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  • Build a Real-time Stream Processing Pipeline with Apache . . .
    Apache Flink is an open source project that is well-suited to form the basis of such a stream processing pipeline It offers unique capabilities that are tailored to the continuous analysis of streaming data
  • Build a Real-time Stream Processing Pipeline with Apache . . .
    Build a Real-time Stream Processing Pipeline with Apache Flink on AWS scalable, and reliable stream processing pipeline that is based on Apache Flink using Amazon EMR, you want to optimize the operations by analyzing the gathered data in real time and making data-based decisions You would like, for instance, to identify hot spots
  • Building a Real-Time Streaming ETL Pipeline in 20 Minutes . . .
    Building a Real-Time Streaming ETL Pipeline in 20 Minutes Let’s consider an application that does some real-time stateful stream processing with the Kafka Streams API We’ll run through a specific example of the end-to-end reference architecture and show you how to: Enhance Security with Apache Kafka 2 0 and Confluent Platform 5 0
  • Build a Real-time Stream Processing Pipeline with Apache . . .
    The increasing number of available data sources in today’s application stacks created a demand to continuously capture and process data from various sources to quickly turn high volume streams of raw data into actionable insights Apache Flink addresses many of the challenges faced in this domain as it’s specifically tailored to distributed computations over streams
  • Building real-time data pipeline using Apache Spark . . .
    However, to process large amounts of real-time or streaming data requires you to build a data processing pipeline The main challenge in building such a pipeline is to minimize latency achieve near real-time processing rate to process high-throughput data
  • Reference architecture for real-time stream processing . . .
    You build a real-time stream processing pipeline that continuously receives information from a fleet of taxis operating in New York City Using this data, you want to optimize the operations by analyzing the gathered data in real time and making data-based decisions
  • Real-Time Streaming Data Pipelines with Apache APIs: Kafka . . .
    Real-time Data Processing Using Spark Streaming Spark Streaming brings Spark's APIs to stream processing, letting you use the same APIs for streaming and batch processing Data streams can be processed with Spark’s core APIs, DataFrames, GraphX, or machine learning APIs, and can be persisted to a file system, HDFS, MapR-FS, MapR-DB, HBase, or
  • Part 2: Steps to Building a Streaming ETL Pipeline with . . .
    KSQL is the open-source SQL streaming engine for Apache Kafka, and makes it possible to build stream processing applications at scale, written using a familiar SQL interface In this talk we’ll explain the architectural reasoning for Confluent Platform and the benefits of real-time integration, and we’ll build a streaming data pipeline




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