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8 Essential Data Pipeline Design Patterns You Should Know

Monte Carlo

Whether it’s customer transactions, IoT sensor readings, or just an endless stream of social media hot takes, you need a reliable way to get that data from point A to point B while doing something clever with it along the way. That’s where data pipeline design patterns come in. Lambda Architecture Pattern 4.

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Aggregator Leaf Tailer: An Alternative to Lambda Architecture for Real-Time Analytics

Rockset

That meant a system that was sufficiently nimble and powerful to execute fast SQL queries on raw data, essentially performing any needed transformations as part of the query step, and not as part of a complex data pipeline. Most processing in the Lambda architecture happens in the pipeline and not at query time.

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Data News — Week 23.12

Christophe Blefari

📺 Watch the full replay Here are my takeaways about the event: Mage and Kestra have been both developed with Airflow flaws in mind, especially about deployment complexity, reusability and data sharing between tasks. Out of the box Mage provide all-in-one web editor to write data pipelines with a great UX.

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Beyond Kafka: Conversation with Jark Wu on Fluss - Streaming Storage for Real-Time Analytics

Data Engineering Weekly

Confluent Tableflow can bridge Kafka and Iceberg data, but that is just a data movement that data integration tools like Fivetran or Airbyte can also achieve. On the other hand, Fluss is a Kappa Architecture ; it stores one copy of data and presents it as a stream or a table, depending on the use case.

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Exploring Processing Patterns For Streaming Data Integration In Your Data Lake

Data Engineering Podcast

If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days.

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Data Pipeline Architecture: Understanding What Works Best for You

Ascend.io

Data pipelines are integral to business operations, regardless of whether they are meticulously built in-house or assembled using various tools. As companies become more data-driven, the scope and complexity of data pipelines inevitably expand. Ready to fortify your data management practice?

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An Exploration Of The Expectations, Ecosystem, and Realities Of Real-Time Data Applications

Data Engineering Podcast

Data stacks are becoming more and more complex. This brings infinite possibilities for data pipelines to break and a host of other issues, severely deteriorating the quality of the data and causing teams to lose trust. Data stacks are becoming more and more complex.