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One of the goals of LucidWorks Big Data (LWBD) is to facilitate interoperation of components within the Hadoop ecosystem. One pathway we use commonly in LWBD is reading and writing data in HBase through Pig. At the most basic level, this is made possible through the HBaseStorage load/store backend provided by Pig. We have found there is a bit of an impedance mismatch between the way HBaseStorage returns records and the way Pig manipulates data. Specifically, HBaseStorage returns associative arrays (maps) corresponding to columns, but Pig is very record oriented and lacks support for map manipulation.
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Great post by Paul Doscher, LucidWorks CEO at Wired.com about how new open source tools and interfaces are making it easier, and without a major investment, for developers to rapidly design, create and deploy prototype applications.
Finding the right solution requires building a framework around the needs of users and the business. You need to have deeper insights into users question, by leveraging existing internal knowledge in a cost-effective way.
Read More Here.
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Yesterday, I get an e-mail from one of our Apache Lucene/Solr committers asking me what I meant by my comment on the Apache JIRA. For those of you unfamiliar with this JIRA, it is where open source developers go to collaborate on Apache projects, post questions, new ideas for a project, and generally engage in thread discussions around the Apache projects.
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See this new research note from Ventana Research discussing how LucidWorks Big Data connects with and integrates Mapr. The author, Mark Smith, discusses how LucidWorks Big Data integrates both MapR and key Apache Open Source technologies to produce a developer-friendly Big Data application platform.
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In this post we will discuss how to create a visualized workflow graph for Oozie. Oozie is a workflow management system for Hadoop jobs. Oozie Workflow jobs are DAG (Directed Acyclical Graphs) of actions: oozie.apache.org
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