it to the next level and touts the power of the Qlik Platform APIs.

 

Solr

 

In case you haven’t seen it – there is a super powerful unstructured search platform used within the big data ecosystem called Solr, built on the Apache Lucene search engine library. What’s great about Solr is that it can index just about anything, text, xml, JSON, PDF, Word, Excel, including almost any kind of text based data. That means you can drop just about anything into Solr and make it searchable using the power of Lucene core which powers the Solr platform.

 

So, where does Qlik fit in you may ask? Well, let’s observe what a Solr query output looks like:

 

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Standard Solr query output

 

Hmmm, not very user friendly, not to mention it was somewhat slow to execute. Here is a little bit about what we’re looking at:

 

  • This data is the collective set of Enron emails from its infamous collapse in early 2000’s.
  • We’ve loaded this data set into our Cloudera cluster and indexed it using Solr.
  • Once this data was loaded and indexed we tested with a series of queries.
  • A full query on someone with a lot of references such as Ken Lay can run upwards of 15 minutes to bring back every email that contains a reference to him.

 

Imagine 10’s or 100’s of users each waiting 10-15 minutes for a single question to be answered, it clearly dilutes the effectiveness of the engine as a business tool.

 

Enter Qlik

 

Qlik has a tremendously powerful REST connector that is perfectly suited for connecting to sources such as Solr. (A great resource created by Mike Tarallo on the Qlik REST connector can be found here: Working with the Qlik REST Connector, Pagination and Multiple JSON Schemas - check it out to understand the basics of how it works and how the response data is assembled within Qlik)

 

What follows is how we are using the Qlik REST Connector to connect to Solr.

 

Qlik In-Memory Analytics with Solr

 

Now that we are armed with the Qlik REST Connector, and the appropriate Solr REST API connection parameters, we can pull the entire Enron email data set into the Qlik engine via Solr. (Refer to the Apache Solr Documentation to learn more,)

 

2.pngQlik REST Connector configuration

 

By pulling the entire data set, and loading it into Qlik, we now ensure that all users have sub-second access to all the data down to the most granular level, and thanks to our associative search technology – all the data has been indexed and correlated in-memory.  We can gain further insights by incorporating stock market data. Combining Enron’s stock performance with their emails tells an interesting story of rising email volume along with collapsing stock prices and elevating trade volumes.

 

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Power of Qlik Data Visualization - Enron stock performance correlated with email volume


Using a mix of visualization techniques, we can see a pretty interesting collection of data, including the famous “deleted emails” gap on the bottom right chart.

 

Performing some additional analysis, we can drill in on the height of the crash that also correlates with the spike in email volume, followed by a rapid drop in volume.

 

7.pngDrop in trade volume

 

Making a few more selections we can dive down into a specific name, or comment to filter down the result sets further.

 

6.png Detail and specifics - name, email address

 

This associative search allows us to dive down into the details of the “TO” elements of the data set and see the metrics affiliated with those names.  We can also jump over to the final sheet of the Qlik Sense app and look at the individual emails body content filtered by our prior selections made in the application.

 

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QIX API Powered Solr Search

 

The above approach of using Qlik in-memory to front end the Solr search engine is just one of the many ways Qlik can access unstructured data in big data systems. Let’s consider another application also using Qlik with Solr – this time with just the Qlik API’s. As a quick refresher, the Qlik engine (called QIX) is a fully API enabled engine with tremendous extensibility that allows Qlik to plug into any web based technology (like Solr). Using the awesome QlikSocial framework from the esteemed Johannes Sunden he adapted the webapp to connect to Solr on demand and build a full webapp from scratch. This is a great example of what we call Custom Analytics.

 

We start with a search box… And our name(s) of interest:

 

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Now unlike the formatted Qlik Sense app, when a user hits the “search” bar – everything will happen dynamically on the fly using the API’s.

 

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Qlik will dynamically generate a REST connection to Solr, create and load the requesting data into memory, and then build a web app around the data using bootstrap.js and angular.

 

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The webapp is still using the Qlik engine, so selections and the search engine are still available – but all the charts and graphics are html and d3js charts – not Qlik. We’re just powering the app and the data interactivity with the QIX engine!

 

Summary

 

Solr is an extremely powerful unstructured search engine that can benefit from the speed and structure of Qlik analytics. It can provide a focusing lens on the core Solr search technology. That data can be consumed in a number of formats including a completely structured Qlik Sense app, or as an API powered web application without any Qlik UI components.

