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Showing posts with the label Bank ATM Analytics - SAP Hana

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8 Ways to Optimize AWS Glue Jobs in a Nutshell

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  Improving the performance of AWS Glue jobs involves several strategies that target different aspects of the ETL (Extract, Transform, Load) process. Here are some key practices. 1. Optimize Job Scripts Partitioning : Ensure your data is properly partitioned. Partitioning divides your data into manageable chunks, allowing parallel processing and reducing the amount of data scanned. Filtering : Apply pushdown predicates to filter data early in the ETL process, reducing the amount of data processed downstream. Compression : Use compressed file formats (e.g., Parquet, ORC) for your data sources and sinks. These formats not only reduce storage costs but also improve I/O performance. Optimize Transformations : Minimize the number of transformations and actions in your script. Combine transformations where possible and use DataFrame APIs which are optimized for performance. 2. Use Appropriate Data Formats Parquet and ORC : These columnar formats are efficient for storage and querying, signif
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Bank ATM Analytics - SAP Hana

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Problem: ATM(Automated Teller Machine) has become an integral part of our society. Recent estimates developed by ATMIA place the number of ATMs in use currently at over 2.2 million, or 1 ATM per around over 3000 people in the world. If we think of very specific banks than a recent information in wikipedia states that SBI Group (State Bank Of India) has around 45000+ ATM. So we see the numbers are huge. But there is a very big problem and i would like to use a typical example from India to illustrate this. Here, many times the ATM runs out of currency notes of specific denomination as lets say Rs 100. The end consumer is left with the option of either accepting currency of higher denomination or seek a different ATM. Here we also have a charge applied by the bank if a consumer withdraws money from a ATM of a bank different from the one in which he holds his account. Now this is a major inconvenience for the end consumer and also a loss for the bank as their reputation is effecte
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