Real-time data streams can provide the enterprise with extraordinary access to timely business insight but having the right data streaming technology is critical to extracting information from vast amounts of Big Data.

What is data streaming? Data streaming, or database streaming, involves processing large volumes of data quickly and using that information to react to changing conditions in real time. Real-time data streams may come from sources that include sensors on devices connected to the Internet of Things (IoT), e-commerce transactions, data from financial trading floors, instrumentation in data centers, social network data, data from mobile and web applications, and much more. By harnessing the power of data stream processing, enterprises can turn real-time data streams into business intelligence that can help to optimize operations, improve customer service, take advantage of business opportunity and compete more effectively.

Managing real-time data streams with Apache Kafka

Extracting insight from real-time data streams requires a powerful streaming architecture and tools for efficiently ingesting and processing streaming data. For many organizations, Apache Kafka provides a high-scale, low-latency platform for managing database streaming.

But while Kafka solves several challenges in working with real-time data streams, it also can adversely impact source system performance and require complex custom coding that place a strain on scarce IT resources.

To enable enterprises to easily process real-life data streams with Apache Kafka, Qlik (Attunity) provides a powerful solution to resolve these challenges.

Qlik Replicate (formerly Attunity Replicate): enabling analytics for real-time data streams

Qlik (Attunity) provides a software solution their works with Apache Kafka to accelerate data ingestion from multiple database systems. Qlik (Attunity) low-impact CDC technology and zero-footprint architecture eliminates the need to install agents in source database systems. Qlik (Attunity) feeds live database changes to Kafka message brokers with low latency, while an intuitive and configurable graphical user interface eliminates the need for manual coding when setting up data feeds.

In addition to Apache Kafka, Qlik (Attunity) enables databases to publish events to other major streaming services, including Confluent, Azure Event Hub, Amazon Kinesis and MapR-ES.

Advantages of Qlik (Attunity) for processing real-time data streams

With Qlik (Attunity), enterprises can more easily process real-time data streams to:

  • Achieve greater value of streaming data ingest with Kafka.
  • Accelerate data delivery to enable real-life time analytics.
  • Use replication in database systems to turn databases into live feeds for streaming ingest and processing.
  • Reduce the administrative burden on IT by minimizing the skill level and training required to manage ingestion of real-time data streams.

Streaming Change Data Capture