 

For more information, visit our demo site at cloudera.qlik.com

 

Enjoy!

 

Regards,

David Freriks (@dlfreriks) | Twitter
Emerging Technology Evangelist

Wow that's some title huh? Ooooh - "Qlik Sense Cloud Business and the Web Connectors" - sounds like the title for a fantasy adventure novel. Seriously, Denise LaForgia and I are back with a Qlik Sense Cloud Business update including some new videos to briefly introduce you to some really cool and new capabilities available in Qlik Sense Cloud Business - our new Web Connectors starting with access to data for Google Analytics, Twitter and Facebook. Take it away Denise!

 

Thanks Mike!

 

As promised in my blog last month, I’m back with more exciting updates about new features in Qlik Sense Cloud Business. Following our launch of REST Connectivity, I’m excited to announce that Facebook, Twitter and Google Analytics data sources are now also available in Qlik Sense Cloud Business under our new Web Connectors package.

 

For business users in particular, these connectors provide an easy way to bring together and analyze multiple data sources and data sets that are critical to sales, marketing, and other business initiatives. While some tools for social and sentiment analysis might allow you to analyze data from those sources individually, the power of Qlik Sense lets you associate this data about social and digital activity with other information about your customers, sales, marketing campaigns, customer service, and more.

 

Here’s an overview of the type of data each connector can return:

 

Google Analytics

The data returned includes many of the fields you’d see in the Google Analytics dashboard, such as page views, top landing pages, most visited pages, etc. You can retrieve data on any Google Analytics-enabled website.

  • Accounts: returns the accounts that the user has access to
  • DataFromQueryURI; enter a query URI on this table
  • DataFromTemplateQuery: returns a report from one of the available prebuilt queries
  • WebProfiles: returns the profiles that the user has access to
  • WebProperties: returns the web properties the user has access to

 

Watch Mike's brief video to get a general idea of how it works:

 

Community page and video download

 

 

Twitter


The content returned includes all tweets that include a hashtag or search term, and you can use Twitter query operators to pull data for more specific, detailed searches.

  • Mentions: returns up to 800 tweets for a Twitter screen name
  • Search: returns tweets based on a search term and other parameters
  • SearchAdvanced: returns tweets based on search term and other parameters and returns more columns than simple search
  • UserSearch: returns information about an account based on a named user search or topic search

 

In this video Mike shows how simple it is to get after Twitter data:

 

Community page and video download

 

Facebook Fan Pages and Groups

 

The data retrieved includes textual content (posts and comments) as well as counts of likes and shares.

 

  • Feed: returns the feed of posts (including status updates) and links published by the selected page, or by other users on the page.
  • Page: returns a single page
  • User Info: returns a single user - note - user identify / vanity id is used as the parameter

 

In the final video we close the loop on the last of the connectors by simply getting access to Facebook data:

 

Community page and video download

 

How to Get Started

 

While in your Qlik Sense Cloud Business workspace, you can set up your connections within your app by going into the data load editor and selecting the Create New Connection button. You’ll have to authenticate each connection using credentials from an account – your personal account, or one belonging to your business, group or organization. Once the connection is established, you can begin retrieving data.

 

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We’re rolling out additional connectors in the next few weeks, so stay tuned for additional information!

 

Learn more


Of course check out the videos and for more detailed information and instructions, visit these resources:


Enjoy your day!

 

Denise LaForgia

Senior Product Marketing Manager

Qlik

 

Resources:

 

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Good Tuesday Qlik Community! Today I have a fun and entertaining way to showcase the power of the Qlik Sense platform, its APIs and the art of the possible. But first, let me give you some background. In March, Qlik participated in the Gartner BI Bake Off (direct highlights here) - and for the 'innovation' portion of the presentation, my colleague Josh Good demonstrated a neat concept, showing what is possible with the Qlik platform and it's robust set of APIs - a Qlik Sense Bot - developed by my colleague Juan Gerado Cabeza. Since then we are getting numerous requests to see and learn more about what was shown on that day, so I have create this brief video to share some of its insights.

 

4-18-2017 7-38-28 AM.pngWhat is a Bot you may ask?

 

You already know some popular bots – Siri, Cortana, Alexa and of course Google. These guys are programs designed to perform tasks, such as setting an alarm, telling you the weather, searching online or even ordering a pizza. So why not have a bot tell you who your top sales people are, alert you when profit margin reaches 20%  – or - even have it send you charts and reports directly to your device?  The possibilities are endless. Juan as done just that, creating an analytical assistant - that in actuality turns Qlik data visualization into conversational analytics.

 

Now understand the Qlik Sense Bot - IS NOT a product provided by Qlik, but rather a neat concept that demonstrates the art of the possible when using the Qlik platform and its APIs. Now - neatness, coolness and Qlik platform superiority aside, what I like the most about this innovation is that it provides many real world applications. Bots can be used by anyone, anywhere, on any device and at anytime – scheduling automated tasks and providing access to information when and where you need it – reducing complexities, and increasing availability of insight by simply having a conversation. So in my opinion I do see this as a viable product offering, but you never know. Watch this brief video to learn more about this awesome concept. If you want even more information about this superior innovation, please contact us at qlik.com, or you can reach out to us on Twitter or on the Qlik Community.

 

Have a great day!

 

Regards,

Michael Tarallo (@mtarallo) | Twitter

 

Pushing the Boundaries of Analytics  - Qlik Sense Bot

 

We want to hear from you. Please leave your comments and questions in the comments section below.

 

NOTE: To increase resolution or size of the video, select the YouTube logo at the bottom right of the player. You will be brought directly to YouTube where you can increase the resolution and size of the player window. Look for the 'settings' gears icon in the lower right of the player once at YouTube.

 

NOTE:  Can't see the video? Download the .mp4 to play on your machine or mobile device.

 

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Happy Tuesday everyone! Thanks for joining me in this week’s Qlik Community Design Blog. Today I have the pleasure of introducing our newest guest blogger, Denise LaForgia. Denise is a colleague of mine in the Product Marketing group and is a Senior Product Marketing Manager focused on our cloud solutions. In this week's edition she will be covering our new REST connectivity recently made available to Qlik Sense Cloud Business subscribers. On an occasional basis, Denise will share updates on our Qlik Sense Cloud solutions. Take it away Denise.

 

Hi Everyone,

 

Welcome to this first installment of what I would like to refer as our Qlik Sense Cloud Update blog. I plan on bringing you all the news about the latest updates in Qlik Sense Cloud as well as some tips and tricks to help you get the most out of your Qlik Sense Cloud subscription. Occasionally, I might even ask Mike to embed a supplemental video to go along with the topic as we have done in this article. Please note that I will also provide continuous updates in the Qlik Sense Cloud Community Section along with the occasional appearance here. We have a lot of exciting features rolling-out, so stay tuned!

 

This week we’re excited to announce the launch of REST Connectivity in Qlik Sense Cloud Business. We know Qlik Sense Cloud Business users are eager for additional data connectivity options in order to automatically import and associate data sets from multiple sources. REST connectivity provides flexibility to a wide range of connectivity options with many of the applications you may be using in your business or project group or team.

 

So what is REST?

 

REST stands for Representational State Transfer, a modern and lightweight, secure communications protocol used to transfer data over the web. The Qlik Sense Cloud Business REST connector is designed to load data into a Qlik Sense app from a service that supports REST. It can return data in many formats such as JSON, XML, or CSV. Most web-based applications, social media channels, cloud-based CRM systems and even Google Analytics are REST-enabled, which means you can now build a connection between Qlik Sense Cloud Business and those data sources.

 

How does it work?

 

The Qlik Sense Cloud Business REST Connector can be considered a 'generic' connector, meaning it gives you the flexibility to configure a connection with any REST-enabled source you’d like to pull data from. Depending on which application you want to connect to, you can navigate to its developer area and configure that application’s settings to open up a REST connection. Visit this area in our help section to read examples on how to do that for LinkedIn, Twitter, Facebook, and Google Analytics. (included in video) Once you have the query parameters, head to the data manager or data load editor in Qlik Sense Cloud Business to complete the connection.

 

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You can also use the REST Connector to load data files directly from public web files, such as DropBox, by simply entering the file’s URL in the REST Connector configurator.  The Qlik Sense Cloud Business REST Connector loads the data into your app and automatically parses the information into appropriate table and field structures so that it’s easily used with your application’s data model. And, you can use the scheduled refresh feature in Qlik Sense Cloud Business to ensure your data files from the REST Connector are always up to date.

 

Ready to learn more - webinars, videos:

 

Watch Mike's video below or go to the Set Up Select Sources for REST Connectivity page for more information about how to connect to different data sources – including Facebook, Twitter, and Google Analytics - using REST. Mike will also be presenting a Tips and Trick's webinar on REST Connectivity with a LIVE Q&A on May 10th at 1PMEST - you can learn more about it and register HERE.

