Driving Business Growth with Apache Kafka – Flink – Druid

Irisidea builds real-time data streaming, processing and analytics applications using Apache Kafka-Flink-Druid

Why Kafka+Flink+Druid?

Organizations are increasingly looking for real-time performance from data teams.

Thus, the entire data workflow must be reconsidered. That explains why so many companies are embracing Kafka-Flink-Druid as the default free and open-source data architecture for building real-time data streaming, processing and analytics applications.

Batch-Processing-Vs-Stream-Processing

A batch workflow, calls for waiting at all stages and cant satisfy real-time data processing and analytics demands.

From data delivery and processing and to data analysis, the batch workflow needs waiting at every step, such as delivering data to ETL tools, bulk processing, importing data to the data warehouse, and querying the data. This makes it hard for teams, working with data employing batch workflows, to satisfy real-time data processing and analytics demands.

When used together, Apache Kafka, Flink, and Druid form a real-time data architecture

That removes all of those wait states. Combining all of these tools enables a diverse set of real-time applications.

forging-Kafka-flink-druid-together

Architecting real-time applications

Interactive Query Engine

Kafka-Flink-Druid builds a data architecture capable of delivering data youthfulness, magnitude and dependable performance throughout the data workflow, from the occurrence to data analysis to application.

Tiering & QoS

These are complementary stream-native technologies that can handle an extensive variety of real-time use cases.

Optimized Data Format

This architecture simplifies the development of real-time applications like visibility, customer-facing insights, security detection/diagnostics, IoT and telemetry analytics, and tailored suggestions.

Setting up Kafka-Flink-Druid together

Success Stories

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