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A few months ago I wrote a blog post about event skew and how dangerous it is for a stateful streaming job. Since it was a high-level explanation, I didn't cover Apache Spark Structured Streaming deeply at that moment. Now the watermark topic is back to my learning backlog and it's a good opportunity to return to the event skew topic and see the dangers it brings for Structured Streaming stateful jobs.
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Do types actually make developers more productive? Or is it just more typing on the keyboard? To answer that question were revisiting Diff Authoring Time (DAT) how Meta measures how long it takes to submit changes to a codebase. DAT is just one of the ways e measure developer productivity and this latest episode of the Meta Tech Podcast takes a look at two concrete use cases for DAT, including a type-safe mocking framework in Hack.
Do types actually make developers more productive? Or is it just more typing on the keyboard? To answer that question were revisiting Diff Authoring Time (DAT) how Meta measures how long it takes to submit changes to a codebase. DAT is just one of the ways e measure developer productivity and this latest episode of the Meta Tech Podcast takes a look at two concrete use cases for DAT, including a type-safe mocking framework in Hack.
A Deep Dive into Databricks Labs’ DQX: The Data Quality Game Changer for PySpark DataFrames Recently, a LinkedIn announcement caught my eyeand honestly, it had me on the edge of my seat. Databricks Labs has unveiled DQX, a Python-based Data Quality framework explicitly designed for PySpark DataFrames. Finally, a Dedicated Data Quality Tool for PySpark […] The post PySpark Data Quality on Databricks with DQX. appeared first on Confessions of a Data Guy.
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In this new webinar, Tamara Fingerlin, Developer Advocate, will walk you through many Airflow best practices and advanced features that can help you make your pipelines more manageable, adaptive, and robust. She'll focus on how to write best-in-class Airflow DAGs using the latest Airflow features like dynamic task mapping and data-driven scheduling!
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With the ever-growing focus on GenAI, many legacy BI tools have failed to invest in the analyst. By focusing solely on AI experiences for business teams, theyve alienated data teams, relegating analysts to disjointed tools and data silos. When in reality, businesses still need people who can help decision-makers assess messy data to diagnose and evaluate business problems.
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Large enterprises face unique challenges in optimizing their Business Intelligence (BI) output due to the sheer scale and complexity of their operations. Unlike smaller organizations, where basic BI features and simple dashboards might suffice, enterprises must manage vast amounts of data from diverse sources. What are the top modern BI use cases for enterprise businesses to help you get a leg up on the competition?
PodPrep AI, an AI-powered research assistant, leverages EDA and real-time streaming data using Confluent and Flink, in order to help its author with podcast preparation.
Questions that guide architectural decisions to balance functional requirements with non-functional ones, like latency and scalability Continue reading on Towards Data Science
Angel Vargas | Software Engineer, API Platform; Swati Kumar | Software Engineer, API Platform; Chris Bunting | Engineering Manager, APIPlatform The inside of the Pinterest lobby in Mexico City, showing a patterned ceiling, a receptionist deck with a plant on it, a light above it, and a gallery of images of pins youd find on Pinterest, behind it. To the left, a glowing Pinterest P sign hovers in front of a glasswall.
Whether you’re creating complex dashboards or fine-tuning large language models, your data must be extracted, transformed, and loaded. ETL and ELT pipelines form the foundation of any data product, and Airflow is the open-source data orchestrator specifically designed for moving and transforming data in ETL and ELT pipelines. This eBook covers: An overview of ETL vs.
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Read Time: 2 Minute, 33 Second Snowflakes PARSE_DOCUMENT function revolutionizes how unstructured data, such as PDF files, is processed within the Snowflake ecosystem. Traditionally, this function is used within SQL to extract structured content from documents. However, Ive taken this a step further, leveraging Snowpark to extend its capabilities and build a complete data extraction process.
Speaker: Jay Allardyce, Deepak Vittal, Terrence Sheflin, and Mahyar Ghasemali
As we look ahead to 2025, business intelligence and data analytics are set to play pivotal roles in shaping success. Organizations are already starting to face a host of transformative trends as the year comes to a close, including the integration of AI in data analytics, an increased emphasis on real-time data insights, and the growing importance of user experience in BI solutions.
Twenty years ago, data was little more than fuel for forecasting. A few marketing insights here. A couple financial reports there. Today, data doesnt simply support your productsmore often than not, it is the product. In the age of AI, data isnt just another cost centerits a value creator. Data teams arent service providerstheyre essential technology partners.
DareData will close 2024 with a 5% revenue growth compared to 2023. At first glance, given the rapid growth in our market, one might be tempted to classify this year as underwhelming. However, 2024 has been a transformative year for us. We started the year as a 100% consulting business. Consulting is highly dependent on people, and in small boutique firms like ours, this often means being heavily reliant on the partners.
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