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Day: October 24, 2022

Designing Event Streams for Kafka

Dave Shook announces a new course:

Properly designing your events and event streams is essential for any event-driven architecture. Precisely how you design and implement them will significantly affect not only what you can do today, but what you can do tomorrow. For such a critical part of any data infrastructure, most event streaming tutorials gloss over event design.

In the new course on Confluent Developer, events and event streams are put front and center. We’re going to look at the dimensions of event and event stream design and how to apply them to real-world problems. But dimensions and theory are nothing without best practices, so we are also going to take a look at these to help keep you clear of pitfalls and set you up for success. This course also includes hands-on exercises, during which you will work through use cases related to the different dimensions of event design and event streaming.

Click through to learn more about what’s in the course and to check it out–it is free, after all.

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Bitemporal Modeling and Running Totals

John Mount solves a running total problem in Python:

An example of this is wanting to know any many reservations for a San Francisco Symphony concert scheduled for December 4th 2022 are known to have been made by October 22nd 2022. This could be used as part of an attendance demand model that is evaluated on October 22nd 2022. The “fifty-cent word” for this is “bitemporal” modeling or data.

As I read through the solution, my initial thought is that, if the data is in a relational database, a running total operation SUM(reservation_count) OVER (PARTITION BY target_date ORDER BY action_date ROWS BETWEEN UNBOUNDED PRECEDING TO CURRENT ROW) would form the basis of a solution. Still, this is an interesting exercise in translating a SQL operation into equivalent Python and just how much we get to take for granted.

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Fine-Tuning Hugging Face for Named Entity Recognition in Japanese

Tsuyoshi Matsuzaki tries out a named entity recognition project with the Hugging Face library:

Now a lot of AI companies (such as, OpenAI, NLP Cloud, Google, NVIDIA, etc) are providing pre-trained large language models including methods that tune to enable models trained. Among such tools and framework, HuggingFace is widely used and providing over 20,000 transformer-based models.

In this post, I’ll show you brief fine-tuned example of transformer models in Hugging Face for your beginning.
In the last part of this post, I’ll also optimize training with DeepSpeed which is well integrated with HuggingFace transformers.

Click through for the results of this analysis.

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Transferring Data between Dedicated SQL and Spark Pools in Synapse

Sidney Cirqueria shows off a connector available to us in Azure Synapse Analytics:

Usually, customers do this kind of operation using Synapse Apache Spark to load data to Dedicated Pool within Azure Synapse Workspace, but today, I would like to reproduce a different scenario that I was working on one of my support cases.  Consider a scenario where you are trying to load data from Synapse Spark to Dedicated pool (formerly SQL DW) using Synapse Pipelines, and additionally you are using Synapse Workspace deployed with Managed Virtual Network.

The intention of this guide is to help you with which configuration will be required if you need to load data from Azure Synapse Apache Spark to Dedicated SQL Pool (formerly SQL DW). If you prefer take advantage of the new feature-rich capabilities now available via the Synapse workspace and Studio and load data directly from Azure Apache Spark to Dedicated Pool in Azure Synapse Workspace is recommended that you enable Synapse workspace features on an existing dedicated SQL pool (formerly SQL DW).

Read on for a few tips a nd a step-by-step walkthrough of the process.

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Azure SQL DB GP Compute Optimized Performance Comp

Reitse Eskens continues a series on comparing the performance of different Azure SQL DB tiers:

The compute optimized tier starts at 8 cores as a minimum and goes up to 72.
The maximum storage starts at 1 TB and  goes up to 4 TB. The number of TempDB files is undisclosed but the size starts at 37 GB and goes up to 333 GB. A huge difference with the regular provisioned one! The disk limitations are disclosed as well. Log rate starts at 36 Mbps and maxing out at 50. The same goes for the data; starting at 2560 iops and maxing out at 12.800. One iop (or Input Output oPeration) is connected to the disk cluster size. As these are 4 kb, reading or writing one data page (8Kb) equals two iops. 2560 Iops equals something of 10 MB per second. The top end goes to about 50 MB per second. Keep this in mind when you’re working out which tier you need, because usually disk performance can be essential.

Read on to see how it performs on Reitse’s standardized test workload.

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Diagnosing Performance Problems with EvaluateAndLog

Chris Webb wants to sort out some performance issues on calculation groups:

A few weeks ago I wrote a post showing how you can use the new EvaluateAndLog DAX function to diagnose performance problems relating to the use of the Switch function. Did you know that calculation groups can experience similar performance problems though? In some scenarios limited evaluation takes place for all calculation items, not just the one you expect; luckily you can use EvaluateAndLog to diagnose this too. In this post I’ll show you a simple example.

Read on for the example.

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