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Day: August 30, 2023

Flink Streaming Use Cases for Kafka Users

Jean-Sebastien Brunner gives us some use cases:

In Part One of our “Inside Flink” blog series, we explored the critical role of stream processing and why developers are increasingly choosing Apache Flink® over other frameworks. 

In this second installment, we’ll showcase how innovative teams across every industry and size are putting stream processing into practice – from streaming data pipelines to train ML models or more timely analytics to fraud detection in finance and real-time inventory management in retail. We’ll also discuss how Flink is uniquely suited to support a wide spectrum of use cases and helps teams uncover immediate insights in their data streams and react to events in real time.

This article stays more at the “art of the possible” level rather than drilling into how we can do it.

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Visualizing when Lower is Better

Alex Velez inverts a common experience:

When quickly scanning, I wonder why the direct and indirect sales teams underperformed in 2022. Mostly, they fell below the goal of 90 days, exceeding their target only three times. 

Now, pausing to think more critically about the context of this scenario, I realize I’ve misread the graph—specifically the goal line. Targets and goals are often seen as minimum thresholds, not maximum limits. But in the sales industry, the goal is to close a deal as quickly as possible. In this visual, below the goal line is actually a good thing!

This graph challenges my standard construct of targets and goals, which could lead to confusion or, worse, the wrong conclusions if I’m not careful. 

Read on for five alternative ways to display this graph and (hopefully) reduce confusion.

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The Concept of Schema in Relational Databases

Adron Hall explains how different relational database management systems describe schemas:

From the viewpoint of someone familiar with the general idea of a schema, it can indeed seem unusual that databases like SQL Server, Oracle, MariaDB/MySQL, and PostgreSQL each interpret and implement schemas in slightly (or sometimes, vastly) different ways. While the core idea behind a schema as a structured container or namespace for database objects remains somewhat consistent, the exact nature, utility, and behavior of schemas vary across these systems.

Read on for an overview of these for four products, as well as what the ANSI standard indicates.

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Setting Table and Matrix Column Widths in Power BI

Kurt Buhler controls the horizontal, Kurt Buhler controls the vertical:

One challenge of the table and matrix visuals in Power BI is that it’s difficult to precisely and consistently set column widths. Unlike in Excel, where you can set the row and column widths in a spreadsheet, you have no option in the visual interface to control the column width property. However, it’s still possible to control it in the report metadata, which is exposed in the officially supported Power BI Projects format (.pbip) which is in preview. Notably, however, opening and modifying report metadata from this format isn’t yet supported. Despite that fact, it still works reliably, so I thought I’d demonstrate how to do this.

There are a fair number of steps involved but it all makes sense in the end.

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Comparing the Microsoft Fabric Data Wrangler and Power Query Editor

Reza Rad performs a comparison:

Power Query Editor and Data Wrangler are data transformation and preparation tools in Microsoft Fabric. There are similarities between these two tools. However, there are differences, too. It is essential to know the capabilities of each tool to understand which one should be used for what purpose and scenario. In this article, this is our quest.

Reza includes a video and an article. Reza also has a summary chart at the bottom.

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Data Type Conversions and Snowflake Performance

Kevin Wilkie is implicit in this whole thing:

One of the ways we can get better at speed is to attempt several slightly different ways that can get you (hopefully) the same data. Some tables work better with one query while some work better with another query.

Let’s work through a scenario in Snowflake and we’ll see which one is faster under “normal” conditions.

Click through for a few query examples and how they end up performing.

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