Azure Cosmos DB – HTAP using Azure Synapse Link

I haven’t done much blogging in the last 6 months. My last article was posted in January, on Data Governance in the Covid era. After changing jobs in March, I prioritized getting settled into my new role. That said, I have had the writer’s itch for a while and thought what better topic to break the rut with than Cosmos DB. I am a big fan of Cosmos DB – Microsoft’s multi-model NoSQL database service in Azure, and I have written a few articles on the topic.

Recent stats of my blog show more interest in data movement out of Cosmos DB, possibly for reporting purposes. Check out my 2019 article on real-time data movement from Cosmos DB using Change Feed and Azure Functions, where I explained how to persist container changes to SQL Server. Well, that is one way to enable real-time analytics. Cosmos DB has evolved a lot since then, and there are new and smarter ways to achieve similar results. Last year (Dec 2020), Microsoft announced the general availability of Azure Synapse Analytics – the unified analytics service in Azure.

sqlroadie – 2021 blog stats

HTAP capabilities of Cosmos DB using Synapse Analytics

In this article, we will explore the HTAP capabilities of Cosmos DB. The goal is to derive real-time insights from transactional changes made to Cosmos DB, in a cost-effective manner with minimal overhead. I want the solution to be scalable, reliable and overall simple to maintain.

What is HTAP?

HTAP stands for hybrid transaction/analytical processing. Traditionally, data systems can be classified as either OLTP – Online Transaction Processing or OLAP – Online Analytical Processing. Amazon shopping website is a good example of an OLTP system, as it manages millions of customer orders daily in real-time. However, Amazon would have billions of historical customer order details in their OLAP data warehouse. This helps them to create product recommendations by analyzing historical order data, and serve it to the OLTP system through analytics APIs.

Ordinarily, transaction data has to be copied or replicated from OLTP -> OLAP databases through ETL processes, and reporting/insights/recommendations generated in the OLAP system at a latency that made business sense. This is a tedious process and involves a lot of overhead. Majority of enterprises still use this approach. However, advancements in computing have enabled a new age HTAP architecture.

Gartner defines HTAP as “a hybrid transaction/analytical processing (HTAP) architecture is best enabled by in-memory computing techniques and technologies to enable analytical processing on the same data store that is used to perform transaction processing. By removing the latency associated with moving data from operational databases to data warehouses and data marts for analytical processing, this architecture enables real-time analytics and situation awareness on live transaction data as opposed to after-the-fact analysis on stale data.

This is exciting stuff, and quite a game-changer in the world of databases. Enough said, how do we go ahead and design a solution using Cosmos DB’s HTAP capabilities!

Synapse Link for Cosmos DB

Synapse Link for Cosmos DB is a cloud-native HTAP capability that creates a tight seamless integration between Cosmos DB and Synapse Analytics. It gives you the ability to gain near real-time analytics and insights over operational data in Cosmos DB with no ETL and no impact to the performance of OLTP transactions in Cosmos DB containers.

Let us break that down. Cloud-native (if you didn’t click the link) is the modern approach, where you design systems using cloud services to scale and perform well consistently. Synapse Analytics (if you are not familiar) can be thought of as Microsoft’s cloud data warehouse, for starts. And that is because Synapse is a lot more than that, but we have to start somewhere, don’t we?

So, Synapse Link creates a seamless, automagic integration between Cosmos DB and Synapse Analytics, thus removing the need for ETL. How does that work? Because a picture is worth a thousand words, I have referred to a diagram from Microsoft’s official doco on Synapse Link. The highlighted portion is new and works behind the scenes to deliver near-real time analytics with no additional ETL. When we create a container in an Azure Cosmos DB account that has Synapse Link enabled, an analytical store is automatically provisioned. This analytical store helps to ensure that there is no impact to OLTP transactions against the Cosmos DB container.

Image courtesy: Microsoft

This is a robust mechanism to perform large-scale analytics over data in Cosmos DB and implement an archival policy efficiently to keep the containers sleek without losing access to valuable historical data. It is worth noting that the transactional and analytical stores get their own, separate TTL (Time to Live) properties. TTL is one of my favorite features of Cosmos DB. Self-destruction can be so powerful sometimes! I will cover this in more detail in my sample solution.

