MENGENAI EPISOD INI
This episode of Techsplainers explores data streaming platforms and how they help organizations continuously capture, process, analyze and deliver data in real time or near-real time. The episode explains why these platforms have become essential as businesses shift from slower batch-based workflows to real-time decision-making powered by live data from applications, databases, sensors, logs and digital interactions.
Listeners are guided through the business context behind this shift, including the rise of big data, event-driven architectures and the need for faster analytics, AI responsiveness and operational agility. The episode also examines how data streaming platforms support AI use cases such as fraud detection, recommendation engines, predictive maintenance and AI agents that depend on current context.
From there, the discussion breaks down the four core architectural layers of a data streaming platform—source and ingestion, processing, destination and serving, and governance and management—while highlighting key characteristics such as scalability, fault tolerance, low latency and high throughput. The episode also reviews major open-source and managed technologies, including Apache Kafka, Apache Flink, Apache Spark, Confluent, Amazon Kinesis, Google Cloud Dataflow and Azure Stream Analytics.
Find more information at https://www.ibm.com/think/topics/data-streaming-platform
Narrated by Ian Smalley
Bahasa Inggeris
Amerika Syarikat
TRANSKRIP 🔗
Are you the producer of this podcast?
Add a podcast transcript
Need Audio-to-Text?
Transcribe with Listen411 in Just 60 Seconds
CARI EPISOD LEPAS
Cari episod lepas bagi Techsplainers by IBM .
EPISOD LAIN DALAM PODCAST INI
This episode of Techsplainers explores AI storage and shows how storage infrastructure changes when it is designed around modern AI workloads. Building on earlier discussions of data storage, storage architectures, flash, intelligent storage and edge storage, the episode shifts the focus to th ...
This episode of Techsplainers explores infrastructure automation as the broader goal behind infrastructure as code practices. It explains how automation allows organizations to provision, configure and manage infrastructure through repeatable, code-driven workflows instead of manual processes, ...
This episode of Techsplainers explores edge storage and why storing data closer to where it is created has become increasingly important in modern IT environments. As part of a larger data storage series, the episode explains how edge storage works in locations such as factories, retail stores, ...
This episode of Techsplainers explores immutable storage, the storage approach that protects data by preventing it from being changed or deleted for a defined period of time, or in some cases permanently. Positioned within a broader data storage series, the episode explains how immutable stora ...
This episode of Techsplainers explores how Terraform is used to implement infrastructure as code, focusing on the practical mechanics that turn infrastructure from a manual process into a repeatable engineering discipline. It explains how Terraform’s declarative model lets teams define the end ...
Penafian: Podcast dan karya seni yang dibenamkan pada halaman ini adalah daripada IBM, yang merupakan hak milik pemiliknya dan tidak bergabung dengan atau disokong oleh Listen Notes, Inc.
SUNTING
Terima kasih kerana membantu memastikan pangkalan data podcast dalam keadaan terkini.
