Fast Visualization of JSON Data

Dean Compher

30 January 2016




If your applications are generating data using JSON, but you are unable to do good analysis in a timely manner because all the good analytics tools like Cognos and your own SQL scripting only work on relational or delimited data, then you should consider the Bluemix cloud solution.  With this solution you can store your JSON documents in the Cloudant NoSQL database and automatically get that data into the dashDB relational warehouse with little effort.  Once there you can use Watson Analytics to visualize the data without even writing queries, or use Cognos or other tools to visualize data using your queries. This solution uses the Bluemix Schema Discovery Process (SDP) to create a relational schema in dashDB based on the Cloudant JSON documents and then begins copying the data automatically.  Since dashDB does not use indexes, materialized views or other complex data warehouse objects to get great performance, you donít need to worry about doing a lot of schema design work.  Cloudant, dashDB and Watson Analytics have many advantages individually, but taken together they provide results amazingly fast.  This is also a fast and inexpensive way to see if there is anything worth analyzing in your JSON data before investing in a larger scale analytics system. 


Use Cases


Since JSON is becoming used more and more to exchange data between applications within and between organizations, it is becoming quite widely used and known to developers.  Because of this, being able to store and query transactional data in those JSON documents without converting them to relational or other database formats saves considerable resources.  Web applications and applications on mobile devices are already using JSON stores in a big way and much investment is being made in JSON stores, and Cloudant is no exception.  However, these data stores do not lend themselves well to complex analytics queries.  So if you already have an application that uses JSON documents or you are developing a new application and you want to see what the data can tell you, then using the Cloudant, SDP and dashDB process is a great way to enable analytics without a lot of effort or expense.  Here are a couple of uses cases that come mind.  Iím sure that there are many others.


Case 1

I am writing a new application for mobile devices that I believe will be wildly popular, so I need a JSON store that can expand as fast as the growth of my application and allow me to analyze what people are doing so I can optimize the experience.  To maximize the growth of my application I not only need to analyze how people are using my application, I need to combine that usage information with what my users like and donít like based on what they are saying on Twitter. 


Because I need to start small due to lack of current funds, need a data store that can grow as fast as my app, and am going to use JSON anyway, Cloudant is a great choice for my JSON store.  Further, I want to do analytics from a relational database, but donít really know anything about data warehouses, so using dashDB and the SDP to load it from Cloudant is great choice for me too.  Twitter data from my users is also easy to add to my dashDB database because the Insights for Twitter service is also on Bluemix and I just need to deploy it when Iím ready as shown in this video.  This service is great because text analytics is not my forte and the service ďreadsĒ the Tweets and can easily add user sentiment to my dahsDB database. 


Once I have the data from my application and Twitter sentiment data in my warehouse, Iím going to us Watson Analytics explore my data in dashDB using natural language questions as well as using Watson Analyticsí predictive capabilities.  From this Iíll be able to see what complex queries I might want to write from my favorite report writer like Cognos. 


Case 2

I have a web application and I want to analyze user behavior to see which content on the site lead to them taking actions that I want such as making a purchase or clicking a certain set of links.  I also suspect that weather has an impact on behavior and I may want to measure that impact later.  I already know how to write good analytic queries in SQL, but I donít have the time or funds to buy equipment and design a data warehouse and then write Extract, Transform, and Load jobs to populate that warehouse. 


In this case I could deploy appropriate sized Cloudant and dashDB systems in the cloud and make some small modifications to my applications to capture what users do as they do it and store those transactions in the Cloudant store.  I would also invoke the SDP process to create the relational schema in dashDB.  Once I began gathering the data I can do simple queries against the Cloudant database to determine things like which pages are the customers visiting.  After I feel enough data is in the dashDB warehouse, I can begin launching my complex analytical queries to show which content and offers placed on my site lead to the desired behavior for various types of customers.  This allows me to determine which content to display for various types of customers and under what circumstances.  Because Cloudant also allows queries, I can watch for the patterns of usage on the site discovered in my analytics process and dynamically change the customer experience to drive the behavior I want.   If I had historical data stored somewhere else that I thought would help the dashDB analysis, I could either load it into Cloudant letting it flow through to dashDB, or just load it into dashDB directly. 


Once my users start seeing a lot of value from my analysis and reports they start asking for lots of other data to be added to my warehouse to get a better picture of user behavior.  Since one of those data sets is weather data and Iím using IBM Bluemix, I can easily start adding that data to the warehouse because the Insights for Weather service is available.  I just need to deploy it.  Now my analytics queries can show how weather impacted user behaviors from the historical data, and then I can use the weather forecast data to begin predicting behavior, using the Predictive Analytics service against my dashDB consolidated set of data.


