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Day: July 3, 2024

Tips for Choosing a Classifier

I’ve wrapped up yet another series:

In this video, I wrap up the series on classification and provide some quick-and-dirty tips on when to use each of the classification algorithms we have discussed.

This was a series I really enjoyed. I’ve had a talk on the topic for a few years, but getting the opportunity to dig in deeper and spend a few hours on the topic was nice. It also helped me fill in some gaps in my understanding and fix a few long-standing bugs in my demo code, so it’s got that going for it as well.

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Parallel Download in Oracle Object Storage

Brendan Tierney continues a series on Oracle Object Storage:

In previous posts, I’ve given example Python code (and functions) for processing files into and out of OCI Object and Bucket Storage. One of these previous posts includes code and a demonstration of uploading files to an OCI Bucket using the multiprocessing package in Python.

Building upon these previous examples, the code below will download a Bucket using parallel processing. Like my last example, this code is based on the example code I gave in an earlier post on functions within a Jupyter Notebook.

Click through for the code.

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Export Azure SQL DB to Blob Storage

Josephine Bush runs an import-export business and wants a database to “fall off a truck”:

After a data migration, we needed to decommission the old Azure SQL DBs, but we wanted to keep a copy in case we needed anything later. Enter exporting an Azure SQL DB to storage!

Click through for an example of how it works. Given that we’re getting bacpac files out, I wonder what it would look like with a really large database.

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Lakehouse Table Partitioning in Microsoft Fabric

Gilbert Quevauvilliers performs a split:

When loading data, it is always important to load the data with performance and scalability in mind.

For lakehouse tables to return queries quickly and to scale it is essential to load your lakehouse tables with partitions.

What I am going to show you in my blog post today is how to load data into a Lakehouse table where the table will be automatically partitioned by Year/Month/Day.

Click through for the example.

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Suspend and Resume Microsoft Fabric Capacity

Olivier Van Steenlandt saves some cash:

With only a limited budget for exploring and testing new tools, I had to figure out how to use my budget efficiently. Therefore, before making any decisions, I looked at the Microsoft Fabric pricing and possibilities.

If you want to take a look at the Microsoft Fabric pricing models, you can find an overview via the following link: Microsoft Fabric – Pricing | Microsoft Azure

To avoid any surprises and to be as cost-effective as possible, I created an easy Python script that I can use to pause and start my Microsoft Fabric capacity, or better said resume and suspend.

I highly recommend this for any organization that does not need 24/7 uptime for Fabric capacity. If you run your system 12 hours a day instead of 24, it takes your F64 capacity from $8k a month to $4k.

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Bit Column Order and Data Length

Brent Ozar performs an experiment:

At the PGConf.dev, where Postgres developers get together and strategize the work they wanna do for the next version, I attended a session where Matthias van de Meent talked about changing the way Postgres stores columns. As of right now (Postgres 17), columns are aligned in 8-bit intervals, so if you create a table with alternating columns:

Read on to see an example, and then Brent performs a test to see how SQL Server handles this scenario. The comments also mention that at least older versions of Oracle behaved like Postgres.

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Postgres Tuning Settings

Semab Tariq shares a few tips:

PostgreSQL is a widely used database known for its robust performance and reliability. To get the most out of PostgreSQL, tuning its parameters is crucial.

In this blog, we will explore the various PostgreSQL performance-related parameters and how to tune them effectively. By measuring Transactions Per Second (TPS) before and after tuning, and analyzing the results, we will demonstrate the significant impact of tuning on PostgreSQL performance.

Click through for some of the sorts of settings you might want to review. In Semab’s case, a simple server achieved nearly 30% better throughput after making these changes, so that’s not bad for the level of effort.

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