How to load data from New York Times to Databricks Lakehouse

Learn how to use Airbyte to synchronize your New York Times data into Databricks Lakehouse within minutes.

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Set up a New York Times connector in Airbyte

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

Set up Databricks Lakehouse for your extracted New York Times data

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

Configure the New York Times 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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How to Sync New York Times to Databricks Lakehouse Manually

How to Sync New York Times to Databricks Lakehouse Manually - Method 2:

FAQs

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.

The Times Developer Network is our API clearinghouse and community. You need to read the API documentation and browse the application gallery to get the latest news about the New York Times API. If you do not agree to any of the terms below or the NYT Terms of Service, NYT does not grant you a license to use the NYT API. In the event of any inconsistency between these Terms of Use and the Terms of Service, these Terms of Use control.

The New York Times API provides access to a wide range of data categories, including:  

1. Articles: Full-text articles from the New York Times, including news, opinion, and feature pieces.  
2. Multimedia: Images, videos, and other multimedia content from the New York Times.  
3. Best Sellers: Lists of best-selling books, both fiction and non-fiction, as compiled by the New York Times.  
4. Movie Reviews: Reviews of movies from the New York Times, including ratings and summaries.  
5. TimesTags: A comprehensive list of tags used by the New York Times to categorize articles and other content.  
6. Times Newswire: A real-time feed of breaking news stories from the New York Times.  
7. Top Stories: A list of the most popular articles on the New York Times website, updated in real-time.  
8. Archive: Access to the New York Times archive, including articles dating back to 1851.  
9. Times Insider: Exclusive content from the New York Times, including behind-the-scenes stories and interviews with journalists.  

Overall, the New York Times API provides a wealth of data for developers and researchers interested in exploring the content and history of one of the world's most respected news organizations.

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 New York Times 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 New York Times 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. 

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.

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.

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Connectors Used

New York Times
Databricks Lakehouse

Related Syncs with New York Times

New York Times to AWS Datalake
New York Times to BigQuery
New York Times to Databricks Lakehouse
New York Times to Redshift
New York Times to S3
New York Times to S3 Glue
New York Times to Snowflake Data Cloud
New York Times to Teradata

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How to load data from 

New York Times to Databricks Lakehouse