Simon Späti

MAY 26, 2023

Welcome to the third and final installment of our series “Data Modeling: The Unsung Hero of Data Engineering.” If you’ve journeyed with us from Part 1, where we dove into the importance and history of data modeling, or joined us in Part 2 to explore various approaches and techniques, I’m delighted you’ve stuck around. In this third part, we’ll delve into data architecture patterns and their influence on data modeling.

Architecture 130

Architecture Data Engineering Data Engineer Engineering 130

Simon Späti

MARCH 9, 2021

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. The goal is to touch on the common data engineering challenges and using promising new technologies, tools or frameworks, which most of them I wrote about in Business Intelligence

Data Engineering 130

Data Engineering Data Engineer Engineering Project 130

Simon Späti

OCTOBER 19, 2022

Will Rust kill Python for Data Engineers? If you only came here to know this, my answer is no. Betteridge’s Law strikes again! But then again, you have to ask: was Python made for Data Engineering in the first place? Rust may not replace Python outright, but it has consumed more and more of JavaScript tooling and there are increasingly many projects trying to do the same with Python/Data Engineering.

Data Engineering 130

Data Engineering Data Engineer Engineering Programming Language 130

Simon Späti

JANUARY 9, 2023

In part I, The Open Data Stack Distilled into Four Core Tools, we discussed how to quickly set up a data stack, tackling end-to-end data analytics challenges. As a manager or developer working with data at a mid- to large-sized enterprise, you might ask why aren’t we using any of these tools. In this article, we dive into what mid-to-large-sized companies are using instead, the struggle of setting up a Modern Data Stack (MDS) for an enterprise size, and the opportunities of a free-of-charge and

Data Analytics 130

Data Analytics Data Management 130

Simon Späti

APRIL 3, 2023

Amidst the excitement and hype surrounding artificial intelligence, the significance of data engineering and its critical foundation—data modeling—can often be overlooked. This article is the first in a three-part series that will shine a spotlight on the fascinating world of data modeling, delving into its crucial importance within the broader context of data engineering.

Data Engineering 130

Data Engineering Data Engineer Engineering Data Architecture 130

Simon Späti

MAY 3, 2023

In case you missed Part 1, An Introduction to Data Modeling, make sure to check first, where we discussed the importance of data modeling in data engineering, the history, and the increasing complexity of data. We have also touched upon the significance of understanding the data landscape, its challenges, and much more. As we delve deeper into this topic, Part 2 will focus on data modeling approaches and techniques.

Data Engineering 130

Data Engineering Data Engineer Engineering Structured Data 130

Simon Späti

MAY 26, 2023

Welcome to the third and final installment of our series “Data Modeling: The Unsung Hero of Data Engineering.” If you’ve journeyed with us from Part 1, where we dove into the importance and history of data modeling, or joined us in Part 2 to explore various approaches and techniques, I’m delighted you’ve stuck around. In this third part, we’ll delve into data architecture patterns and their influence on data modeling.

Architecture 130

Architecture Data Engineering Data Engineer Engineering 130

Simon Späti

JANUARY 3, 2023

In this article, we are going to explore core open-source tools that are needed for any company to become data-driven. We’ll cover integration, transformation, orchestration, analytics, and ML tools as a starter guide to the latest open data stack. Let’s start with the Modern Data Stack. Have you heard of it or where the term came from?

Data 130

Data IT 130

Simon Späti

SEPTEMBER 29, 2022

A semantic layer is something we use every day. We build dashboards with yearly and monthly aggregations. We design dimensions for drilling down reports by region, product, or whatever metrics we are interested in. What has changed is that we no longer use a singular business intelligence tool; different teams use different visualizations (BI, notebooks, and embedded analytics).

BI 130

BI Business Intelligence Designing Building 130

Simon Späti

OCTOBER 19, 2022

Will Rust kill Python for Data Engineers? If you only came here to know this, my answer is no. Betteridge’s Law strikes again! But then again, you have to ask: was Python made for Data Engineering in the first place? Rust may not replace Python outright, but it has consumed more and more of JavaScript tooling and there are increasingly many projects trying to do the same with Python/Data Engineering.

Data Engineering 130

Data Engineering Data Engineer Engineering Programming Language 130

Simon Späti

FEBRUARY 25, 2023

As I sit down to write this article, I’m filled with a sense of vulnerability and excitement. You see, this is a story that only I can tell. It’s a tale of finding my Pathless Path and discovering who I am in the process. I have learned that some of my best decision-making comes from following my gut, heart, and intuition, a place of inner knowing.

Process 289

Process IT 289

Simon Späti

NOVEMBER 23, 2018

Are you on the lookout for a replacement for the Microsoft Analysis Cubes, are you looking for a big data OLAP system that scales ad libitum, do you want to have your analytics updated even real-time? In this blog, I want to show you possible solutions that are ready for the future and fits into existing data architecture. What is OLAP? OLAP is an acronym for Online Analytical Processing.

Big Data 130

Big Data Data Architecture Architecture Systems 130

Simon Späti

NOVEMBER 28, 2018

These days, everyone talks about open-source. However, this is still not common in the Data Warehouse (DWH) field. Why is this? In my recent blog, I researched OLAP technologies, for this post I chose some open-source technologies and used them together to build a full data architecture for a Data Warehouse system. I went with Apache Druid for data storage, Apache Superset for querying and Apache Airflow as a task orchestrator.

Data Warehouse 130

Data Warehouse Data Storage Data Architecture Architecture 130

Simon Späti

SEPTEMBER 28, 2021

Taking notes helps you not to forget things, teaches you to express yourself, brainstorms your thoughts, research a topic, and so many more things. I used to take notes all my life. Maybe it’s because I’m Swiss, they say we are well organised. I used to write in OneNote for 10+ years. I have notebooks for my bachelor studies and every workplace I worked.

IT 130

IT 130

Simon Späti

APRIL 6, 2022

With burnout and mental stress at every level of our lives, I find my Personal Knowledge Management (PKM) system even more valuable. As a human, I forget lots of things. As a dad, I have more responsibilities with remembering all things related to my kid. As a developer and knowledge worker, I re-use code snippets or create new things. That’s why a PKM system such as a Second Brain to store all of it in a sustainable way is crucial to me.

Management 189

Management Coding Systems IT 189

Simon Späti

JUNE 14, 2020

Today we have more requirements with ever-growing tools and framework, complex cloud architectures, and with data stack that is changing rapidly. I hear claims: “Business Intelligence (BI) takes too long to integrate new data”, or “understanding how the numbers match up is very hard and needs lots of analysis”. The goal of this article is to make business intelligence easier, faster and more accessible with techniques from the sphere of data engineering.

Business Intelligence 130

Business Intelligence Data Engineering Data Engineer Technology 130

Simon Späti

MARCH 9, 2021

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. The goal is to touch on the common data engineering challenges and using promising new technologies, tools or frameworks, which most of them I wrote about in Business Intelligence

Data Engineering 189

Data Engineering Data Engineer Engineering Project 189

Simon Späti

JANUARY 22, 2022

Image by Mohammad Bagher Adib Behrooz on Unsplash Why GraphQL for data engineers, you might ask? GraphQL solved the problem of providing a distinct interface for each client by unifying it to a single API for all clients such as web, mobile, web apps. The same challenge we’re now facing in the data world, where we integrate multiple clients with numerous backend systems.

Data Engineering 147

Data Engineering Data Engineer Engineering Building 147

Simon Späti

JUNE 20, 2022

Data consumers, such as data analysts, and business users, care mostly about the production of data assets. On the other hand, data engineers have historically focused on modeling the dependencies between tasks (instead of data assets) with an orchestrator tool. How can we reconcile both worlds? This article reviews open-source data orchestration tools (Airflow, Prefect, Dagster) and discusses how data orchestration tools introduce data assets as first-class objects.

Data Pipeline 147

Data Pipeline Data Data Engineering Data Engineer 147

Simon Späti

SEPTEMBER 30, 2022

Image by Rachel Claire on Pexels Ever wanted or been asked to build an open-source Data Lake offloading data for analytics? Asked yourself what components and features would that include. Didn’t know the difference between a Data Lakehouse and a Data Warehouse? Or you just wanted to govern your hundreds to thousands of files and have more database-like features but don’t know how?

Data Lake 130

Data Lake Data Warehouse Government Data 130

Simon Späti

JANUARY 3, 2023

I used VS Code, Sublime, Notepad++, TextMate, and others, but the shortcut with cmd(+shift)+end, jumping with option+arrow-keys from word to word, needed to be faster at some point. I was hitting my limits. Everything I was doing I did decently fast, but I didn’t get any faster. Vim is the only editor you get faster with time. Vim is based solely on shortcuts.

Data Engineering 195

Data Engineering Data Engineer Engineering Coding 195