Data Engineering Weekly

DECEMBER 3, 2023

Visit rudderstack.com to learn more. Github writes an excellent blog to capture the current state of the LLM integration architecture. The blog is an excellent read to understand late-arriving data, backfilling, and incremental processing complications. I experienced similar drawbacks to what Lyft is talking about in Druid.

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Cloudera

NOVEMBER 29, 2023

In the dynamic world of machine learning operations (MLOps), staying ahead of the curve is essential. That’s why we’re excited to announce the Cloudera Model Registry as generally available, a game-changer that’s set to transform the way you manage your machine learning models in production environments.

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Machine Learning Metadata Cloud Project 82

RandomTrees

APRIL 12, 2024

Model retraining is a critical component of any robust MLOps stack, playing a fundamental role in ensuring the longevity and effectiveness of machine learning models. Model retraining, in essence, involves the creation of a new iteration of a machine learning model by rerunning the training pipeline with updated data.

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Machine Learning Datasets Systems Process 52

Databand.ai

JULY 17, 2023

The main objectives of DataOps include: Collaboration: Facilitating better communication between different teams involved in the data pipeline such as engineers, analysts, scientists, and business stakeholders. Learn more in our detailed guide to DataOps tools (coming soon) What Is MLOps?

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Christophe Blefari

JANUARY 20, 2023

Thank you for every recommendation you do about the blog or the Data News. In between the Hadoop era, the modern data stack and the machine learning revolution everyone—but me—waits for. Data Engineering job market in Stockholm — Alexander shared on a personal blog his job research in Sweden.

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DoorDash Engineering

NOVEMBER 28, 2023

It is amusing for a human being to write an article about artificial intelligence in a time when AI systems, powered by machine learning (ML), are generating their own blog posts. I frequently check Pipeline Runs and Sensor Ticks, but, often verify with Dagit.”

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Data Engineering Weekly

SEPTEMBER 24, 2023

Request Access to Data Validate with Exploration [link] Ramp: How Ramp Accelerated Machine Learning Development to Simplify Finance Ramp writes about its machine learning infrastructure and the choice of Metaflow for running the ML workload. It’s the same with machine learning.

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Christophe Blefari

JANUARY 20, 2024

Learn data engineering, all the references ( credits ) This is a special edition of the Data News. But right now I'm in holidays finishing a hiking week in Corsica 🥾 So I wrote this special edition about: how to learn data engineering in 2024. My advice on this point is to learn from others.

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Lyft Engineering

JUNE 28, 2023

In early 2022, Lyft already had a comprehensive Machine Learning Platform called LyftLearn composed of model serving , training , CI/CD, feature serving , and model monitoring systems. Lyft is a real-time marketplace and many teams benefit from enhancing their machine learning models with real-time signals.

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Knowledge Hut

MARCH 28, 2024

In this blog, I will explore Azure data engineer jobs and the top 10 job roles in this field where you can begin your career. This is the perfect time to search for Azure data engineer jobs for freshers and to start learning Azure for Dat a Engineers out there. Machine learning frameworks (e.g., Let’s get started.

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Data Engineering Data Engineer Engineering Data Warehouse 52

Data Engineering Podcast

JUNE 14, 2021

Summary When you build a machine learning model, the first step is always to load your data. In this episode Paige Roberts explains the benefits of pushing the machine learning processing into the database layer and the approach that Vertica has taken for their implementation. bayesian inference, deep learning, etc.)

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Machine Learning Database Data Warehouse Hadoop 100

Ripple Engineering

MARCH 30, 2023

Summary Managing the entire lifecycle of a machine learning (ML) model from inception to deployment in production can be a daunting task involving multiple systems and lots of moving parts. This blog outlines Ripple’s general design and approach for machine learning model lifecycle management using Databricks.

