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Read The ReportExtract single or multiple CSV files from Azure Data Lake Gen2, configure CSV settings, and manage destination tables in Keboola Storage. Options include incremental loading, primary key settings, and processing settings like decompression and filename column addition.
The Azure Data Lake Gen2 extractor allows users to efficiently extract files from Azure Data Lake Gen2 and store them in Keboola. Key features include file system semantics, file-level security, and the ability to handle large-scale data. This integration simplifies data extraction and processing, enabling users to focus on building impactful data products.
Extract single or multiple CSV files from Azure Data Lake Gen2, configure CSV settings, and manage destination tables in Keboola Storage. Options include incremental loading, primary key settings, and processing settings like decompression and filename column addition.
A retail company uses the Azure Data Lake Gen2 extractor to pull sales data stored in CSV format. By configuring incremental loads and primary keys, they ensure only new data is processed, reducing redundancy and improving data accuracy. This setup allows the analytics team to focus on generating insights rather than managing data extraction.
A financial services firm extracts transaction data from Azure Data Lake Gen2 and combines it with customer data in Snowflake. This enriched dataset is then used to create detailed customer profiles, enabling personalized marketing strategies and improved customer engagement.
A manufacturing company extracts production data from Azure Data Lake Gen2 and integrates it with OneDrive Excel Sheets. This setup allows real-time reporting and visualization of production metrics, facilitating quick decision-making and operational efficiency improvements.
Efficiently manage and process large datasets with Azure Data Lake Gen2 and Keboola.