Various tools, services, and processes have been developed over the years to help address these challenges. Often the format is different, or the data needs to be shaped or cleaned before loading it into its final destination. The destination might not be the same type of data store as the source. Then you'd need to move it to one or more data stores. Table2 = etl.A common problem that organizations face is how to gather data from multiple sources, in multiple formats. Reach out to our Support Team if you have any questions. Free Trial & More Informationĭownload a free, 30-day trial of the Twitter Python Connector to start building Python apps and scripts with connectivity to Twitter data. With the CData Python Connector for Twitter, you can work with Twitter data just like you would with any database, including direct access to data in ETL packages like petl. In the following example, we add new rows to the Tweets table. Table2 = etl.sort(table1,'Retweet_Count') In this example, we extract Twitter data, sort the data by the Retweet_Count column, and load the data into a CSV file. With the query results stored in a DataFrame, we can use petl to extract, transform, and load the Twitter data. Sql = "SELECT From_User_Name, Retweet_Count FROM Tweets WHERE From_User_Name = 'twitter'"Įxtract, Transform, and Load the Twitter Data In this article, we read data from the Tweets entity. Use SQL to create a statement for querying Twitter. Use the connect function for the CData Twitter Connector to create a connection for working with Twitter data.Ĭnxn = mod.connect("InitiateOAuth=GETANDREFRESH OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")") You can now connect with a connection string. Code snippets follow, but the full source code is available at the end of the article.įirst, be sure to import the modules (including the CData Connector) with the following: Once the required modules and frameworks are installed, we are ready to build our ETL app. Pip install pandas Build an ETL App for Twitter Data in Python Use the pip utility to install the required modules and frameworks: pip install petl See the Getting Started chapter in the help documentation for a guide to using OAuth.Īfter installing the CData Twitter Connector, follow the procedure below to install the other required modules and start accessing Twitter through Python objects. Obtain the OAuthAccessToken and OAuthAccessTokenSecret directly by registering an app. If you intend to communicate with Twitter only as the currently authenticated user, then you can To authenticate using OAuth, you can use the embedded OAuthClientId, OAuthClientSecret, and CallbackURL or you can register an app to obtain your own. You can connect using your User and Password or OAuth. For this article, you will pass the connection string as a parameter to the create_engine function.Īll tables require authentication. Create a connection string using the required connection properties. When you issue complex SQL queries from Twitter, the driver pushes supported SQL operations, like filters and aggregations, directly to Twitter and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).Ĭonnecting to Twitter data looks just like connecting to any relational data source. With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Twitter data in Python. This article shows how to connect to Twitter with the CData Python Connector and use petl and pandas to extract, transform, and load Twitter data. With the CData Python Connector for Twitter and the petl framework, you can build Twitter-connected applications and pipelines for extracting, transforming, and loading Twitter data. The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively.
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