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Machine Learning AWS Metadata Data Collection 98

Data Engineering Weekly

OCTOBER 1, 2023

Dropbox writes an exciting approach to applying machine learning to identify the date formats in file names to standardize naming conventions. link] Agoda: How to Design and Maintain a High-Performing Data Pipeline I liked this article because it covers the basic 101 of data pipelines on maintaining a healthy infrastructure.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Pipeline 52

Cloudera

APRIL 9, 2021

This is part 4 in this blog series. This blog series follows the manufacturing and operations data lifecycle stages of an electric car manufacturer – typically experienced in large, data-driven manufacturing companies. The second blog dealt with creating and managing Data Enrichment pipelines. The ML Challenge.

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Machine Learning Manufacturing Data Collection Data Science 98

Data Engineering Podcast

JULY 10, 2022

In this episode Chris Riccomini shares his experiences building and scaling data operations at WePay and LinkedIn, as well as the lessons he has learned working with other teams as they automated their own systems. The Machine Learning Podcast helps you go from idea to production with machine learning.

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Data Engineering Weekly

JULY 2, 2023

This conference isn't just about talks - it's about collaboration, learning, and leading the way. 🌟 Submit Your Conference Talk Proposal Here LinkedIn: Declarative Data Pipelines with Hoptimator Data pipeline often requires multiple hop through the system to serve the end customers.

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Cloudera

JUNE 2, 2021

Data pipelines are in high demand in today’s data-driven organizations. As critical elements in supplying trusted, curated, and usable data for end-to-end analytic and machine learning workflows, the role of data pipelines is becoming indispensable.

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Databand.ai

JUNE 8, 2023

Observability in Your Data Pipeline: A Practical Guide Eitan Chazbani June 8, 2023 Achieving observability for data pipelines means that data engineers can monitor, analyze, and comprehend their data pipeline’s behavior. This ensures the reliability and accuracy of data-driven decision-making processes.

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Pinterest Engineering

MARCH 20, 2024

To achieve it, we developed LinkSage, which is a Graph Neural Network (GNN) based model that learns the semantics of landing page contents. The GNN can learn from nearby landing pages/nodes to improve accuracy. The text and crawled image features are split in the transformer encoder to let the model learn the cross attention of them.

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Machine Learning Engineering Software Engineer Software Engineering 72

Cloudera

JANUARY 6, 2021

Python is used extensively among Data Engineers and Data Scientists to solve all sorts of problems from ETL/ELT pipelines to building machine learning models. In this blog series, we will explain how to configure PySpark and HBase together for basic Spark use as well as for jobs maintained in CDSW. Introduction.

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Data Engineering Weekly

FEBRUARY 19, 2023

Data Engineering Weekly Is Brought to You by RudderStack RudderStack provides data pipelines that make it easy to collect data from every application, website, and SaaS platform, then activate it in your warehouse and business tools. The blog mostly focuses on how it optimizes the batch pipeline using Airflow and BigQuery.

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 52

Pinterest Engineering

SEPTEMBER 20, 2023

Machine Learning Engineer; Rohan Mahadev | Machine Learning Engineer II; Sujay Khandagale | Machine Learning Engineer II; Abhay Varmaraja | Machine Learning Engineer II Pinterest’s mission as a company is to bring everyone the inspiration to create a life they love. Pedro Silva | Sr.

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Building Pipeline-centric Machine Learning Datasets 84

Databand.ai

DECEMBER 13, 2022

Whether you’re a data engineering pro looking to stay up to date on the latest trends or new to the space and want to learn more, following the right leaders and joining the right conversations can make all the difference when it comes to plugging into the data engineering community. You’ve come to the right place.

Data Engineering 52

Data Engineering Data Engineer Engineering AWS 52

Cloudera

JUNE 26, 2023

For data engineering teams, Airflow is regarded as the best in class tool for orchestration (scheduling and managing end-to-end workflow) of pipelines that are built using programming languages like Python and SPARK. So which open source pipeline tool is better, NiFi or Airflow? Visit our Data and IT Leaders page to learn more.

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ETL Tools Programming Language Datasets Data Pipeline 75

Workfall

MAY 16, 2023

In this blog, we shall look at how we can create a Dockerfile to create an image with this executable. In this blog, we will cover: Docker AWS EC2 Hands-on Conclusion Docker We are going to containerize our application using Docker to make it easier to deploy on different target platforms.

