Show Me How: Build Streaming Data Pipelines for Real-Time Data Warehousing | Register Today
While open source Apache Kafka provides the technical foundation for collecting and processing real-time data streams, it doesn’t offer a simple solution for implementing business use cases fully end-to-end. Instead, enterprises attempt to solve this by custom coding or integrating several distributed systems together; these architectures often include one system for event capture, one for event storage, one for stream processing, and one traditional database for serving point-in-time queries. Even then, this doesn’t account for the wide range of capabilities needed for enterprise-wide event streaming. Confluent allows enterprises to:
Problem: Stream processing requires development teams to adopt a sophisticated mental model and understand how to create “always-running” stream processing applications. Many times the logic will be complex or cumbersome to express in Java, deterring many development teams of taking advantage of stream processing. SQL, on the other hand, is a high-level language that is easier to write in because it is declarative. To help development teams take advantage of the power of stream processing, Confluent allows enterprises to:
Problem: Typically when moving data in and out of Kafka, development teams will need to either develop their own connectors using the Kafka Connect framework or leverage existing open source connectors already built by the community. The challenge is the time and effort required to build and maintain a new connector. On average, it takes 3-6 months for a FTE, who is a Kafka expert, to design, develop, test, and maintain one connector. And if something were to break, none of the connectors have support, so teams will spend more time fixing custom code than on innovation. Confluent allows enterprises to:
Ensure your data architecture is future-proof, elastic, global, secure, and reliable with a complete fully-managed event streaming platform.
Build applications that respond immediately to events. Receive real-time push updates, or pull current state on demand.
Quickly integrate a diverse set of data sources and sinks with Kafka to de-risk and accelerate time to market.
Discover and use pre-built expertly designed assets from the Confluent ecosystem.
Enable data governance by programmatically validating and enforcing schemas at broker-level.
Build streaming apps with same ease and familiarity as traditional apps on a relational database.
Delivers Kafka-native connectivity into IoT devices without the need for intermediate MQTT brokers.
A single online marketplace to easily browse, search, and filter from 120+ pre-built connectors.
Enable application development compatibility by enforcing consistent data formats.
How do you distribute real-time events across the globe and make them accessible from anywhere?
How do you reduce the risk of security breaches that can result in app downtime or costly data leaks throughout the Kafka operational lifecycle?
How do you maximize the value of your real-time data and harness the full power of event streaming?
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