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Using Streams Replication Manager Prefixless Replication for Kafka Topic Aggregation

Cloudera

For example, if there are separate clusters, and there are topics with the same purpose in the different clusters, then it is useful to aggregate the content into one topic. This blog post walks you through how you can use prefixless replication with Streams Replication Manager (SRM) to aggregate Kafka topics from multiple sources.

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Streams Replication Manager Prefixless Replication

Cloudera

Streams Replication Manager (SRM) is an enterprise-grade replication solution that enables fault tolerant, scalable, and robust cross-cluster Kafka topic replication. SRM replicates data at high performance and keeps topic properties in sync across clusters. This makes it difficult to manage multiple clusters.

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Druid Deprecation and ClickHouse Adoption at Lyft

Lyft Engineering

In this particular blog post, we explain how Druid has been used at Lyft and what led us to adopt ClickHouse for our sub-second analytic system. External dependencies for Druid were managed by our persistence teams and Amazon S3 was utilized for deep storage of our segments. Written by Ritesh Varyani and Jeana Choi at Lyft.

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API-First Approach to Kafka Topic Creation

DoorDash Engineering

DoorDash’s Engineering teams revamped Kafka Topic creation by replacing a Terraform/Atlantis based approach with an in-house API, Infra Service. DoorDash’s Real-Time Streaming Platform, or RTSP, team is under the Data Platform organization and manages over 2,500 Kafka Topics across five clusters.

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Current 2023 Announcements

Jesse Anderson

A few examples: Everything should use Kafka – No, it shouldn’t. Kafka isn’t for everyone and everything. Kafka is easy – It’s not that simple. Confluent Cloud makes Kafka operations easier but doesn’t change the difficulty for architecture or development. The talks went from ksqlDB and Kafka Streams to Flink.

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Accelerating Deployments of Streaming Pipelines – Announcing Data in Motion on Kubernetes

Cloudera

Organizations are challenged today to become both more data driven and more nimble to adapt quickly to changing conditions. These challenges are the driving forces behind much of their digital transformation or “modernization” efforts. This new release will deliver both of those strategic capabilities.

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Staying in the Zone: How DoorDash used a service mesh to manage  data transfer, reducing hops and cloud spend

DoorDash Engineering

In this blog post, we describe the journey DoorDash took using a service mesh to realize data transfer cost savings without sacrificing service quality. Kubernetes cluster deployment: Each cell consists of multiple Kubernetes clusters; each microservice is deployed exclusively to one cluster within a given cell.

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