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Lyft Engineering

APRIL 3, 2023

This blog post focuses on the scope and the goals of the recommendation system, and explores some of the most recent changes the Rider team has made to better serve Lyft’s riders. This blog mostly focuses on the mode selector to explain how rankings have evolved in the past years and briefly touches on the post request cross-sells.

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Systems Pipeline-centric Machine Learning Transportation 87

LinkedIn Engineering

MARCH 21, 2023

Our members and customers experience the insights from our Skills Graph in the tools they use everyday on LinkedIn, including Recruiter and LinkedIn Learning. Let’s look at “machine learning” for example. Our taxonomy includes machine learning (skill concept), the skill ID (a number assigned to each skill), aliases (e.g.

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databricks

FEBRUARY 15, 2023

As Machine Learning usage continues to rise across industries and applications, the sophistication of the Machine Learning pipelines is also increasing. Many of.

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Data Engineering Weekly

MARCH 19, 2023

The blog discusses implementing Type-2 SCD modeling and strategies to generate surrogate keys and bridge tables to handle many-to-many relationships. link] Open Questions on Type-2 modeling I keep thinking about the Type-2 SCD and the complexity of the data pipeline. Much of it focuses on model training, evaluation, and scoring.

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Data Engineering Weekly

MARCH 12, 2023

When I read articles for Data Engineering Weekly, there are many instances I say to myself, this is amazing work; I want to talk to the author to learn more from them or discuss it. We take three articles and do an in-depth analysis, and we hope to bring the author of the blogs to discuss more. We call it Data Engineering Weekly Radio.

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Data Engineering Data Engineer Engineering SQL 52

Databand.ai

AUGUST 30, 2023

These tools play a vital role in data preparation, which involves cleaning, transforming, and enriching raw data before it can be used for analysis or machine learning models. Data testing tools are software applications designed to assist data engineers and other professionals in validating, analyzing, and maintaining data quality.

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Knowledge Hut

MARCH 27, 2024

Over the last few decades, machine learning has fundamentally altered how systems function and decisions are made. These days, practically every industry effectively employs various machine learning ideas in one way or another. What is a Machine Learning cheat sheet?

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Machine Learning Deep Learning Algorithm Certification 52

Striim

NOVEMBER 17, 2023

In today’s data-driven world, the ability to leverage real-time data for machine learning applications is a game-changer. Meanwhile, Google BigQuery ML is a machine learning service provided by Google Cloud, allowing you to create and deploy machine learning models using SQL-like syntax directly within the BigQuery environment.

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Machine Learning Data Process PostgreSQL Process 52

Databand.ai

JULY 5, 2023

Data observability tools are software solutions that oversee, analyze, and improve the performance of data pipelines. Ensuring Data Quality Data accuracy is a critical factor in the success of any analytics or machine learning initiative. Faster Troubleshooting Data pipeline failures are expensive and damaging to organizations.

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Data Pipeline Data Lake Data Warehouse Datasets 52

Data Engineering Weekly

MARCH 10, 2024

Google: Croissant- a metadata format for ML-ready datasets Google Research introduced Croissant, a new metadata format designed to make datasets ML-ready by standardizing the format, facilitating easier use in machine learning projects. link] NVIDIA: What Is Trustworthy AI?

Data Engineering 52

Data Engineering Data Engineer Engineering Datasets 52

Databand.ai

AUGUST 30, 2023

These tools play a vital role in data preparation, which involves cleaning, transforming and enriching raw data before it can be used for analysis or machine learning models. There are several types of data testing tools. This is part of a series of articles about data quality. In this article: Why are data testing tools important?

Data Cleanse 52

Data Cleanse Data Pipeline Datasets Data Validation 52

Databand.ai

AUGUST 30, 2023

This may involve the use of data pipelines, data integration platforms, or data processing frameworks. This can include statistical analysis, machine learning, artificial intelligence, and other advanced analytics techniques.

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Architecture Data Ingestion Data Governance Data Cleanse 52