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How to Screen and Interview Fintech Data Engineer

Mobilunity

When it comes to financial technology, data engineers are the most important architects. As fintech continues to change the way standard financial services are done, the data engineer’s job becomes more and more important in shaping the future of the industry.

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10 Steps to Achieve Enterprise Machine Learning Success

Cloudera

You’ve probably heard it more than once: Machine learning (ML) can take your digital transformation to another level. We recently published a Cloudera Special Edition of Production Machine Learning For Dummies eBook. Let your teams experiment rapidly, fail early and often, continuously learn, and try new things.

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The Modern Data Lakehouse: An Architectural Innovation

Cloudera

The promise of a modern data lakehouse architecture. Imagine having self-service access to all business data, anywhere it may be, and being able to explore it all at once. Imagine quickly answering burning business questions nearly instantly, without waiting for data to be found, shared, and ingested.

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Machine Learning with Python, Jupyter, KSQL and TensorFlow

Confluent

Building a scalable, reliable and performant machine learning (ML) infrastructure is not easy. It takes much more effort than just building an analytic model with Python and your favorite machine learning framework. Impedance mismatch between data scientists, data engineers and production engineers.

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Evolving from Rule-based Classifier: Machine Learning Powered Auto Remediation in Netflix Data…

Netflix Tech

Operational automation–including but not limited to, auto diagnosis, auto remediation, auto configuration, auto tuning, auto scaling, auto debugging, and auto testing–is key to the success of modern data platforms. Right Sizing is in progress and will be covered with more details in a dedicated technical blog post later. Stay tuned.

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Make the leap to Hybrid with Cloudera Data Engineering

Cloudera

When we introduced Cloudera Data Engineering (CDE) in the Public Cloud in 2020 it was a culmination of many years of working alongside companies as they deployed Apache Spark based ETL workloads at scale. Each unlocking value in the data engineering workflows enterprises can start taking advantage of. Usage Patterns.

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Snowflake Best Practices for Data Engineering

Perficient

Introduction: We often end up creating a problem while working on data. So, here are few best practices for data engineering using snowflake: 1.Transform Each data model has its own advantages and storing intermediate step results has significant architectural advantages.