7 Must Have Items on Your Checklist Before You Consider Machine Data Analysis

These are the 7 key factors that highlight whether performing machine data analysis realizes credible value for you.

1. Prepare the raw data —

Most of machine data are in the form of logs. Industrial machines are constantly producing valuable operational data (call-home data) on configuration, performance, usage, and other important parameters that define the very life of the device in the field.

IOT analytics fuel the brick and mortar retail revolution

Brick and mortar retailers are in a life struggle with online marketplaces. Brick and mortar retailers operate with the added expense of maintaining a physical facility, keeping inventory on hand and hiring service staff to support customers.

Savvy retailers are continuously seeking out new information about their customers; how they browse the store, what they buy, when they buy it, and which marketing “levers” are responsible for those sales. Some are even turning to technologies as elaborate as creating virtual store shelves and studying shoppers’ eye movements.

Glassbeam 4.8 is here!

Glassbeam 4.8 is the latest and greatest version of our next generation machine data analytics platform. We’re excited to announce it’s launch – specially so because it packs so much punch it almost feels like 5.0!

For those who like a release notes kind of narrative, please check out our WHAT’S NEW page.

Here are few things, that makes 4.8 so cool:

Geting the most out of akka clusters

Anyone serious about distributed systems or building one, commonly encounters issues such as replication, consistency, availability and partition tolerance (CAP) [1]. In a real life scenario, partition tolerance is inevitable. So the system must be able to handle partition tolerance when there are network outages. Therefore, ‘P’ in the CAP is a must for any distributed system. This has been backed by Peter Deutsch in his (EIGHT FALLACIES OF DISTRIBUTED COMPUTING).

Elastically adding and removing nodes using akka cluster

This post explores a pull-based master/worker architecture – one that is suitable for anyone who is looking to elastically provision nodes when the load is higher than normal and under-provision when the load is below normal. In this post, the master accepts RSS links from frontend which can be a user submitting links and worker accept one RSS link and extract information such as article’s published date, title and a brief description. All this information is indexed into ELASTICSEARCH.

PRDS and user stories, iot style

Network World spoke with us earlier this month and ran a NICE PIECE on how the IOT revolution is positively impacting new product development.

ABCS of log file analytics

A. Aggregate data from all log files – All log files are not the same – Most people think of sys logs when they think of log files – no thats not all. Logs from product and software companies are bundles containing many files each of different types and formats. Some are time series data of events but others can be stats, session information, usage metrics, configuration etc.

Big data and dsl ( domain specific language )

DOMAIN SPECIFIC LANGUAGES have been around for a long time – a great example of a DSL is SQL for RDBMS. A DSL is differentiated from a general-purpose language such as C, Java or Python since a DSL is geared towards a specific domain.

Skills needed for big data analysis?

A recent article in FORBES opined that Big Data Analysis required Data Scientists and Quant/Excel jockeys. We agree – to an extent.

While these skills are important for an organization that wishes to do everything in-house, there are companies like us that are obviating the need for acquiring this talent. Especially if you are a manufacturer of computer-centric technology products you don’t need to look beyond our SaaS-based OFFERING to easily collect and analyze data.