Real-Time Data Streaming Tools And Technologies

Real-time data holds potentially high value for business but it also comes with a perishable expiration date. If the value of this data is not realized in a certain window of time, its value is lost and the decision or action which was needed as a result never occurs. Such data comes continuously and quite quickly, therefore, we call it streaming data. Data streaming requires special attention as sensor reading changing rapidly, blip in log file, sudden price change holds immense value but only if it alerted in time.

Although there are many technologies available, still while considering streaming in a data lake it is necessary to have a well-executed data lake which offers strict rules and processes in terms of ingestion.

Here are some real time data streaming tools and technologies.

Apache Flink is a streaming data flow engine which aims to provide facilities for distributed computation over streams of data. Treating batch processes as a special case of data streaming, Flink is effective both as a batch and real-time processing framework but it puts streaming first. Flink offers a number of APIs which includes static data API like DataStream API, DataSet API for Java, Scala and Python and SQL-like query API for embedding in Java, Scala static API code. Flink also has its own machine learning library called FlinkML, its own SQL Query called MRQL as well as graph processing libraries.

Compared to Spark and Storm, Flink is more stream-oriented. It is something of a hybrid between Spark and Storm. Spark operates in batch mode. Flink also provides a highly flexible streaming window for the continuous streaming model. This ensures that both batch and the real-time streaming gets integrated into one system.

Apache Storm is a distributed real-time computation system. Its applications are designed as directed acyclic graphs. Storm can be used with any programming language. It is known for processing over one million tuples per second per node which is highly scalable and provides processing job guarantees. Storm is written in Clojure which is the Lisp-like functional-first programming language.

Storm is used for distributed machine learning, real-time analytics, and numerous other cases, especially with high data velocity. Storm runs on YARN and integrates with Hadoop ecosystems. Storm is a stream processing engine without batch support, a true real-time processing framework, taking in a stream as an entire ‘event’ instead of series of small batches. Storm has low latency and is well-suited to data which must be ingested as a single entity. Storm does suffer from a lack of direct YARN support. Storm is a bridge between batch processing and stream processing, which Hadoop is not natively designed to handle.

Kafka and Kinesis are very similar. Although Kafka is free and requires you to make it into an enterprise-class solution for your organization. But Amazon came to the rescue by offering Kinesis as an out of the box streaming data tool. Kinesis comprises of shards which Kafka calls partitions.