Top 10 Real-Time Data Processing Frameworks for 2021

Are you looking for the best real-time data processing frameworks to use in 2021? Look no further! In this article, we will be discussing the top 10 real-time data processing frameworks that you should consider using this year.

Real-time data processing is becoming increasingly important in today's world, as businesses and organizations need to make quick decisions based on real-time data. Real-time data processing frameworks are designed to handle large volumes of data in real-time, allowing businesses to make informed decisions quickly.

Without further ado, let's dive into the top 10 real-time data processing frameworks for 2021.

1. Apache Spark

Apache Spark is a popular real-time data processing framework that has been around for several years. It is an open-source framework that is designed to handle large volumes of data in real-time. Spark is known for its speed and ease of use, making it a popular choice for many businesses.

Spark supports a variety of programming languages, including Java, Scala, and Python. It also supports a variety of data sources, including Hadoop Distributed File System (HDFS), Apache Cassandra, and Amazon S3.

2. Apache Beam

Apache Beam is another popular real-time data processing framework that is gaining popularity in 2021. It is an open-source framework that is designed to handle both batch and streaming data processing. Beam is known for its flexibility and portability, making it a popular choice for many businesses.

Beam supports a variety of programming languages, including Java, Python, and Go. It also supports a variety of data sources, including HDFS, Apache Cassandra, and Google Cloud Storage.

3. Apache Kafka

Apache Kafka is a popular real-time data streaming platform that is designed to handle large volumes of data in real-time. It is an open-source platform that is known for its scalability and reliability, making it a popular choice for many businesses.

Kafka supports a variety of programming languages, including Java, Scala, and Python. It also supports a variety of data sources, including HDFS, Apache Cassandra, and Amazon S3.

4. Apache Flink

Apache Flink is a popular real-time data processing framework that is designed to handle both batch and streaming data processing. It is an open-source framework that is known for its speed and scalability, making it a popular choice for many businesses.

Flink supports a variety of programming languages, including Java, Scala, and Python. It also supports a variety of data sources, including HDFS, Apache Cassandra, and Amazon S3.

5. Apache Storm

Apache Storm is a popular real-time data processing framework that is designed to handle large volumes of data in real-time. It is an open-source framework that is known for its speed and scalability, making it a popular choice for many businesses.

Storm supports a variety of programming languages, including Java, Clojure, and Python. It also supports a variety of data sources, including HDFS, Apache Cassandra, and Amazon S3.

6. Apache NiFi

Apache NiFi is a popular real-time data processing framework that is designed to handle data flow between systems. It is an open-source framework that is known for its ease of use and flexibility, making it a popular choice for many businesses.

NiFi supports a variety of data sources, including HDFS, Apache Cassandra, and Amazon S3. It also supports a variety of data formats, including JSON, XML, and CSV.

7. Apache Samza

Apache Samza is a popular real-time data processing framework that is designed to handle large volumes of data in real-time. It is an open-source framework that is known for its simplicity and ease of use, making it a popular choice for many businesses.

Samza supports a variety of programming languages, including Java and Scala. It also supports a variety of data sources, including HDFS, Apache Cassandra, and Amazon S3.

8. Apache Apex

Apache Apex is a popular real-time data processing framework that is designed to handle both batch and streaming data processing. It is an open-source framework that is known for its speed and scalability, making it a popular choice for many businesses.

Apex supports a variety of programming languages, including Java and Scala. It also supports a variety of data sources, including HDFS, Apache Cassandra, and Amazon S3.

9. Apache Kylin

Apache Kylin is a popular real-time data processing framework that is designed to handle large volumes of data in real-time. It is an open-source framework that is known for its speed and scalability, making it a popular choice for many businesses.

Kylin supports a variety of programming languages, including Java and Scala. It also supports a variety of data sources, including HDFS, Apache Cassandra, and Amazon S3.

10. Apache Druid

Apache Druid is a popular real-time data processing framework that is designed to handle large volumes of data in real-time. It is an open-source framework that is known for its speed and scalability, making it a popular choice for many businesses.

Druid supports a variety of programming languages, including Java and Scala. It also supports a variety of data sources, including HDFS, Apache Cassandra, and Amazon S3.

In conclusion, these are the top 10 real-time data processing frameworks that you should consider using in 2021. Each framework has its own strengths and weaknesses, so it's important to choose the one that best fits your business needs. Whether you're looking for speed, scalability, or ease of use, there's a real-time data processing framework out there for you.

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