Browsing Tag

kafka

179 posts
Multi-Cloud Replication in Real-Time with Apache Kafka and Cluster Linking
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  • 5 minute read

Multi-Cloud Replication in Real-Time with Apache Kafka and Cluster Linking

Multiple Apache Kafka clusters are the norm; not an exception anymore. Hybrid integration and multi-cloud replication for migration or disaster recovery are common use cases. This blog post explores a real-world success story from financial services around the transition of a large traditional bank from on-premise data centers into the public cloud for multi-cloud data sharing between AWS and Azure.
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One Apache Kafka Cluster Type Does NOT Fit All Use Cases
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  • 13 minute read

Apache Kafka Cluster Type Deployment Strategies

Organizations start their data streaming adoption with a single Apache Kafka cluster to deploy the first use cases. The need for group-wide data governance and security but different SLAs, latency, and infrastructure requirements introduce new Kafka clusters. Multiple Kafka clusters are the norm, not an exception. Use cases include hybrid integration, aggregation, migration, and disaster recovery. This blog post explores real-world success stories and cluster strategies for different Kafka deployments across industries.
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Apache Iceberg Open Table Format for Data Lake Lakehouse Streaming wtih Kafka Flink Databricks Snowflake AWS GCP Azure
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  • 11 minute read

Apache Iceberg – The Open Table Format for Lakehouse AND Data Streaming

An open table format framework like Apache Iceberg is essential in the enterprise architecture to ensure reliable data management and sharing, seamless schema evolution, efficient handling of large-scale datasets and cost-efficient storage. This blog post explores market trends, adoption of table format frameworks like Iceberg, Hudi, Paimon, Delta Lake and XTable, and the product strategy of leading vendors of data platforms such as Snowflake, Databricks (Apache Spark), Confluent (Apache Kafka / Flink), Amazon Athena and Google BigQuery.
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Energy Trading with Apache Kafka and Flink at Uniper ReAlto Powerledger
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  • 9 minute read

Energy Trading with Apache Kafka and Flink

Energy trading and data streaming are connected because real-time data helps traders make better decisions in the fast-moving energy markets. This data includes things like price changes, supply and demand, smart IoT meters and sensors, and weather, which help traders react quickly and plan effectively. As a result, data streaming with Apache Kafka and Apache Flink makes the market clearer, speeds up information sharing, and improves forecasting and risk management. This blog post explores the use cases and architectures for scalable and reliable real-time energy trading, including real-world deployments from Uniper, re.alto and Powerledger.
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RAG and Kafka Flink to Prevent Hallucinations in GenAI
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Real-Time GenAI with RAG using Apache Kafka and Flink to Prevent Hallucinations

How do you prevent hallucinations from large language models (LLMs) in GenAI applications? LLMs need real-time, contextualized, and trustworthy data to generate the most reliable outputs. This blog post explains how RAG and a data streaming platform with Apache Kafka and Flink make that possible. A lightboard video shows how to build a context-specific real-time RAG architecture. Also, learn how the travel agency Expedia leverages data streaming with Generative AI using conversational chatbots to improve the customer experience and reduce the cost of service agents.
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My Data Streaming Journey with Kafka and Flink - 7 Years at Confluent
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  • 11 minute read

My Data Streaming Journey with Kafka & Flink: 7 Years at Confluent

Time flies… I joined Confluent seven years ago when Apache Kafka was mainly used by a few tech giants and the company had ~100 employees. This blog post explores my data streaming journey, including Kafka becoming a de facto standard for over 100,000 organizations, Confluent doing an IPO on the NASDAQ stock exchange, 5000+ customers adopting a data streaming platform, and emerging new design approaches and technologies like data mesh, GenAI, and Apache Flink. I look at the past, present and future of my personal data streaming journey. Both, from the evolution of technology trends and the journey as a Confluent employee that started in a Silicon Valley startup and is now part of a global software and cloud company.
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Apache Kafka and Snowflake Cost Efficiency and Data Governance
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  • 10 minute read

Apache Kafka + Flink + Snowflake: Cost Efficient Analytics and Data Governance

Snowflake is a leading cloud data warehouse and transitions into a data cloud that enables various use cases. The major drawback of this evolution is the significantly growing cost of the data processing. This blog post explores how data streaming with Apache Kafka and Apache Flink enables a “shift left architecture” where business teams can reduce cost, provide better data quality, and process data more efficiently. The real-time capabilities and unification of transactional and analytical workloads using Apache Iceberg’s open table format enable new use cases and a best of breed approach without a vendor lock-in and the choice of various analytical query engines like Dremio, Starburst, Databricks, Amazon Athena, Google BigQuery, or Apache Flink.
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Snowflake with Apache Kafka and Iceberg Connector
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  • 8 minute read

Snowflake Data Integration Options for Apache Kafka (including Iceberg)

The integration between Apache Kafka and Snowflake is often cumbersome. Options include near real-time ingestion with a Kafka Connect connector, batch ingestion from large files, or leveraging a standard table format like Apache Iceberg. This blog post explores the alternatives and discusses its trade-offs. The end shows how data streaming helps with hybrid architectures where data needs to be ingested from the private data center into Snowflake in the public cloud.
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Snowflake and Apache Kafka Data Integration Anti Patterns Zero Reverse ETL
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  • 9 minute read

Snowflake Integration Patterns: Zero ETL and Reverse ETL vs. Apache Kafka

Snowflake is a leading cloud-native data warehouse. Integration patterns include batch data integration, Zero ETL and near real-time data ingestion with Apache Kafka. This blog post explores the different approaches and discovers its trade-offs. Following industry recommendations, it is suggested to avoid anti-patterns like Reverse ETL and instead use data streaming to enhance the flexibility, scalability, and maintainability of enterprise architecture.
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Google Apache Kafka for BigQuery GCP Cloud Service
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When (Not) to Choose Google Managed Service for Apache Kafka?

Google announced its Apache Kafka for BigQuery cloud service at its conference Google Cloud Next 2024 in Las Vegas. Welcome to the data streaming club joining Amazon, Microsoft, IBM, Oracle, Confluent, and others. This blog post explores this new managed Kafka offering for GCP, reviews the current status of the data streaming landscape, and shares some criteria to evaluate when Kafka in general and Google Apache Kafka in particular should (not) be used.
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