Look around you. Look closer. Pay more attention. What do you see? When I look around me I can see activity trackers, digital cameras, smart watches, interconnected devices, virtual reality gadgets, wearable technology, smart elevators, energy saving light systems, intelligent traffic lights, smart cars with over the air updates and that can gather data on your driving habits, intelligent houses, eco friendly buildings and more. All of these generate massive amounts of data. But let’s hold that thought for a minute. Now this is just what’s happening around you. What do you have in your pocket or in your hand right now? Most likely a smart phone. It is your portal to the digital world and even though it became second nature – pretty much everyone walks around with a phone in their hand now a days – it is a relatively new phenonem. It is highly likely that you use your smartphone constantly to check Facebook, Twitter, Instagram or search the web using Google among a few other applications. This generates humongous amounts of data. Let’s throw out a few numbers just to put it in perspective. Facebook has 1.6 billion users- yes, that is with a B – […]
Big Data Big deal? Big hype? Or big change in our world? I think that the answer can be all of the above. “Hype” you might be thinking? Well, here is the deal. Our world has changed in unimaginable ways. The amount of information created daily is reaching levels that just a few years ago would’ve been considered science fiction or even plain old crazy. Lots and Lots of Data To make it even more interesting, a lot of it is unstructured data. Which can be kind of a problem if we think about it, because the success of relational databases has taught a lot of us to think in a columnar and relational way. And this is not bad… at all. It is nice to have all your data and metadata organized neatly. You can use select, join, where, group by and more to get what you need. But the success of relational databases can also create a blind spot for many. Just a few days ago I was talking with the VP and cofounder of a company related to migrations and artificial intelligence software whose company has faced success (as well as a few failures or learning experiences) […]
Here is a collection of some interesting or fun articles that I have found on Big Data – 5 Reasons to Move to Big Data (and 1 Reason Why It Won’t Be Easy): gives an easy to understand set of selling points on why to adopt Big Data, but making it clear some of the issues you might face. – The Most Practical Big Data Use Cases Of 2016: covers some interesting use cases of Big Data. Remember, Big Data is sexy! – Why ‘Big Data’ Means Nothing Without ‘Little Data’: Little Data is regular performance metrics. – Why Big Data is the new competitive advantage: provides good points on how Big Data can help give you an edge. – Big Data: What is it and Why it Matters: goes straight to the point to explain the basics. – Big Data Analytics: What is it and Why it Matters: explains what is Big Data Analytics.
I have been working with Solr for a while, mainly from the .NET world and I basically love it. I use SolrNet which I think it is a very mature and stable library. I was asked today if I have ever used SolrExpress and if I recommend it over SolrNet. The short answer is no, I have not used it. Therefore I can’t give a facts based recommendation, but looking over the source code of both libraries it is my opinion that SolrNet is still more complete. So I still believe SolrNet to be a more sensible choice. It is worth mentioning that is a biased point of view, as I have used SolrNet multiple times and it really has made my life a lot easier. Having said that, besides using it several times, I have authored a few things around Solr and SolrNet and used it extensively. It works fine and I know it pretty well. It basically gets the job done, it is pretty mature and almost complete (pending SolrCloud and a few minor things like a breaking change on collation). Some of the things I created I created a Solr training for Pluralsight https://www.pluralsight.com/courses/enterprise-search-using-apache-solr Getting Started with Enterprise Search […]
Yesterday I was coming back from the beautiful mountains of Monteverde in Costa Rica, feeling full of energy after a relaxing weekend. Monteverde is one of the most beautiful places I’ve been. Newsweek has declared Monteverde the world’s #14 Place to Remember Before it Disappears.” Anyway, on the drive back I stop and decide to check my email, as usual, and I see a contact form from my blog so I decide to check it. This is what I found, a note from Robert Stevens:
Whenever you want to start Solr or any other search or big data application, you need to have as prerequisite the Java Runtime Environment, known as JRE. How do you find out if you have the JRE? Open the command line and run java -version
I received a question today on stemming and multi language. Basically, “why do we need multiple fields in our Solr in different languages and how do I test multi language stemming?”. First of all, let’s explain what stemming is. Stemming involves reducing words to their stem (or base or root) during indexing and querying in an effort to improve recall. For example, if a document includes the following phrase “Xavier walked to work every morning from Westside Parkway” and a user searches for walk then the results will correctly include the document that has walk.