Kamu Documentation > Kamu CLI > Ingesting Data > Input Formats

Input Formats

kamu supports multiple data sources and data formats that in combination with custom preparation steps can be used ingest all kinds of data.

Note that kamu is not made to be or replace data workflow tools like Apache Airflow or Apache Ni-Fi, or data extraction tool like Debezium. Utilities described below are here only to simplify the initial data ingestion step - the very first step in the data’s journey through a web of structured stream processing pipelines.

CSV Variants

You can customize a lot of formatting options for CSV format parser.

For example, a tab-separated file can be read as:

read:
  kind: Csv
  separator: "\t"
  quote: '"'

See: ReadStep::Csv

JSON Document

If you have a JSON document such as this:

{
  "nested": {
    "values": [
        {"id": 1, "key": "value"},
        {"id": 2, "key": "value"},
    ]
  }
}

first consider if you can use NDJSON instead, as ordinary JSON documents cannot be split and read efficiently.

If not, use:

read:
  kind: Json
  subPath: nested.values

Where subPath points at the array of records withing the document.

See: ReadStep::Json

NDJSON

NDJSON, aka newline-delimited JSON file such as:

{"id": 1, "key": "value"}
{"id": 2, "key": "value"}

Can be read using:

read:
  kind: NdJson
  schema:
  - id BIGINT
  - key STRING

See: ReadStep::NdJson

GeoJSON Document

Simply use:

read:
  kind: GeoJson

The reader expects one FeatureCollection object in the root and will create a record per each Feature inside it, extracting the properties into individual columns and leaving the feature geometry in its own column.

See: ReadStep::GeoJson

NDGeoJSON Document

Simply use:

read:
  kind: NdGeoJson

It is similar to GeoJSON format but instead of FeatureCollection object in the root it expects every individual Feature to appear on its own line.

See: ReadStep::NdGeoJson

Esri Shapefile

GIS data in ESRI format can be read as:

read:
  kind: EsriShapefile
  subPath: specific_data-*.shp
# Use preprocess to optionally convert between different projections
preprocess:
  kind: Sql
  engine: spark
  query: |
    SELECT
      ST_Transform(geometry, "epsg:3157", "epsg:4326") as geometry,
      ...
    FROM input

See: ReadStep::EsriShapefile

Compressed Data & Archives

Use decompress preparation step to extract data from gzip, zip archives.

prepare:
- kind: Decompress
  format: Gzip

In case of a multi-file archive you can specify which file should be extracted:

prepare:
- kind: Decompress
  format: Zip
  subPath: specific-file-*.csv  # Note: can contain glob patterns

See: PrepStep::Decompress

Other Formats

If you have to work with formats that are not natively supported you’ll need to transcode them.

Using the pipe preparation step you can specify a custom program or a script that will get data via STDIN and output result to STDOUT.

For example here’s how transcoding a JSON document into CSV using jq may look like:

prepare:
- kind: Pipe
  command:
  - 'jq'
  - '-r'
  - '.values[] | [.id, .key] | @csv'
read:
  kind: Csv
  schema:
  - id BIGINT
  - key STRING

See: PrepStep::Pipe

Directory of Timestamped Files

The FetchStep::FilesGlob is used in cases where directory contains a growing set of files. Files can be periodic snapshots of your database or represent batches of new data in a ledger. In either case file content should never change - once kamu processes a file it will not consider it again. It’s OK for files to disappear - kamu will remember the name of the file it ingested last and will only consider files that are higher in order than that one (lexicographically based on file name, or based on event time as shown below).

In the example below data inside the files is in snapshot format, and to complicate things it does not itself contain an event time - the event time is written into the file’s name.

Directory contents:

db-table-dump-2020-01-01.csv
db-table-dump-2020-01-02.csv
db-table-dump-2020-01-03.csv

Fetch step:

fetch:
  kind: FilesGlob
  path: /home/username/data/db-table-dump-*.csv
  eventTime:
    kind: FromPath
    pattern: 'db-table-dump-(\d+-\d+-\d+)\.csv'
    timestampFormat: '%Y-%m-%d'
  cache:
    kind: Forever

See: FetchStep::FilesGlob

Dealing with API Keys

Sometimes you may want to parametrize the URL to include things like API keys and auth tokens. For this kamu supports basic variable substitution:

fetch:
  kind: Url
  url: "https://api.etherscan.io/api?apikey=${{ env.ETHERSCAN_API_KEY }}"

Using Ingest Scripts

Sometimes you may need the power of a general purpose programming language to deal with particularly complex API, or when doing web scraping. For this kamu supports containerized ingestion tasks:

fetch:
  kind: Container
  image: "ghcr.io/kamu-data/fetch-my-dataset:0.1.0"
  env:
    - name: ETH_NODE_PROVIDER_URL

The specified container image is expected to conform to the following interface:

Produce data to stdout
Write warnings / errors to sterr
Use following environment variables:
- ODF_LAST_MODIFIED - last modified time of data from the previous ingest run, if any (in RFC3339 format)
- ODF_ETAG - caching tag of data from the previous ingest run, if any
- ODF_BATCH_SIZE - is the recommended number of records, for ingest scripts that provide continuous stream of data and can resume from previous state
  - default value is 10 000, can be overridden via env
- ODF_NEW_LAST_MODIFIED_PATH - path to a text file where ingest script may write new Last-Modified timestamp
- ODF_NEW_ETAG_PATH - path to a text file where ingest script may write new eTag
- ODF_NEW_HAS_MORE_DATA_PATH - path to a text file which ingest script can create to indicate about having more data for the next batch
  - ⚠️ Please note: if the file is created, one of the following output marks must also be present: eTag or Last-Modified timestamp

Need More Examples?

To give you more examples on how to deal with different ingest scenarios we’ve created an experimental repository where we publish Root Dataset manifests for a variety of Open Data sources - check out kamu-contrib repo.

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