 

Regards,

 

Denise LaForgia

Senior Product Marketing Manager

Qlik

 

 

 

 

Introduction to the Qlik Sense Cloud Business REST Connector and JSON Schemas

 

Can't see the video? Download the .mp4 to play on your machine or mobile device.

 

Additional Qlik Sense Cloud Connectivity Resources

 

How-To Guides:

 

How-To Videos:

Well guys - it is release time again and today I am please to announce the arrival of Qlik Sense 3.2. Now if you have been using Qlik Sense Cloud since February 2017, some of these features may seem familiar to you already. Going forward we may not always roll out everything in one big bang for a dot/feature release. In fact, as we continue to build on our strength in the cloud, we are beginning to make features available in Qlik Sense Cloud on a continuous basis so there is really no need to wait for the ‘big day’ to get the latest features. So if you are eager to try out new features and capabilities in Qlik Sense, there is always a chance they could already be in Qlik Sense Cloud ahead of the Desktop and Enterprise software. So stay tuned and connected to us for the latest news.

 

In this blog I wanted to demonstrate and present what's new in Qlik Sense 3.2. As one of our community members puts it, it is "a pretty beefy dot release". To keep it plain and simple here are the highlights:

 

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  • Advanced coloring
    Ability to more easily choose user defined colors for visualizations, measures, and KPI objects

 

  • Calendar measures (my favorite)
    Automatically generated fields for comparing date ranges for measures without writing Set Analysis expressions. (BTW, if you don't know what Set Analysis is, I suggest you check out this primer video:A Beginners' Introduction to Set Analysis (video) as the concepts learned can still apply to other analysis.)

 

  • GeoAnalytics
    This is technically not "in" 3.2 - but is ready to be used with it. I make mention of this in the video and you can learn more here: Introducing Qlik GeoAnalytics

  • Shared persistence
    A new installation option for Qlik Sense Enterprise when deploying multi-node sites that allows nodes access to centralized storage, improving performance and stability with larger deployments.

 

  • Supported desktop client
    Allows Qlik Sense Desktop to authenticate against a Qlik Sense Enterprise server NOW with full support by Qlik in production environments. (No Qlik Sense server? - then you must register for a QlikID and use those credentials)

 

  • Additional visualization, navigation, and search improvements

 

So, sit back and enjoy this short video that describes and demonstrates these aforementioned features in more detail. Leave your comments and feedback to below, we'd love to hear from you.

 

If you want to get Qlik Sense Desktop 3.2 today - you can get it from this temporary link Qlik Sense Desktop here - we are currently updating our Qlik Sense Desktop products page on our website. You can also experience 3.2 without downloading any software by using the Qlik Sense Cloud.

 

For additional help on these features and more check out our Qlik Help Channel on YouTube. (I don't manage this, so videos are not always available as .mp4)

 

Regards,

Michael Tarallo (@mtarallo) | Twitter

Qlik

 

 

What's New in Qlik Sense 3.2


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In this week's edition of the Qlik Design Blog, I'd like to introduce Mitul Vadgama. Mitul is a member of our Qlik Consulting Services team in the UK. He has worked on a variety of consulting projects using both Qlik Sense and QlikView with many enterprise customers. His passion is transforming data into valuable business insights, knowledge sharing, and enabling customers to get the most value out of Qlik products. Mitul is going to show you how to optimize a powerful and unique capability in Qlik Sense, Smart Search. Thanks for your valuable contribution Mitul!

 

Smart Search

 

Smart Search provides powerful functionality allowing users to search dimensions and values found within an application’s data model. When working with customers on consulting projects I have found that sometimes the business users, data architects, and app designers want to control which fields are included or excluded in the search. Did you know that you can control which fields are included or excluded from Smart Search?

 

Here is an example using Smart Search in action where I search for “Rob” in my sales application:

 

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You can see Qlik Sense finds the value “Rob” in the following fields:

 

•    First Name

•    SalesPerson

•    CompanyName

•    ContactName

 

Consider a scenario where you want Smart Search to exclude searching in the “ContactName” field when the users search. You can achieve this by inserting the exclude clause in the Search statement in your script.

 

The Search statement

 

The Search statement is a script function used for including or excluding fields in smart search.

 

It can contain two clauses:

 

Syntax:

  • Search Include *fieldlist
  • Search Exclude *fieldlist

In our case I want to exclude ContactName from my results. I do this by opening my data load editor and typing in the following right after the SET statements:

 

Search exclude ContactName;

 

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....and reload.

