Browsing Tag

Reverse ETL

4 posts
Apache Iceberg Open Table Format for Data Lake Lakehouse Streaming wtih Kafka Flink Databricks Snowflake AWS GCP Azure
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  • 12.0K views
  • 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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The Shift Left Architecture
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  • 15.0K views
  • 8 minute read

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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  • 4.0K views
  • 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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Reverse ETL Anti Pattern vs Event Streaming with Apache Kafka
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  • 6.8K views
  • 9 minute read

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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