In a previous post, we walked through how to implement a custom Java transformation in Oracle Big Data Discovery. While that post was more technical in nature, this follow up post will highlight a detailed use case for the transformations and illustrate how they can be used to augment an existing dataset.
In our first post introducing Oracle Big Data Discovery, we highlighted the data transform capabilities of BDD. The transform editor provides a variety of built in functions for transforming datasets. While these built in functions are straightforward to use and don't require any additional configuration, they are also limited to a predefined set of transformations. Fortunately, for those looking for additional functionality during transform, it is possible to introduce custom transformations that can leverage external Java libraries by implementing a custom Groovy script. The rest of this post will walk through the implementation of a basic example, and a subsequent post will go in depth with a few real world use cases.
Ok. Now it is time to showoff. Check out some of the secret sauce Ranzal brings to the solution around unstructured data and custom visualizations...
We're long overdue for a "public service" post dedicated to sharing best practices around how Ranzal does certain things during one of our implementation cycles. Past installments have covered installation pitfalls, temporal analysis and the Endeca Extensions for Oracle EBS.
In this post, we're sharing our internal playbook (adapted from our internal Wiki) for deploying custom portlets (such as our Advanced Visualization Framework or our Smart Tagger) inside of an Oracle Endeca Studio instance on WebLogic.
The documentation is pretty light in this area so consider this our attempt to fill in the blanks for anyone looking to deploy their own portlets (or ours!) in a WebLogic environment. More after the jump...
The newest version of Oracle's Endeca Information Discovery (OEID v3.1), Oracle's data discovery platform, was released yesterday morning. We'll have a lot to say about the release, the features, and what an upgrade looks like in the coming weeks (for the curious, Oracle's official press release is here) but top of our minds right now is: "How do I get this installed and up and running?"
For almost a decade, the core Endeca MDEX engine that underpins Oracle Endeca Information Discovery (OEID) has supported one-time indexing (often referred to as a Baseline Update) as well as incremental updates (often referred to as partials). Through all of the incarnations of this functionality, from "partial update pipelines" to "continuous query", there was one common limitation. Your update operations were always limited to act on "per-record" operations.
If you're a person coming from a SQL/RDBMS background, this was a huge limitation and forced a conceptual change in the way that you think about data. Obviously, Endeca is not (and never was) a relational system but the freedom to update data whenever and where ever you please, that SQL provided, was often a pretty big limitation, especially at scale. Building an index nightly for 100,000 E-Commerce products is no big deal. Running a daily process to feed 1 million updated records into a 30 million record Endeca Server instance just so that a set of warranty claims could be "aged" from current month to prior month is something completely different.
Thankfully, with the release of the latest set of components for the ETL layer of OEID (called OEID Integrator), huge changes have been made to the interactions available for modifying an Endeca Server instance (now called a "Data Domain"). If you've longed for a "SQL-style experience" where records can be updated or deleted from a data store by almost any criteria imaginable, OEID Integrator v3.0 delivers.
I've recently been working with a customer who has a larger number of Endeca CAS crawls (300+) defined for their Endeca-driven enterprise search application. Occasionally, the need arises to rerun all of their crawls consecutively.
Ranzal launches the first of our Performance Analysis Tools: Phoenix
So, you’ve got this great Endeca Commerce implementation powering your online sales and delivering a world-class experience to your customers. Or you’ve got a terrific Data Discovery application built on the Oracle Endeca Information Discovery (OEID, for short) platform and it’s enabling your users to unlock all kinds of value from your structured and unstructured data. Things are humming along and life is grand. However, one day, you decide to implement some changes. Maybe you’re rolling out a second business release or a whole new set of data sources or products. Maybe you’re enabling record-level-security. Post-rollout, you start to get the dreaded emails from your users: