How to load data from SFTP Bulk to Databricks Lakehouse

Learn how to use Airbyte to synchronize your SFTP Bulk data into Databricks Lakehouse within minutes.

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Set up a SFTP Bulk connector in Airbyte

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

Set up Databricks Lakehouse for your extracted SFTP Bulk data

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

Configure the SFTP Bulk to Databricks Lakehouse 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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TL;DR

set up SFTP Bulk as a source connector (using Auth, or usually an API key)
set up Databricks Lakehouse as a destination connector
define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is SFTP Bulk

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.

What is Databricks Lakehouse

Databricks is an American enterprise software company founded by the creators of Apache Spark. Databricks combines data warehouses and data lakes into a lakehouse architecture.

Integrate SFTP Bulk with Databricks Lakehouse in minutes

Try for free now

Prerequisites

A SFTP Bulk account to transfer your customer data automatically from.
A Databricks Lakehouse account.
An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including SFTP Bulk and Databricks Lakehouse, for seamless data migration.

When using Airbyte to move data from SFTP Bulk to Databricks Lakehouse, it extracts data from SFTP Bulk using the source connector, converts it into a format Databricks Lakehouse can ingest using the provided schema, and then loads it into Databricks Lakehouse via the destination connector. This allows businesses to leverage their SFTP Bulk data for advanced analytics and insights within Databricks Lakehouse, simplifying the ETL process and saving significant time and resources.

Step 1: Set up SFTP Bulk as a source connector

1. Open the Airbyte platform and navigate to the "Sources" tab on the left-hand side of the screen.
2. Click on the "Create Connection" button and select the "SFTP Bulk" source connector.
3. Enter a name for the connection and click on the "Next" button.
4. In the "Connection Configuration" section, enter the following information:
- Host: the hostname or IP address of the SFTP server
- Port: the port number used for SFTP (usually 22)
- Username: the username used to access the SFTP server
- Password: the password associated with the username
- Private Key: (optional) the private key used for authentication
- Passphrase: (optional) the passphrase associated with the private key
- Path: the path to the directory containing the files to be synced
5. Click on the "Test" button to ensure that the connection is successful.
6. If the test is successful, click on the "Create Connection" button to save the connection.
7. Once the connection is created, you can configure the sync settings by selecting the desired sync mode (full refresh or incremental) and setting the sync schedule.
8. Finally, click on the "Create" button to start the sync process.

Step 2: Set up Databricks Lakehouse as a destination connector

1. First, navigate to the Airbyte website and log in to your account.
2. Once you are logged in, click on the "Destinations" tab on the left-hand side of the screen.
3. Scroll down until you find the "Databricks Lakehouse" connector and click on it.
4. You will be prompted to enter your Databricks Lakehouse credentials, including your account name, personal access token, and workspace ID.
5. Once you have entered your credentials, click on the "Test" button to ensure that the connection is successful.
6. If the test is successful, click on the "Save" button to save your Databricks Lakehouse destination connector settings.
7. You can now use the Databricks Lakehouse connector to transfer data from your source connectors to your Databricks Lakehouse destination.
8. To set up a data transfer, navigate to the "Sources" tab and select the source connector that you want to use.
9. Follow the prompts to enter your source connector credentials and configure your data transfer settings.
10. Once you have configured your source connector, select the Databricks Lakehouse connector as your destination and follow the prompts to configure your data transfer settings.
11. Click on the "Run" button to initiate the data transfer.

Step 3: Set up a connection to sync your SFTP Bulk data to Databricks Lakehouse

Once you've successfully connected SFTP Bulk as a data source and Databricks Lakehouse as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
Choose your source: Select SFTP Bulk from the dropdown list of your configured sources.
Select your destination: Choose Databricks Lakehouse from the dropdown list of your configured destinations.
Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
Select the data to sync: Choose the specific SFTP Bulk objects you want to import data from towards Databricks Lakehouse. You can sync all data or select specific tables and fields.
Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from SFTP Bulk to Databricks Lakehouse according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your Databricks Lakehouse data warehouse is always up-to-date with your SFTP Bulk data.

