Remove fraud-detection-with-cloudera-stream-processing-part-1
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Fraud Detection with Cloudera Stream Processing Part 1

Cloudera

In a previous blog of this series, Turning Streams Into Data Products , we talked about the increased need for reducing the latency between data generation/ingestion and producing analytical results and insights from this data. Building real-time streaming analytics data pipelines requires the ability to process data in the stream.

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Fraud Detection With Cloudera Stream Processing Part 2: Real-Time Streaming Analytics

Cloudera

In part 1 of this blog we discussed how Cloudera DataFlow for the Public Cloud (CDF-PC), the universal data distribution service powered by Apache NiFi, can make it easy to acquire data from wherever it originates and move it efficiently to make it available to other applications in a streaming fashion. Data decays!

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Digital Transformation is a Data Journey From Edge to Insight

Cloudera

Most of what is written though has to do with the enabling technology platforms (cloud or edge or point solutions like data warehouses) or use cases that are driving these benefits (predictive analytics applied to preventive maintenance, financial institution’s fraud detection, or predictive health monitoring as examples) not the underlying data.

Data 103
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The Future Is Hybrid Data, Embrace It

Cloudera

In the past decade, the amount of structured data created, captured, copied, and consumed globally has grown from less than 1 ZB in 2011 to nearly 14 ZB in 2020. If you haven’t looked at Cloudera in a while, it’s time you do . For Cloudera this is a back to the future moment. We live in a hybrid data world.

Data 106
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And the winners are…. Congratulations to the Sixth Annual Data Impact Awards winners

Cloudera

The week is typically filled with exciting announcements from Cloudera and many partners and others in the data management, machine learning and analytics industry. Voya Financial prevented millions of dollars of fraudulent transactions by deploying predictive analytic capabilities on Cloudera. Technical Impact.

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Data Science use cases & tools

Apiumhub

Pattern detection and grouping (e.g., Anomaly detection (e.g., fraud detection). Automated processes and decision-making (e.g., Modeling is also a part of ML and involves identifying which algorithm is the most suitable to solve a given problem and how to train these models. What is Data Science?

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5 Key Takeaways from #Current2023

Cloudera

With few conferences curating content specific to streaming developers, Current has historically been an important event for anyone trying to keep a pulse on what’s happening in the streaming space. I will cover key takeaways from Current 2023 and offer Cloudera’s perspective. The answer from the community is a resounding yes.