Interesting links
Here are some interesting links for you! Enjoy your stay :)Pages
Categories
Archive
- March 2025
- February 2025
- January 2025
- December 2024
- November 2024
- October 2024
- September 2024
- August 2024
- July 2024
- June 2024
- May 2024
- April 2024
- March 2024
- February 2024
- January 2024
- December 2023
- November 2023
- October 2023
- September 2023
- August 2023
- July 2023
- June 2023
- May 2023
- April 2023
- March 2023
- February 2023
- January 2023
- December 2022
- November 2022
- October 2022
- September 2022
- August 2022
- July 2022
- June 2022
- May 2022
- April 2022
- March 2022
- February 2022
- January 2022
- December 2021
- November 2021
- October 2021
- September 2021
- August 2021
- July 2021
- June 2021
- May 2021
- April 2021
- March 2021
- February 2021
- January 2021
- December 2020
- November 2020
- October 2020
- September 2020
- August 2020
- July 2020
- June 2020
- May 2020
- April 2020
- March 2020
- January 2020
- December 2019
- November 2019
- October 2019
- September 2019
- August 2019
- July 2019
- May 2019
- March 2019
- February 2019
- January 2019
- December 2018
- November 2018
- October 2018
- September 2018
- July 2018
- June 2018
- May 2018
- April 2018
- March 2018
- February 2018
Part 2 – How Technology Platforms affect your Data Warehouse, Data Lake, and Lake Houses.
This is a start of a 5 part series on Demystifying Data Warehouses / Data Lakes / Lake Houses. In Part 2 We are digging into the common Big Data tools and how those technologies have a direct impact on Data Models and what kind of Datastore ends up being designed. Part 1 – What […]
5 Part Series – Demystifying Data Warehouses / Data Lakes / Lake Houses
Even I get confused these days. Data Warehouse, Data Lake, and Lake Houses … why do we have three, what are the differences? Is it all just marketing huff-a-luff? Technology and life in the data world seem to be changing fast these days. Lot’s of new vendors on the streets trying to hawk their tools […]
2 Useful PySpark Functions
I’ve come to have a great love for PySpark, it’s such an easy and powerful tool to use. I use it every day to crunch tens to hundreds of terabytes of data, without even blinking an eye. And all this with the ease of Python, it’s almost too good to be true. I have to […]
DataFrames vs SparkSQL – Which One Should You Choose?
I’ve been amazed at the growth of Spark over the last few years. I remember 5 years when I first started writing about Spark here and there, it was popular, but still not used that widely at smaller companies. AWS Glue was just starting to get popular, it seemed the barrier to widely adopted Spark […]
Performance Testing Postgres Inserts with Python
Sometimes I get to feeling nostalgic for the good ol’ days. What days am I talking about? My Data Engineering days when all I had to worry about was reading files with Python and throwing stuff into Postgres or some other database. The good ol’ days. The other day I was reminiscing about what I […]
Hive Metastore in Databricks – What To Know.
Hive is like the zombie apocalypse of the Big Data world, it can’t be killed, it keeps coming back. More specifically the lesser-known Hive Metastore is the little sneaker that has wormed its way into a lot of Big Data tooling and platforms, in a quasi behind the scenes way. Many people don’t realize it, […]
Lessons Learned from MERGE operations with Billions of Records on Databricks Spark
Something happens with you starting working with 10’s of billions of records and data sets that are hundreds of TBs in size. Do you know what happens? Things stop working, that’s what. I miss the days where 1-10 TBs were considered large and in charge. the good ole days. I want to talk about lessons […]
CTE vs SubQuery
What to choose what to choose? The age-old problem that has plagued data engineers forever, ok maybe like 10 years, should you use CTE’s or Sub-Queries when writing your SQL code. This has become even more of a relevant topic with the rise of SparkSQL, Snowflake, Redshift, and BigQuery. Funny how some things never change. […]
ORM’s are the Cigarettes of the Data Engineering World.
Seriously, just don’t do it, they are bad for you. Listen to your mother, just say no. The dreaded ORM’s ( Object Relational Mapping ) that do all the hard SQL work for you. But, they come with many unintended consequences that are bad for your health and wellness in the long term. Many unsuspecting […]
3 Tips for Unit Testing PySpark Pipelines
I’m not sure what it is, but some prevailing evil in the Data Engineering world has made it not so common for PySpark pipelines to be unit tested. Who knows, it’s probably a combination of things. Data Engineers have been accused of not having good Software Engineering principles. Functional testing is a hot commodity in […]