This story was originally published on HackerNoon at: https://hackernoon.com/how-to-enable-auto-start-for-apache-dolphinscheduler.
To set DolphinScheduler to start automatically upon system boot, you typically need to configure it as a system service.
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This story was written by: @williamguo. Learn more about this writer by checking @williamguo's about page, and for more stories, please visit hackernoon.com.

To set DolphinScheduler to start automatically upon system boot, you typically need to configure it as a system service. The following are general steps, which may vary depending on your operating system.

This story was originally published on HackerNoon at: https://hackernoon.com/benchmarking-apache-kafka-performance-per-price.
This is a study comparing environments for Apache Kafka. The ultimate goal is to find the most effective setup and achieve the best price-performance ratio.
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This story was written by: @mishaepikhin. Learn more about this writer by checking @mishaepikhin's about page, and for more stories, please visit hackernoon.com.

ARM rocks. Modern expensive architecture does not always mean “better”.

This story was originally published on HackerNoon at: https://hackernoon.com/when-and-when-not-to-use-apache-kafka-as-a-database.
Discover how Apache Kafka’s data retention and querying capabilities make it similar to a database and learn when to use Kafka for database-like use cases.
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This story was written by: @aahil. Learn more about this writer by checking @aahil's about page, and for more stories, please visit hackernoon.com.

Apache Kafka, while not a traditional database, has database-like properties such as data retention and querying capabilities. This article explores when Kafka can be used for database-like purposes and when it is best suited as a streaming platform.

This story was originally published on HackerNoon at: https://hackernoon.com/a-leaders-guide-to-data-driven-success.
Transform data from a source of frustration into a powerful business tool with this practical guide for executives.
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This story was written by: @liorb. Learn more about this writer by checking @liorb's about page, and for more stories, please visit hackernoon.com.

Despite having more information than ever, making informed decisions seems increasingly challenging. This guide is designed to help you transform data from a source of frustration into a powerful tool for driving business growth. From my own experience, I've seen professionals dedicating up to 50% of their workweek to validating data.

This story was originally published on HackerNoon at: https://hackernoon.com/seamlessly-migrate-your-on-premise-data-pipeline-to-azure-with-these-key-steps.
Scaling AI/ML Data Needs: Migrating On-Premise Data Engineering Workloads to Azure Cloud
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This story was written by: @amlanpatnaik. Learn more about this writer by checking @amlanpatnaik's about page, and for more stories, please visit hackernoon.com.

This guide details the process of migrating an on-premise Cloudera data system to Azure, covering key considerations, challenges, and best practices to ensure a smooth and secure transition.

This story was originally published on HackerNoon at: https://hackernoon.com/data-collection-for-product-managers.
Discover how product managers can bridge the gap between intuition and data to optimize product improvement with best practices and real-world examples.
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This story was written by: @carolinagarcia. Learn more about this writer by checking @carolinagarcia's about page, and for more stories, please visit hackernoon.com.

Discover how product managers can bridge the gap between intuition and data to optimize product improvement. This guide explores the importance of data-driven decision-making, offering best practices and real-world examples from companies like NuBank, Monzo, Deliveroo, and Booking.com. Learn how to acquire insights from customer feedback, track performance metrics, monitor market trends, and refine product roadmaps through iterative experimentation. Become a data-driven PM and create products that users will love.

This story was originally published on HackerNoon at: https://hackernoon.com/data-collection-for-product-managers.
Discover how product managers can bridge the gap between intuition and data to optimize product improvement with best practices and real-world examples.
Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data-collection, #startups, #data-product-management, #data-driven-insights, #data-driven-decision-making, #product-manager, #product-management-tips, #how-to-collect-data, and more.

This story was written by: @carolinagarcia. Learn more about this writer by checking @carolinagarcia's about page, and for more stories, please visit hackernoon.com.

Discover how product managers can bridge the gap between intuition and data to optimize product improvement. This guide explores the importance of data-driven decision-making, offering best practices and real-world examples from companies like NuBank, Monzo, Deliveroo, and Booking.com. Learn how to acquire insights from customer feedback, track performance metrics, monitor market trends, and refine product roadmaps through iterative experimentation. Become a data-driven PM and create products that users will love.

This story was originally published on HackerNoon at: https://hackernoon.com/leveraging-data-granularity-distribution-and-modeling-for-effective-product-management.
These three fundamental concepts are exceptionally needed for being able to use data to enhance product strategy.
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This story was written by: @gevorgkazaryan. Learn more about this writer by checking @gevorgkazaryan's about page, and for more stories, please visit hackernoon.com.

Granularity determines the level of detail available in the data, which directly impacts what you can observe and analyze. For instance, finer granularity provides more detailed insights but may require more sophisticated handling and processing techniques. Distribution helps identify the patterns and spread of data, which is critical for selecting the appropriate analysis techniques and ensuring the accuracy of predictive models. Data Modeling uses the insights gained from understanding granularity and distribution to build predictive or descriptive models that inform decision-making and strategy.