Walker Rowe – BMC Blogs http://www.bmc.com/blogs BMC Software Fri, 17 Nov 2017 16:42:07 +0000 en-US hourly 1 http://blogs.bmc.com/wp-content/uploads/2016/04/bmc_favicon-300x300-150x150.png Walker Rowe – BMC Blogs http://www.bmc.com/blogs 32 32 What is a Neural Network? Introduction to Neural Networks Part I http://www.bmc.com/blogs/neural-network-tensor-flow/ Wed, 15 Nov 2017 08:12:23 +0000 http://www.bmc.com/blogs/?p=11465 We want to explore machine learning on a deeper level by discussing neural networks. We will do that by explaining how you can use Tensor Flow to recognize handwriting. But to do that we first must understand what are neural networks. We begin our discussion, based upon our knowledge of linear models, and draw some […]]]> Introduction to TensorFlow and Logistic Regression http://www.bmc.com/blogs/introduction-to-tensorflow-and-logistic-regression/ Mon, 06 Nov 2017 09:57:49 +0000 http://www.bmc.com/blogs/?p=11421 Here we introduce TensorFlow, an opensource machine learning library developed by Google. We explain what it does and show how to use it to do logistic regression. Background TensorFlow has many applications to machine learning, including neural networks. One application of neural networks is handwriting analysis. Another is facial recognition. TensorFLow is design to allow […]]]> Working with MongoDB Aggregate Functions http://www.bmc.com/blogs/working-with-mongodb-aggregate-functions/ Tue, 31 Oct 2017 05:15:31 +0000 http://www.bmc.com/blogs/?p=11386 This is the second part of the tutorial on how to use NodeJS with MongoDB. Here we switch to using the regular MongoDB shell and commands to make the study of aggregate functions simpler. To show how to use aggregate functions, we will first explain how to do basic queries. Then we will show how […]]]> MongoDB Sharding Explained http://www.bmc.com/blogs/mongodb-sharding-explained/ Mon, 30 Oct 2017 15:20:59 +0000 http://www.bmc.com/blogs/?p=11398 MongoDB is designed to be scalable, meaning you can run it in a cluster across a distributed platform. That is called sharding. You assign different parts of the data to different servers using an index.  For example, records with the index customers could be on one set of servers and vendors on the other.  But […]]]> How to Use Mongoose for MongoDB and NodeJS http://www.bmc.com/blogs/how-to-use-mongoose-for-mongodb-and-nodejs/ Fri, 27 Oct 2017 10:40:41 +0000 http://www.bmc.com/blogs/?p=11347 Here we show how to use Mongoose to save data to a MongoDB. This is a two part blog post. In the second post, we will show how to run aggregation functions. The last previous post was an intro to MongoDB, so read that first. Mongoose In this example, we will use NodeJS. Do not […]]]> MongoDB Overview: Getting Started with MongoDB http://www.bmc.com/blogs/mongodb-overview-getting-started-with-mongodb/ Tue, 24 Oct 2017 09:45:27 +0000 http://www.bmc.com/blogs/?p=11293 Here we provide an overview of the MongoDB database. In subsequent posts we will give more in depth examples of how to use MongoDB. First, MongoDB is a noSQL big data database. It fits the definition of big data, because it scales (i.e., can be made larger) simply by adding more servers to a distributed […]]]> Using Apache Hive with ElasticSearch http://www.bmc.com/blogs/using-apache-hive-with-elasticsearch/ Tue, 24 Oct 2017 09:20:10 +0000 http://www.bmc.com/blogs/?p=11260 Here we explain how to use Apache Hive with ElasticSearch. We will copy an Apache webserver log into ElasticSearch then use Hive SQL to query it. Why do this? Hive lets you write user defined functions and use SQL (actually HQL) which is easier to work with and provides more functions that ElasticSearch, whose query […]]]> Using Apache Pig and Hadoop with ElasticSearch with The Elasticsearch-Hadoop Connector http://www.bmc.com/blogs/using-apache-pig-and-hadoop-with-elasticsearch-with-the-elasticsearch-hadoop-connector/ Wed, 11 Oct 2017 09:10:20 +0000 http://www.bmc.com/blogs/?p=11219 Here we show how to retrieve data from ElasticSearch using Apache Pig. The reason for doing that is Pig is much easier to use that Java, Scala, and other tools for doing data extraction and transformation ElasticSearch. (You can read our introduction to Apache Pig here.) Also you can construct complex queries and sets using […]]]> Using ElasticSearch with Apache Spark http://www.bmc.com/blogs/using-elasticsearch-with-apache-spark/ Wed, 11 Oct 2017 08:30:53 +0000 http://www.bmc.com/blogs/?p=11316 ElasticSearch is a JSON database popular with log processing systems. For example, organizations often use ElasticSearch with logstash or filebeat to send web server logs, Windows events, Linux syslogs, and other data there. Then they use the Kibana web interface to query log events. All of this is important for cybersecurity, operations, etc. Now, since […]]]> Using Spark with Hive http://www.bmc.com/blogs/using-spark-with-hive/ Fri, 15 Sep 2017 11:00:12 +0000 http://www.bmc.com/blogs/?p=11173 Here we explain how to use Apache Spark with Hive. That means instead of Hive storing data in Hadoop it stores it in Spark. The reason people use Spark instead of Hadoop is it is an all-memory database. So Hive jobs will run much faster there. Plus it moves programmers toward using a common database […]]]>