Apache NiFiĪpache NiFi is a powerful, reliable, and easy to use system used to process and distribute data. It is an open-source framework for fast and versatile data analytics in clusters. Pache Flink can be used for batch processing, interactive processing, real-time stream processing, graph processing, iterative processing, in-memory processing. It processes events at very high speed with low latency. It is a highly scalable, fast, and reliable large scale data processing engine.Īpache Flink supports both batches as well as real-time stream analytics in one system. It gains its popularity because of its accuracy in data ingestion, and its feature of recoverability from failures. Apache FlinkĪpache Flink is an open-source streaming platform. Apache Kafka can be integrated with Apache Storm and Apache Spark for real-time streaming data analysis. ![]() The real-time streaming applications transform or react to the data streams. The real-time streaming pipelines get data between applications or systems. It is useful building real-time streaming data pipelines and real-time streaming applications. It enables users to pass messages from one end-point to another.Īpache Kafka suits for offline as well as online message consumption. It is an open-source distributed streaming platform and a robust queue that is capable of handling high volumes of data. Apache KafkaĪpache Kafka is a distributed publish-subscribe based messaging system. Apache Storm can be integrated with the database technologies already in use. Apache Storm is a fast, fault-tolerant, and scalable tool. It is amongst the top tools for real-time analytics, ETL, continuous computation, online machine learning, and many more. It is a simple tool that we can use with any programming language. With Apache Storm, we can easily process unbounded streams of data, that is, data that have to start but no end. Apache StormĪpache Storm is an open-source real-time computation system. We can use it for collecting logs, parsing them, and storing them for later use. It is a tool for managing logs and events. This ensures a more powerful analysis and helps in generating business value. It identifies the named fields for building structure and transforms them to bring on a common format. Logstash filters parse each event as the data travels from the source to the store. Logstash excludes or anonymizes sensitive fields, and thus provides ease for overall processing. ![]() It can derive decipher geo coordinates from the IP addresses. Logstash can derive structure from the unstructured data with grok. It dynamically ingests, transforms, and ships our data without worrying about data format or complexity. ![]() It dynamically transforms and prepares our data regardless of data format and complexity. Apache Spark can process data in Hadoop HDFS, Hive, HBase, Cassandra, and any Hadoop InputFormat. It is best for its in-memory computation. It provides a single platform for all the big data-related problems. Apache Spark is perfect for distributed SQL like applications. It performs faster processing than Apache Hadoop. It is the best tool for batch processing as well as real-time stream processing, interactive queries, and machine learning.Īpache Spark runs on any of Hadoop YARN, EC2, Apache Mesos, etc. It is a unified analytical engine for processing large scale data. Apache Spark is an open-source data analytics tool.
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