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Data Engineering Weekly #161

Data Engineering Weekly

Here is the agenda, 1) Data Application Lifecycle Management - Harish Kumar( Paypal) Hear from the team in PayPal on how they build the data product lifecycle management (DPLM) systems. 3) DataOPS at AstraZeneca The AstraZeneca team talks about data ops best practices internally established and what worked and what didn’t work!!!

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How to Become a Data Engineer in 2024?

Knowledge Hut

Data Engineering is typically a software engineering role that focuses deeply on data – namely, data workflows, data pipelines, and the ETL (Extract, Transform, Load) process. What is the role of a Data Engineer? Data Engineers are skilled professionals who lay the foundation of databases and architecture.

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Data Lineage Tools: Key Capabilities and 5 Notable Solutions

Databand.ai

This capability is useful for businesses, as it provides a clear and comprehensive view of their data’s history and transformations. Data lineage tools are not a new concept. In this article: Why Are Data Lineage Tools Important? One of the unique features of Atlan is its human-centric design.

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How to Become an Azure Data Engineer? 2023 Roadmap

Knowledge Hut

The demand for data-related professions, including data engineering, has indeed been on the rise due to the increasing importance of data-driven decision-making in various industries. Becoming an Azure Data Engineer in this data-centric landscape is a promising career choice.

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Azure Synapse vs Databricks: 2023 Comparison Guide

Knowledge Hut

It offers a wide range of services, including computing, storage, databases, machine learning, and analytics, making it a versatile choice for businesses looking to harness the power of the cloud. This cloud-centric approach ensures scalability, flexibility, and cost-efficiency for your data workloads.

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Centralize Your Data Processes With a DataOps Process Hub

DataKitchen

Data organizations often have a mix of centralized and decentralized activity. DataOps concerns itself with the complex flow of data across teams, data centers and organizational boundaries. It expands beyond tools and data architecture and views the data organization from the perspective of its processes and workflows.

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Data Contracts and 4 Other Ways to Overcome Schema Changes

Monte Carlo

But perhaps one of the most common reasons for data quality challenges are software feature updates and other changes made upstream by software engineers. These are particularly frustrating, because while they are breaking data pipelines constantly, it’s not their fault. What should that solution look like?