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4 DataWarehouse-ish Functions For Your PySpark Dataframes

Ever felt like just exploring documentation… seeing what you can find? That’s what you do on a cold, first snowstorm of the year Sunday afternoon. After the initial fun has warn off, the kids don’t want to go outside anymore, and Netflix has nothing new to offer up. So I thought I might as well spend some time poking around the PySpark Dataframe API, seeing what strange wonders I can uncover. I did find a few methods that took me back to my SQL Data Warehouse days. Memories of my old school Data Analyst and Business Intelligence days in Data Warehousing… the endless line of SQL queries being written day after day. Anyways lets dive into the 4 analytical methods you can call on your PySpark Dataframe, buried in the documentation like some tarnished gem.

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Intro to Apache Cassandra for Data Engineers

Who who? Apache Cassandra, who?

Hmm… yet another distributed database …. will it ever end? Probably not. It’s hard to keep up with them all, even the old ones. That brings me to Apache Cassandra. Of all the popular big data distributed databases Cassandra seems to be kind of that student who always sits in the back row and never says anything… you forget they are there…. until someone says their name….. Apache Cassandra. I honestly didn’t even know what space Cassandra fit in before trying to install and use it… so this should fun. What Is Cassandra? Distributed NoSQL.

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Database/SQL Fundamentals for Data Engineers

I’ve meet my fair share of snooty people who poo poo SQL and databases as second class hand-me-downs. I still remember talking to an academic computer science grad who was explaining to me how he refused to teach database classes, he was just too good for that. Whatever. Apparently refusing to accept how 90% of companies are able to operate as data driven businesses just isn’t important to some people. There is probably nothing more important in the tool belt of a data engineer than being above average at SQL and databases. Tuning queries, writing queries, indexing, designing data warehouses. I’m sure there are some Hadoop data engineers who skipped this step of RDBMS world, but that is not the normal path of a data engineer. Let’s dive into the fundamentals of SQL and databases.

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Apparently Apache Hive is still a thing…. I should probably learn it.

So what’s up with Apache Hive? It’s been around a long time…but all the sudden it seems like it’s requirement in every other job posting these days. “It’s not you… it’s me.” That’s what I would tell Hive if it suddenly materialized as Mr. Smith via the Matrix that I’m pretty sure is the new reality these days. I’ve been around Hadoop and Spark for awhile now and I feel like Hive is that weird 2nd cousin who shows up at Thanksgiving. You know you should like and be nice to him, but you’re not sure why. It seems like Hive sits in a strange world. It’s not a RDBMS, although it does ACID, but it’s touted as a Data Warehousing tool. Time to dig in.

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PySpark SQLContext….tired of your decades old ETL process?

Seriously. Haven’t you had enough of SSIS, SAP Data Services, Informatica, blah blah blah? It’s been the same old ETL process for the last 20 years. CSV files appear somewhere, some poor old aged and angry Developer soul in a cubicle pulls up the same old GUI ETL tool, maps a bunch of columns to some SQL Server, if you’re in a forward thinking shop…maybe Postgres. This is after painstakingly designing the Data Warehouse with good ole’ Kimball in mind. Data flows from some staging table to some facts and dimensions. Eventually some SQL queries are run and a Data Mart is produced summarizing a years worth of data for a crabby Sales or Product department. Brings a tear to my eye. And this is all because Apache Spark sounds scary to some people?

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SQL Database (RDBMS) Design for Data Engineers

Database design… hmmm. There is probably nothing more all over the board in tech. Data warehousing, analytics, OLTP… everyone with their own “defend this hill to the death” ideas. Kimball vs Inmon. Hmmm.. what to do, what to do? After defending my own hills to the death over the years and arguing over whiteboards I’ve come to a conclusion. The right answer is somewhere in the middle. Understanding a few basic design principals will help any data engineer master writing DDL for anything from a Data Warehouse to a high load OLTP systems… across all RDBMS platforms.

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Columnstore Indexes – Always Faster Uh?

Columnstore indexes promise to be the savior of every data warehouse. So, what are they, when should you use them, when to stay away? Columnstore indexes are just what they sound like, data physically stored in a columnar way. This is what makes them so fast when it comes aggregating large amounts of data. The data is compressed and similar values are stored together, the database engine can grab all the values it needs to SUM for example, very quickly, this all leads to faster query results.

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Python and Apache Parquet. Yes Please.

Update: Check out my new Parquet post.
Recently while delving and burying myself alive in AWS Glue and PySpark, I ran across a new to me file format. Apache Parquet.

It promised to be the unicorn of data formats. I’ve not been disappointed yet.

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Where Good Data Warehousing Goes Wrong.

It wouldn’t be the first time.

The story is usually the same, lots of people, contractors, software installation, months of ETL work, months of database work, testing testing and more testing. And then it arrives, a beautiful spiffy Enterprise Data Warehouse with all it’s facts and dimensions in all their Kimball glory.

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T-SQL Basics : Running Totals

Some of the most unused yet powerful functions in T-SQL are Window functions. These functions are powerful because they allow calculations on a Window of the data you specify, even while the calculation scrolls through your data.

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