Bringing Data Discovery To Hadoop – Part 2

The most exciting thing about Oracle Big Data Discovery is its integration with all the latest tools in the Hadoop ecosystem. This includes Spark, which is rapidly supplanting MapReduce as the processing paradigm of choice on distributed architectures. BDD also makes clever use of the tried and tested Hive as a metadata layer, meaning it has a stable foundation on which to build its complex data processing operations.

In our first post of this series, we showcased some of BDD’s most handy features, from its streamlined UI to its very flexible data transformation abilities. In this post, we’ll delve a little deeper into BDD’s underlying mechanics and explain why we think the application might be a great solution for Hadoop users.


Much of the backbone for BDD’s data processing operations lie in Hive, which effectively acts as a robust metastore for BDD. While operations on the data itself are not performed using Hive functions (which currently run on MapReduce), Hive is a great way to store and retrieve information about the data: where it lives, what it looks like, and how it’s formatted.

For organizations that are already running data in Hive, the integration with BDD couldn’t be simpler. The application ships with a data processing tool that can automatically import databases and tables from Hive, all while keeping data types intact. The tool can also sync up with a Hive database so that when new tables are created a user can automatically work with that data in BDD. If a table is dropped, BDD deletes that particular data set from its index. Currently, the 1.0 version doesn’t support updates to existing Hive tables, but we hope to see that feature in an upcoming release.

BDD can also upload data to HDFS and create a new table with that data in Hive to work with. It does this whenever a user uploads a file through the UI. For example, here’s what we saw in Hive with the consumer complaints data set from the last post after BDD imported it:

Example of an auto-generated Hive table by BDD

This easy integration with Hive makes BDD a good option for both experienced Hadoop users who are using Hive already, as well as less technical users.


While Hive provides a solid foundation for BDD’s operations, Spark is the workhorse. All data processing operations are run through Spark, which allows BDD to analyze and transform data in-memory. This approach effectively sidesteps the launching of slower MapReduce jobs through Hive and gives the processing engine direct access to the data.

When a user commits a series of transforms to a data set via the BDD UI, those transforms are interpreted into a Groovy script that are then passed to Spark through an Oozie job. Here, we can see how some date strings are converted to datetime objects behind the scenes:


After Spark has done its handiwork, the data is then written out to HDFS as a new set of files, serialized and compressed in Avro. The original collection stays intact in another location in case we want to go back to it in the future.

From this point, the data is then loaded into the Dgraph.


The Dgraph is basically an in-memory index, and is what enables the real-time, dynamic exploration of data in BDD. This concept might be familiar to those who have used Oracle Endeca Information Discovery, where the Dgraph also played a key role, and this lineage means BDD inherits some very nice features: quick response, keyword search, impromptu querying, and the ability to unify metrics, structured and unstructured data in a single interface. The biggest difference now is that users have the ability to apply these real-time search and analytic capabilities to data sitting on Hadoop.

We think the marriage of this kind of discovery application with Hadoop makes a lot of sense. For starters, Hadoop has enabled organizations to store vast amounts of data cheaply without necessarily knowing everything about its structure and contents. BDD, meanwhile, offers a solution to indexing exactly this kind of data — data that is irregular, inconsistent and varied.

There’s also the issue of access. Currently, most data tools in the Hadoop ecosystem require a moderate level of technical knowledge, meaning wide swaths of an organization might have little to no view of all that data on HDFS. BDD offers a system to connect more people to that data, in a way that’s straightforward and intuitive.

If you would like to learn more about Oracle Big Data Discovery and how it might help your organization, please contact us at info [at]

Bringing Data Discovery To Hadoop – Part 1

We have been anticipating the intersection of big data with data discovery for quite some time. What exactly that will look like in the coming years is still up for debate, but we think Oracle’s new Big Data Discovery application provides a window into what true discovery on Hadoop might entail.

We’re excited about BDD because it wraps data analysis, transformation, and discovery tools together into a single user interface, all while leveraging the distributed computing horsepower of Hadoop.