Sample Solution

Confucius says, “I hear and I forget. I see and I remember. I do and I understand”. So, I built a sample solution that would ingest data into Cosmos DB at scale, and I was impressed at just how quickly data was available downstream in Synapse Analytics. The sample solution features a Python Notebook, running on Databricks and ingesting the popular New York City taxi trip data into a Cosmos DB container using Cosmos DB Spark 3 OLTP connector for SQL API. The trip data that became available in Synapse Analytics was used to build a Power BI dashboard, thus effectively producing near real-time analytics with no ETL and minimal code. I will give a step-by-step breakdown of the sample solution. Refer to this guide by Microsoft for more details.

Sample Cosmos DB HTAP solution
Sample solution using NYC Taxi trip data

1. Cosmos DB container

Firstly, we want to enable Synapse Link in the Cosmos DB account using the Settings blade. It is worth noting that at the time of writing this article (Oct 2021), Synapse Link and analytical store is supported only for SQL and MongoDB APIs.

Enable Azure Synapse Link

At the moment, analytical store can be turned ON only for new containers. If you wish to make use of Cosmos DB’s HTAP capabilities for an existing container, you will need to migrate data to a new container. Note that containers cannot be renamed. Auto-sync latency is within 2 minutes, as per Microsoft documentation. In my tests, I did not see a delay of more than a minute or so for data to be available downstream in Synapse Analytics.

New Cosmos DB container with Analytical store turned ON

Analytical store does not need request units (RU/throughput) to be allocated, nor does it consume the RU assigned to the transactional store – 1000 in the snip above. Analytical store follows a consumption-based pricing model, which is based on data storage and analytical read/write operations and queries executed. Storage can be optimized by setting the Analytical Store Time To Live property in the Data Explorer blade under Scale & Settings option. This dictates for how long data would be retained in the analytical store, and is independent from the container TTL property. You should set the Analytical Store TTL to a value that gives you sufficient time to process the data and persist or derive insights from it as required. At the time of writing this article (Oct 2021), analytical store does not support backup and restore, i.e. if a Cosmos DB backup is restored, only the transactional store will be recovered. It is also worth noting that schema representation in the analytical store could be different from the container schema.

Analytical Store Time to Live

2. Databricks – Python notebook to write NYC Taxi trip data to Cosmos DB

In the sample solution, I used a Python notebook to write data to Cosmos DB using the Spark 3 OLTP Connector for SQL API. This was just to simulate incoming OLTP application requests.

Python notebook
Sample data

3. Synapse Analytics

In the Synapse Workspace, Cosmos DB analytical store may be accessed using the Spark pool or built-in serverless SQL pool. I used the serverless SQL pool, which is a pay per query distributed data processing system built for big data and computational functions.

In the Synapse Studio, I created a linked service by connecting to the external Cosmos DB resource.

Connect to Azure Cosmos DB

In this case, I chose Managed Identity as the authentication method.

New Linked Service – Cosmos DB SQL API

In a little while, the linked service was added. At this stage, we are almost ready to query the underlying analytical store of GreenTaxiTrip container.

The next step was to create a user database in the serverless SQL pool, so that a CREDENTIAL could be created to access the Cosmos DB database securely. You cannot create the credential in master database.

4. Data Ingestion

I scheduled a Databricks job to execute the Python notebook to ingest NYC Taxi trip data from DBFS to Cosmos DB using the Spark 3 OLTP connector for SQL API. You may also execute it manually. Once data became available in Synapse Analytics through the analytical store, I wrote a couple of queries over the data using familiar T-SQL and converted them to views.

Databricks Job
Cosmos DB query

Note the number of records, and the corresponding timestamp in the transactional store. When the record count query completed in Synapse Analytics 9 seconds later, the number of records were out by only 545! Auto-sync latency is 2 minutes, as per Microsoft doco. It is worth noting that even if a document had a TTL set to lower than 2 minutes, it would still appear in the analytical store however, I have not tested it.

Cosmos DB – transactional store record count

My test dataset had 7 million records, and I left the job running to see how well the analytical store would perform. I was quite satisfied with the performance of Synapse Link. In this case, I used only one container but it would be interesting to see how well auto-sync holds up at scale.

5. Near real-time analytics using Power BI

Having validated that trip data written to the Cosmos DB container were visible in Synapse Analytics in near real-time, I proceeded to create 2 simple views using the OPENROWSET function, and a Power BI dashboard that used the views as its data source. Please excuse my lazy visualization efforts, but the point is you can easily get near real-time insights over operational data at scale using Synapse Link. All without any ETL!