 

When I search again for “Rob” you can now see ContactName excluded from the results:

 

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Tip: If you want to exclude multiple fields, simply list the fields you want to exclude using comma separators while ensuring your field name syntax is correct.

 

Search exclude field1,field2,field3;

 

Another common scenario may be you want to hide most of the ID fields from your Smart Search (maybe because they are only needed for the data model building process).

 

A possible way you can achieve this is through the following syntax:

 

4.png

 

The above contains two lines of code:

 

  • Search Exclude '*ID';
  • This excludes all fields ending with ID from searches in Smart Search.

 

  • Search Include CustomerID;
  • This includes the field CustomerID in searches in Smart Search.

 

This way I am ensuring all but one ID field is included in my Smart Search.

 

We hope through this post you can see how the Search syntax along with Exclude and Include keyword clauses is a simple yet powerful way to control which fields a user can search on your Qlik Sense application. It also speeds up the search index creation when the data model is first loaded.

 

For a full list of services offered by Qlik Consulting check out: http://www.qlik.com/us/services/qlik-consulting.

 

Regards,

 

Mitul Vadgama

Consulting Services, UK

 

For more information on the Smart Search feature check out these videos:

 

Simon Kirby.jpg
In this week's design blog I have the pleasure of introducing our newest guest blogger, Simon Kirby. Simon is a Director of SI Industry Solutions with our Financial Services group out of the UK and has prepared an introduction and video that shows you how Qlik Sense can be used to analyze car insurance claims data.

 

Insurance Whiplash Fraud

 

It was recently announced in the UK that car insurance premiums are at their highest ever levels (The Actuary Magazine) and one of the key factors for this is an increase in whiplash injury claims. In addition to legitimate whiplash injury claims, insurance companies are struggling to cope with an increasing trend in fraudulent whiplash claims. These claims occur when a third party deliberately creates an accident by causing another car to crash into the back of them. They then feign a whiplash injury and receive thousands in compensation. It’s been discovered that these people repeat these accidents regularly and effectively earn a living from scamming the insurance industry.

 

How Qlik Sense can help

 

A key weapon for insurers in identifying these fraud perpetrators is the analysis of data. Insurers need to be able to search for associations in data between similar types of claims, in similar locations, including something unique like a mobile phone number. These associations between the data can lead to a significant increase in identifying the groups of people that commit these types of fraud. This is exactly where Qlik Sense can play an important role in this activity.  The QIX Associative engine, at the heart of Qlik Sense, is designed to make the discovery of these associations easy and intuitive. Together with the ability to quickly create drag and drop visualizations, Qlik Sense can help Insurance Fraud Analysts identify trends, patterns and examples of fraudulent Whiplash claims. (view more about Qlik's Associative Model: Qlik Snippets - The Qlik Associative Model - YouTube and Qlik's Associative Model - Part I - YouTube)

 

See and try it for yourself

 

The next step for insurers would be to combine the power of Qlik Sense with their fraud detection models.  The result would be a Qlik Sense visualization embedded into the claims system that showed the claims handler the real-time results of the fraud model at the time of the Notification of Loss.  This would allow the insurer to push suspicious claims to a dedicated fraud management team for further investigation which could potentially save the company millions.

 

If you would like to see how Qlik Sense can be used to identify claims fraud, take a look at our video on YouTube and the attached Qlik Sense app. For more details on this app, please visit this link available on the Qlik Financial Services Community site: Insurance Claims App

 

Regards,

Simon Kirby

Director, SI Industry Solutions - Financial Services

Qlik

 

 

NOTE: To increase resolution or size of the video, select the YouTube logo at the bottom right of the player. You will be brought directly to YouTube where you can increase the resolution and size of the player window. Look for the 'settings' gears icon in the lower right of the player once at YouTube.

 

Can't see the video, YouTube blocked? Download the attached .mp4.

