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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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The Shift Left Architecture
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The Shift Left Architecture – From Batch and Lakehouse to Real-Time Data Products with Data Streaming

Data integration is a hard challenge in every enterprise. Batch processing and Reverse ETL are common practices in a data warehouse, data lake or lakehouse. Data inconsistency, high compute cost, and stale information are the consequences. This blog post introduces a new design pattern to solve these problems: The Shift Left Architecture enables a data mesh with real-time data products to unify transactional and analytical workloads with Apache Kafka, Flink and Iceberg. Consistent information is handled with streaming processing or ingested into Snowflake, Databricks, Google BigQuery, or any other analytics / AI platform to increase flexibility, reduce cost and enable a data-driven company culture with faster time-to-market building innovative software applications.
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Snowflake and Apache Kafka Data Integration Anti Patterns Zero Reverse ETL
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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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The State of Data Streaming for Healthcare in 2023 with Apache Kafka and Flink
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The State of Data Streaming for Healthcare with Apache Kafka and Flink

This blog post explores the state of data streaming for the healthcare industry in 2023 powered by Apache Kafka and Apache Flink. IT modernization and innovation with pioneering technologies like sensors, telemedicine, or AI/machine learning are explored. I look at enterprise architectures and customer stories from Humana, Recursion, BHG (former Bankers Healthcare Group), and more. A complete slide deck and on-demand video recording are included.
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Data Streaming with Apache Kafka at Airlines - Lufthansa Case Study
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How Lufthansa uses Apache Kafka for Middleware and Analytics

Aviation and travel are notoriously vulnerable to social, economic, and political events, as well as the ever-changing expectations of consumers. The coronavirus was just a piece of the challenge. This post explores how Lufthansa leverages data streaming powered by Apache Kafka as cloud-native middleware for mission-critical data integration projects and as data fabric for AI/machine learning scenarios such as real-time predictions in fleet management. An interactive conversation with Lufthansa as an on-demand video is added at the end as a highlight if you want to learn more.
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Streaming ETL with Apache Kafka in Healthcare
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Streaming ETL with Apache Kafka in the Healthcare Industry

IT modernization and innovative new technologies change the healthcare industry significantly. This blog series explores how data streaming with Apache Kafka enables real-time data processing and business process automation. This is part three: Streaming ETL. Examples include Babylon Health and Bayer.
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Apache Camel vs Apache Kafka Comparison
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When to use Apache Camel vs. Apache Kafka?

Should I use Apache Camel or Apache Kafka for my next integration project? The question is very valid and comes up regularly. This blog post explores both open-source frameworks and explains the difference between application integration and event streaming. The comparison discusses when to use Kafka or Camel, when to combine them, when not to use them at all. A decision tree shows how you can quickly qualify out one for the other.
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Is Apache Kafka an iPaaS or is Event Streaming its own Software Category?

This post explores why Apache Kafka is the new black for integration projects, how Kafka fits into the discussion around cloud-native iPaaS solutions, and why event streaming is a new software category. A concrete real-world example shows the difference between event streaming and traditional integration platforms respectively iPaaS.
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Reverse ETL Anti Pattern vs Event Streaming with Apache Kafka
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When to Use Reverse ETL and when it is an Anti-Pattern

This blog post explores why software vendors (try to) introduce new solutions for Reverse ETL, when Reverse ETL is really needed, and how it fits into the enterprise architecture. The involvement of event streaming to process data in motion is a key piece of Reverse ETL for real-time use cases.
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