, , , ,

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. […]

Read more
, , ,

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 […]

Read more
, , ,

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 […]

Read more
, , ,

6 Tips for Optimizing Databricks Cluster and Pipeline Performance and Cost

Databricks, easily the hotest tool these days for Data Lakes and Data Warehousing, it’s a beast. As with any new technology there are always growing pains, learnings, and tips and tricks that might not be obvious to those dipping their toes in the water. Not understand certain concepts, and being unware of specific configurations can […]

Read more
, , ,

Data Modeling – Relational Databases (SQL) vs Data Lake (File Based)

Data Modeling is a topic that never goes away. Sometimes I do reminisce about the good ol’ days of Kimball-style data models, it was so simple, straightforward, just the same thing for years. Then Big Data happened, Spark happened. Things just changed. There is a lot of new content coming out around Data Lakes and […]

Read more
, , , ,

Review of Airbyte for Data Engineers

It’s hard to keep up with the never-ending stream of new Data Engineering tools these days. Always something new around the next bend. I find it interesting to kick the tries on the new kids on the block. It’s always interesting to see what angle or pain point a new tool tries to hone in […]

Read more
, , ,

Bitwise Operations for Data Engineers

Ugh. Cursed bitwise operations … something usually reserved for the low-level mythical engineers writing code no one should have to write. I’ve escaped all but twice during my meager existence, recently I had to use a bitwise operation while converting a Python hashing algorithm into PySpark code. It made my brain hurt. What is this […]

Read more
, ,

gRPC for Data Engineers

If you’ve been around Data Engineering for a while, like me, you’ve noticed a few trends in the industry at wide, and in individual data engineers themselves. There seem to be a few types of data engineers, and it depends on where you’ve worked, and what your projects have looked like that put you here […]

Read more
, , , ,

The Wild West of Parallel Computing – Review of Bodo.ai

It truly is the Wild West of parallel computing these days. It seems that big data has brought out an onslaught of companies trying to either take advantage of making it easier to use any number of big data platforms or making up their own. Most of them usually take shots at tools like Spark […]

Read more
, , , ,

Dask vs PySpark – Performance and Other Thoughts.

Every once in awhile I see someone talking about their wonder distributed cluster of Dask machines, and my curiosity gets aroused. I know plenty of people use Dask, mostly on their local machines, but it seems like the meteoric rise of Spark, especially with tools like EMR and Databricks, that Dask is slowly slipping into […]

Read more