{"id":57493,"date":"2023-06-27T08:33:13","date_gmt":"2023-06-27T03:03:13","guid":{"rendered":"https:\/\/www.tothenew.com\/blog\/?p=57493"},"modified":"2023-06-27T08:33:13","modified_gmt":"2023-06-27T03:03:13","slug":"no-code-data-ingestion-framework-using-apache-flink","status":"publish","type":"post","link":"https:\/\/www.tothenew.com\/blog\/no-code-data-ingestion-framework-using-apache-flink\/","title":{"rendered":"No Code Data Ingestion Framework Using Apache-Flink\u00a0"},"content":{"rendered":"

The conveyance of data from many sources to a storage medium where it may be accessed, utilized, and analyzed by an organization is known as data ingestion. Typically, the destination is a data warehouse, data mart, database, or document storage. Sources can include RDBMS such as MySQL, Oracle, and Postgres. The data ingestion layer serves as the foundation for any data engineering framework.<\/span><\/p>\n

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What is Apache Flink?<\/b><\/h3>\n

Apache Flink is a free and open-source distributed stream processing framework that may be used for real-time analytics and massive dataset processing. It was created to handle batch and stream processing cohesively, making it an excellent solution for use cases that require both. Flink accepts data from various sources, including event streams, databases, and file systems. It includes filters, maps, joins, and aggregations among its data transformation and analysis operations. One of Flink’s primary strengths is its capacity to process streaming data in real-time, which means it can manage large amounts of data with minimal delay. It also enables fault tolerance, ensuring that processing can continue even if the system fails.<\/span><\/p>\n

How are we using Flink? \u00a0 <\/b>\u00a0<\/span>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<\/span><\/h3>\n

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TO THE NEW has automated the data ingestion process using PyFlink. We wrote a Python wrapper on the top of the SQL code, which automates the process of ingestion, where the user only needs to pass the JSON file with the appropriate details that establish the connection between the source and destination so that ingestion can occur. Our framework offers various features such as reading data from RDBMS sources using PyFlink and writing it to S3, HDFS using the Flink tool, and incremental load.<\/span><\/p>\n

Basic Features<\/b><\/h2>\n

Reading data from RDBMS sources using PyFlink and writing it to S3 , HDFS.<\/span><\/p>\n