Data Pipeline Engineering Metadata 
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FEBRUARY 22, 2024
Data Pipeline Logging Best Practices 3.1. Metadata: Information about pipeline runs, & data flowing through your pipeline 3.2. Introduction 2. Setup & Logging architecture 3. Obtain visibility into the code’s execution sequence using text logs 3.3. Monitoring UI & Traceability 3.5.
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.
Data Engineering Podcast
JUNE 19, 2022
Summary Metadata is the lifeblood of your data platform, providing information about what is happening in your systems. In order to level up their value a new trend of active metadata is being implemented, allowing use cases like keeping BI reports up to date, auto-scaling your warehouses, and automated data governance.
Christophe Blefari
MARCH 15, 2024
Cognition AI introduced Devin — Devin is the first AI software engineer, Devin can, unassisted, do software engineering tasks like fixing Github issues (13% of success, previously best was ~5%), apply to jobs on Upwork, train and fine-tune its own models. Arrow doing a lot of the data operation heavy lifting.
Databand.ai
JUNE 28, 2023
Data Pipeline Observability: A Model For Data Engineers Eitan Chazbani June 29, 2023 Data pipeline observability is your ability to monitor and understand the state of a data pipeline at any time. We believe the world’s data pipelines need better data observability.
LinkedIn Engineering
JUNE 26, 2023
However, we've found that this vertical self-service model doesn't work particularly well for data pipelines, which involve wiring together many different systems into end-to-end data flows. Data pipelines power foundational parts of LinkedIn's infrastructure, including replication between data centers.
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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