Big Data and Hadoop can play a significant role in the banking industry, especially in managing and analyzing large amounts of financial data. The end-to-end processing pipeline in a Big Data and Hadoop-based banking system typically involves the following steps:

1. Data Ingestion: This is the first step in the pipeline where financial data from various sources such as ATM transactions, credit card transactions, and bank statements, is collected and stored in a Hadoop Distributed File System (HDFS).
2. Data Storage: The collected data is stored in HDFS, which is a scalable and fault-tolerant data storage system. HDFS stores data in a distributed manner, which makes it easy to process large amounts of data in parallel.
3. Data Processing: Once the data is stored, it can be processed using MapReduce, Spark, or other Big Data processing frameworks. This step involves transforming, aggregating, and cleaning the data to prepare it for analysis.
4. Data Analysis: The processed data is then analyzed to uncover patterns, relationships, and trends that can be used to make informed business decisions. This step can involve various techniques such as machine learning, statistical analysis, and data visualization.
5. Data Visualization: The results of the analysis can be visualized using various tools such as Tableau, PowerBI, or QlikView to help business analysts, data scientists, and other stakeholders understand and interpret the data.
6. Output Delivery: The final step in the pipeline is delivering the output to the relevant stakeholders. The output can be in the form of reports, dashboards, or interactive visualizations that provide insights into the financial data and help inform business decisions.

By using Big Data and Hadoop, banks can process and analyze large amounts of financial data in real-time, enabling them to make informed decisions, improve customer experience, and enhance their overall operational efficiency.

here is a high-level diagram that illustrates the end-to-end processing pipeline in a Big Data and Hadoop-based banking system:

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In this diagram, financial data is collected from various sources and ingested into the Hadoop-based system. The data is then stored in HDFS, which is a scalable and fault-tolerant data storage system. The data is processed using Big Data processing frameworks such as MapReduce or Spark, and then analyzed to uncover patterns, relationships, and trends. The results of the analysis are visualized using data visualization tools, and finally, the output is delivered to the relevant stakeholders in the form of reports, dashboards, or interactive visualizations.