How does it work?




The deployments of the Cloudant JSON store and dashDB relational data warehouse are done as you would any other cloud databases, but I describe each of them a bit more below.  The really interesting thing about this is the Schema Discovery Process (SDP).  This process first scans your JSON documents for the data elements and generates a relational schema and deploys it to the dashDB database.  Once that is done it will copy the data from your Cloudant store.  After that it will copy new data at an interval that you specify keeping the dashDB warehouse up to date.  There are a few reasons why this saves so much time and effort including:


1.     The SDP automates the process of translating the JSON schema into a relational database for data warehousing.  That means that it creates a set of tables and columns in those tables instead of you having to do that.  This can be a very time consuming to do this right if you had to do it manually.  Itís not magic though.  The better you name the elements in the JSON documents the better your table and column manes will be in dashDB.  However, with a little additional work you can get the column names you want.

2.     The transformation of data from the JSNO NoSQL documents into relational tables is not a trivial task.  Having the SDP create this transformation automatically not only saves a lot of time and testing, it also means that you donít need to purchase an ETL tool.  Further, automating the replication of the data with a fully managed service means that you donít need to configure and monitor those jobs.

3.     dashDB is a deployment of DB2 BLU technology and that means that you get extremely fast performance without the need for indexes, materialized query tables and other data warehousing objects that take a lot of time and skill to design properly. 


This link provides a good description of how you configure the Schema Discovery Process.  This article was written for the beta testers, so a number of the restrictions like the size of the databases and a single copy of the data have been lifted.  It is a good description of the process, none the less, and has a link at the top that you can click to see exactly what is now available. 


This architecture is also quite flexible, meaning that you can add data to your dashDB data warehouse from a variety of sources as shown in the diagram above.  These include data from your own databases including transactional systems and services provided by Bluemix like the Twitter and weather feeds.  Just add this data to different tables.  You do not want to modify the tables being loaded by the SDP.  Further, you can query the data in a variety of ways.   For transactional type queries you can use Cloudantís query language and get results based on up-to-date data.  For more complex queries you can use your favorite business analytics tool like Cognos.  If you just want to ask questions and not have to use any query language then you can use Watson Analytics to explore your data. 


Here is a bit more information on some of the components discussed here.



Cloudant is a NoSQL JSON data store based on CouchDB.  While there is an on-premises version, it is primarily used in the cloud needs to be in the cloud to use the SDP feature discussed in this article.  The full paid version allows for redundant copies of the data across data centers.  You can start small with an inexpensive deployment, but grow very quickly if needed. 



dashDB is a fully managed relational database in the cloud built on DB2 BLU technology with integrated analytics features built in like R, Spatial and others.  dashDB is great in this scenario because you donít really need to engineer the database schema to get great performance.  Part of the reason is that the SDP does an adequate job of creating the tables, but mostly it is because of the DB2 BLU technology that allows for fast query response without the need for indexes, materialized query views, and the many other objects that take a lot of time and expertise to develop.  This technology that brings together several complementary features that allow the right data to be scanned extremely quickly including columnar tables, compression, in-memory algorithms, data skipping and others.  If you used a different database technology, you would need to engineer all of these objects yourself to get decent performance.  You do not need to limit the dashDB data to the Cloudant feed.  You can augment that data with weather data, Twitter data and others from Bluemix services or feeds from your own databases as shown in the diagram above.  Even if you donít have good SQL skills, Watson Analytics can easily be connected so you can just ask questions and get answers about your data. 


Try Before You Buy


Does this sound too good to be true?  Then try it for yourself.  You can deploy a 30 day trial for free to see the functionality.  There are limitations on the size, but you can see all functionality.  You will start by signing up for Bluemix.   Then from the Bluemix dashboard deploy a ďCloudant NoSQL DBĒ and then launch it.  From there you follow the instructions in the Schema Discovery Process page that include a step for deploying your dashDB database. 


Once you see that this will work, you can buy services that allow for a production sized data set with better performance characteristics.  Even with the purchased plans you can get an analytics database going quickly and at low cost using the process described here.  This is a great way to proceed if you know that there are secrets locked in your data, but even if you are not sure, this is a very cost-effective way to find out.  Even if you find that there is no real value in the data, you can discontinue the service after a short time and you only paid for what you used. 




These tools are undergoing tremendous development in an agile methodology so new features are appearing all the time and restrictions are being lifted.   So if you see any great features that I missed or that appear after I post this article, please post them to my Facebook Page or db2Dean and Friends Community


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