Views created in Synapse serverless SQL pool over data in analytical store

Other alternatives

When it comes to enabling analytics over operational data in Cosmos DB, I cannot think of a better way to do it. The Change Feed way of doing it is not as scalable or easily maintainable as using Synapse Link. The obvious difference is that you will need a Synapse Analytics account to make use of Synapse Link. If you are a Microsoft house, this would make sense as Synapse is the unified analytics service and integrates directly with several services such as Azure Machine Learning, Cognitive Services and Power BI.


At the time of writing this article (Oct 2021), Synapse Link for Cosmos DB is only supported for the SQL and Mongo DB APIs. I think it will be really cool to have it enabled for the Gremlin API. Graph databases are getting increasingly popular, and graph-enabled analytics such as fraud detection and money laundering is bound to become more common place.

More limitations are available here. The most glaring one is the lack of support for backup and restore of data in the analytical store.

The road ahead

HTAP is made possible by the power of cloud computing. I believe it will not be too long before HTAP capability is available for Azure SQL databases. That will be quite a game changer!

Aside from the basic Business Intelligence feature I demonstrated in the sample solution, Synapse Analytics also enables batch scoring using native scoring for models trained using RevoscalePy and RevoscaleR packages, AutoML to develop a Regression/Classification/Time Series Forecasting model using the Azure Machine Learning linked service, and multi language support for advanced analytics using the Spark pool.

Cosmos DB’s HTAP capability is fascinating, and I can think of several use cases to enable real-time decision intelligence at scale. I hope you had a good read. Please leave a comment if you have questions or any feedback.

References – Jovan Popovic – Microsoft Mechanics

Demo: Predictive Modeling using R and SQL Server Machine Learning Services

Late last year, I wrote a series of articles about Predictive Modeling using R and SQL Server Machine Learning Services. At the time, I thought MLS was an underutilized feature of SQL Server. With a view to sharing my learning on the topic, I presented a session at the local SQL Server User Group earlier this month.


MLS at QLD SQL Server User Group

This article will be focused on content presented at the User Group meeting. Below is the presentation I put together. If you are new to Machine Learning on premises, it will help you understand the capabilities of this powerful feature of SQL Server.


As a typical data nerd, I was more excited about the demo than the session itself. For the demo, I chose to predict heart disease risk using the popular heart disease data set from UCI.

  1. Using R, a predictive model is trained and tested against known results.
  2. After testing and comparing a few Machine Learning models, the R scripts were wrapped in SQL Server Stored Procedures letting us execute R scripts through Stored Procedures
  3. The trained models were stored in a SQL Server table, and were used to perform Machine Learning predictions through Stored Procedure calls
  4. Last step in the demo covered Native Scoring using the native C++ extension capabilities in SQL Server 2017

Demo solution is available for download at

Solution contains the following files:

  • – UCI dataset attached for reference heart-disease.names – Data description. Go through this file to understand what the variables mean
  • PredictiveModelingUsingR.r – R script (with comments wherever applicable) that builds the predictive Model using RevoScaleR package. Go through this to understand how the models are created and used for prediction.
  • PredictiveModelingUsingMLS.sql – SQL script that uses R code covered in the previous file to build a Machine Learning predictive model that is executed in the SQL on premises instance

Predictive Modeling using R and SQL Server Machine Learning Services

If you need a hand with the demo, please drop me a note. LinkedIn is the best way to get in touch with me. Happy learning!

Thanks to Wardy IT and their Marketing Manager, Michaela Murray, for their continued efforts on organizing the user group meetings.

Analyzing Heart Disease risk using Key Influencers AI visual in Power BI

The Gartner Magic Quadrant for 2019, announced earlier this month, names Microsoft the leader in Analytics and Business Intelligence Platforms. Microsoft also coincidentally announced the public preview release of its first AI-driven visual for Power BI Key Influencers – this month, among a number of new features for Feb 2019. Inbuilt integration of Power BI with many Azure data products would catapult Power BI miles ahead of Tableau in the long run.


