Data streaming is revolutionizing analytics for customer engagement, operational decisions, supply chain optimization, fraud detection, and much more. In most cases, the closer organizations can get to real-time data, the more valuable the data is for gaining predictive insights, running AI/ML-driven applications, and providing situation awareness to users of dashboards and data analytics platforms. Adoption of Apache Kafka to run on cloud platforms, on premises, or a hybrid of both has proven critical to expanding the range of use cases that can benefit from real-time insights.

However, to achieve the potential of data and event streaming, organizations need to address key challenges:

  • Complexity. Data teams need to reduce confusion and have better visibility so they can prepare multiple data streams for Kafka-based platforms.
  • Efficient operations at scale: Organizations need to minimize operational complexity to ensure high performance and scalability as data streaming grows.
  • Self-service and less manual coding. Data teams can’t afford delays and errors due to heavy manual coding.
  • Make it easy to build event streaming applications. Organizations need to be able to build applications that deliver fresh insights from semi-structured data faster.
  • Legacy data availability. Mainframe databases and older enterprise applications can’t be left behind.

Join this webinar to learn how your organization can harness the power of data and event streaming for important initiatives. You will hear from David Stodder, TDWI senior director of research for business intelligence, Rankesh Kumar, partner solution engineer, Confluent, and John Neal, partner engineering team, Qlik.

                   Register today »