Metadata Driven Inputs

We have several data suppliers reporting monthly sales numbers in CSV files.

Details of file formats differ across suppliers. File encoding, headers, separators, order and names of columns, date and number formats - they are all different.

Every file contains at least the following fields: date, sku, quantity, and price.

Our task is to consolidate all files and create one CSV file that contains all that data in a single format.

In this tutorial, we’ll accomplish that with a single data flow.

Pick up the (424.7 KB) files to follow along. The archive contains all input files, the metadata file, and the solution flow.

The files

├── bishop_unlimited
│   ├── 2019-01.csv
│   ├── 2019-02.csv
|   | ...
│   └── 2019-12.csv
├── chan_and_co
│   ├── 2019-01.csv
│   ├── 2019-02.csv
|   | ...
│   └── 2019-12.csv
├── parkes_industries
│   ├── 2019-01.csv
│   ├── 2019-02.csv
|   | ...
│   └── 2019-12.csv
└── raymond_corp
    ├── 2019-01.csv
    ├── 2019-02.csv
    | ...
    └── 2019-12.csv

4 directories, 48 files

We have four different suppliers, each with their own file format. Let’s take a look at them:

Raymond Corp

Their files look like this:

  • there is a header line
  • ; is the separator
  • uuuu-MM-dd is the date format
  • price is prepended with a dollar sign
  • files are in UTF-8 encoding

Bishop Unlimited

Their files look like this:

  • there is a header line with abbreviated field names
  • fields are in order quantity, price, sku, date
  • " is used to enclose fields
  • , is the separator
  • MM/dd/uuuu is the date format
  • files are in UTF-8 encoding

Parkes Industries

Their files look like this:

20002080|USD 615.23|dev/s/bk|20190120|77
20002307|USD 295.63|dev/s/bk|20190120|37
  • there is no header line
  • fields are in order serial, price, sku, date, quantity
  • there is an extra column containing a serial number that is of no interest to us
  • | is the separator
  • price is prepended by USD
  • date format is uuuuMMdd
  • files are in UTF-16 little endian encoding

Chan and Co

Their files look like this:

--- Data Export for 01/2019
lix/s/bl	425.27	43	2019-01-19
dev/s/bk	441.45	65	2019-01-19
  • there are extra three lines of prologue
  • there is no header line
  • fields are in order sku, price, quantity, date
  • the tab character \t is the separator
  • uuuu-MM-dd is the date format
  • files are in UTF-8 encoding

The Target Format

We need to consolidate all files into a single output file of the following format:


The Solution

We’ll keep track of the metadata about our file formats and use it to dynamically determine how to process a file at runtime.

Our metadata boils down to the following information:

source charset skip_lines named_columns separator enclosure date_col date_format sku_col quantity_col price_col price_format
raymond_corp UTF-8 0 true ; date uuuu-MM-dd sku quantity price $0.00
bishop_unlimited UTF-8 0 true , " d MM/dd/uuuu sku n p 0.00
parkes_industries UTF-16LE 0 false | 3 uuuuMMdd 2 4 1 USD 0.00
chan_and_co UTF-8 3 false TAB 3 uuuu-MM-dd 0 2 1 0.##

We’ll keep that information in a JSON file structured like so:

    "source": "raymond_corp",
    "charset": "UTF-8",
    "skip_lines": 0,
    "separator": ";",
    "named_columns": true,
    "enclosure": "",
    "columns": {
      "date": {
        "index": "date",
        "format": "uuuu-MM-dd"
      "sku": {
        "index": "sku"
      "quantity": {
        "index": "quantity"
      "price": {
        "index": "price",
        "format": "'$'0.00"
  ... additional supplier formats ...

With that information to hand, we can apply the necessary configuration dynamically for each processed file.

Part 1 - finding files to read

We first want to get a list of files to read. They all reside in our ./input directory.

The name of their subdirectory i.e. ./input/raymond_corp tells us which supplier we are looking at.

We can find all *.csv files, and extract the name of their parent directory.

Part 2- reading metadata

We now need to pull in the metadata file. We can just read the JSON file into memory, and index them by source name for easy lookup.

We will eventually have to parse dates and prices using different formats. We create parsers for each format as well, and put them into rows.

We can then look up the correct format definition for each file.

Part 3 - reading CSV lines

Reading CSV lines

We’d ready to read the CSV file now. We just have to put the information from our format definition into the corresponding settings of the CSV input step.

The CSV input step now reads the lines as configured by our format, and gives us a list of field values for each record.

If our format includes a header line, we can also access the field values by field names as included in the file.

We output a field called record which will be a dict in case we have field names, and a list if we do not.

Part 4 - extracting fields

We can now extract our fields of interest from CSV lines, based on our format information.


The sku field is easiest. It’s just a string that we need to grab from the right place.


The quantity field needs to converted to an integer number. We’re always getting a string made entirely of digits int this field, so we can just cast it to a long.


The price field needs to be parsed into a decimal number. The location and parser are part of our metadata, so we get the value by invoking the parser on our input string.


The date field needs to be parsed into a datetime value. The location and parser are part of our metadata again.

Writing the output file

Once we’re done extracting fields, we can thin out the row structure and look at our data.

With all data rows present, we can output them to CSV using the CSV output step.

In Conclusion

We’ve created a consolidated version of all files. Our output.csv contains all data rows from various data inputs and looks as required: