Insurance Portfolio Unstructured Data 
Knowledge Hut
MAY 31, 2023
Whether you are a beginner or an experienced programmer, these top database projects with source codes can help you improve your skills and add value to your portfolio. Maintaining accurate records of train movements, routes, timetables, and passenger data is essential for ensuring safe and efficient operations. So, Let's get started!
Knowledge Hut
APRIL 23, 2024
Big Data Use Cases in Banking and Financial Services Fraud Detection and Prevention: By examining vast amounts of transaction data and looking for patterns and anomalies suggestive of possible fraud, big data analytics assists banks and financial organizations in finding fraudulent activity.
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Knowledge Hut
MARCH 26, 2024
Responsibilities: Define data architecture strategies and roadmaps to support business objectives and data initiatives. Design data models, schemas, and storage solutions for structured and unstructured data. Evaluate and recommend data management tools, database technologies, and analytics platforms.
ProjectPro
MARCH 15, 2016
These days we notice that many banks compile separate data warehouses into a single repository backed by Hadoop for quick and easy analysis. Hadoop clusters are used by banks to create more accurate risk analysis models for the customers in its portfolio. Hadoop allows us to store data that we never stored before.
ProjectPro
JULY 13, 2015
Apache Hadoop is the framework of choice for JPMorgan - not only to support the exponentially growing data size but more importantly for the fast processing of complex unstructured data. JP Morgan has massive amounts of data on what its customers spend and earn. Hadoop allows us to store data that we never stored before.
ProjectPro
APRIL 1, 2016
eWeek.com Syncsort has made it easy for mainframe data to work in Hadoop and Spark by upgrading its DMX-h data integration software. Syncsort has delivered this because some of the companies in industries like financial services, banking, and insurance needed to maintain their mainframe data in native format.
ProjectPro
MARCH 16, 2015
Thus, the computing technology and infrastructure must be able to render a cost efficient implementation of: Parallel Data Processing that is unconstrained. Provide storage for billions and trillions of unstructured data sets. In such circumstances, the data and the outcomes are always dynamic and changing.
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