MapR, DataStax offer options for container persistence
If it's lack of options for persistence that's keeping you from using containers, maybe it's time you reconsider.
Read More →Staying agile: data-driven IT operations
Would you like to have an end-to-end picture of your IT operations, but are lost in translation among a myriad monitoring solutions and metrics? Your monitoring should be as agile as your operations, and OpsDataStore says it can help you get there.
Read More →Streaming hot: Real-time big data architecture matters
What is streaming in big data processing, why you should care, and what are your options to make this work for you?
Read More →Mix and match analytics: data, metadata, and machine learning for the win
Creating winning analytics solutions means combining and making the most of different approaches and techniques. Taking a look at how Google does this for YouTube can provide inspiration and set a framework for analytics solutions.
Read More →Googling prescriptive analytics: YouTube recommendations and the analytics continuum
Understanding prescriptive analytics is complicated, let alone implementation. Would turning to Google help? Seeing how it works for Google raises questions, but may also lead to some answers.
Read More →Big Data, Crystal Balls and Looking Glasses: Reviewing 2016, predicting 2017
End-of-year reviews are boring — and everyone does them. Predictions are boring — and they are hard. Of course, this is different — because big data.
Read More →Why finance craves big data: A perfect storm of disruption and opportunity
The financial services domain, where real-time is measured in milliseconds, holds particular interest for big data applications and vendors for a number of reasons: architecture, regulation, transparency, decision-making, and the need for speed.
Read More →Predictive analytics and machine learning: A dynamic duo
Predictive analytics and machine learning are seen as the pair of tools to save the day for most organizations currently. We try to de-mystify both, taking a look at what they are, how they work, and what they are good for.
Read More →Out of the Hadoop box: SQL everywhere and AtScale
AtScale has made a name for itself by providing an access layer on top of Hadoop that enables it to be used directly as a data warehouse. AtScale is now announcing support for Teradata DW and Google Dataproc and BigQuery, offering what it calls a Unified Analytics Platform. Why this move now, how does it work and what does it mean?
Read More →To the cloud, big data sisters and brothers, to the cloud
While reports of big data's death have been greatly exaggerated, the skepticism is not unwarranted. The cloud may have some of the answers, but it won't solve all of big data's problems.
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