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Added data enrichment part two currency conversion docs
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---
title: Currency-conversion data enrichment
description: Converting the monetary value in one currency to another using the latest market rates.
keywords: [data enrichment, currency conversion, latest market rates]
---

# Data enrichment part two: Currency conversion data enrichment

Currency conversion data enrichment means adding additional information to currency-related data. Often, you have a data set of monetary value in one currency. It would be best to have these amounts in a different currency for different reasons (like reporting, analysis, or operations in a global context).

## Setup Guide

To enable this is currency conversion data enrichment. A few important steps are involved:

1. Define base and target currencies. e.g., USD (base) to EUR (target).
1. Obtain current exchange rates from a reliable source like a financial data API.
1. Convert the monetary values at obtained exchange rates.
1. Include metadata like conversion rate, date, and time.
1. Save the updated dataset in a data warehouse or lake using a data pipeline.

We use the [ExchangeRate-API](https://app.exchangerate-api.com/) to fetch the latest currency conversion rates. However, you can use any service you prefer.

:::note
ExchangeRate-API free tier offers 1500 free calls monthly. For production, consider upgrading to a higher plan.
:::

## Creating data enrichment pipeline
You can either follow the example in the linked Colab notebook or follow this documentation to
create the currency conversion data enrichment pipeline.

### A. Colab notebook
The Colab notebook combines three data enrichment processes for a sample dataset, it's second part contains "Data
enrichment part two: Currency conversion data enrichment".

Here's the link to the notebook:
**[Colab Notebook](https://colab.research.google.com/drive/1ZKEkf1LRSld7CWQFS36fUXjhJKPAon7P?usp=sharing).**

### B. Create a pipeline
Alternatively, to create a data enrichment pipeline, you can start by creating the following directory structure:

```text
currency_conversion_enrichment/
├── .dlt/
│ └── secrets.toml
└── currency_enrichment_pipeline.py
```

### 1. Creating resource

`dlt` works on the principle of [sources](https://dlthub.com/docs/general-usage/source)
and [resources.](https://dlthub.com/docs/general-usage/resource)

1. The last part of our data enrichment (part one) involved enriching the
data with user-agent device data. This included adding two new columns
to the dataset as folows:

- `device_price_usd`: average price of the device in USD.

- `price_updated_at`: time at which the price was updated.

2. The columns initially present prior to the data enrichment were:

- `user_id`: Web trackers typically assign unique ID to users for
tracking their journeys and interactions over time.

- `device_name`: User device information helps in understanding the user base's device.

- `page_refer`: The referer URL is tracked to analyze traffic sources and user navigation behavior.

3. Here's the resource that yields the sample data as discussed above:

```python
@dlt.resource()
def enriched_data_part_two():
data_enrichment_part_one = [
{"user_id": 1, "device_name": "Sony Experia XZ",
"page_referer": "https://b2venture.lightning.force.com/",
"device_price_usd": 313.01,
"price_updated_at": "2024-01-15 04:08:45.088499+00:00"},
]
"""
Similar data for the other users.
"""
for user_data in data_enrichment_part_one:
yield user_data
```
> `data_enrichment_part_one` holds the enriched data from part one. It can also be directly
used in part two as demonstrated in
**[Colab Notebook](https://colab.research.google.com/drive/1ZKEkf1LRSld7CWQFS36fUXjhJKPAon7P?usp=sharing).**

### 2. Create `converted_amount` function
This function retrieves conversion rates for currency pairs that either haven't been fetched before or were last updated more than 24 hours ago from the ExchangeRate-API, using information stored in the dlt state.

The first step is to register on [ExhangeRate-API](https://app.exchangerate-api.com/) and obtain the API token.

