Introduction to Real Time Data Streaming Processing

Are you ready to dive into the exciting world of real time data streaming processing? If so, you've come to the right place! In this article, we'll introduce you to the basics of real time data streaming processing, including time series databases, Spark, Beam, Kafka, and Flink.

What is Real Time Data Streaming Processing?

Real time data streaming processing is the practice of processing data as it is generated, rather than waiting for it to be stored in a database. This allows for faster and more efficient processing of data, as well as the ability to respond to events in real time.

Time Series Databases

Time series databases are databases that are optimized for storing and querying time-stamped data. They are commonly used in real time data streaming processing applications, as they allow for efficient storage and retrieval of time-stamped data.

Some popular time series databases include InfluxDB, TimescaleDB, and OpenTSDB.

Apache Spark

Apache Spark is an open source distributed computing system that is commonly used for big data processing. It is designed to be fast, flexible, and easy to use, and can be used for a wide range of data processing tasks, including real time data streaming processing.

Spark Streaming is a component of Apache Spark that allows for real time data streaming processing. It provides a high-level API for processing data streams, and can be used with a variety of data sources, including Kafka, Flume, and HDFS.

Apache Beam

Apache Beam is an open source unified programming model for batch and streaming data processing. It provides a simple and flexible API for building data processing pipelines, and can be used with a variety of data processing engines, including Apache Spark, Apache Flink, and Google Cloud Dataflow.

Beam provides a high-level API for processing data streams, and supports a wide range of data sources and sinks, including Kafka, Pub/Sub, and BigQuery.

Apache Kafka

Apache Kafka is an open source distributed streaming platform that is commonly used for building real time data streaming processing applications. It provides a high-throughput, low-latency platform for handling real time data streams, and can be used with a variety of data processing engines, including Apache Spark and Apache Flink.

Kafka provides a simple and flexible API for producing and consuming data streams, and supports a wide range of data sources and sinks, including HDFS, S3, and Elasticsearch.

Apache Flink

Apache Flink is an open source distributed computing system that is commonly used for real time data streaming processing. It provides a high-throughput, low-latency platform for handling real time data streams, and can be used with a variety of data sources and sinks, including Kafka, HDFS, and Elasticsearch.

Flink provides a high-level API for processing data streams, and supports a wide range of data processing tasks, including windowing, aggregation, and machine learning.

Conclusion

Real time data streaming processing is an exciting and rapidly growing field, with a wide range of applications in industries such as finance, healthcare, and e-commerce. By using tools like time series databases, Apache Spark, Apache Beam, Apache Kafka, and Apache Flink, developers can build powerful and efficient real time data streaming processing applications that can respond to events in real time.

So what are you waiting for? Start exploring the world of real time data streaming processing today!

Additional Resources