Key Influencers is the first of many AI visuals Microsoft would release I assume, in their efforts to democratize AI and make their customers look cool 🙂 In this article, we will go over the various features of this new visual using a publicly available dataset, and get familiar with interpreting the results. Download a copy of Power BI Desktop file for the example I am using in this article and try it out yourself using the free Power BI Desktop tool.

Key Influencers

Key Influencers is a powerful Power BI visual that lets us understand the factors that drive a metric. Power BI analyzes data, ranks the factors that matter, and displays them as key influencers. Under the hood, Power BI uses ML.NET to run logistic regression to calculate the key influencers. Logistic regression is a statistical model that compares different groups to each other, also taking into consideration the number of data points available for a factor.

As the visual is still in preview, there are a number of limitations. My first attempt to use Key Influencers using a survey responses dataset was rather unimpressive.

In my second attempt, I used the popular Heart Disease dataset from UCI to identify key influencers affecting heart disease, and achieved good results.

Heart Disease - Key Influencers Power BI.jpg


Before we delve any further, let us take a look at the limitations that apply in the public preview phase of the visual. Pay attention here to avoid frustration as you explore the visual.

Following features are not supported:

  • Analyzing metrics that are aggregates/measures
  • Direct Query / Live Connection / Row Level Security – support
  • Consuming the visual in Power BI Embedded and Power BI mobile apps

Using the Key Influencers Visual

As a first time user, I found the Key Influencers visual intuitive and self-explanatory. It hardly takes a few minutes to set up the visual once you have clean data. Check out Microsoft documentation to understand all aspects of the visual. You could also download a copy of Power BI Desktop file for the example I am using in this article.

Note: Keep column names readable as this will help interpret the visual better

Getting Familiar

There are 2 tabs available within the visual – Key influencers and Top Segments.

The Key influencers tab displays the key factors affecting the metric value selected. In this case, the top factor that affects positive diagnosis of Heart Disease, based on our dataset, is Reversible Defect Thalassemia – increasing the risk of heart disease by 2.83 times when the value of Reversible Defect Thalassemia is 7.

On the right hand side, there is a column chart showing distribution of the selected factor. The check box at the bottom lets you display only influential factor values. We could click-select a different factor to see how it contributes to heart disease.

Heart Disease - Key Influencers Power BI - Getting Familiar.jpg

The Top segments tab displays different segments identified by Power BI within the population, for the metric value selected. Click-select a segment to view more details such as the factor values that define the segment, and how the segment compares against the average. We could also drill down further into the segment to split by additional fields.

Under the hood, Power BI uses ML.NET to run a decision tree to find interesting subgroups. The objective of the decision tree is to end up with a subgroup of datapoints that is relatively high in the metric we are interested in – in our case, the patients who  are suspected to have heart disease.

Heart Disease - Key Influencers Power BI - Top Segment.jpg


Heart Disease - Key Influencers Power BI - Top Segment Details.jpg

First Impression

Considering that it is still in preview and is only going to get better, Key Influencers ticks the right boxes. The rationale behind choosing a popular dataset, such as the Heart Disease dataset from UCI, for my example was to allow for comparison of results to Machine Learning models that are already publicly available. Power BI seems to identify influencers correctly and does a good job at presentation. I’m thoroughly impressed by this new feature.

Suggested Reading

If you enjoyed this article, consider reading my other articles on Azure data products.


Download the Power BI workbook used in the example –
Intro to Key Influencers by Microsoft:
Power BI Feb 2019 feature summary –

Heart Disease Data source
Donor:  David W. Aha (aha ‘@’ (714) 856-8779

  • Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D.
  • University Hospital, Zurich, Switzerland: William Steinbrunn, M.D.
  • University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D.
  • V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D.

Azure Databricks – Introduction (Free Trial)

Microsoft’s Azure Databricks is an advanced Apache Spark platform that brings data and business teams together. In this introductory article, we will look at what the use cases for Azure Databricks are, and how it really manages to bring technology and business teams together.


Before we delve deeper into Databricks, it is good to have a general understanding of Apache Spark.

Apache Spark is an open-source, unified analytics engine for big data processing, maintained by the Apache Software Foundation. Spark and its RDDs were developed in 2012 in response to limitations of MapReduce

Key factors that make Spark ideal for big data processing are:

  • Speed – up to 100X faster
  • Ease of use – code in Java, Scala, Python, R and SQL
  • Generality – use SQL, streaming and complex analytics
Apache Spark Ecosystem.jpg
Pic courtesy: Microsoft

Databricks – the company – was founded by creators of Apache Spark. Databricks provides a web-based platform for working with Spark, with automated cluster management and IPython-style notebooks. It is aimed at unifying data science and engineering across the Machine Learning (ML) life cycle from data preparation, to experimentation and deployment of ML applications. Databricks, by virtue of its big data processing capabilities, also facilitates big data analytics. Databricks, as the name implies, thus lets you build solutions using bricks of data.

Azure Databricks

Azure Databricks combines Databricks and Azure to allow easy set up of streamlined workflows and an interactive work space that lets data teams and business collaborate. If you’ve been following data products on Azure, you’d be nodding your head along, imagining where Microsoft is going with this 🙂

Azure Databricks enables integration across a variety of Azure data stores and services such as Azure SQL Data Warehouse, Azure Cosmos DB, Azure Data Lake Store, Azure Blob storage, and Azure Event Hub. Add rich integration with Power BI, and you have a complete solution.

Azure Databricks Overview
Pic courtesy: Microsoft

Why use Azure Databricks?

By now, we understand that Azure Databricks is an Apache Spark-based analytics platform that has big data processing capabilities and brings data and business teams together. How exactly does it do that, and why would someone use Azure Databricks?

  1. Fully managed Apache Spark clusters: With the serverless option, create clusters easily without having to set up your own data infrastructure. Dynamically auto-scale clusters up and down, and auto-terminate inactive clusters after a predefined period of inactivity. Share clusters with your teams, reduce time spent on infrastructure management and improve iteration time.

  2. Interactive workspace: Streamline data processing using secure workspaces, assign relevant permissions to different teams. Mix languages within a notebook – use your favorite out of R, Python, Scala and SQL. Explore, model and execute data-driven applications by letting Data Engineers prepare and load data, Data Scientists build models, and business teams analyze results. Visualize data in a few clicks using familiar tools like Matplotlib, ggplot or take advantage of the rich integration with Power BI.

  3. Enterprise security: Use SSO through Azure Active Directory integration to run complete Azure-based solutions. Roles-based access control enables fine-grained user permissions for notebooks, clusters, jobs, and data.

  4. Schedule notebook execution: Build, train and deploy AI models at scale using GPU-enabled clusters. Schedule notebooks as jobs, using runtime for ML that comes preinstalled and preconfigured with deep learning frameworks and libraries such as TensorFlow and Keras. Monitor job performance and stay on top of your game.

  5. Scale seamlessly: Target any amount of data or any project size using a comprehensive set of analytics technologies including SQL, Streaming, MLlib and GraphX. Configure number of threads, select number of cores and enable autoscaling to dynamically scale processing capabilities leveraging a Spark engine that is faster and performant through various optimizations at the I/O layer and processing layer (Databricks I/O).

Of course, all of this comes at a price. If this article has piqued your interest, hop over to Azure Databricks homepage and avail the 14 day free trial!

Azure Databricks - Free Trial 14 days.jpg

Suggested learning path:

  1. Read more about Azure Databricks –
  2. Create a Spark cluster and run a Spark job on Azure Databricks –
  3. ETL using Azure Databricks –
  4. Stream data into Azure Databricks using Event Hubs –
  5. Sentiment analysis on streaming data using Azure Databricks –

I hope you found the article useful. Share your learning experience with me. My next article will be on Real-time analytics using Azure Databricks.

Azure Databricks - Real time analytics.jpg
Azure Databricks


Global AI Bootcamp – Developing AI, responsibly

Global AI Bootcamp, Brisbane 2018

Yesterday, I attended the Global AI Bootcamp Brisbane (at the Precinct, Valley) along with nearly 100 other technology enthusiasts. The event was well organized by David Alzamendi of Wardy IT Solutions and Thiago Passos of SSW Consulting. I rocked up to the event hoping to get an update on the rapidly evolving Data Platform offerings from Microsoft. While the event did meet most of my expectations, it planted one particular seed of thought in my head. As I walked away at end of the day, I was enthralled about the rigorous, almost paranoic, awareness and research of the social responsibility that AI developers and solution providers should exert.


Role of Ethics in AI

The event started with playback of the recorded keynote address by distinguished researchers of Microsoft AI. It was probably the small shot of long black coffee I had just had, I sat there wide-eyed and amazed by the wise words of Hanna Wallach, Principal Researcher at Microsoft Research, NYC. Hanna’s research covers a broad range of topics; she was clearly passionate about the impact of AI on society – FATE (Fairness, Accountability, Transparency and Ethics in AI). I had never thought about ethics in AI the same way, but it made perfect sense.

The one reaction that the average Joe has to AI is the notion that it is almost magical, but always reliable and authentic. That’s a dangerous prejudice! AI, much like any other branch of science, can be used for good or bad. The elevated status that AI enjoys amongst the masses, thanks to Hollywood movies, and research companies pitching AI as the field of science that would shape 21st century, leads to the belief that AI = TRUTH! Those in the know, are aware that inherent biases in training data sets lead to biases in scoring. My heart skips a beat just to think how a technologically illiterate person may be led to believe utter lies, much like the predictions of this highly controversial Israeli company – Faception. They claim to be able to apply facial personality analytics technology to predict a person’s IQ, their personality – whether they are an academic researcher or a terrorist, for instance, just by looking at their face.

Utilizing advanced machine learning techniques we developed and continue to evolve an array of classifiers. These classifiers represent a certain persona, with a unique personality type, a collection of personality traits or behaviors. Our algorithms can score an individual according to their fit to these classifiers (sic).

When Vanessa Love, Assistant Director of Integration and DevOps at Australian Bureau of Statistics, talked about Faception during her session – I ain’t afraid of no terminator – at the Bootcamp, my initial impression was that the company was called out on its claims and was obviously identified as a scam. I could resonate with her frustration and anger as she went on to explain how Faception was working with governments, and clients in Fintech and Retail. There are numerous such shocking applications of AI. For instance, Stanford researchers built an AI solution that could predict a person’s sexuality from facial analysis. The only aspect that is more appalling than the intent of their research is the fact that the average Joe doesn’t read the T&Cs – in this case, their model was correct only 81% and 71% of the times in predictions for males and females respectively. So, what about the 48 wrong predictions for every 152 correct predictions? Vanessa also mentioned Amazon’s AI enabled recruiting tool that was stood down due to racial and sexist biases. In this case, AI helped to reveal the truth about inherent historical bias in recruitment practices at one of the biggest technology companies. So, sometimes AI = TRUTH. Tricky? Food for thought!

Will AI enslave human beings?

The age-old question! This is a recurring question I am asked when I discuss AI with less technologically-literate acquaintances. I usually go on to explain how Machine Learning works, and the differences between Supervised and Unsupervised learning. The key point I try to drive home is that AI is not a person or a thing, and more importantly, like all software solutions, it is error prone and not to be taken for granted. When we do take technology for granted, self-driving cars kill people and auto-pilot programs crash planes. Technology is meant to aid and assist, not render humanity obsolete!

Developing AI, responsibly

Luckily, researchers like Hanna Wallach and Yoshua Bengio are actively working on building a code of conduct for AI research and application. A result of that vigil is the Montreal Declaration for Responsible Development of Artificial Intelligence, inked earlier this month. At the time, I read about it and quickly slid that thought to the slow sectors of my brain. I signed the declaration a little while ago. As a technologist, I not only have the responsibility to develop AI responsibly, but also educate others about the pros and cons of  AI solutions.

Other interesting learnings from the Bootcamp

Jernej Kavka, Software Architect at SSW Consulting, presented his experiments with Real-Time Face Recognition using Microsoft Cognitive Services. He explained how his team successfully reduced costs by 99% by applying caching and pre-processing. I found his session remarkable.

Joseph Zhou, Data Scientist and Solution Development Consultant at Avanade – talked about drag-and-drop AI using Azure Machine Learning Services. I found his session crisp and relevant. Later, Yousry Mohamed, Consultant at Readify, explained how to apply DevOps practices in Azure Machine Learning and automating model-selection using “a bit of simple code”. As always, Yousry’s presentation was animated and wonderful.

A day well spent!

Overall, it was a day well spent. Thanks to all sponsors and volunteers for making the event happen! I could tell everyone was excited to be there, and we all went home with various thoughts in our little heads, a little wiser than we were at start of the day. The thought in my head was – what about the Tesla driver who relied on the self-driving capability, what about the black Facebook employee who the soap dispenser denied, what happens when AI goes wrong?