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Month: March 2023

Applying Quality Assurance Practices to Data Science

Devin Partida bridges the gap:

The world runs on data. Data scientists organize and make sense of a barrage of information, synthesizing and translating it so people can understand it. They drive the innovation and decision-making process for many organizations. But the quality of the data they use can greatly influence the accuracy of their findings, which directly impacts business outcomes and operations. That’s why data scientists must follow strong quality assurance practices.

Read on for seven practices which can help data scientists achieve better outcomes.

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The Legacy of Big Data

Adam Bellemare looks back:

Big Data was going to change the way everything worked. We were about to solve every financial, medical, scientific, and social problem known to humankind. All it would take was a great big pile of data and some way to process it all. 

But somewhere along the line, the big data revolution just sort of petered out, and today you barely hear anything about big data. 

Click through for Adam’s explanation, which is a more detailed form of “Some stuff worked out and became ubiquitous in other ways; others fell off the map.”

But I’m going to snag one more quotation here from Adam:

And finally, big data has shown us that no matter how hard we try, there’s simply no escaping from the inevitable convergence to a full SQL API.

Me: Laughs in Feasel’s Law.

Feasel’s Law – Any sufficiently advanced data retrieval process will eventually have a SQL interface.

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Estimating and Managing Pod Spread in AKS

Joji Varghese talks pod distribution in Azure Kubernetes Service:

In Azure Kubernetes Service (AKS), the concept of pod spread is important to ensure that pods are distributed efficiently across nodes in a cluster. This helps to optimize resource utilization, increase application performance, and maintain high availability.

This article outlines a decision-making process for estimating the number of Pods running on an AKS cluster. We will look at pod distribution across designated node pools, distribution based on pod-to-pod dependencies and distribution where pod or node affinities are not specified. Finally, we explore the impact of pod spread on scaling using replicas and the role of the Horizontal Pod Autoscaler (HPA). We will close with a test run of all the above scenarios.

Read on for tips, as well as a few web tools, which you can use to estimate and control pod spread in AKS.

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Tips for Using a Data Lakehouse

James Serra shares some advice:

As I mentioned in my Data Mesh, Data Fabric, Data Lakehouse presentation, the data lakehouse architecture, where you use a data lake with delta lake as a software layer and skip using a relational data warehouse, is becoming more and more popular. For some customers, I will recommend “Use a data lake until you can’t”. What I mean by this is to take the following steps when building a new data architecture in Azure with Azure Synapse Analytics:

Click through for six notes.

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Parallelization in DirectQuery

Chris Webb shares some insight:

Recently we announced an important new optimisation for DirectQuery datasets: the ability to run (some) of the queries generated by a single DAX query in parallel. You can read the blog post here:

A few of us on the Power BI CAT team have tested this out with customers and seen some great results, so I thought I’d write a post illustrating the effect this optimisation can have and explaining when it can and can’t help.

Chris has examples of great success, as well as not-so-great success and utter failure, and explains the why behind each outcome.

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Using Security Groups with Power BI Row-Level Security

Soheil Bakhshi has a recommendation for us:

However, managing RLS roles can be challenging if you have a large number of users or if your user base changes frequently. You need to manually assign each user account to one or more roles, which can be time-consuming and error-prone. Moreover, if a user changes their position or leaves the organisation, you must update their role membership accordingly.

This is where Security Groups become handy. 

Soheil explains why and then gives us a step-by-step guide on what we can do to use security groups instead.

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Customizing Shiny Apps with shinydashboard

Mandy Norrbo isn’t satisfied with the defaults:

Using {shinydashboard} is great for creating dashboard prototypes with a header-sidebar-body layout. You can quickly mock up a professional looking dashboard containing a variety of outputs, including plots and tables.

However, after a while, you’ll probably have had enough of the “50 shades of blue” default theme. Or, you might have been asked to to follow company branding guidelines, so you need to replace the default colours with custom ones.

Click through for a walkthrough of what is available for customization and how to do it.

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Startup Pains with Large Memory-Optimized Tables

Brent Ozar takes us through a problem:

Well, here it is in 2023, and recently I’ve talked to a couple of architects who wish they could go back in time and watch that video. In both cases, they suffered from the same issue.

The short story is that the more data you put into durable In-Memory OLTP tables – and even just 5GB of data can hit this issue – the more your startups, failovers, and restores turn into long stories, to the point where other databases on your SQL Server are practically unusable.

Click through for the scenario. In-Memory OLTP is one of those features which frustrates me to no end. It had the potential to be outstanding, but due to the difficulty of further development (e.g., getting cross-database queries to work when you have a mix of memory-optimized and non-optimized databases and tables) and the limitations of what it actually made faster (mostly inserts, not selects), the actual number of great use cases for the product is a lot lower than I think it could have been.

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Unmasking Dynamic Data Masking via Powershell

Jana Sattainathan needs to see all the details:

Today, I had to unmask all the columns I had helped mask using Dynamic Data Masking. This simple post assumes that you are a privileged user with the ability to drop “Column Masking”!

In other words, this isn’t exploiting the mechanics of Dynamic Data Masking to view data you shouldn’t be able to; it’s about removing Dynamic Data Masking from columns with it enabled.

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Performance Tuning a Dedicated SQL Pool

Sarath Sasidharan has some guidance for us:

Synapse Dedicated pools have been battle tested at enterprise customers across the globe. We deal with data in the magnitude of PetaBytes. Synapse can provide you with the scale of the cloud and the high performance required for your enterprise-grade requirements.  The key to maximizing your performance is to follow best practices, check out best practices for dedicated SQL pools in Azure Synapse Analytics

Failure to do so causes performance issues. In such scenarios, is it important to understand where the bottlenecks are. This blog focuses on the different steps a query goes through; from the time the query is fired from the client until it returns back.  Delay caused in any of the steps would impact the overall run-time of the query and hence indicate degraded performance.

Click through for a walkthrough of each step along the way, potential problems you could run into, and remediations for those problems. Much of the advice is similar to what you’d get with SQL Server, though there are differences interspersed throughout each level.

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