An Enterprise Data Architecture Framework

One of the challenges facing enterprise architecture is maintaining consistency across the enterprise. This is challenging because data comes from disparate sources and systems representing different purposes, focuses, and objectives. On top of that, there needs to be more clarity regarding the terms Data Lake, Data Warehouse, Operational Data Store, etc. To overcome these challenges at an enterprise level, a framework is necessary to apply data uniformly, consistently, and meaningfully. The framework will transform data from multiple, disparate data sources into an operational data store, a consistent, homogeneous environment, as the single dependable source of truth for all reporting and analytics.

This session will present that framework and discuss the following:
• The challenges facing data analytics and reporting due to multiple disparate data sources
• An introduction to the various terms used today for data storage
• The purpose of an Operational Data Store and how it improves data usability
• The data zones for Raw, Refined, and Published data
• How data standards simplify and enhance data usability
• A method to provide primary and foreign keys across disparate data

Migrating AdventureWorks to Postgres

One of the advantages that SQL Server developers have enjoyed is a comprehensive sample database that touches on all aspects and operations of a database. That same sample database, AdventureWorks, is now available for Postgres! However, it is not just the data in tables but all views, triggers, procedures, functions, and other database objects. Data type differences, procedural differences, and others are highlighted and discussed. This session highlights several differences between SQL Server and Postgres using AdventureWorks as an example.

"There Be Whales Here!" -- Big Data in Relational Databases

Due to varying definitions of Big Data, which confuse data volume and organization, the common perception is that Big Data requires using Hadoop, MongoDB, or another NoSQL database. Relational Databases can easily store and manipulate large volumes of data and perform exceptionally well, provided the database is appropriately set up and tuned. In this session, we'll cover some of the definitions and misconceptions of Big Data, look at some Big Data solutions that went wrong, and review a case where Big Data was successfully implemented in Relational Databases. Finally, we'll look under the covers to see how Relational Databases best handle Big Data through proper data models, table designs, performance tuning, and stored procedure techniques.

The Power of Window Functions

Window functions have nothing to do with the Windows operating system but provide a "window" in a data set when used in SQL queries. They are powerful, set-based methods to view data in unexpected ways. Window functions provide easy, set-based solutions to solve some common problems:
• Using running totals
• Performing operations on intervals
• Identifying data gaps and islands
• Performing aggregates without losing detail

Many people are confused by window functions and do not know how to use them properly. This session will explore window functions, how they work, and how to use them. Demonstrations with several examples will solidify how window functions can simplify queries and make them more powerful.

The Lost Science of Set Theory

Many database developers today do not have a Computer Science degree and may also have begun their career developing software using procedural programming languages (Java, C#, etc.). As a result, they may not have the background needed to conceptualize solutions in terms of sets of data and therefore may not be able to develop efficient set-based queries. In this session, we will review the basics of set theory, relational algebra and how they apply to database development to turn cursors, loops, and other procedural programming practices into efficient set-based operations that make the most use of a relational database engine.

Tally Ho! — Explore the Varied Uses of Tally Tables

Relational databases are designed to operate efficiently with set-based queries; they do not perform as well with loops and cursors. However, there are situations where you need a loop of some fashion. Tally tables give you the best of both worlds by allowing you to effectively “loop” through records in a set-based manner. We’ll explore what tally tables are, how they work, and, more importantly, review some of the many ways you can use them to turn your loops and cursors into efficient set-based operations.

SQL Team Six: How to Build Effective Teams

Teamwork is important in all areas, including development, QA or other teams that we interact with daily. Lessons learned from highly effective teams show that there are six major impacts to team effectiveness: chain of command, team cohesion, standard operating procedures, training, mission objectives and after action analysis. A failure in any of these areas can impact team effectiveness. In this session, learn what can improve or destroy team effectiveness and what you can do about it.

This session describes the attributes of successful leadership and team effectiveness, highlights the issues that can cause their downfall, and provides steps to improve them.

How to Give a Technical Presentation

Most people don’t like to speak in public and technical folks are certainly no exception. Yet, you’ve got some ideas for a great presentation but just don’t know how to go about putting it all together. On top of that, how do you go about giving the presentation itself? This session will cover how to pull your material together in an organized fashion and present it such that your audience (both in person and virtual) can follow along and get the message you want to relay. We’ll also discuss how to deal with issues that cause presenters stress to help you to make the process enjoyable and rewarding for all.

Defining What’s Normal — The Basics of Database Normalization

Determining how to efficient design database structures for a given application has always relied on the same old answer: “It depends!” So, how do you determine what level of normalization your data requires? This session will review the principles of data normalization, show the impact that normalization can have on performance and data integrity, and just as importantly, show you when it’s OK to break the rules and denormalize. We will cover some case situations where it’s better not to be normal!

SQL Team Six: How to Build Effective Teams (Workshop)

Teamwork is essential in all areas of our work life, including how we interact with other teams daily. Lessons learned from highly effective teams, such as SEAL Team Six, show six significant impacts on effectiveness:
• Chain of Command
• Team Cohesion
• Standard Operating Procedures
• Training
• Mission Objectives
• After-Action Analysis

Failure in any of these areas can impact team effectiveness. This workshop explores these six areas to understand what can build or destroy team effectiveness. The exercises and discussions teach several techniques that will enable you to become better leaders, build better teams, and be more effective team members.

Each attendee will walk away with a better understanding of the six significant impacts on team effectiveness and how they can affect change to improve each impact. The discussions and exercises in this session apply to both leaders and team members. The knowledge and skills gained can be directly used for an immediate impact.

Data Governance Begins with Data Architecture

Most organizations who successfully recognize a need for a data governance program do so after the fact -- after all critical source systems are in operation. They may feel they are done once they have a data catalog in place and their data governance goals have been met. However, proper data governance is not just cataloging data assets. Having the catalog on the virtual bookshelf does not do anyone any good, regardless of how much attention is given to it. Proper data governance is proactive and guides data projects before, during, and after implementation. In most industries, it is essential. In healthcare, it is critical! It all begins with data architecture before systems are designed or purchased. This session will address many of the questions you have:

• Who Should Be Involved?
• What Is Data Governance?
• When Should Data Governance Begin?
• Where Is Data Governance Needed Most?
• Why Begin with Data Architecture?
• How Do I Get Started?

Learn what data governance is and how to do it initially and during all data projects instead of only at the end. Leverage your data assets to support compliance and reporting requirements.