1. In the `.dlt`folder, there's a file called `secrets.toml`. It's where you store sensitive
information securely, like access tokens. Keep this file safe. Here's its format for service
account authentication:

```python
[sources]
api_key= "Please set me up!" #ExchangeRate-API key
```
1. Create the `converted_amount` function as follows:
```python
def converted_amount(record):
"""
Converts an amount from base currency to target currency using the latest exchange rate.
This function retrieves the current exchange rate from an external API and
applies it to the specified amount in the record. It handles updates to the exchange rate
if the current rate is over 12 hours.
Args:
record (dict): A dictionary containing the 'amount' key with the value to be converted.
Yields:
dict: A dictionary containing the original amount in AED, converted amount in USD,
the exchange rate, and the last update time of the rate.
Note:
The base currency (AED) and target currency (USD) are hard coded in this function,
that can be changed
The API key is retrieved from the DLT (Data Lineage Tool) secrets.
"""

# Hardcoded base and target currencies
base_currency = "EUR"
target_currency = "INR"

# Retrieve the API key from DLT secrets
api_key = dlt.secrets.get("sources.api_key")

# Initialize or retrieve the state for currency rates
rates_state = dlt.current.resource_state().setdefault("rates", {})
currency_pair_key = f"{base_currency}-{target_currency}"
currency_pair_state = rates_state.setdefault(currency_pair_key, {
"last_update": datetime.min,
"rate": None
})

# Update the exchange rate if it's older than 12 hours
if (
currency_pair_state.get("rate") is None
or (datetime.utcnow() - currency_pair_state["last_update"] >= timedelta(hours=12))):
url = f"https://v6.exchangerate-api.com/v6/{api_key}/pair/{base_currency}/{target_currency}"
response = requests.get(url)
if response.status_code == 200:
data = response.json()
currency_pair_state.update({
"rate": data.get("conversion_rate"),
"last_update": datetime.fromtimestamp(data.get("time_last_update_unix"))
})
print(f"The latest rate of {data.get('conversion_rate')} for the currency pair {currency_pair_key} is fetched and updated.")
else:
raise Exception(f"Error fetching the exchange rate: HTTP {response.status_code}")

# Convert the amount using the retrieved or stored exchange rate
amount = record['device_price_usd']
rate = currency_pair_state["rate"]
yield {
"actual_amount": amount,
"base_currency": base_currency,
"converted_amount": round(amount * rate, 2),
"target_currency": target_currency,
"rate": rate,
"rate_last_updated": currency_pair_state["last_update"],
}
```
1. Next, follow the instructions in [Destinations](https://dlthub.com/docs/dlt-ecosystem/destinations/duckdb) to add credentials for your chosen destination. This will ensure that your data is properly routed to its final destination.

### 3. Create your pipeline

1. In creating the pipeline, the `converted_amount` can be used in the following ways:
- Add map function
- Transformer function

The `dlt` library's `transformer` and `add_map` functions serve distinct purposes in data
processing.

`Transformers` used to process a resource and are ideal for post-load data transformations in a
pipeline, compatible with tools like `dbt`, the `dlt SQL client`, or Pandas for intricate data
manipulation. To read more:
[Click here.](../../general-usage/resource#process-resources-with-dlttransformer)

Conversely, `add_map` used to customize a resource applies transformations at an item level
within a resource. It's useful for tasks like anonymizing individual data records. More on this
can be found under
[Customize resources](../../general-usage/resource#customize-resources) in the
documentation.

1. Here, we create the pipeline and use the `add_map` functionality:

```python
# Create the pipeline
pipeline = dlt.pipeline(
pipeline_name="data_enrichment_two",
destination="duckdb",
dataset_name="currency_conversion_enrichment",
)

# Run the pipeline with the transformed source
load_info = pipeline.run(enriched_data_part_two.add_map(converted_amount))

print(load_info)
```

:::info
Please note that the same outcome can be achieved by using the transformer function. To
do so, you need to add the transformer decorator at the top of the `converted_amount` function.
For `pipeline.run`, you can use the following code:

```python
# using fetch_average_price as a transformer function
load_info = pipeline.run(
enriched_data_part_two | converted_amount,
table_name="data_enrichment_part_two"
)
```

This will execute the `converted_amount` function with the data enriched in part one and return the converted
currencies.
:::

### Run the pipeline

1. Install necessary dependencies for the preferred
[destination](https://dlthub.com/docs/dlt-ecosystem/destinations/), For example, duckdb:

```
pip install dlt[duckdb]
```

1. Run the pipeline with the following command:

```
python currency_enrichment_pipeline.py
```

1. To ensure that everything loads as expected, use the command:

```
dlt pipeline <pipeline_name> show
```

For example, the "pipeline_name" for the above pipeline example is `data_enrichment_two`; you can use
any custom name instead.
1 change: 1 addition & 0 deletions docs/website/sidebars.js
Original file line number Diff line number Diff line change
Expand Up @@ -226,6 +226,7 @@ const sidebars = {
label: 'Data enrichments',
items: [
'general-usage/data-enrichments/user_agent_device_data_enrichment',
'general-usage/data-enrichments/currency_conversion_data_enrichment'
]
},
{
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