 

If using Qlik Sense Desktop please copy .qvf file to your C:\Users\<user profile>\Documents\Qlik\Sense\Apps and refresh Qlik Sense Desktop with F5. If using Qlik Sense Enterprise Server please import .qvf into your apps using the QMC - Qlik Management Console.

starwars.pngRecently, I worked with a Qlik Community member to help them understand the Qlik REST Connector with Qlik Sense and QlikView. At first it appeared simple, but then he soon realized he needed to understand a bit more about how the data came back (the response), what the pagination settings were (pages of data used to retrieve more rows) and finally how to link (join, associate) other attributes that came back from the results of multiple REST API endpoints / resources. We got it all working and the results were pleasing. Needless to say were able to perform text analytics from a barrage of Facebook comments.  However, as I finalized all this in my head, I wanted to share what I've learned but in the simplest way possible. So I decided to find a very simple, publicly available RESTful service API in which I can demonstrate my findings easily. The below video presents those findings in a educational and entertaining way using the Star Wars API. Yes, that is correct, I said the Star Wars API. As a bonus, stick to the end of the video to see the Media Box Extension in action.

 

See this video on YouTube as well. Using the Qlik REST Connector - Pagination and Multiple JSON Schemas - YouTube

 

 

 

 

Do you know of other simple and fun, publicly available RESTful services? Share them with the Qlik Community in the comments below.

 

Regards,

Michael Tarallo (@mtarallo) | Twitter

Qlik

 

Special shout out to: Paul Hallett    (@phalt_) | Twitter - for creating an awesome resource http://swapi.co/about that allowed me to easily demonstrate the Qlik Sense REST Connector.

 

Resources used in this video:

 

 

 

Other Resources:

 

 

If using Qlik Sense Desktop please copy .qvf file to your C:\Users\<user profile>\Documents\Qlik\Sense\Apps and refresh Qlik Sense Desktop with F5. If using Qlik Sense Enterprise Server please import .qvf into your apps using the QMC - Qlik Management Console.

 

Disclaimer: Star Wars, the Star Wars logo, all names and pictures of Star Wars characters, vehicles and any other Star Wars related items are registered trademarks and/or copyrights of Lucasfilm Ltd., or their respective trademark and copyright holders.


Hello Qlik Community! image1.pngIn my last blog entry Introducing Qlik GeoAnalytics - I um...well....introduced our latest product offering... Qlik GeoAnalyitcs. I also included a promo video showcasing its various capabilities.  (In case you have not seen it, I suggest you start with that first.)

 

I've been working with Qlik GeoAnalytics for a few weeks now and I am really enjoying it. I'm gathering tremendous knowledge (thanks to the fantastic Idevio team who is now part of the Qlik family) and compiling it so I can share it with you so you can get started quickly. You can see some of the efforts in this new video index which provides a few primers to help you get started with Qlik GeoAnalytics for both Qlik Sense and QlikView. If you have Qlik GeoAnalytics related questions you can also start a discussion in our new section on the Qlik Sense forums: Qlik GeoAnalytics Community.

 

Over the next few weeks I'll be presenting other topics in a series of blogs and community posts that will help you learn more about Qlik GeoAnalytics, presenting its various capabilities. To that point, I recently discovered the Qlik GeoAnalytics Connector which can grab data from a variety of external geo-data services to return route information, distance, time and spatial relationships and associate them with your 'decision making data'. Look at this fun example using Qlik GeoAnalytics Line, Bubble (flags), and Heatmap layers to represent long and short travel routes, along with a fictional "area of concern" depicted by the heatmap. With Qlik Sense small devices mode it even fits and reacts perfectly on my iPhone too, I did not have to create a separate mobile-version of the app....."Oh, no - I don't want to take that shortcut through Central Park, there are way too many street performers along that route!!"  ...but more on that later. What I love the most about Qlik GeoAnalytics, it's more than just plotting simple dots on a map, it moves Qlik beyond visualization and supports a broad range of advanced geoanalytic use cases. I hope you will see the value and benefits it has to offer and that you have as much fun with it as I.

 

To kick of the knowledge share, in this below video I show you how to create a very simple Area map, which can also be known as a boundary or choropleth map, that you can drill-down into.

 

 

If you cannot see the video, or you would like the sample data, you can find both here: Qlik GeoAnalytics - Creating a Drill-down Area Map

 

Let me know what you think and I looking forward to joining the conversation on the Qlik GeoAnalytics Community.

 

Regards,

Michael Tarallo (@mtarallo) | Twitter

Senior Marketing Manager
Qlik

Today - Jeff Goldberg is back and will introduce us to using session attributes to pass security and other information to a Qlik Sense session.


Introduction

Did you know with Qlik Sense security rules, it's possible to use attributes delivered to Qlik Sense Enterprise during the authentication process?  Yup, it's true. While the conventional approach to using attributes is to import them using a User Directory Connector, session attributes are a flexible alternative to storing attributes in the repository to control capabilities and authorization in Qlik Sense Enterprise.

