In my previous post I looked the latest release of Oracle Stream Analytics (OSA), and saw how it provided a graphical interface to “Fast Data”. Users can analyse streaming data as it arrives based on conditions and rules. They can also transform the stream data, publishing it back out as a stream in its own right. In this article we’ll see how OSA can be used with Kafka.
A project that I've been working on for a while now involves the use of Oracle Text, Oracle R Enterprise and Oracle Data Mining. Oracle Text comes with your Oracle Database licence. Oracle R Enterprise and Oracle Data Mining are part of the Oracle Advanced Analytics (extra cost) option.
What I will be doing over the course of 4 or maybe 5 blog posts is how these products can work together to help you gain (more...)
Oracle Stream Analytics (OSA) is a graphical tool that provides “Business Insight into Fast Data”. In layman terms, that translates into an intuitive web-based interface for exploring, analysing, and manipulating streaming data sources in realtime. These sources can include REST, JMS queues, as well as Kafka. The inclusion of Kafka opens OSA up to integration with many new-build data pipelines that use this as a backbone technology.
Previously known as Oracle Stream Explorer, it is (more...)
Since the release of OBIEE 12c in 2015, we have received countless inquiries about how we would be offering our training. Our customers are familiar with our ability to provide on-site private training for a team and we are well known for hosting training classes in our offices in the UK and the US. But what most people aren’t aware of is that we now offer OBI remote training.
The need to monitor and control the system performances is not new. What is new is the trend of clever, lightweight, easy to setup, open source metric collectors in the market, along with timeseries databases to store these metrics, and user friendly front ends through which to display and analyse the data.
In this post I will compare Telegraf, Collectl and Topbeat as lightweight metric collectors. All of them do a great job of collecting variety (more...)
Recently at Rittman Mead we have been asked a lot of questions surrounding Oracle’s new Data Visualization Desktop tool and how it integrates with OBIEE. Rather than referring people to the Oracle docs on DVD, I decided to share with you my experience connecting to an OBIEE 12c instance and take you through some of the things I learned through the process.
In a previous blog, I went though database connections with Data Visualization (more...)
Oracle’s Big Data Discovery encompasses a good amount of exploration, transformation, and visualisation capabilities for datasets residing in your organisation’s data reservoir. Even with this though, there may come a time when your data scientists want to unleash their R magic on those same datasets. Perhaps the data domain expert has used BDD to enrich and cleanse the data, and now it’s ready for some statistical analysis? Maybe you’d like to use R’s excellent forecast(more...)
Big Data Discovery (BDD) is a great tool for exploring, transforming, and visualising data stored in your organisation’s Data Reservoir. I presented a workshop on it at a recent conference, and got an interesting question from the audience that I thought I’d explore further here. Currently the primary route for getting data into BDD requires that it be (i) in HDFS and (ii) have a Hive table defined on top of it. From there, (more...)
This is the fourth and last blog post in a series that looks at how you can examine the details of predicted clusters using Oracle Data Mining. In the previous blog posts I looked at how to use CLUSER_ID, CLUSTER_PROBABILITY and CLUSTER_SET.
In this blog post we will look at CLUSTER_DISTANCE. We can use the function to determine how close a record is to the centroid of the cluster. Perhaps we can use this to (more...)
I've recently come across the 'googleVis' R package. This allows you to create a variety of different (typical and standard) charts in R but with the look and feel of the charts we can get from a number of different Google sites.
I won't bore you with some examples in the post but I'll point you to a good tutorial on the various charts.
With my recent role as CTO/Advisor with www.analytos.com, during most of my conversation with Analytics leaders within the company, all are concern over security. At a recent conversation with another entrepreneur friend, one of his solution was stalled due to SQL injection issue on the cloud ( a valid concern , but is it valid ?) .
During my recent startup sting, cloud Allergy word was coined, and it did make sense, because allergies do exist and (more...)
June is the perfect month: summer begins, major football (and futbol) tournaments are in full swing, and of course, KScope16 is on! Rittman Mead have participated in many of the past KScope conferences and will continue that tradition this year with a wide range of presentation and training topics across the Business Intelligence, Data Warehousing, Big Data, and Advanced Analytics subject areas. This year the event is held in Chicago at the Sheraton Grand Chicago, a (more...)
Gartner’s latest version of the Magic Quadrant for Business Intelligence and Analytics Platforms (#BIAMQ) caused a lot of noise in the Analytics Market. Gartner and Oracle have different views of this market. Gartner sees a difference; “…between a modern BI and Analytics Platform and a traditional, IT-centric Reporting and Analysis Platform”. It’s clear that Oracle and Gartner have a…Read more Oracle Business Analytics from a different angle
New in Big Data Discovery 1.2 is the addition of BDD Shell, an integration point with Python. This exposes the datasets and BDD functionality in a Python and PySpark environment, opening up huge possibilities for advanced data science work on BDD datasets. With the ability to push back to Hive and thus BDD data modified in this environment, this is important functionality that will make BDD even more useful for navigating and exploring (more...)
New in Big Data Discovery 1.2 is the addition of BDD Shell, an integration point with Python. This exposes the datasets and BDD functionality in a Python and PySpark environment, opening up huge possibilities for advanced data science work on BDD datasets, particularly when used in conjunction with Jupyter Notebooks. With the ability to push back to Hive and thus BDD data modified in this environment, this is important functionality that will make BDD (more...)
In a previous blog post I gave some details of how you can examine some of the details behind a prediction made using a classification model. This seemed to spark a lot of interest. But before I come back to looking at classification prediction details and other information, this blog post is the first in a 4 part blog post on examining the details of Clusters, as identified by a cluster model created using Oracle (more...)