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Data Engineering Weekly #196

Data Engineering Weekly

The challenges around memory, data size, and runtime are exciting to read. Sampling is an obvious strategy for data size, but the layered approach and dynamic inclusion of dependencies are some key techniques I learned with the case study. This count helps to ensure data consistency when deleting and compacting segments.

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5 Free Courses to Master Data Engineering

KDnuggets

Data engineers must prepare and manage the infrastructure and tools necessary for the whole data workflow in a data-driven company.

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New Fivetran connector streamlines data workflows for real-time insights

ThoughtSpot

Those coveted insights live at the end of a process lovingly known as the data pipeline. The pathway from ETL to actionable analytics can often feel disconnected and cumbersome, leading to frustration for data teams and long wait times for business users. Keep reading to see how it works. What is a SpotApp?

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Snowflake’s New Python API Empowers Data Engineers to Build Modern Data Pipelines with Ease

Snowflake

In today’s data-driven world, developer productivity is essential for organizations to build effective and reliable products, accelerate time to value, and fuel ongoing innovation. This allows your applications to handle large data sets and complex workflows efficiently.

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Data Engineering Weekly #191

Data Engineering Weekly

Airbnb: Sandcastle - data/AI apps for everyone Product ideas powered by data and AI must go through rapid iteration on shareable, lightweight live prototypes instead of static proposals. link] Grab: Enabling conversational data discovery with LLMs at Grab.

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Effective Pandas Patterns For Data Engineering

Data Engineering Podcast

Summary Pandas is a powerful tool for cleaning, transforming, manipulating, or enriching data, among many other potential uses. As a result it has become a standard tool for data engineers for a wide range of applications. The only thing worse than having bad data is not knowing that you have it.

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Establish A Single Source Of Truth For Your Data Consumers With A Semantic Layer

Data Engineering Podcast

Summary Maintaining a single source of truth for your data is the biggest challenge in data engineering. Different roles and tasks in the business need their own ways to access and analyze the data in the organization. Dagster offers a new approach to building and running data platforms and data pipelines.

Data Lake 162