Confessions of a Data Guy

MARCH 29, 2024

Have you ever wondered at a high level what it’s like to build production-level data pipelines on Databricks? The post Building Databricks Data Pipelines 101 appeared first on Confessions of a Data Guy. What does it look like, what tools do you use?

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Snowflake

AUGUST 24, 2023

The data engineers are now able to conduct preemptive, automated data validation so the data scientists can say with confidence that all their data is correct, updated, and in a healthy state. The move significantly improved performance because it kept the data and the processing on a single platform. “We

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KDnuggets

SEPTEMBER 5, 2023

Build a streaming data pipeline using Formula 1 data, Python, Kafka, RisingWave as the streaming database, and visualize all the real-time data in Grafana.

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Data Engineering Podcast

JUNE 25, 2023

Contact Info tobymao on GitHub @captaintobs on Twitter Website Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? __init__ covers the Python language, its community, and the innovative ways it is being used. Closing Announcements Thank you for listening!

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Towards Data Science

MARCH 9, 2024

A gentle introduction to unit testing, mocking and patching for beginners Continue reading on Towards Data Science »

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Lyft Engineering

MARCH 6, 2024

DEED In this post, we’ll cover how Lyft upgrades Python at scale — 1500+ repos spanning 150+ teams — and the latest iteration of the tools and strategy we’ve built to optimize both the overall time to upgrade and the work required from our engineers. linters) and libraries as they drop old Pythons or only work on the newest Pythons (e.g.

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Data Engineering Podcast

SEPTEMBER 11, 2022

Building reliable data pipelines is a complex and costly undertaking with many layered requirements. In order to reduce the amount of time and effort required to build pipelines that power critical insights Manish Jethani co-founded Hevo Data. Data stacks are becoming more and more complex.

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Start Data Engineering

APRIL 22, 2024

Test types for data pipelines 2.2. pytest: A powerful Python library for testing 2.2.1. Use fixture to create fake data for testing 2.2.4. Introduction 2. Ensure the code’s logic is working as expected with tests 2.1. Set context, run code, check results & clean up 2.2.2.

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Towards Data Science

APRIL 6, 2023

Today’s post follows the same philosophy: fitting local and cloud pieces together to build a data pipeline. And, when it comes to data engineering solutions, it’s no different: They have databases, ETL tools, streaming platforms, and so on — a set of tools that makes our life easier (as long as you pay for them). not sponsored.

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Ascend.io

AUGUST 18, 2023

This not only jeopardizes the integrity and robustness of production environments but also compounds challenges for both data scientists and engineers. This article delves into the reasons behind our assertion: data science notebooks are not your best choice for production data pipelines. What Are Jupyter Notebooks?

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Workfall

AUGUST 14, 2023

Reading Time: 7 minutes In today’s data-driven world, efficient data pipelines have become the backbone of successful organizations. These pipelines ensure that data flows smoothly from various sources to its intended destinations, enabling businesses to make informed decisions and gain valuable insights.

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Data Engineering Podcast

APRIL 19, 2021

Summary Most of the time when you think about a data pipeline or ETL job what comes to mind is a purely mechanistic progression of functions that move data from point A to point B. Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code.

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Team Data Science

MAY 10, 2020

I'll use Python and Spark because they are the top 2 requested skills in Toronto. Kafka, while not in the top 5 most in demand skills, was still the most requested buffer technology requested which makes it worthwhile to include it.

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Ascend.io

MARCH 17, 2023

These engineering functions are almost exclusively concerned with data pipelines, spanning ingestion, transformation, orchestration, and observation — all the way to data product delivery to the business tools and downstream applications. Pipelines need to grow faster than the cost to run them.

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Ascend.io

MARCH 17, 2023

These engineering functions are almost exclusively concerned with data pipelines, spanning ingestion, transformation, orchestration, and observation — all the way to data product delivery to the business tools and downstream applications. Pipelines need to grow faster than the cost to run them.

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Dataquest

NOVEMBER 4, 2019

If you’ve ever wanted to learn Python online with streaming data, or data that changes quickly, you may be familiar with the concept of a data pipeline. Data pipelines allow you transform data from one representation to another through a series of steps. Run python log_generator.py.

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Ascend.io

MAY 24, 2023

This article is based on a presentation given by Sarwat Fatima , Principal Data Engineer at Biome Analytics, at the Data Pipeline Automation Summit 2023. Dive right into Sarwat’s full presentation at the Data Pipeline Automation Summit 2023. Table of Contents Data is the lifeblood of the healthcare industry.

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Ascend.io

JULY 28, 2023

Data pipelines are integral to business operations, regardless of whether they are meticulously built in-house or assembled using various tools. As companies become more data-driven, the scope and complexity of data pipelines inevitably expand. Ready to fortify your data management practice?

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Towards Data Science

MAY 2, 2023

I know the manual work you did last summer Photo by EJ Yao on Unsplash Introduction A few weeks ago, I wrote a post about developing a data pipeline using both on-premise and AWS tools. This post is part of my recent effort in bringing more cloud-oriented data engineering posts. Adding a module to the Glue job Our main.tf

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Monte Carlo

JUNE 14, 2023

In this post, we will help you quickly level up your overall knowledge of data pipeline architecture by reviewing: Table of Contents What is data pipeline architecture? Why is data pipeline architecture important? What is data pipeline architecture? Why is data pipeline architecture important?

