In Big Data management, data streaming is the continuous high-speed transfer of large amounts of data from a source system to a target. By efficiently processing and analyzing real-time data streams to glean business insight, data streaming can provide up-to-the-second analytics that enable businesses to quickly react to changing conditions.
With the rise of Big Data, data streaming and database streaming have become essential data management tools for enterprises seeking to optimize processes, streamline operations, improve service, identify opportunities and reduce time to innovation.
But what is data streaming exactly, and how can it help transform an organization to become more competitive? Here's a short introduction to "What is data streaming?" that provides an overview of this important technology.
Data streaming involves the processing of vast amounts of real-time data from hundreds or thousands of sources. Streaming data may originate with sensors on IOT devices, within mobile or web applications, from e-commerce transactions, from information about financial markets, through social media and from many other sources.
Data stream processing ingests these various streams of data and extracts important and relevant information and analytics, putting real-time intelligence at the fingertips of decision-makers and employees throughout an organization.
Managing vast amounts of data that is growing exponentially is not an easy task. Even more difficult – processing and analyzing thousands of data streams in real-time to extract valuable information. To benefit from data streaming, enterprises require a sophisticated streaming architectures with powerful Big Data tools for ingesting and processing information.
Apache Kafka is a fast, scalable and durable publish-subscribe messaging system that provides a low-latency platform for ingesting and processing live data streams. Enterprises can use Kafka to support analytics or data lake initiatives. While Kafka is powerful – it can process and execute more than 100,000 transactions per second – it can also place a strain on source database systems and require complex custom coding that can drain IT resources.
Qlik (Attunity) provides a software solution for ingesting, replicating, synchronizing, distributing and consolidating data. Qlik (Attunity) supports the industry's broadest range of sources and targets, including on-premise and cloud replication. Qlik (Attunity) supports data streaming with Apache Kafka by leveraging low-impact CDC technology to minimize the impact on source systems, and by enabling administrators to use an intuitive and configurable GUI that eliminates the need for manual coding when setting up ingestion and replication tasks. With Qlik (Attunity) and Apache Kafka, enterprises can more successfully realize the value from data streaming technology.