Use Cases to transfer your SFTP Bulk data to Databricks Lakehouse

Integrating data from SFTP Bulk to Databricks Lakehouse provides several benefits. Here are a few use cases:

Advanced Analytics: Databricks Lakehouse’s powerful data processing capabilities enable you to perform complex queries and data analysis on your SFTP Bulk data, extracting insights that wouldn't be possible within SFTP Bulk alone.
Data Consolidation: If you're using multiple other sources along with SFTP Bulk, syncing to Databricks Lakehouse allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
Historical Data Analysis: SFTP Bulk has limits on historical data. Syncing data to Databricks Lakehouse allows for long-term data retention and analysis of historical trends over time.
Data Security and Compliance: Databricks Lakehouse provides robust data security features. Syncing SFTP Bulk data to Databricks Lakehouse ensures your data is secured and allows for advanced data governance and compliance management.
Scalability: Databricks Lakehouse can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding SFTP Bulk data.
Data Science and Machine Learning: By having SFTP Bulk data in Databricks Lakehouse, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
Reporting and Visualization: While SFTP Bulk provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to Databricks Lakehouse, providing more advanced business intelligence options. If you have a SFTP Bulk table that needs to be converted to a Databricks Lakehouse table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

Configure a SFTP Bulk account as an Airbyte data source connector.
Configure Databricks Lakehouse as a data destination connector.
Create an Airbyte data pipeline that will automatically be moving data directly from SFTP Bulk to Databricks Lakehouse after you set a schedule

With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.

We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!

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How to Sync SFTP Bulk to Databricks Lakehouse Manually

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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.

What is SFTP Bulk?

What data can you extract from SFTP Bulk?

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.

How do I transfer data from SFTP Bulk?

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:
1. Set up SFTP Bulk to Databricks Lakehouse as a source connector (using Auth, or usually an API key)
2. Choose a destination (more than 50 available destination databases, data warehouses or lakes) to sync data too and set it up as a destination connector
3. Define which data you want to transfer from SFTP Bulk to Databricks Lakehouse and how frequently
You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

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.

Back to all tutorials

Warehouses and Lakes

Others

How to load data from SFTP Bulk to Databricks Lakehouse

Learn how to use Airbyte to synchronize your SFTP Bulk data into Databricks Lakehouse within minutes.

Try Airbyte Cloud free

Deploy Airbyte Open Source

TL;DR

What is SFTP Bulk

What is Databricks Lakehouse

Prerequisites

Step 1: Setup SFTP Bulk

Step 2: Setup Databricks Lakehouse

Step 3: Setup a connection Use Cases Wrapping Up

Example H2

Example H3

Sync data between SFTP Bulk and Databricks Lakehouse

Get your data syncing in minutes

Try Airbyte free

TL;DR

set up SFTP Bulk as a source connector (using Auth, or usually an API key)
set up Databricks Lakehouse as a destination connector
define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is SFTP Bulk

What is Databricks Lakehouse

Databricks is an American enterprise software company founded by the creators of Apache Spark. Databricks combines data warehouses and data lakes into a lakehouse architecture.

Integrate SFTP Bulk with Databricks Lakehouse in minutes

Try for free now

Prerequisites

A SFTP Bulk account to transfer your customer data automatically from.
A Databricks Lakehouse account.
An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Step 1: Set up SFTP Bulk as a source connector

Step 2: Set up Databricks Lakehouse as a destination connector

Step 3: Set up a connection to sync your SFTP Bulk data to Databricks Lakehouse

Once you've successfully connected SFTP Bulk as a data source and Databricks Lakehouse as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
Choose your source: Select SFTP Bulk from the dropdown list of your configured sources.
Select your destination: Choose Databricks Lakehouse from the dropdown list of your configured destinations.
Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
Select the data to sync: Choose the specific SFTP Bulk objects you want to import data from towards Databricks Lakehouse. You can sync all data or select specific tables and fields.
Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from SFTP Bulk to Databricks Lakehouse according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your Databricks Lakehouse data warehouse is always up-to-date with your SFTP Bulk data.

Use Cases to transfer your SFTP Bulk data to Databricks Lakehouse

Integrating data from SFTP Bulk to Databricks Lakehouse provides several benefits. Here are a few use cases:

Advanced Analytics: Databricks Lakehouse’s powerful data processing capabilities enable you to perform complex queries and data analysis on your SFTP Bulk data, extracting insights that wouldn't be possible within SFTP Bulk alone.
Data Consolidation: If you're using multiple other sources along with SFTP Bulk, syncing to Databricks Lakehouse allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
Historical Data Analysis: SFTP Bulk has limits on historical data. Syncing data to Databricks Lakehouse allows for long-term data retention and analysis of historical trends over time.
Data Security and Compliance: Databricks Lakehouse provides robust data security features. Syncing SFTP Bulk data to Databricks Lakehouse ensures your data is secured and allows for advanced data governance and compliance management.
Scalability: Databricks Lakehouse can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding SFTP Bulk data.
Data Science and Machine Learning: By having SFTP Bulk data in Databricks Lakehouse, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
Reporting and Visualization: While SFTP Bulk provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to Databricks Lakehouse, providing more advanced business intelligence options. If you have a SFTP Bulk table that needs to be converted to a Databricks Lakehouse table, Airbyte can do that automatically.