BDD’s roots clearly extend from Oracle Endeca Information Discovery, and some of the best aspects of that application — ad-hoc analysis, fast response times, and instructive visualizations — have made it into this new product. But while BDD has inherited a few of OEID’s underpinnings, it’s also a complete overhaul in many ways. OEID users would be hard-pressed to find more than a handful of similarities between Endeca and this new offering. Hence, the completely new name.

The biggest difference of course, is that BDD is designed to run on the hottest data platform in use today: Hadoop. It is also cutting edge in that it utilizes the blazingly fast Apache Spark engine to perform all of its data processing. The result is a very flexible tool that allows users to easily upload new data into their Hadoop cluster or, conversely, pull existing data from their cluster onto BDD for exploration and discovery. It also includes a robust set of functions that allows users to test and perform transformations on their data on the fly in order to get it into the best possible working state.

In this post, we’ll explore a scenario where we take a basic spreadsheet and upload it to BDD for discovery. In another post, we’ll take a look at how BDD takes advantage of Hadoop’s distributed architecture and parallel processing power. Later on, we’ll see how BDD works with an existing data set in Hive.

We installed our instance of BDD on Cloudera’s latest distribution of Hadoop, CDH 5.3. From our perspective, this is a stable platform for BDD to operate on. Cloudera customers also should have a pretty easy time setting up BDD on their existing clusters.


Getting started with BDD is relatively simple. After uploading a new spreadsheet, BDD automatically writes the data to HDFS, then indexes and profiles the data based on some clever intuition:What you see above displays just a little bit of the magic that BDD has to offer. This data comes from the Consumer Financial Protection Bureau, and details four years’ worth of consumer complaints to financial services firms. We uploaded the CSV file to BDD in exactly the condition we received it from the bureau’s website. After specifying a few simple attributes like the quote character and whether the file contained headers, we pressed “Done” and the application got to work processing the file. BDD then built the charts and graphs displayed above automatically to give us a broad overview of what the spreadsheet contained.

As you can see, BDD does a good job presenting the data to us in broad strokes. Some of the findings we get right from the start are the names of the companies that have the most complaints and the kinds of products consumers are complaining about.

We can also explore any of these fields in more detail if we want to do so:


Now we get an even more detailed view of this date field, and can see how many unique values there are, or if there are any records that have data missing. It also gives us the range of dates in the data. This feature is incredibly helpful for data profiling, but we can go even deeper with refinements.


With just a few clicks on a couple charts, we have now refined our view of the data to a specific company, JPMorgan Chase, and a type of response, “Closed with monetary relief”. Remember, we have yet to clean or manipulate the data ourselves, but already we’ve been able to dissect it in a way that would be difficult to do with a spreadsheet alone. Users of OEID and other discovery applications will probably see a lot of familiar actions here in the way we are drilling down into the records to get a unique view of the data, but users who are unfamiliar with these kinds of tools should find the interface to be easy and intuitive as well.


Another way BDD differentiates itself from some other discovery applications is with the actions available under the “Transform” tab.

Within this section of the application, users have a wealth of common transformation options available to them with just a few clicks. Operations like converting data types, concatenating fields, and getting absolute values now can be done on the fly, with a preview of the results available in near real-time.

BDD also offers more complex transformation functions in its Transformation Editor, which includes features like date parsing, geocoding, HTML formatting and sentiment analysis. All of these are built-in to the application; no plug-ins required. Another nice feature BDD provides is an easy to way group (or bin) attributes by value. For example, we can find all the car-related financing companies and group them into a single category to refine by later on:


Another nice added feature of BDD is the ability to preview the results of a transform before committing the changes to all the data. This allows a user to fine tune their transforms with relative ease and minimal back and forth between data revisions.

Once we’re happy with our results, we can commit the transforms to the data, at which point BDD launches a Spark job behind the scenes to apply the changes. From this point, we can design a discovery interface that puts our enriched data set to work.