 

Qlik Sense supports session attributes through SAML, ticketing, and session authentication schemes.  Session attributes are attributes sent in the request for access to the Qlik Sense server.  The attributes are not stored in the repository, but in the user's Qlik Sense session.  Session attributes can be referenced in security rules using the user.environment (e.g. user.environment.attributename) syntax.  In addition, if there is an attribute named group sent to Qlik Sense using SAML, ticketing, or session, it can be used in Section Access data reduction.

 

Session Attributes in SAML

When you setup a SAML virtual proxy in Qlik Sense, there is an additional attributes section where you can add attribute names from the saml response sent by the identity provider, and the name you want to use in Qlik Sense.  Here you can see I have a SAML integration with Okta.  I have an additional attribute that comes in from Okta named Groups.  In Qlik Sense I have it use the name group.  The name on the left must match what is sent by the idp.  The name on the right can be whatever you want it to be.

 

jeff1.png

How do you identify the names of the attributes contained in the SAML response?  I use SAML Message Decoder chrome extension.  It's a great tool for reading through the messages sent to an IDP and sent to the Qlik Sense server.

 

jeff2.png

 

<?xml version="1.0" encoding="UTF-8"?>
<saml2p:Response xmlns:saml2p="urn:oasis:names:tc:SAML:2.0:protocol"
        Destination="https://gss.qlikpoc.com:443/okta/samlauthn/"
        ID="id1026813850591869499238360"
        InResponseTo="_583b079d-39d2-44ab-9824-1336e628770e"
        IssueInstant="2016-12-13T14:03:23.898Z"
        Version="2.0"
        xmlns:xs="http://www.w3.org/2001/XMLSchema">
<...>
    <saml2 :AttributeStatement xmlns:saml2="urn:oasis:names:tc:SAML:2.0:assertion">
      <saml2 :Attribute Name="email"
             NameFormat="urn:oasis:names:tc:SAML:2.0:attrname-format:unspecified">
        <saml2 :AttributeValue xmlns:xs="http://www.w3.org/2001/XMLSchema"
               xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
               xsi:type="xs:string">goldbergjeffrey@yahoo.com</saml2:AttributeValue>
      </saml2:Attribute>
          <saml2 :Attribute Name="groups" NameFormat="urn:oasis:names:tc:SAML:2.0:attrname-format:unspecified">
          <saml2 :AttributeValue xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" 
xsi:type="xs:string">QlikGroup</saml2:AttributeValue>
          <saml2 :AttributeValue xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" 
xsi:type="xs:string">Everyone</saml2:AttributeValue>
        </saml2:Attribute>
    </saml2:AttributeStatement>
</saml2p:Response>



 

In Qlik Sense, I've created a stream for members of the QlikGroup group to access in the Qlik Sense Hub. The security rule I put in place uses the user.environment.group attribute and evaluates it against the QlikGroup group value.

 

jeff3.png

 

When I log into Qlik Sense,  you see that I have access to the stream I created because in my SAML response I am a member of the QlikGroup group.

 

jeff4.png

 

Session Attributes in Ticketing and Session

 

When ticketing and session authentication schemes are used session attributes are sent as part of the request. Here is a snippet from some sample code that demonstrates how to send attributes in a ticketing request. Notice that in the JSON message there is the UserDirectory and UserId properties and then the attributes property which is an array inside you specify an additional JSON object for each key value pair that's used to send an attribute to Qlik Sense.

 

ticket request message body
{...}
//The body message sent to the Qlik Sense Proxy api will add the session to Qlik Sense for authentication
string body = "{ 'UserId':'" + user + "','UserDirectory':'" + userdirectory +"',";
    body+= "'Attributes': [{"group":"QlikGroup"},{"group":"Sales"}],";
    body+= "}";

byte[] bodyBytes = Encoding.UTF8.GetBytes(body);
{...}



 

Once a session attribute has been sent through the ticketing or session request system those attributes are stored for the duration of the users access and are used for security rules just like in the SAML example. Let's take a quick at how this works. I'll navigate to my webpage that uses the ticketing code.  Inside the ticketing code I've supplied a group attribute that will grant me access to the QlikGroup stream. During authentication, security rules will evaluate and determine that I have a group attribute and the security rule uses user.environment.group to grant me access to the QlikGroup stream.

