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Cloud-Native

34 posts
Telecom OSS Modernization with Data Streaming using Apache Kafka and Flink for Cloud-Native BSS and OTT Integration
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  • 7 minute read

Telecom OSS Modernization with Data Streaming: From Legacy Burden to Cloud-Native Agility

OSS is critical for service delivery in telecom, yet legacy platforms have become rigid and costly. They slow innovation just as 5G, cloud native networks and OTT partnerships demand agility. This article explores how a data streaming platform with Apache Kafka and Flink helps telcos modernize OSS step by step, cut costs, accelerate time to market and turn OSS into the real time backbone for AI and event driven operations.
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The Rise of Diskless Apache Kafka with Object Storage and No Brokers
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  • 8 minute read

The Rise of Diskless Kafka: Rethinking Brokers, Storage, and the Kafka Protocol

Apache Kafka has evolved from a data lake pipeline into the backbone of real-time transactional systems. The shift from broker-based storage to Tiered Storage and now to Diskless Kafka using cloud object storage redefines Kafka’s role. This blog explores the business value, technical architecture, and use cases of running Kafka without brokers, using the Kafka protocol as the foundation for scalable, cost-efficient event streaming.
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Apache Kafka Deployment Options - Serverless vs Self-Managed vs BYOC Bring Your Own Cloud
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  • 9 minute read

Deployment Options for Apache Kafka: Self-Managed, Fully-Managed / Serverless and BYOC (Bring Your Own Cloud)

BYOC (Bring Your Own Cloud) is an emerging deployment model for organizations looking to maintain greater control over their cloud environments. Unlike traditional SaaS models, BYOC allows businesses to host applications within their own VPCs to provide enhanced data privacy, security, and compliance. This approach leverages existing cloud infrastructure. It offers more flexibility for custom configurations, particularly for companies with stringent security needs. In the data streaming sector around Apache Kafka, BYOC is changing how platforms are deployed. Organizations get more control and adaptability for various use cases. But it is clearly NOT the right choice for everyone!
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The State of Data Streaming for the Public Sector in 2023
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  • 5 minute read

The State of Data Streaming for the Public Sector

This blog post explores the state of data streaming for the public sector and government. Data streaming provides consistency across all layers and allows integrating and correlating data in real-time at any scale. I look at public sector trends to explore how Apache Kafka helps as a business enabler, including case studies from the US Department of Defense (DoD), NASA, Deutsche Bahn (German Railway), and others. A complete slide deck and on-demand video recording are included.
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The State of Data Streaming for Digital Natives in 2023
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  • 8 minute read

The State of Data Streaming for Digital Natives in 2023

This blog post explores the state of data streaming in 2023 for digital natives born in the cloud. Data streaming allows integrating and correlating data in real-time at any scale to improve the most innovative applications leveraging Apache Kafka. I explore how data streaming helps as a business enabler, including customer stories from New Relic, Wix, Expedia, Apna, Grab, and more. A complete slide deck and on-demand video recording are included.
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The State of Data Streaming for Telco in 2023
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  • 7 minute read

The State of Data Streaming for Telco

This blog post explores the state of data streaming for the telco industry. The evolution of telco infrastructure, customer services, and new business models requires real-time end-to-end visibility, fancy mobile apps, and integration with pioneering technologies like 5G for low latency or augmented reality for innovation. Learn about customer stories from Dish Network, British Telecom, Globe Telecom, Swisscom, and more. A complete slide deck and on-demand video recording are included.
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Apache Kafka vs Redpanda Comparison
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  • 18 minute read

When to choose Redpanda instead of Apache Kafka?

Data streaming emerged as a new software category. It complements traditional middleware, data warehouse, and data lakes. Apache Kafka became the de facto standard. New players enter the market because of Kafka’s success. One of those is Redpanda, a lightweight Kafka-compatible C++ implementation. This blog post explores the differences between Apache Kafka and Redpanda, when to choose which framework, and how the Kafka ecosystem, licensing, and community adoption impact a proper evaluation.
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Best Practices for Data Analytics with AWS Azure Googel BigQuery Spark Kafka Confluent Databricks
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  • 10 minute read

Best Practices for Building a Cloud-Native Data Warehouse or Data Lake

The concepts and architectures of a data warehouse, a data lake, and data streaming are complementary to solving business problems. Unfortunately, the underlying technologies are often misunderstood, overused for monolithic and inflexible architectures, and pitched for wrong use cases by vendors. Let’s explore this dilemma in a blog series. This is part 5: Best Practices for Building a Cloud-Native Data Warehouse or Data Lake.
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Case Studies for Cloud Native Analytics with Data Warehouse Data Lake Data Streaming Lakehouse
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  • 7 minute read

Case Studies: Cloud-native Data Streaming for Data Warehouse Modernization

The concepts and architectures of a data warehouse, a data lake, and data streaming are complementary to solving business problems. Unfortunately, the underlying technologies are often misunderstood, overused for monolithic and inflexible architectures, and pitched for wrong use cases by vendors. Let’s explore this dilemma in a blog series. This is part 4: Case Studies for cloud-native data streaming and data warehouses.
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Data Warehouse and Data Lake Modernization with Data Streaming
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  • 9 minute read

Data Warehouse and Data Lake Modernization: From Legacy On-Premise to Cloud-Native Infrastructure

The concepts and architectures of a data warehouse, a data lake, and data streaming are complementary to solving business problems. Unfortunately, the underlying technologies are often misunderstood, overused for monolithic and inflexible architectures, and pitched for wrong use cases by vendors. Let’s explore this dilemma in a blog series. This is part 3: Data Warehouse Modernization: From Legacy On-Premise to Cloud-Native Infrastructure.
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