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Many companies have huge investment in Data Warehouse and BI tools and want to leverage those investments to process data collected by applications in MongoDB. For example, a company may need to......
Many companies have huge investment in Data Warehouse and BI tools and want to leverage those investments to process data collected by applications in MongoDB. For example, a company may need to blend clickstream data collected by distributed MongoDB data storage with personal data from Oracle into the Data Warehouse system or Analytics platform to provide timely marketing reports. Most of the time the job requires converting a MongoDB JSON document structure into a traditional relational model. Traditional ETL (Extract Transform Load) process still needed to be developed for loading and conversion of unstructured data into traditional analytical tools or Hadoop. In this talk we discuss how to develop a real-time, scalable, fault-tolerant ETL process to integrate MongoDB with traditional RDBMS storage using the open-sourced Twitter Storm project. We will be capturing data streamed by MongoDB oplog or capped collections, transforming it into tables, rows and columns and loading it into a SQL database. We will discuss mongoDB oplog and Storm architecture. The principles discussed in the talk can be used for many other applications - like advanced analytics, continuous computations and so on. We will be using Java as our language of choice but you can use the same software stack with any language.