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How LotteON built a personalized recommendation system using Amazon SageMaker and MLOps

AWS Machine Learning Blog

With Amazon EMR, which provides fully managed environments like Apache Hadoop and Spark, we were able to process data faster. Event-based pipeline automation After the preprocessing batch was complete and the training/test data was stored in Amazon S3, this event invoked CodeBuild and ran the training pipeline in SageMaker.

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Spark Vs. Hadoop – All You Need to Know

Pickl AI

Introduction Apache Spark and Hadoop are potent frameworks for big data processing and distributed computing. While both handle vast datasets across clusters, they differ in approach. Hadoop relies on disk-based storage and batch processing, while Spark uses in-memory processing, offering faster performance.

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The Backbone of Data Engineering: 5 Key Architectural Patterns Explained

Mlearning.ai

In data engineering, the Pub/Sub pattern can be used for various use cases such as real-time data processing, event-driven architectures, and data synchronization across multiple systems. The company can use the Pub/Sub pattern to process customer events such as product views, add to cart, and checkout.

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Introduction to Apache NiFi and Its Architecture

Pickl AI

Guaranteed Delivery : NiFi ensures that data delivered reliably, even in the event of failures. It maintains a write-ahead log to ensure that the state of FlowFiles preserved, even in the event of a failure. Provenance Repository : This repository records all provenance events related to FlowFiles. Is Apache NiFi Easy to Use?

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Top 5 Challenges faced by Data Scientists

Pickl AI

It contains data clustering, classification, anomaly detection and time-series forecasting. Some of the tools used by Data Science in 2023 include statistical analysis system (SAS), Apache, Hadoop, and Tableau. Additionally, you should attend conferences and events like webinars and learn from your peers and experts.

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Discover the Most Important Fundamentals of Data Engineering

Pickl AI

Among these tools, Apache Hadoop, Apache Spark, and Apache Kafka stand out for their unique capabilities and widespread usage. Apache Hadoop Hadoop is a powerful framework that enables distributed storage and processing of large data sets across clusters of computers.

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Top 15 Data Analytics Projects in 2023 for beginners to Experienced

Pickl AI

Diagnostic Analytics Projects: Diagnostic analytics seeks to determine the reasons behind specific events or patterns observed in the data. 3. Predictive Analytics Projects: Predictive analytics involves using historical data to predict future events or outcomes. Root cause analysis is a typical diagnostic analytics task.