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Data Engineering Podcast

JUNE 1, 2021

Summary Every part of the business relies on data, yet only a small team has the context and expertise to build and maintain workflows and data pipelines to transform, clean, and integrate it. RudderStack’s smart customer data pipeline is warehouse-first. Closing Announcements Thank you for listening!

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Towards Data Science

MARCH 6, 2023

On-premise and cloud working together to deliver a data product Photo by Toro Tseleng on Unsplash Developing a data pipeline is somewhat similar to playing with lego, you mentalize what needs to be achieved (the data requirements), choose the pieces (software, tools, platforms), and fit them together.

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Ascend.io

MAY 8, 2023

The costs of developing and running data pipelines are coming under increasing scrutiny because the bills for infrastructure and data engineering talent are piling up. For data teams, it is time to ask: “How can we have an impact on these runaway costs and still deliver unprecedented business value?”

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KDnuggets

NOVEMBER 18, 2021

This is the guide on how to build a serverless data pipeline on AWS with a Machine Learning model deployed as a Sagemaker endpoint.

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Data Engineering Podcast

SEPTEMBER 30, 2019

Summary Building an end-to-end data pipeline for your machine learning projects is a complex task, made more difficult by the variety of ways that you can structure it. Interview Introduction How did you get involved in the area of data management? Interview Introduction How did you get involved in the area of data management?

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Ascend.io

SEPTEMBER 14, 2023

As the demand to efficiently collect, process, and store data increases, data engineers have started to rely on Python to meet this escalating demand. In this article, our primary focus will be to unpack the reasons behind Python’s prominence in the data engineering domain. Why Python for Data Engineering?

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Grouparoo

OCTOBER 26, 2021

As a result, data has to be moved between the source and destination systems and this is usually done with the aid of data pipelines. What is a Data Pipeline? A data pipeline is a set of processes that enable the movement and transformation of data from different sources to destinations.

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Data Engineering Podcast

JANUARY 26, 2020

Summary Data pipelines are complicated and business critical pieces of technical infrastructure. What are some of the types of checks and assertions that can be made about a pipeline using Great Expectations? __init__ to learn about the Python language, its community, and the innovative ways it is being used.

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Ascend.io

MARCH 2, 2023

It won with ease of use by our analysts, the comprehensive scope of its automation, and the speed of end-to-end pipeline development. Terry Dougal Intelligent Data Pipelines at Audacy Instead of hiring more data engineers to patch over all the tooling with more bespoke code, Terry shifted gears and looked for a new solution.

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Data Engineering Podcast

SEPTEMBER 24, 2021

Summary Python has beome the de facto language for working with data. In this episode Ehsan Totoni explains how he built the Bodo project to bring the speed and processing power of HPC techniques to the Python data ecosystem without requiring any re-work. RSVP today – you don’t want to miss it!

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Data Engineering Podcast

MAY 18, 2024

Contact Info Ryan LinkedIn Paul LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? __init__ covers the Python language, its community, and the innovative ways it is being used. Closing Announcements Thank you for listening!

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Snowflake

JUNE 6, 2024

Snowflake Notebooks aim to provide a convenient, easy-to-use interactive environment that seamlessly blends Python, SQL and Markdown, as well as integrations with key Snowflake offerings, like Snowpark ML, Streamlit, Cortex and Iceberg tables. Discover valuable business insights through exploratory data analysis.

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Marc Lamberti

SEPTEMBER 29, 2023

Then you will set up and run your local development environment using the Astro CLI to create your first data pipeline. Concretely, you must create data pipelines to produce valuable data for later analytics or machine learning. To create, schedule, and monitor this kind of data pipeline you need a tool.

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Marc Lamberti

OCTOBER 4, 2023

Let’s say you have the following data pipeline: A quick explanation about the DAG: the first three tasks train machine learning models. It evaluates the condition that is itself in a Python callable function. The BranchPythonOperator is useful as it allows solving new use cases for you data pipelines.

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Ascend.io

MARCH 2, 2023

It won with ease of use by our analysts, the comprehensive scope of its automation, and the speed of end-to-end pipeline development. Terry Dougal Intelligent Data Pipelines at Audacy Instead of hiring more data engineers to patch over all the tooling with more bespoke code, Terry shifted gears and looked for a new solution.

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Marc Lamberti

SEPTEMBER 22, 2023

Stemming from this analogy, “you” is the orchestrator in data orchestration, and the recipe is the data pipeline. It was created in 2014 by Airbnb and has since been widely adopted by the data engineering community, primarily as it was the first orchestrator allowing to author data pipelines programmatically.

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Data Engineering Podcast

DECEMBER 16, 2018

As the organization grows and gains more customers, the requirements for that pipeline will change. In this episode Christian Heinzmann, Head of Data Warehousing at Grubhub, discusses the various requirements for data pipelines and how the overall system architecture evolves as more data is being processed.

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