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Data pipelines

Dataconomy

Data pipelines are essential in our increasingly data-driven world, enabling organizations to automate the flow of information from diverse sources to analytical platforms. What are data pipelines? Purpose of a data pipeline Data pipelines serve various essential functions within an organization.

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Unlocking near real-time analytics with petabytes of transaction data using Amazon Aurora Zero-ETL integration with Amazon Redshift and dbt Cloud

Flipboard

While customers can perform some basic analysis within their operational or transactional databases, many still need to build custom data pipelines that use batch or streaming jobs to extract, transform, and load (ETL) data into their data warehouse for more comprehensive analysis. or a later version) database.

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Data sips and bites: An evening of data insights

Dataconomy

Hosted at one of Mindspace’s coworking locations, the event was a convergence of insightful talks and professional networking. Mindspace , a global coworking and flexible office provider with over 45 locations worldwide, including 13 in Germany, offered a conducive environment for this knowledge-sharing event.

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Hybrid Vs. Multi-Cloud: 5 Key Comparisons in Kafka Architectures

Smart Data Collective

Kafka And ETL Processing: You might be using Apache Kafka for high-performance data pipelines, stream various analytics data, or run company critical assets using Kafka, but did you know that you can also use Kafka clusters to move data between multiple systems. A three-step ETL framework job should do the trick.

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How Kakao Games automates lifetime value prediction from game data using Amazon SageMaker and AWS Glue

AWS Machine Learning Blog

The result of these events can be evaluated afterwards so that they make better decisions in the future. With this proactive approach, Kakao Games can launch the right events at the right time. Kakao Games can then create a promotional event not to leave the game. However, this approach is reactive.

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Real‑time data streaming architecture: The essential guide to AI‑ready pipelines and instant personalization

Dataconomy

Six core principles of a realtime streaming pipeline Drawing on Matus Tomleins stepbystep Implementation Guide: Building an AIReady Data Pipeline Architecture , you can anchor any streaming stack around six nonnegotiables: Explicit data requirements. Schemafirst design. Robust ingestion. Duallayer storage.

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Supercharging Your Data Pipeline with Apache Airflow (Part 2)

Heartbeat

Image Source —  Pixel Production Inc In the previous article, you were introduced to the intricacies of data pipelines, including the two major types of existing data pipelines. You might be curious how a simple tool like Apache Airflow can be powerful for managing complex data pipelines.