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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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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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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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How to Manage Unstructured Data in AI and Machine Learning Projects

DagsHub

Apache Kafka Apache Kafka is a distributed event streaming platform for real-time data pipelines and stream processing. Kafka is highly scalable and ideal for high-throughput and low-latency data pipeline applications. It allows unstructured data to be moved and processed easily between systems.

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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.