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"With Airbyte, we could just push a few buttons, allow API access, and bring all the data into Google BigQuery. By blending all the different marketing data sources, we can gain valuable insights."
First, ensure that the Google Cloud SDK is installed on your local machine or server. The SDK provides the `gcloud` and `gsutil` command-line tools, which are necessary for interacting with Google Cloud services, including Google Cloud Storage (GCS) and BigQuery.
Use an SFTP client or command-line tool to download files from the SFTP server to your local machine or server. For command-line usage, you can use the `sftp` command:
```bash
sftp username@host:/path/to/files /local/path
```
Once the files are downloaded locally, upload them to a Google Cloud Storage bucket. This step acts as an intermediary to prepare the data for BigQuery. Use the `gsutil` command:
```bash
gsutil cp /local/path/ gs://your-bucket-name/path/
```
Before importing data, ensure that a BigQuery dataset and table are ready. If not, create them using the `bq` command. For example:
```bash
bq mk --dataset your-project-id:your_dataset
bq mk --table your-project-id:your_dataset.your_table schema.json
```
Replace `schema.json` with the path to your table schema file.
Use the `bq` command to load data from GCS into BigQuery. This command will load the data into the specified dataset and table:
```bash
bq load --source_format=CSV your-project-id:your_dataset.your_table gs://your-bucket-name/path/ schema.json
```
Adjust `source_format` based on your data format (e.g., CSV, JSON, AVRO).
After loading, verify that the data has been correctly imported into BigQuery. Use the BigQuery Console or `bq` command-line tool to run queries and check the integrity and completeness of the data:
```bash
bq query --nouse_legacy_sql 'SELECT FROM your_dataset.your_table LIMIT 10'
```
Optionally, to save storage costs and maintain organization, delete temporary files from your local system and the GCS bucket after confirming that the data has been successfully loaded into BigQuery:
```bash
rm /local/path/
gsutil rm gs://your-bucket-name/path/
```
Following these steps will allow you to move data from an SFTP server to BigQuery without using third-party connectors or integrations.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
SFTP is basically based on the SSH2 protocol, which uses a binary encoding of messages over a secure channel. SFTP Bulk contains the setup guide and reference information for the FTP source connector. SFTP Bulk fetches files from an FTP server matching a folder path and defines an optional file pattern to bulk ingest files into a single stream. It incrementally loads files into your destination from an FTP server based on when files were last added or modified.
SFTP Bulk's API provides access to a wide range of data related to file transfer and management. The following are the categories of data that can be accessed through the API:
1. File Transfer Data: This includes information related to the transfer of files such as file name, size, transfer status, and transfer time.
2. User Data: This includes user-related information such as user ID, username, and password.
3. Server Data: This includes server-related information such as server name, IP address, and port number.
4. Security Data: This includes security-related information such as encryption algorithms used, authentication methods, and access control policies.
5. Error Data: This includes information related to errors that occur during file transfer such as error codes, error messages, and error descriptions.
6. Audit Data: This includes information related to auditing and compliance such as user activity logs, file transfer logs, and security logs.
Overall, SFTP Bulk's API provides access to a comprehensive set of data that can be used to monitor, manage, and secure file transfers.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
