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Data Engineering Weekly #173

Data Engineering Weekly

[link] Meta: Composable data management at Meta Meta writes about its transition to a composable data management system to improve interoperability, reusability, and engineering efficiency. It is a long standing question on people wondering In what situations should you use SQL instead of Pandas as a data scientist?

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Reflecting On The Past 6 Years Of Data Engineering

Data Engineering Podcast

In that time there have been a number of generational shifts in how data engineering is done. Sign up now for early access to Materialize and get started with the power of streaming data with the same simplicity and low implementation cost as batch cloud data warehouses.

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Building Self Serve Business Intelligence With AI And Semantic Modeling At Zenlytic

Data Engineering Podcast

In this episode Paul Blankley and Ryan Janssen explore the power of natural language driven data exploration combined with semantic modeling that enables an intuitive way for everyone in the business to access the data that they need to succeed in their work. Business intelligence is a crowded market.

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The Future of Data Engineering as a Data Engineer

Monte Carlo

In the world of data engineering, Maxime Beauchemin is someone who needs no introduction. Currently, Maxime is CEO and co-founder of Preset , a fast-growing startup that’s paving the way forward for AI-enabled data visualization for modern companies. Enter, the data engineer. What is a data engineer today?

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Let Your Business Intelligence Platform Build The Models Automatically With Omni Analytics

Data Engineering Podcast

Summary Business intelligence has gone through many generational shifts, but each generation has largely maintained the same workflow. Data analysts create reports that are used by the business to understand and direct the business, but the process is very labor and time intensive.

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Building a Data Engineering Project in 20 Minutes

Simon Späti

This post focuses on practical data pipelines with examples from web-scraping real-estates, uploading them to S3 with MinIO, Spark and Delta Lake, adding some Data Science magic with Jupyter Notebooks, ingesting into Data Warehouse Apache Druid, visualising dashboards with Superset and managing everything with Dagster.

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Modern Data Engineering

Towards Data Science

In this article, I want to talk about crucial things that affect data engineers. I’d like to discuss some popular Data engineering questions: Modern data engineering (DE). Does your DE work well enough to fuel advanced data pipelines and Business intelligence (BI)? What is it?