"Extract, Transform, and Load Big Data with Apache Hadoop"

| May 19, 2017

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Over the last few years, organizations across public and private sectors have made a strategic decision to turn big data into competitive advantage. At the heart of this challenge is the process used to extract data from multiple sources, transform it to fit your analytical needs, and load it into a data warehouse for subsequent analysis, a process known as “Extract, Transform & Load” (ETL). The nature of big data requires that the infrastructure for this process can scale cost-effectively. Apache Hadoop* has emerged as the de facto standard for managing big data. This whitepaper examines some of the platform hardware and software considerations in using Hadoop for ETL.

Spotlight

Smarter Data, Inc.

At SmarterData, we work with Enterprise Customers on projects that help them stay relevant. Initiatives that are focused on turning data into insight with sophisticated analytics technologies like big data, predictive analytics, data science and machine learning. As an Authorized IBM Business Partner, we design and implement IBM Information Management and Business Analytics solutions including IBM SPSS, BigInsights, PureData, Netezza and so forth. We have experience in working with Hadoop, Spark and a variety of popular open source tools like R, Python, etc. We are proficient in working with game changing datasets like Weather, Social and Machine data. We can help integrate analytics with mobile and cloud to drive value at the point of impact.

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A solid impact by nations to curb illicit cash movement has brought about a huge yet amazingly little part of money laundering being recognized — a triumph rate of about 2% average. Dutch banks — ABN Amro, Rabobank, ING, Triodos Bank, and Volksbank announced in September 2019 to work toward a joint transaction monitoring to stand-up fight against Money Laundering. A typical challenge in transaction monitoring, for instance, is the generation of a countless number of alerts, which thusly requires operation teams to triage and process the alarms. ML models can identify and perceive dubious conduct and besides they can classify alerts into different classes such as critical, high, medium, or low risk. Critical or High alerts may be directed to senior experts on a high need to quickly explore the issue. Today is the immense number of false positives, gauges show that the normal, of false positives being produced, is the range of 95 and 99%, and this puts extraordinary weight on banks. The examination of false positives is tedious and costs money. An ongoing report found that banks were spending near 3.01€ billion every year exploring false positives. Establishments are looking for increasing productive ways to deal with crime and, in this specific situation, Machine Learning can end up being a significant tool. Financial activities become productive, the gigantic sum and speed of money related exchanges require a viable monitoring framework that can process exchanges rapidly, ideally in real-time.   What are the types of machine learning algorithms which can identify money laundering transactions? Supervised Machine Learning, it is essential to have historical information with events precisely assigned and input variables appropriately captured. If biases or errors are left in the data without being dealt with, they will get passed on to the model, bringing about erroneous models. 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Bayesian networks: It makes a model of the user’s previous activities, and this model will be a measure of future customer activities. In the event that the exchange or user financial transactions have. Cluster-based local outlier factor algorithm: The money laundering detection utilizing clustering techniques combination and Outliers.   Conclusion For banks, now is the ideal opportunity to deploy ML models into their ecosystem. Despite this opportunity, increased knowledge and the number of ML implementations prompted a discussion about the feasibility of these solutions and the degree to which ML should be trusted and potentially replace human analysis and decision-making. In order to further exploit and achieve ML promise, banks need to continue to expand on its awareness of ML strengths, risks, and limitations and, most critically, to create an ethical system by which the production and use of ML can be controlled and the feasibility and effect of these emerging models proven and eventually trusted.

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Spotlight

Smarter Data, Inc.

At SmarterData, we work with Enterprise Customers on projects that help them stay relevant. Initiatives that are focused on turning data into insight with sophisticated analytics technologies like big data, predictive analytics, data science and machine learning. As an Authorized IBM Business Partner, we design and implement IBM Information Management and Business Analytics solutions including IBM SPSS, BigInsights, PureData, Netezza and so forth. We have experience in working with Hadoop, Spark and a variety of popular open source tools like R, Python, etc. We are proficient in working with game changing datasets like Weather, Social and Machine data. We can help integrate analytics with mobile and cloud to drive value at the point of impact.

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