How to load data from Parquet File to Teradata

Learn how to use Airbyte to synchronize your Parquet File data into Teradata within minutes.

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Set up a Parquet File connector in Airbyte

Connect to or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up Teradata for your extracted Parquet File data

Select where you want to import data from your source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Parquet File to Teradata in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.

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How to Sync to Manually

Step 1: Prepare Environment with Necessary Tools

Install the necessary tools on your local machine or server. Ensure you have Apache Arrow installed for processing Parquet files and Teradata client utilities like BTEQ (Basic Teradata Query) for interacting with a Teradata database. Python is commonly used for handling Parquet files, so ensure you have Python and pip installed.

Use Python and the PyArrow library to read the Parquet file. PyArrow can efficiently handle Parquet files and convert them into Pandas DataFrames, making it easy to manipulate and prepare data for loading. Use the following snippet to read a Parquet file:
```python
import pyarrow.parquet as pq
import pandas as pd

parquet_file = 'yourfile.parquet'
table = pq.read_table(parquet_file)
df = table.to_pandas()
```

Ensure that the data types in your DataFrame match those expected by the Teradata table. This might include converting data types or formatting date/time fields. Use Pandas to modify your DataFrame as needed. For example:
```python
df['date_column'] = pd.to_datetime(df['date_column']).dt.strftime('%Y-%m-%d')
df['integer_column'] = df['integer_column'].astype(int)
```

Convert your DataFrame to a CSV format, which can be easily ingested by Teradata. Use Pandas to output the DataFrame to a CSV file:
```python
csv_file = 'output.csv'
df.to_csv(csv_file, index=False)
```

Use a secure file transfer method like SCP (Secure Copy Protocol) or FTP to move the CSV file to a location accessible by the Teradata server. Ensure that the Teradata environment has access to this file, and it is placed in the correct directory.

Utilize Teradata's BTEQ or FastLoad utilities to import the CSV data into a staging table in your Teradata database. You can execute a BTEQ script from the command line as follows:
```bash
bteq < .LOGON your_teradata_server/your_user,your_password;
.IMPORT VARTEXT ',' FILE='output.csv';
.SET RECORDMODE OFF
.SET ERRORLEVEL 3807 SEVERITY 0

USING (col1 VARCHAR(100), col2 INTEGER, col3 DATE FORMAT 'YYYY-MM-DD')
INSERT INTO staging_table (col1, col2, col3)
VALUES (:col1, :col2, :col3);

.LOGOFF;
EOF
```

Once the data is in the staging table, use SQL commands within Teradata to transform and move the data to your target table. Execute these SQL commands through BTEQ or any Teradata query tool:
```sql
INSERT INTO target_table (col1, col2, col3)
SELECT col1, col2, col3
FROM staging_table;
```

Ensure proper error handling and data validation during this step to confirm that the data has been successfully transferred and is accurate.