Netflix Tech

DECEMBER 3, 2022

by Jasmine Omeke , Obi-Ike Nwoke , Olek Gorajek Intro This post is for all data practitioners, who are interested in learning about bootstrapping, standardization and automation of batch data pipelines at Netflix. You may remember Dataflow from the post we wrote last year titled Data pipeline asset management with Dataflow.

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Netflix Tech

NOVEMBER 14, 2023

By Abhinaya Shetty , Bharath Mummadisetty At Netflix, our Membership and Finance Data Engineering team harnesses diverse data related to plans, pricing, membership life cycle, and revenue to fuel analytics, power various dashboards, and make data-informed decisions.

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Data Engineering Weekly

JUNE 16, 2024

Experience Enterprise-Grade Apache Airflow Astro augments Airflow with enterprise-grade features to enhance productivity, meet scalability and availability demands across your data pipelines, and more. link] Picnic: Open-sourcing dbt-score: lint model metadata with ease!

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Data Engineering Weekly

AUGUST 25, 2024

Take Astro (the fully managed Airflow solution) for a test drive today and unlock a suite of features designed to simplify, optimize, and scale your data pipelines. Try For Free → Conference Alert: Data Engineering for AI/ML This is a virtual conference at the intersection of Data and AI.

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Data Engineering Weekly

NOVEMBER 3, 2024

Data Engineering Weekly readers get 15% discount by registering the following link, [link] Gustavo Akashi: Building data pipelines effortlessly with a DAG Builder for Apache Airflow Every code-first data workflow grew into a UI-based or Yaml-based workflow.

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Data Engineering Podcast

NOVEMBER 10, 2021

Summary A significant source of friction and wasted effort in building and integrating data management systems is the fragmentation of metadata across various tools. Data engineers struggling with unreliable data need look no further than Monte Carlo, the world’s first end-to-end, fully automated Data Observability Platform!

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Data Engineering Weekly

SEPTEMBER 22, 2024

Uber: QueryGPT – Natural Language to SQL Using Generative AI Companies are increasingly adopting the natural language interface to interact with the enterprise data. link] Sponsored: IMPACT - Have you seen the agenda for IMPACT: The Data Observability Summit yet?

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Snowflake

JANUARY 25, 2023

Over the multiple decades I’ve spent in the data industry, one observation has remained nearly constant: the majority of the work in building a data analytics platform revolves around data transformations (what we used to call “the T in ETL or ELT”). We cannot scale our expertise as fast as we can scale the Data Cloud.

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Data Engineering Weekly

SEPTEMBER 15, 2024

Whether or not Data Mesh is a separate product is debatable, but it is certainly an impactful framework for scaling data platforms. link] Miro: Data Products Reliability - The Power of Metadata Miro writes about its adoption of Data Contracts.

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Data Engineering Weekly

OCTOBER 6, 2024

The learning goes back to the fundamentals of pipeline design principles. Regularly review if pipelines are still required. Minimize the data used in pipelines, aka do incremental data pipeline design. Optimize pipeline schedules. Filter data effectively to make sure the query uses partition pruning.

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Data Science Blog: Data Engineering

MAY 20, 2024

Continuous Integration and Continuous Delivery (CI/CD) for Data Pipelines: It is a Game-Changer with AnalyticsCreator! The need for efficient and reliable data pipelines is paramount in data science and data engineering. They transform data into a consistent format for users to consume.

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Data Engineering Weekly

JULY 7, 2024

Experience Enterprise-Grade Apache Airflow Astro augments Airflow with enterprise-grade features to enhance productivity, meet scalability and availability demands across your data pipelines, and more. The author highlights Paimon’s consistency model by examining the metadata model.

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Ascend.io

APRIL 26, 2023

Data teams worldwide are building data pipelines with the point solutions that make up the “modern data stack.” Pipelines built with this approach are slow and require constant manual reprogramming and updates. For a deep dive into this level of automation, take a look at our What Is Data Pipeline Automation paper.

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Data Engineering Podcast

OCTOBER 15, 2021

Summary The binding element of all data work is the metadata graph that is generated by all of the workflows that produce the assets used by teams across the organization. The DataHub project was created as a way to bring order to the scale of LinkedIn’s data needs. How is the governance of DataHub being managed?

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Data Engineering Podcast

JUNE 16, 2024

In this episode Kevin Liu shares some of the interesting features that they have built by combining those technologies, as well as the challenges that they face in supporting the myriad workloads that are thrown at this layer of their data platform. What are the other systems that feed into and rely on the Trino/Iceberg service?

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Data Engineering Podcast

JANUARY 30, 2022

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. You can observe your pipelines with built in metadata search and column level lineage.

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Ascend.io

JULY 23, 2024

Data engineering is evolving rapidly, driven by advancements in AI and automation. This article (based on the webinar below) explores how data engineering teams can leverage AI and automation to enhance productivity and tackle current challenges. This sobering statistic has haunted well-intentioned data teams for years.

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Ascend.io

FEBRUARY 28, 2024

The rise of generative AI is changing more than just technology; it’s reshaping our professional landscapes — and yes, data engineering is directly experiencing the impact. How does AI recalibrate the workload and priorities of data teams? How can data engineers harness the power of AI?

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Ascend.io

SEPTEMBER 25, 2023

European data teams can now build and automate data pipelines with the ease of a managed SaaS offering while meeting stringent data sovereignty requirements. As Ascend’s partners Snowflake and Databricks continue to expand in the region, the demand for scalable data pipeline solutions has skyrocketed.

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Data Engineering Weekly

DECEMBER 18, 2023

Lorin Hochstein: “Human error” means they don’t understand how the system worked The post is not directly related to Data Engineering but system operations in general. It is great to see an internal tech talk with a series focus on data engineering. Visit rudderstack.com to learn more.

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Data Engineering Podcast

MAY 22, 2022

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode.

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Data Engineering Podcast

AUGUST 28, 2022

Summary The dream of every engineer is to automate all of their tasks. For data engineers, this is a monumental undertaking. Orchestration engines are one step in that direction, but they are not a complete solution. Atlan is the metadata hub for your data ecosystem.

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Data Engineering Weekly

MARCH 10, 2024

Pradheep Arjunan - Shared insights on AZ's journey from on-prem to the cloud data warehouses. 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.

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Ascend.io

MARCH 17, 2023

As a result, stakeholders limit their reliance on data, making decisions based on gut instinct rather than facts. To shift this harmful approach, companies need to make fundamental changes to their data engineering function and start running at speed and with agility. Pipelines need to grow faster than the cost to run them.

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Ascend.io

MARCH 17, 2023

As a result, stakeholders limit their reliance on data, making decisions based on gut instinct rather than facts. To shift this harmful approach, companies need to make fundamental changes to their data engineering function and start running at speed and with agility. Pipelines need to grow faster than the cost to run them.

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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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Ascend.io

NOVEMBER 28, 2023

The terms ‘data orchestration’ and ‘data pipeline orchestration’ are often used interchangeably, yet they diverge significantly in function and scope. Data orchestration refers to a wide collection of methods and tools that coordinate any and all types of data-related computing tasks.

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Data Engineering Weekly

AUGUST 18, 2024

Take Astro (the fully managed Airflow solution) for a test drive today and unlock a suite of features designed to simplify, optimize, and scale your data pipelines. Hudi maintains the current snapshot in the metadata table. Try Fully Managed Apache Airflow for FREE Run Airflow without the hassle and management complexity.

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Data Engineering Podcast

AUGUST 13, 2022

In this episode Shinji Kim discusses the challenges of data discovery and how to collect and preserve additional context about each piece of information so that you can find what you need when you don’t even know what you’re looking for yet. Data stacks are becoming more and more complex.

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Cloudera

FEBRUARY 8, 2021

Below is the entire set of steps in the data lifecycle, and each step in the lifecycle will be supported by a dedicated blog post(see Fig. 1): Data Collection – data ingestion and monitoring at the edge (whether the edge be industrial sensors or people in a vehicle showroom). Conclusion.

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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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Data Engineering Podcast

NOVEMBER 20, 2022

Summary The majority of blog posts and presentations about data engineering and analytics assume that the consumers of those efforts are internal business users accessing an environment controlled by the business. Atlan is the metadata hub for your data ecosystem.

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Data Engineering Weekly

JULY 2, 2023

Data Engineering Weekly Is Brought to You by RudderStack RudderStack Profiles takes the SaaS guesswork, and SQL grunt work out of building complete customer profiles, so you can quickly ship actionable, enriched data to every downstream team. So, let's shape the future of Data Engineering together.

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Ascend.io

FEBRUARY 2, 2023

2, 2023 — Ascend.io, the leader in Data Pipeline Automation for building the world’s most intelligent data pipelines, is expanding its global presence and has hired Robin Rostratter as Head of EMEA sales to better engage and support the company’s customer base across Europe. . SAN FRANCISCO , Feb.

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Ascend.io

FEBRUARY 2, 2023

2, 2023 — Ascend.io, the leader in Data Pipeline Automation for building the world’s most intelligent data pipelines, is expanding its global presence and has hired Robin Rostratter as Head of EMEA sales to better engage and support the company’s customer base across Europe. . SAN FRANCISCO , Feb.

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Data Engineering Podcast

DECEMBER 18, 2022

In this episode Abe Gong brings his experiences with the Great Expectations project and community to discuss the technical and organizational considerations involved in implementing these constraints to your data workflows. Atlan is the metadata hub for your data ecosystem. Struggling with broken pipelines? Missing data?

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Jesse Anderson

NOVEMBER 14, 2023

A Guest Post by Ole Olesen-Bagneux In this blog post I would like to describe a new data team, that I call ‘the data discovery team’. 1) The data discovery team must work on discovering the IT landscape. That is done via a careful examination of all metadata repositories describing data sources.

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