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How to load data from Pocket to Databricks Lakehouse

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

TL;DR

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 Pocket as a source connector (using Auth, or usually an API key)
  2. set up Databricks Lakehouse as a destination connector
  3. 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 Pocket

Pocket, the premier Save for Later app, lets you consume and share content whenever you want, wherever you want, even without an internet connection. When you come across an article, video or a webpage you'd like to readbut can't at that time, save it to Pocket. You can then read or watch it whenever you have a moment, whether it's on the couch, during your commute, on the plane, train, or practically anywhere.

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 Pocket with Databricks Lakehouse in minutes

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Prerequisites

  1. A Pocket account to transfer your customer data automatically from.
  2. A Databricks Lakehouse account.
  3. 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 Pocket and Databricks Lakehouse, for seamless data migration.

When using Airbyte to move data from Pocket to Databricks Lakehouse, it extracts data from Pocket 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 Pocket data for advanced analytics and insights within Databricks Lakehouse, simplifying the ETL process and saving significant time and resources.

Step 1: Set up Pocket as a source connector

1. First, navigate to the Pocket source connector page on Airbyte.com.
2. Click on the "Create a new connection" button.
3. In the "Configuration" tab, enter a name for your connection.
4. Enter your Pocket consumer key and access token. These can be found in your Pocket developer account.
5. Click on the "Test" button to ensure that your credentials are correct and that Airbyte can connect to your Pocket account.
6. Once the test is successful, click on the "Save & Continue" button.
7. In the "Sync Schema" tab, you can choose which fields you want to sync from your Pocket account.
8. Click on the "Save & Continue" button.
9. In the "Scheduling" tab, you can choose how often you want Airbyte to sync your Pocket data.
10. Click on the "Save & Continue" button.
11. In the "Destination" tab, you can choose where you want to send your Pocket data. This could be a data warehouse, a database, or a cloud storage service.
12. Click on the "Save & Continue" button.
13. Finally, click on the "Create Connection" button to complete the process. Your Pocket data will now be synced to your chosen destination on a regular basis according to your scheduling preferences.

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 Pocket data to Databricks Lakehouse

Once you've successfully connected Pocket 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:

  1. Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
  2. Choose your source: Select Pocket from the dropdown list of your configured sources.
  3. Select your destination: Choose Databricks Lakehouse from the dropdown list of your configured destinations.
  4. 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.
  5. Select the data to sync: Choose the specific Pocket objects you want to import data from towards Databricks Lakehouse. You can sync all data or select specific tables and fields.
  6. 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.
  7. Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
  8. Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Pocket 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 Pocket data.

Use Cases to transfer your Pocket data to Databricks Lakehouse

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

  1. Advanced Analytics: Databricks Lakehouse’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Pocket data, extracting insights that wouldn't be possible within Pocket alone.
  2. Data Consolidation: If you're using multiple other sources along with Pocket, 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.
  3. Historical Data Analysis: Pocket has limits on historical data. Syncing data to Databricks Lakehouse allows for long-term data retention and analysis of historical trends over time.
  4. Data Security and Compliance: Databricks Lakehouse provides robust data security features. Syncing Pocket data to Databricks Lakehouse ensures your data is secured and allows for advanced data governance and compliance management.
  5. Scalability: Databricks Lakehouse can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Pocket data.
  6. Data Science and Machine Learning: By having Pocket data in Databricks Lakehouse, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
  7. Reporting and Visualization: While Pocket 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 Pocket 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:

  1. Configure a Pocket account as an Airbyte data source connector.
  2. Configure Databricks Lakehouse as a data destination connector.
  3. Create an Airbyte data pipeline that will automatically be moving data directly from Pocket 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!

What should you do next?

Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:

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What should you do next?

Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:

flag icon
Easily address your data movement needs with Airbyte Cloud
Take the first step towards extensible data movement infrastructure that will give a ton of time back to your data team. 
Get started with Airbyte for free
high five icon
Talk to a data infrastructure expert
Get a free consultation with an Airbyte expert to significantly improve your data movement infrastructure. 
Talk to sales
stars sparkling
Improve your data infrastructure knowledge
Subscribe to our monthly newsletter and get the community’s new enlightening content along with Airbyte’s progress in their mission to solve data integration once and for all.
Subscribe to newsletter

Frequently Asked Questions

What data can you extract from Pocket?

Pocket's API provides access to various types of data related to the user's Pocket account. The categories of data that can be accessed through the API are:  

1. Articles: This includes the full text of articles saved by the user, along with metadata such as title, author, and URL.  
2. Tags: The API allows access to the tags associated with each article, which can be used to organize and filter saved articles.  
3. Favorites: The API provides access to the user's favorite articles, which can be used to highlight important or frequently referenced content.  
4. Reads: The API tracks the user's reading history, including the date and time each article was read.  
5. Recommendations: Pocket's API can provide personalized article recommendations based on the user's reading history and preferences.  
6. Stats: The API provides access to various statistics related to the user's Pocket account, such as the number of articles saved, read, and favorited.  
7. Authentication: The API allows developers to authenticate users and access their Pocket data securely.

What data can you transfer to Databricks Lakehouse?

You can transfer a wide variety of data to Databricks Lakehouse. This usually includes structured, semi-structured, and unstructured data like transaction records, log files, JSON data, CSV files, and more, allowing robust, scalable data integration and analysis.

What are top ETL tools to transfer data from Pocket to Databricks Lakehouse?

The most prominent ETL tools to transfer data from Pocket to Databricks Lakehouse include:

  • Airbyte
  • Fivetran
  • Stitch
  • Matillion
  • Talend Data Integration

These tools help in extracting data from Pocket and various sources (APIs, databases, and more), transforming it efficiently, and loading it into Databricks Lakehouse and other databases, data warehouses and data lakes, enhancing data management capabilities.