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What are the most reliable data processing tools for real-time data?

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Real-time data is data that is generated and processed continuously and rapidly, often from multiple sources and formats. It can provide valuable insights and opportunities for data-driven decision making, but it also poses significant challenges for data science. How can you handle the volume, velocity, variety, and veracity of real-time data? What are the most reliable data processing tools and platforms that can help you ingest, transform, analyze, and visualize real-time data? In this article, you will learn about some of the most popular and powerful data processing tools for real-time data, and how they can help you achieve your data science goals.

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1 Apache Kafka

Apache Kafka is a distributed streaming platform that can handle millions of events per second from various sources. It can act as a message broker, a data pipeline, or a stream processor, depending on your needs. Kafka allows you to publish and subscribe to streams of data, store them in a fault-tolerant way, and process them as they occur. Kafka is widely used by many companies, such as Netflix, LinkedIn, and Uber, to handle real-time data for applications such as analytics, monitoring, and recommendation systems.

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Ali Oraji

Data Analyst | Data Science
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Apache Kafka stands out as a favorite among many Data Engineers and is integral to numerous business models. Its ability to handle vast amounts of real-time data is exceptional. Another tool I’ve had the opportunity to work with is Google Pub/Sub in Google BigQuery. It offers robust message service capabilities for events between independent applications, showcasing its versatility and control in data management.

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2 Apache Spark

Apache Spark is a fast and general-purpose cluster computing framework that can perform batch and stream processing on large-scale data. Spark can run on various platforms, such as Hadoop, Mesos, Kubernetes, or standalone, and supports multiple languages, such as Scala, Python, Java, and R. Spark offers several libraries and APIs for different tasks, such as Spark SQL for structured and semi-structured data, Spark Streaming for stream processing, MLlib for machine learning, and GraphX for graph analytics.

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Pragati Gupta

Data Science | Data Analyst | Machine Learning | Artificial Intelligence | Business Intelligence Analyst | Data Visualization | Tableau | ETL | SQL | Alteryx | Python | PowerBI | Data Analytics
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Apache Spark is like a superhero for big data! It's a super-fast tool that can handle lots of data at once. It works on different platforms and speaks many languages, like Scala, Python, Java, and R. Spark has cool libraries for different tasks, like sorting data, processing streams, doing machine learning, and analyzing graphs.

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3 Apache Flink

Apache Flink is another distributed streaming platform that can process large volumes of data in a low-latency and scalable manner. Flink can handle both batch and stream processing, but it is optimized for streaming scenarios. Flink provides a unified API for both types of processing, and supports various data sources and sinks, such as Kafka, HDFS, Cassandra, and Elasticsearch. Flink also offers libraries and modules for complex event processing, stateful computations, machine learning, and graph analytics.

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4 Apache Storm

Apache Storm is a distributed real-time computation system that can process unbounded streams of data with high throughput and low latency. Storm can integrate with various data sources and sinks, such as Kafka, HBase, MongoDB, and Redis, and supports multiple languages, such as Java, Python, Ruby, and Clojure. Storm can be used for a variety of real-time applications, such as fraud detection, sentiment analysis, social media analytics, and IoT.

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Pragati Gupta

Data Science | Data Analyst | Machine Learning | Artificial Intelligence | Business Intelligence Analyst | Data Visualization | Tableau | ETL | SQL | Alteryx | Python | PowerBI | Data Analytics
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Apache Storm is a powerhouse for "real-time data processing". It handles endless data streams with speed and efficiency, connecting seamlessly to sources like Kafka and databases. Supporting languages like Java and Python, Storm finds applications in fraud detection, sentiment analysis, social media analytics, and IoT, making it a versatile tool in the data world.

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5 Amazon Kinesis

Amazon Kinesis is a cloud-based service that offers several solutions for real-time data processing. Kinesis Data Streams can collect and process streams of data from various sources, such as web servers, mobile devices, sensors, and logs. Kinesis Data Firehose can deliver data streams to various destinations, such as Amazon S3, Amazon Redshift, Amazon Elasticsearch Service, and Splunk. Kinesis Data Analytics can analyze data streams using SQL or Apache Flink. Kinesis Video Streams can capture, process, and store video streams for analytics and machine learning.

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Pragati Gupta

Data Science | Data Analyst | Machine Learning | Artificial Intelligence | Business Intelligence Analyst | Data Visualization | Tableau | ETL | SQL | Alteryx | Python | PowerBI | Data Analytics
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Amazon Kinesis, a cloud service, excels in real-time data processing. Kinesis Data Streams handles data from diverse sources, while Data Firehose sends it to destinations like Amazon S3 and Redshift. Data Analytics uses SQL or Apache Flink for stream analysis. Kinesis Video Streams captures, processes, and stores video for analytics and machine learning.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Sumanth Donthula

Data Science & GenAI, Pandata Group ?? | Bridging the Gap between AI/ML research and Business|
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I would love to use all the tools mentioned. My way of picking up tools would depend on whether I need a broker system or continuous streaming system. How much packet loss can I afford according to the business task.

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What are the most reliable data processing tools for real-time data?

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2

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4

5

6

1 Apache Kafka

2 Apache Spark

3 Apache Flink

4 Apache Storm

5 Amazon Kinesis

6 Here’s what else to consider

Data Science

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What are the most reliable data processing tools for real-time data?

1

2

3

4

5

6

1 Apache Kafka

2 Apache Spark

3 Apache Flink

4 Apache Storm

5 Amazon Kinesis

6 Here’s what else to consider

Data Science

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