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Databricks Access Control – The 3 Most Important Steps

It’s not often I yearn for the good old days of SQL Server, but I’ve had a few of those moments lately. Some things I miss, some I don’t, and it’s probably because I’m getting old and crusty, stuck in my ways, by permissioning is one of those topics where I think about the good old days. Data access control and permissions are topics that we all kinda ignore as not that important … until we actually are trying to do something with them. Then all of sudden we start complaining about complexity and why this isn’t easier. That’s a good way to describe my reaction to having to work on Databricks Access control.

But, I learned a few things and I think they will be helpful for someone. Read on for the basics of handling permissions and access control in Databricks and Delta Lake.

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Part 4 – Keys To Success – Idempotency and Partitioning.

As the road winds on we come to Part 4, of our 5 Part Series on Data Warehouses, Lakes, and Lake Houses. Finally, we are getting to some fun topics after all the boring stuff. Today I want to talk about the two keys to success in your Data Lakes … Idempotency and Partitioning. I firmly believe these two concepts are the cornerstones of the new exciting, or not-so-exciting world of Data Lakes and Lake Houses, without which your data and pipelines go the way of the dodo.

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Databricks + Delta Lake MERGE duplicates – Deterministic vs Non-Deterministic ETL.

Is there any problem more classic to the Data Lakes and Data Warehouses than duplicate records? You would think after doing the same ETL for over a decade I could avoid the issue, apparently not. It’s good never to think too highly of one’s self, the duplicates can get us all. Today I want to talk about a wonderful feature of Databricks + Delta Lake MERGE statements that are perfect for quietly and insidiously injecting duplicates into your Data Warehouse or Data Lake. This is a great trick to play on your unsuspecting coworkers.

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End-to-End Pipeline Integration Testing for Databricks + Delta Lake.

The testing never ends. Tests tests tests, and more tests. When it comes to data engineering and data pipelines it seems good practices are finally catching up after years. In the past, the data engineering community took a lot of heat, and rightly so, for not adopting good software engineering principles, especially in data pipelines.

In the defense of many data engineers, because of the varied backgrounds people come from, some were never taught or realized the importance of good software design and testing practices. Sure, it always “takes more time” upfront to design data pipelines with code that is functional and unit-testable, and worse, able to be integration tested from end to end. It requires some foresight and thought in both data architecture and pipeline design to enable complete testability.

Integration testing end-to-end in an automated manner is a tough nut to crack. How can you do such a thing on massive pipelines that crunch hundreds of TBs of data? With a little creativity.

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Part 3 – Data Modeling in Data Warehouses, Data Lakes, and Lake Houses.

Now we are getting to the crux of the matter. I would say Data Modeling is probably one of the most unaddressed, yet important parts of Data Warehousing, Data Lakes, and Lake Houses. It raises the most questions and concerns and is responsible for the rise and fall of many Data Engineers.

This is what really drives the difference between the”big three”, Data Modeling.

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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 are Data Warehouses, Data Lakes, and Lake Houses?

Part 2 – How Technology Platforms affect your Data Warehouse, Data Lake, and Lake Houses.

Part 3 – Data Modeling in Data Warehouses, Data Lakes, and Lake Houses.

Part 4 – Keys To Sucess – Idemptoency and Partitioning.

Part 5 – Serving Data from your Data Warehouse, Data Lake, or Lake House.

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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 and solutions, each of them pumping out content designed to solve all your data needs.

I’ve seen a lot of content out there by SAAS vendors, and by folks who ascribe to a said vendor, about Data Lakes and Lake Houses, new schema designs and approaches, and it’s hard to know what is just a sales tactic and what is real. I’m going to stir the pot.

This is a start of a 5 part series on Demystifying Data Warehouses / Data Lakes / Lake Houses. Enjoy.

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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 say though, where things get a little dicey is when you need to do something maybe “out-the-box”, say, strange text manipulations, something that is easy in Python becomes a challenge in PySpark using DataFrame API functionality.

Sure, you could use a udf written in Python for that, but we all know the performance penalty for that. Many times I just try to get creative with a combination of PySpark functions to accomplish the same task others would use a udf for.

I want to talk about two wonderful PySpark functions I find myself using a lot, they come in handy and I rarely see them used, hopefully, they come in handy for you!

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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 was the managing of Spark clusters etc. That has all changed the last few years with EMR, Databricks, and the like.

Back in those days, it was common for most Spark pipelines to be written with the DataFrame API, you didn’t see much SparkSQL around. I’m going to talk about how that has changed, what you should be using, and why.

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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, but Hive Metastore is the beating heart behind many systems, including Databricks. It’s one of those topics that sneaks up on you, ignore it happily at your own peril, till all of a sudden you need to know everything about it.

Specifically, I want to talk about Hive MetaStore as related to Databricks, how it works inside the Databricks platform, and what you need to know. I tripped myself up a lot during my initial forays into Databricks at a Production level. When you wander outside the realm of Notebooks, which you should, strange things start to happen. Databricks seems to assume you already have your own Hive Metastore, maybe like the Glue Data Catalog, or that you want to set up your own somewhere. But what if you don’t?

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