Included with BDD are a set of dynamic, advanced data visualizations that can turn any data set into something profoundly more intuitive and usable:


The image above is just a sampling of the kind of visual tools BDD has to offer. These charts were built in a matter of minutes, and because much of the ETL process is baked into the application, it’s easy to go back and modify your data as needed while you design the graphical elements. This style of workflow is drastically different from workflows of the past, which required the back- and front-ends to be constructed in entirely separate stages, usually in totally different applications. This puts a lot of power into the hands of users across the business, whether they have technical chops or not.

And as we mentioned earlier, since BDD’s indexing framework is a close relative to Endeca, it inherits all the same real-time processing and unstructured search capabilities. In other words, digging into your data is simple and highly responsive:


As more and more companies and institutions begin to re-platform their data onto Hadoop, there will be a growing need to effectively explore all of that distributed data. We believe that Oracle’s Big Data Discovery offers a wide range of tools to meet that need, and could be a great discovery solution for organizations that are struggling to make sense of the vast stores of information they have sitting on Hadoop.

If you would like to learn more, please contact us at info [at]

Also be sure to stay tuned for Part 2!

Announcing PowerDrill for Oracle EID 3.1

If you had distill what we at Ranzal’s Big Data Practice do down to its essence, it’s to use technology to make accessing and managing your data more intuitive, more useful.  Often this takes the form of data modeling and integration, data visualization or advice in picking the right technology for the problem at hand.

Sometimes, it’s a lot simpler than that.  Sometimes, it’s just giving users a shortcut or an easy way to do more with the tools they have.  Our latest offering, the PowerDrill for Oracle Endeca Information Discovery 3.1, is the quintessential example of this.

When dealing with large and diverse quantities of data, Oracle Endeca Studio is great for a lot of operations.  It enables open text search, it has data visualization, it enriches data, it surfaces all in-context attributes for slicing and dicing and it helps you find answers both high-level, say “Sales by Region”, and low, like “My best/worst performing product”.  But what about the middle ground?

For example, on our demo site, we have an application that allows users to explore publicly available data related to Parks and Recreation facilities in Chicago.  I’m able to navigate through the data, filter by the types of facilities available (Pools, Basketball Courts, Mini Golf, etc.), see locations on a map, pretty basic exploration.

The Parks of Chicago

The Parks of Chicago

Now, let’s say I’m looking for parks that fit a certain set of criteria.  For example, let’s say I’m looking to organize a 3-on-3 basketball tournament somewhere in the city.  I can use my discovery application to very easily find parks that have at least 2 basketball courts.

Navigate By Courts

Navigate By Courts

This leaves me with 80 potential parks that might be a candidate for my tournament.  But let’s say I live in the suburbs and I’m not all that familiar with the different neighborhoods of Chicago.  Wouldn’t it be great to use other data sets to quickly explore the areas surrounding these parks quickly and easily?  Enter the Power Drill. Continue reading

Data Discovery In Healthcare — 1st Installment

Interested to understand how cutting edge healthcare providers are turning to data discovery solutions to unlock the insights in their medical records?  Check out this real-world demonstration of what a recent Ranzal customer is doing to unlock a 360 degree view of their clinical outcomes leveraging all of their EMR data — both the structured and unstructured information.

Take a look for yourself…

Fun with Shapefiles: The Two Utahs

A little midweek enjoyment, courtesy of our Advanced Visualization Framework.  Below, you can see a county-by-county map of Utah and all of its Oil and Gas Fields.

You can wave over counties and fields and get some basic statistics related to the county or field that you are inspecting.  For fields, we have the oil/gas field status, the year it was opened and other basic information such as whether or not it was merged with another field.

We had to “dumb it down a bit” and put it in to an iframe (WordPress!) but you can still some of the detail.  It’s obviously not as flexible as our “real visualizations” (no zooming, no refining, etc.) that render inside of Oracle Endeca Studio but gives you a sense of how quickly and easily our technology incorporates advanced GIS data into a Data Discovery application.