This article presents a solution for gauging public opinion in tweets. The goal is to create a transformation pipeline that outputs clusters of comments and trending subjects. Apache, Apache NiFi, Apache Hadoop, Apache Hive, and Apache Airflow are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries. No endorsement by The Apache Software Foundation is implied by the use of these marks. The Twitter ingestion pipeline consists of four stages.

This course is all about learning various cloud Analytics and Machine Learning options available on Microsoft AZURE cloud platform. We would be creating resources for Stream Analytics, Spark, HDInsight exploring options. We would be learning all the Analytics services with some use cases. Machine learning and cloud computing are trending domains and also have lot of job opportunities, if you have interest in machine learning as well as cloud computing then this course for you. This course will let you use your machine learning skills deploy in cloud.

Azure Databricks (documentation and user guide) was announced at Microsoft Connect, and with this post I'll try to explain its use case. At a high level, think of it as a tool for curating and processing massive amounts of data and developing, training and deploying models on that data, and managing the whole workflow process throughout the project. It is for those who are comfortable with Apache Spark as it is 100% based on Spark and is extensible with support for Scala, Java, R, and Python alongside Spark SQL, GraphX, Streaming and Machine Learning Library (Mllib). It has built-in integration with Azure Blog Storage, Azure Data Lake Storage (ADLS), Azure SQL Data Warehouse (SQL DW), Cosmos DB, Azure Event Hub, Apache Kafka for HDInsight, and Power BI (see Spark Data Sources). Think of it as an alternative to HDInsight (HDI) and Azure Data Lake Analytics (ADLA).

Learn how to use Apache Hive to process Twitter data. The result is a list of Twitter users who sent the most tweets that contain a certain word. The steps in this document were tested on HDInsight 3.6. Linux is the only operating system used on HDInsight version 3.4 or greater. For more information, see HDInsight retirement on Windows.

Beginning in 2016, Microsoft rolled out a preview of Microsoft R Server (MRS) for Azure HDInsight clusters. Recent blog posts (by Max Kaznady and David Smith) have highlighted how to use and tune this service for large scale machine learning tasks. In this post, we push the envelope and show how to build an end-to-end fully operationalized analytics pipeline using Azure Data Factory (ADF) and MRS with HDInsight (specifically Apache Spark). By integrating Azure Data Factory with Microsoft R Server and Spark, we show how to configure a scalable training and testing pipeline that operates on large volumes of data.

In particular, it is particularly amenable to machine learning and interactive data workloads, and can provide an order of magnitude greater performance than traditional Hadoop data processing tools. In this course, we will provide a deep-dive into Spark as a framework, understand it's design, how to optimally utilize it's design, and how to develop effective machine learning applications with Spark on HDInsight. The course covers the fundamentals of Spark, it's core APIs and design, relational data processing with Spark SQL, the fundamentals of Spark job execution, performance tuning, tracking and debugging. Users will get hands-on experience with processing streaming data with Spark streaming, training machine learning algorithms with Spark ML and R Server on Spark, as well as HDInsight configuration and platform specific considerations such as remote developing and access with Livy and IntelliJ, secure Spark, multi-user notebooks with Zeppelin, and virtual networking with other HDInsight clusters.

Microsoft has a tricky job in the data world. On the one hand, it has a 25-year legacy in the on-premises relational database business with SQL Server and needs to keep that lucrative business relevant and stable. On the other hand, as the company pivots toward the cloud, it needs to proffer relational OLTP, data warehouse, NoSQL, Big Data and machine learning technologies. And it need to make them credible and competitive against offerings from so many startups in the data and analytics world. And then there was Strata... Microsoft also needs to make all of this technology accessible to developers, including its core constituency of .NET developers, but also those working with Java, Node/JavaScript, Python and a slew of other programming platforms.

Today, we announced the general availability of R Server for Azure HDInsight. This gives Azure HDInsight the most comprehensive set of ML algorithms and statistical functions in the cloud that also leverages Hadoop and Spark. R is one of the most popular programming language that helps millions of data scientists solve their most challenging problems in fields ranging from computational biology to quantitative marketing. R Server for Azure HDInsight is a scale-out implementation of R integrated with Spark clusters created from HDInsight. This gives you the familiarity of the R language for machine learning while leveraging the scalability and reliability built into Spark.

As I mentioned yesterday, Microsoft R Server now available for HDInsight, which means that you can now run R code (including the big-data algorithms of Microsoft R Server) on a managed, cloud-based Hadoop instance. Debraj GuhaThakurta, Senior Data Scientist, and Shauheen Zahirazami, Senior Machine Learning Engineer at Microsoft, demonstrate some of these capabilities in their analysis of 170M taxi trips in New York City in 2013 (about 40 Gb). Their goal was to show the use of Microsoft R Server on an HDInsight Hadoop cluster, and to that end, they created machine learning models using distributed R functions to predict (1) whether a tip was given for a taxi ride (binary classification problem), and (2) the amount of tip given (regression problem). The analyses involved building and testing different kinds of predictive models. Debraj and Shauheen uploaded the NYC Taxi data to HDFS on Azure blob storage, provisioned an HDInsight Hadoop Cluster with 2 head nodes (D12), 4 worker nodes (D12), and 1 R-server node (D4), and installed R Studio Server on the HDInsight cluster to conveniently communicate with the cluster and drive the computations from R. To predict the tip amount, Debraj and Shauheen used linear regression on the training set (75% of the full dataset, about 127M rows).