How to read the first line and store header data in Apache Beam Python?
Check out this example. See how the UsCovidDataCsvReader parses the input.
The basic ideas are
read the header and build a schema from it
read the file with skip_header_lines=1
parse the input with the schema to build a PCollection
Related
how can I convert an uploaded CSV to dataframe in foundry using a code workbook? should I use the #transform decorator with spark.read.... (not sure of the exact syntax)?
Thx!!
CSV is a "special format" where Foundry can infer the schema of the CSV and automatically convert it to a dataset. In this example, I uploaded a CSV with hail data from NOAA:
If you hit Edit Schema on the main page, you can use a front end tool to set certain configurations about the CSV (delimiter, column name row, column type etc).
Once it's a dataset, you can import the dataset in Code Workbooks and it should work out of the box.
If you wanted to parse the raw file, I would recommend you use Code Authoring, not Code Workbooks, as it's better suited for production level code, has less overhead and is more efficient for this type of workflow (parsing raw files). If you really wanted to use code workbooks, change the type of your input using the input helper bar, or in the inputs tab.
Once you finish iterating please move this to a Code Authoring repository and repartition your data. File reads at code workbook can substantially slow down your whole code workbook pipeline. Code Authoring offers preview of raw files now, so it's just as fast to develop as using Code Workbooks.
Note: Only imported datasets and persisted datasets can be read in as Python transform inputs. Transforms that are not saved as a dataset cannot be read in as Python transform inputs. Datasets with no schema should be read in as a transform input automatically.
I'm trying to capture the schema of a JSON file that I am generating from a SQL database using Talend. I need to store this schema in a separate file. Does anyone know of a way to capture this?
With the metadata section in repository, you can create a JSON File Schema. Here you can import a json file example , it will then generate a schema that you could reuse in the output of your job, in a twritejsonfields component for example.
I am using google cloud data fusion to ingest a CSV file from AWS, apply directives, and store the resultant file in GCS. Is there a way to dynamically generate the output schema of the CSV file for Wrangler? I am uploading the input path, directives, output schema as a macro to the argument setter. I can see options only to import and export output schema.
Currently there is no way to get the output schema dynamically since the output schema can be wildly different as a result of a parse directive.
Hi I am trying to create an application which would read a binary file and then depending on the data within the binary file I will have to build a sequence of steps.
I have tried using FlatFileItemReader but I understand that in order to read a Binary File you would have to use SimpleBinaryBufferedReaderFactory.
Can some one please help me on how to read the binary data.
Can someone suggest me how to parse EDIFACT format data using Apache spark ?
i have a requirement as every day EDIFACT data will be written to aws s3 bucket. i am trying to find a best way to convert this data to structured format using Apache spark.
In case you have your invoices in EDIFACT format you can read each one of them as one String per Invoice using RDD´s. Then you will have a RDD[String] which represents the distributed invoice collection. Take a look to https://github.com/CenPC434/java-tools with this you can convert the EDIFACT strings to XML. This repo https://github.com/databricks/spark-xml shows how to use XML format as input source to create Dataframes and perform multiples queries, aggregation... Etc.