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I am dividing the whole project in 2 stage Process
A. First Stage Process -
I. ETL can performed in following ways -
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Which is performed over: OpenMRS Database to Enterprise Data-warehouse (EDW)
II. Open Source Tools for ETL - Petaho, Talend, CloverETL, JasperSoft etc.
Where Pentaho and Talend are in my first preferences because Pentaho is faster and easier to use because of its GUI while Talend is more a tool for people who are making already a Java program and want to save lots and lots of time with a tool that generates code for them.
III. Three way to integrate it in OpenMRS -
1. Integration in the Web Application (Dynamic) - We can perform ETL in OpenMRS web application at some regular interval of time. Where a JS library is required which is integrated in JSP pages.
2. Integration in the Server i.e. OpenMRS Standalone (Static) - When any one start the server he/she runs the OpenMRS Standalone. We can perform ETL in standalone at some regular interval of time. Where we have some menu items in Standalone like :
ETL Tool Menu-
Force Update - Escaping whatever the scheduled time and performs ETL
Set Interval - Set interval of time after which a ETL is performed.
3. OpenMRS ETL Tool-kit - This is separate application that doesn't require the running of server. It will have the same functionality as first way integration with some extending capability. The benefit of this method is we can also use other cross platform implementation other than java like in C# for performing ETL. This gives the independency of language selection.
IV. Selection of EDW - Using Apache Hive. Apache Hive is probably the best way to store data in Hadoop as it uses a table concept and have a SQL like language, HiveQL
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Figure 1: MySQL to HDFS Integration
This integration uses a JS Library where user drag and drop objects somehow similar to what one can do in informatica and then generate a sql which will be executed at backend.
It connects to the MySQL OpenMRS database to read the table information and then:
- Fetches the row insert events occurring on the database
- Decodes these events, extracts data inserted into each field of the row, and uses content handlers to get it in the format required
- Appends it to a text file in HDFS.
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Databases are mapped as separate directories, with their tables mapped as sub-directories with a Hive data warehouse directory. Data inserted into each table is written into text files (named as datafile1.txt) in Hive / HDFS. Data can be in comma separated format; or any other, that is configurable by command line arguments.
Figure 2: Mapping between MySQL and HDFS Schema
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