As I posted in the Pentaho Forums last week, I’m running a survey on who uses Kettle to help with a presentation I’m doing for the Boulder Java Users’ Group. If you need those advantages then you'll have to get at your file name another way, such as passing it as a named parameter and adding it to the stream with the Get Variables step. Survey on pentaho data integration/kettle users. Lazy conversion: If you will be reading many fields from the file and many of those fields will not be manipulate, but merely passed through the transformation to land in some other text file or a database, lazy conversion can prevent Kettle from performing unnecessary work on those fields such as converting them into objects such as strings, dates, or numbers.Parallel running: If you configure this step to run in multiple copies or in clustered mode, and you enable parallel running, each copy will read a separate block of a single file allowing you to distribute the file reading to several threads or even several slave nodes in a clustered transformation.NIO: Native system calls for reading the file means faster performance, but it is limited to only local files currently. In the Design tab, under the Big Data section, select and bring over two Hadoop File Input steps.The ETL Metadata Injection step inserts metadata into a template transformation.
The advantages you gain when using the CSV Input are: Pentaho will perform single-row insert/commit pairs using one concurrent connection per running transformation. Metadata Injection in Pentaho Data Integration. Using this step would solve your problem because in the Additional output fields tab you can specify a field in the stream to put your filename, extension, file path, etc. Open a transformation with CSV Input File that includes Timestamp format field In CSV Input File, uncheck the option Lazy Conversion Select Run and Inspect Data Expected / Actual Result: DET opens and the data is shown, including Timestamp.
You can set the Filetype as CSV and select your separator in the Content tab, and list out the fields you want to grab in the Fields tab. With Text File Input there are a lot more options available to you for reading the file. While SAS does support other format types (such as CSV and Excel), sas7bdat is a format most similar to other analytics packages special formats (such as Wekas ARFF file format). Loading customer data from a CSV file is accomplished by dragging a data input node onto the canvas and. Kettle can read files written in SAS specialized data format known as sas7bdat using a new (since Version 4.3) input step called SAS Input. I know it sounds kind of backwards but you probably will want to use the Text File Input step to parse your CSV file, rather than the CSV Input which is a subset of options from the Text File Input with some performance advantages for delimited files. Pentahos Data Integration module allows users to extract data from a number of data sources, transform it so that it is in a standardized format, and load this information into a central database to make it possible for future analysis.
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