Data Lineage for Data Streaming with OpenLineage Apache Kafka and Flink
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  • 11 minute read

Open Standards for Data Lineage: OpenLineage for Batch AND Streaming

One of the greatest wishes of companies is end-to-end visibility in their operational and analytical workflows. Where does data come from? Where does it go? To whom am I giving access to? How can I track data quality issues? The capability to follow the data flow to answer these questions is called data lineage. This blog post explores market trends, efforts to provide an open standard with OpenLineage, and how data governance solutions from vendors such as IBM, Google, Confluent and Collibra help fulfil the enterprise-wide data governance needs of most companies, including data streaming technologies such as Apache Kafka and Flink.
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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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  • 8 minute read

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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Streaming Analytics SQL API with Apache Kafka Confluent ClickHouse Tinybird
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  • 8 minute read

Apache Kafka and Tinybird (ClickHouse) for Streaming Analytics HTTP APIs

Apache Kafka became the de facto standard for data streaming. However, the combination of an event-driven architecture with request-response APIs is crucial for most enterprise architectures. This blog post explores how Tinybird innovates with a REST/HTTP layer on top of the open source analytics database ClickHouse in the cloud. Integrating Kafka with Tinybird, the benefits of fully managed services like Confluent Cloud, and customer stories from Factorial and FanDuel show why Kafka and analytics databases complement each other for more innovation and faster time-to-market.
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When NOT to use Apache Kafka
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  • 3 minute read

When NOT to Use Apache Kafka? (Lightboard Video)

Apache Kafka is the de facto standard for data streaming to process data in motion. With its significant adoption growth across all industries, I get a very valid question every week: When NOT to use Apache Kafka? What limitations does the event streaming platform have? When does Kafka simply not provide the needed capabilities? How to qualify Kafka out as it is not the right tool for the job? This blog post contains a lightboard video that gives you a twenty-minute explanation of the DOs and DONTs.
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The Past Present and Future of Stream Processing
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  • 12 minute read

The Past, Present and Future of Stream Processing

Stream processing has existed for decades. The adoption grows with open source frameworks like Apache Kafka and Flink in combination with fully managed cloud services. This blog post explores the past, present and future of stream processing, including the relation of machine learning and GenAI, streaming databases, and the integration between data streaming and data lakes with Apache Iceberg.
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JavaScript Node JS Apache Kafka for Full Stack Data Streaming in Event Driven Architecture
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  • 10 minute read

JavaScript, Node.js and Apache Kafka for Full-Stack Data Streaming

JavaScript is a pivotal technology for web applications. With the emergence of Node.js, JavaScript became relevant for both client-side and server-side development, enabling a full-stack development approach with a single programming language. Both Node.js and Apache Kafka are built around event-driven architectures, making them naturally compatible for real-time data streaming. This blog post explores open-source JavaScript Clients for Apache Kafka and discusses the trade-offs and limitations of JavaScript Kafka producers and consumers compared to stream processing technologies such as Kafka Streams or Apache Flink.
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