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. Image used under license from Shutterstock.The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. Move data behind a firewall in batches, which may be useful for edge computing and the Internet of Things (IoT).Merge data systems from different departments or companies in a single, reliable repository (e.g.Especially for data warehouse applications Read (extract) data from a database, pull (extract) that data out, write (transform) the data into a target database.Provide quality data sets for machine learning.“A type of data integration that refers to three steps used to blend data from multiple sources.” ( SAS).A service that “simplifies the process for loading data.” ( TechRepublic).“The process of making inaccessible data available by extracting data from multiple sources and making it usable for cleansing, transformation, and finally, business insight.” ( Talend). ![]() “A technique in managing the movement and consolidation of data within and between applications and organizations.” ( Data Management Body of Knowledge).A means to “load data from different systems into a data warehouse for reporting and data analytics.” ( Paul Varley). ![]() “A process of connecting to data sources, integrating data from various data sources, improving Data Quality, aggregating it and then storing it in staging data sources or data marts or data warehouses for consumption of various business applications including BI, analytics, and reporting.” ( Kartik Patel).This could cause issues in understanding the original purpose of the data or prevent staying current with new technologies or contexts. As data is transformed, the meaning and the context of that data changes. While ETL technologies have been popular since the 1970s, ETL as a strategy plays an important role in modern times with new technologies.īeware of introducing unintended semantic drifts when employing ETL. TAKE OUR DATA MANAGEMENT CERTIFICATION PREP COURSES
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