![]() ![]() Specifically, after the data is loaded into the target database, it's already been altered from the source data. ETL is also a very traditional approach that has a significant drawback. For example, if you're migrating from one database system to another, datasets will need to be reformatted when being imported so that the new database can interpret the incoming data. In other cases, you may have data from legacy systems that needs to be transformed in order to be read and interpreted by the destination server. If your marketing team only needs to access certain columns of data, sensitive pieces can be filtered out before getting loaded into a centralized location where the relevant teams can interact with the data. For example, you may have a need to transform sensitive information so it's not sitting unmasked in a data lake or warehouse. ETL is a very systematic approach that is mainly worthwhile to consider in situations where the data needs to be processed prior to being usefully managed. And finally, it's loaded into a data warehouse to be handled from there. Data is first extracted from the source database or server, then transformed in whatever way is needed through cleaning, revising, restructuring, de-duplicating, or otherwise changing the data. The idea is you have source data that needs to be modified in some way so that it can be read by the destination database or server. This is a data integration process that you'll come across mainly when working with data, data warehouses, and analytics. Reach out to our Support Team if you have any questions.Ĭnxn = mod.connect("Excel File='C:/MyExcelWorkbooks/SampleWorkbook.- So, what's ETL? ETL stands for extract, transform, load. Free Trial & More Informationĭownload a free, 30-day trial of the Excel Python Connector to start building Python apps and scripts with connectivity to Excel data. With the CData Python Connector for Excel, you can work with Excel 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 Sheet table. In this example, we extract Excel data, sort the data by the Revenue 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 Excel data. Sql = "SELECT Name, Revenue FROM Sheet WHERE Name = 'Bob'"Įxtract, Transform, and Load the Excel Data In this article, we read data from the Sheet entity. Use SQL to create a statement for querying Excel. ![]() Use the connect function for the CData Excel Connector to create a connection for working with Excel data.Ĭnxn = mod.connect("Excel File='C:/MyExcelWorkbooks/SampleWorkbook.xlsx' ") 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 Excel Data in Python ![]() Use the pip utility to install the required modules and frameworks: pip install petl The ExcelFile, under the Authentication section, must be set to a valid Excel File.Īfter installing the CData Excel Connector, follow the procedure below to install the other required modules and start accessing Excel through Python objects. For this article, you will pass the connection string as a parameter to the create_engine function. Create a connection string using the required connection properties. When you issue complex SQL queries from Excel, the driver pushes supported SQL operations, like filters and aggregations, directly to Excel and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).Ĭonnecting to Excel 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 Excel data in Python. This article shows how to connect to Excel with the CData Python Connector and use petl and pandas to extract, transform, and load Excel data. With the CData Python Connector for Excel and the petl framework, you can build Excel-connected applications and pipelines for extracting, transforming, and loading Excel data. The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |