Thomas LeBlanc's Speaker Profile @ Sessionize

Effective Visualizations with Power BI

Using data visualizations help pen point positives or negatives with almost any data set. Power BI has lots of default visualizations plus custom ones that can be added. The art of selecting the best depends on the kind of data and what has been done to prepare the data. This session will guide attendees through selecting the right visualizations for the given data. Hints will be given on how to better suit the data for effective visualizations.

Power BI: AI and ML

AI & ML in Power BI are clicks not code. This session starts out using simple visuals to dive into descriptive analytics. It progresses to more AutoML like analysis of the data with the AI visuals. Then, we will step thru features added to Dataflows and PowerBI to utilize PowerQuery and AutoML. The preparation of the data will be demonstrated with Dataflows. The analytics retrieve from this feature helps with cleansing and preparation. All is done within Power BI service and skills already possessed by the analyst. The model will be trained and tested before data is used to predict a result.

What is Fabric?

This lighting talk will review the useful items Microsoft has made available in Fabric in the Public Preview. We will talk about the tools for a Data Engineer versus a Business Intelligence developer versus a Data Scientist.

Using Azure Synapse with Azure Data Lake

The modern data warehouse methodology has changed the patterns used for populating data warehouses. One of those is staging the data in a data lake. The data lake is a raw storage (bronze) of exported data from a source system. Some steps include cleaning this raw data and placing in the next stage – silver. The final dimensional model might be formatted into the data lake stage called gold. From there, it might be a one-to-one match in a relational table structure of dimension and fact tables. The Serverless option in Synapse Analytics lets you skip the relational data resource and place external table structures to reference the gold data lake format. You might think of these external tables like a SQL view. Please join me on creating this new paradigm during this session with many demos.

Using Azure Data Factory (ADF) to Load a Slowly Changing Dimension table

Slowly changing type 2 dimensions are typical in data warehouses. This enables historical tracking of attributes in a dimensional model. Power BI does not need a special indicator for this, just good modeling at the dimension and fact levels. Data Factory is the ETL/ELT tool used with Azure data services. Azure Synapse uses the same interface for pipelines. The slowly changing dimensions pattern is required for anyone integrating source data into a data mart. This session will demo a technique for implementing a pipeline for tracking historical changes in a dimension table.

Transition from SSMS to Azure Data Studio (ADS)

It is hard to move to a new tool when one works so well. Well, progress has to be made when the technology changes and the tool (SQL Server Management Studio or SSMS) does not keep up. This session will help navigate the common areas that I see SQL professionals use in SSMS and what the equivalent is ADS. SSMS users will also benefit from the architecture of ADS when it comes to extensions of the product. From queries, to performance tuning, to server management, all bases will be covered and time for Q&A.

The ends and outs of Visualizations

Using data visualizations help pen point positives or negatives with almost any data set. Power BI has lots of default visualizations plus custom ones that can be added. The art of selecting the best depends on the kind of data and what has been done to prepare the data. This session will guide attendees through selecting the right visualizations for the given data. Hints will be given on how to better suit the data for effective visualizations.

The end game: Lakehouse in Fabric

Power BI continues to make strides in enabling the service to do more and more in the data lake space. This session will traverse through the new tools for managing a lakehouse in the Power BI service. We will look at the initial ETL (extract, transform & load) tool (Power Query) to create data flows. Then, show the progression of Power Query used for Data Marts. The session will conclude on the latest release of Power BI to use pipelines and notebooks to manage data for visualizations.

SQL Server Execution Plans and indexes

To execute queries, the database engine of SQL Server analyzes the statement to find the most efficient way to access the required data. Analysis is done in the query optimizer. The input to the query optimizer comprises the query, the table and index definitions, and statistics. The output of the query optimizer is a query execution plan, sometimes referred to as a query plan or just an execution plan.
This session will go thru the history of execution plans as well as demos of using clustered, non-clustered and column-store indexes. After looking at the history, we will see where indexes are used and why they are not – sometimes. A separation of indexes in transitional (OLTP) databases will guide an attendee of the usefulness of a reporting system that uses more indexes than the OLTP system. The Query Store will be a repository of query plans and the dashboards/reports that can assist in performance tuning. Buckle up as we journey through being a better performance tuner in the SQL Server world.

Power BI licensing in 20 minutes

This session will walk thru the Power BI licenses. The main areas will be features, cost and resources. We will navigate the service for options that are enabled or disabled for a licensed user. Each license has a different storage for content and abilities to share - or distribute content.

Power Bi and Dimensional Modeling

What is dimensional modeling? Why is it needed for Power BI? Are there benefits to using dimensional modeling with Power BI? Yes, dimensional modeling is best for modeling in Power BI. The terms most frequently used to describe dimensional modeling is star schema or facts and dimensions. Even though the Kimball Group has ‘retired’, the methodology spelled out by this group still works great in today’s business intelligence reporting. Facts are for the measures with surrogate keys to related dimensions. The dimensions are the place for de-normalized attributes for slicing and dicing data. There won’t be any cons listed during this session, only the benefits like performance improvements and simplicity as we roll through the demos.

Power BI Administration

With the expansion of features and licensing options for Power BI, the administration has become more and more important. Instead of just tweaking the data connection you now have to control roles within the workspace, data gateway as well as capacity(s). Within the workspace, the roles have expanded from admin to member, contributor and viewer with options for re-sharing and a build permission. Beyond roles, we now have to multiple options for monitoring the system and the community has created some cool template apps to help. Let's review these and other hidden gems for managing the Power BI environment at Live!360!

Making Power BI Run Faster!!!

This session will review the areas of performance problems and helpful tools. From data refreshes to model calculations and visual rendering to data model design, performance tuning is vital to keep the attention of a report user. More than 5 or 6 seconds for a refresh can drive someone to not use a report. There are many ways to handle tuning, but it should start in the design. This is not always the case, so internal and external tools are there to guide the current developer. We will talk about query folding, minimizing data loaded as well seeing the difference between the storage and formula engines. Come join me for tuning Power BI reports and the data models.

Introduction to Execution Plans

Using Execution Plans to performance tune SQL Server queries can be a great starting point to help a database application run optimally. Execution Plans have been around for some time now and Microsoft has improved the interface and extra tools with every release.

Join Thomas as he walks through the history of execution plans. The session will progress to Scans and Seeks while dipping into Loops and Lookups. These are the basic skills needed to start using execution plans. We will conclude the session with the new features released with SQL Server 2019.

Intro to Microsoft Fabric

This lighting talk will review the useful items Microsoft has made available in Fabric in the Public Preview. We will talk about the tools for a Data Engineer versus a Business Intelligence developer versus a Data Scientist.

How to become a data analyst in 20 minutes

What are the skills needed to be a data analyst? What jobs are available for a data analyst? What can I look forward to in a career once I outgrow the analyst position?

Faster Power BI Reports

This session will review the areas of performance problems and helpful tools. From data refreshes to model calculations and visual rendering to data model design, performance tuning is vital to keep the attention of a report user. More than 5 or 6 seconds for a refresh can drive someone to not use a report. There are many ways to handle tuning, but it should start in the design. This is not always the case, so internal and external tools are there to guide the current developer. We will talk about query folding, minimizing data loaded as well seeing the difference between the storage and formula engines. Come join me for tuning Power BI reports and the data models.

Azure Synapse Analytics

This all encompassing workspace gives the Data Engineer resources for transforming data into efficient analytics for analyst and data science professionals. This session will step through the setup to create a workspace. Using polybase and SQL Pools will show the power of T-SQL in such an environment. The Spark pool will be discussed for the data scientist using Spark or python as well as the Orchestration from integrated Azure Data Factory. Lastly, Power BI integration will give the users the ability to visualize the analysis

Introduction to SQL Server Query Data Store

SQL Server’s Query Data Store is a brilliant option for databases if you are a DBA or have to do performance tuning on T-SQL queries. This feature of SQL Server 2016 or higher is contained within a database with configuration options for storage of history and query plans. The option has many benefits you can take advantage of today, but also this is an option for future upgrades to new SQL Server versions because of the history contained in the database rather than the instance.

Getting started with SQL Server Analysis Services - Tabular Model

This is a start to finish session on developing an enterprise analytical database with Analysis Services (SSAS) Tabular Model. The session will provide an introduction to Analysis Services and why it should be used. The rest of the time will be spent in live demonstrations building an analytical database. The session will start with a blank project and show the steps for data sources and data sets. The data modeling will include relationships and hierarchies. The DAX language will be used to create measures including a Key Performance Indicator. The more advanced examples will include a many to many relationship as well as a slowly changing dimension table. The reporting will be throughout the session using Excel PivotTables/Chart as well as Power BI.

Time Intelligence in Power BI

Once a model is built in Power BI, the developer will need to understand what's already built to help with date grouping. A simple data table can be added as a dimension table from a Data Mart or built with a DAX formula. This session will look at the Date Table in a Tabular Model in Power BI. The flow will continue into the DAX functions most useful. There will be a guide with example code for groupings and filters. The Time Intelligence in Power BI data models is one option a developer should always include.

Performance Tuning for Developers

This session highlights the starting point for using the execution plans from SQL Server to assist in query tuning. Briefly, we will look at the history to get an idea of how Microsoft has improved the display through Graphical Plans and Missing Index suggestion. What is the difference between a Table and Clustered Index Scan? What is a Lookup? How do you improve performance of Lookups? What are the different types of Loops? How to get more information from the Plan with the properties window?

Data Mining in SQL Server 2017 On-Premise

This demo filled session will step through some predictive modeling functionality built into SQL Server Analysis Services (SSAS). We will start by review the data modeling and data cleansing needed before any predictive model design and implementation. SQL Server provides this with Integration Services, T-SQL and Data Quality Services.

After the data is formatted adequately for predictive modeling, we can start modeling and finding the best fit for examining forecasts and what if scenarios. SSAS provides models for clustering, neural networks, logistic and linear regression, decision tress and Naïve Bayes classifiers. We will not have time for all models, but be stepping through 2 or 3, you will leave with your brain firing on all cylinders about predicting data for your company or consulting firm.

Transition from SSIS to Azure Data Factory

With v2 (and possibly v3), ETL developers are going to wonder how to migrate or re-create data flows in Azure. The session will look at the history of Azure Data Factory and explore the differences between it and SSIS. A new mind set is needed, but just like SSAS from cubes to tabular, it is all for the better.

What's new in SQL Server 2019 for Analytics

SQL Server 2019 has many new features for everybody, but for analytics, this session gets more specific. To increase performance, the column store index has some enhanced Batch mode processing and re-indexing improvements. Analysis Services (SSAS) has been update for the long awaited Calculation Groups which is similar to Scoping in Multidimensional. DAX updates include Single Calculation (filter context) and Sideways recursion. Performance improvements with window aggregate functions for those not using SSAS. The Power BI Report Server has been improved along with more Mobile display options. The session will end with some of the updates for Polybase and data virtualization.

SQL Server Indexes for Developers

If you are designing databases, meaning creating tables, this session is for you. The session will review the normal primary key, clustered and non-clustered indexes. But the meat of the session will help a developer understand some of the new indexing added over the last decade. These will include indexed views, column store indexes as well as filtered Indexes. Included with these indexes will be an overview of the maintenance that needs to be maintained on indexes as well as statistics. Some changes has been made to the maintenance abilities and there are some specifics that need to be known and learnt.

Azure Machine Learning: Likely To Buy A Bike

The session will use a sales database to predict the likelihood of a customer to buy a bike (or another product). The modeling in Azure Machine Learning (AML) provides such an application for this prediction. The cloud-based algorithms involve a multitude of steps along the way. The analyst can use AML to decide the architecture and/or features needed for such an analysis. This includes training the model with data and tuning the model. Azure let's the analyst scale down for testing, then scale up for large data sets. Model versions can be tracked as well as using different parameters. Even though the flow looks complex, the steps are intuitive all the way to deployment to others to use.

Machine Learning in Power BI

The new Machine Learning in Power BI consists of clicks not code. Sounds to good to be true? The session will step thru the additional features added to Dataflows and PowerBI to utilize the ETL built into PowerQuery. The preparation of the data will be demonstrated with Dataflows. The analytics retrieve from this features helps with the necessary data cleansing and preparation needed for a model. Called AutoML, this option enables the data prep integrated into a model for reporting in Power BI. All is done within Power BI and skills already possessed by the analyst. Model quality and visibility is available through these steps while creating a ML in an awesome reporting tool.

Thomas LeBlanc

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Live! 360 Orlando 2021 Sessionize Event

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Thomas LeBlanc

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