 

Section Access


 

In the previous examples I sent along an attribute named group. In reality I could send any attribute I want through the session attributes mechanism. But the group attribute name has some additional capabilities within Qlik Sense. Using the group attribute name allows those values to pass into an application for section access data reduction purposes.

Let's go ahead and open up a sales application as Anne Foster.

 

Her data is reduced to only the United States because the session attribute named group has a value of "SalesUS".

 

jeff567.png

 

When I log in as Eddie Reese, he is only able to see Australia because his the session attribute value for him is "SalesAUS".

 

jeff8910.png

Wrap up

 

Session attributes are a powerful and flexible alternative to user directory connectors when using SAML, ticketing, or session authentication schemes.  Session attributes are accessible in security rules with the user.environment syntax, and when the session attribute is named "group", the values may be used with Section Access data reduction.  To learn more Qlik products and discover additional technical materials, join the conversation on our Qlik Community, Qlik Help YouTube channel and view the video libraries on the Qlik Community: New to Qlik Sense Videos - New to QlikView Videos

 

About Jeff:

IMG_2582.JPG

 

Jeff is a Principal Enterprise Architect on the North America Enterprise Architecture team who has worked in various technology positions for over 16 years. Jeff focuses on integration, deployment, automation, security, and api topics across a wide range of software. Thanks for this valuable contribution Jeff!





(Hey Jeff - this pic is going to give me nightmares!)


Follow us:
Jeff Goldberg (@goldbergjeffrey) | Twitter

Mike Tarallo (@mtarallo) | Twitter




Add the Geographical Dimension to your Business Data

1-9-2017 12-25-48 PM.png


Hello Qlik Community! I know most of you never sleep, but some of us took some mu
ch needed time off. I welcome you back from your much deserved holiday and I wish you all a prosperous new year. Now, let's get back to work!


We are looking forward to a fantastic year @Qlik, and the greatness has already started to roll in. On January 4th we announced the acquisition of Idevio, a provider of geographical-related software and services, and a valued Sweden-based Qlik partner. This acquisition not only extends Qlik’s current mapping capabilities, but also moves its offerings beyond visualization with support for a broader range of advanced geoanalytic use cases. Users can now easily add maps to their apps with automatic geo-data lookup to reveal spatial information and then overlay it with different visualizations. You can drill down into information dense maps that contain millions of points, and with a local or cloud-based service, analyze geo-data in combination with non-geo data for use cases such as determining potential store locations, understanding customer distribution of sales by zip code, or calculating supply chain delivery times. IdevioMaps, which includes Idevio GeoAnalytics for QlikView and Qlik Sense, has been re-branded as Qlik GeoAnalytics and is available immediately in its current offering, with tighter integration in Qlik Sense planned for the second half of 2017. You can learn more about what Qlik GeoAnalytics has to offer in this brief introductory video.


 

 

 

Come visit the new Qlik GeoAnalytics Community to post your questions and join the conversation about Qlik GeoAnalytics.

 

 

My 2¢

 

I am very excited about this capability as part of the Qlik product portfolio. When I joined Qlik in 2012, I was always curious as to why there weren't any mapping visualizations with QlikView. Naturally, with Qlik's disruption to the traditional BI vendors, you would think we would have some sort of maps! Hey, everyone was doing it, why not Qlik? (I was so envious of those pretty maps!) I eventually realized that Qlik knew geospatial analytics required more than just dots on a map and was waiting for consumerization to catch up to mapping. But by the time that happened we were neck deep in developing our next generation data visualization platform "QlikView.Next" which lovingly became Qlik Sense. So that really kept us from bringing the strategic focus to bear that we needed to to build out our own mapping capabilities organically.  (As some of you may recall, this gap was filled in QlikView with a technique that utilized a scatter plot with a Google Maps background.) At that time, our valuable partner network recognized this as an great opportunity and stepped in with a variety of mapping solutions and extensions designed for varying needs. Idevio maps was one of those partners. Just like many companies, Qilk invests in its existing resources where it can excel quickly and provide immediate value to its customers. As time allows, it will continue to grow its expert family and capabilities through acquisitions that will enhance that value to its customers. We have seen this with the acquisition of Expressor (data governance, data management), NComVA (advanced visualizations)  Vizubi / NPrinting (creation, schedule, distribution of operational reports) and now Idevio. Hmmmm, I wonder what's next? Looking forward to a prosperous 2017. Stay well my friends!



Tell me what do you think. Leave your comments below.

 

Regards,

Michael Tarallo

Follow me: https://twitter.com/mtarallo

Qlik

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