Tyler studied data and analytics at the University of North Texas and majored in Business Computer Information Systems.

Tyler has experience solving across every tech stack & enjoys working with IT and non-tech business experts too. Please read more about data warehousing and follow the journey below.

Tyler has studied, researched, and consulted across many tech terms. This page is about his experience with data warehousing. If you have any questions. tyler@dev3lop.com

A data warehouse in analytics offers a simple place to discover new insights, optimize data requirements, and with current technology - most data warehousing projects can be accomplished in minutes VS months.

Tyler has 12 years+ experiencing building classic EDWs, troubleshooting data warehouses, writing SQL, and architecting new data pipelines. Tyler Garrett has experience building production facing data warehouses in over 100+ environments. He has a team of data engineers ready to help you begin and also enjoys playing Senior Consultant at Dev3lop. Tyler works to build easy data warehouses that business users can maintain and automate without engineering support.

Data sizes are continuing to grow every day and finding talent to help you wrangle helps but does your expert understand when to offload or what that means? Whether you are offloading work to an expert or offloading a complex SQL job to your data warehousing solution, you need to delegate. The same is true in the database world. Learning to offload heavy lifting is key to success and we help clients lower the "heavy lifting" to save money in big data.

Large or complex data jobs need to be offloaded capable technology stacks. Optimizing your data environment will decrease time to solve and increase time to access data.

Are you ready to throw away excel and start building a data warehouse with all of your data sources today?

Finding out your solution isn't easy to maintain is the last thing you need to learn about at the end of your project. Organized data warehouses are more than a best practice in the enterprise BI space. Organized data allows developers, data architects, and analysts an easy place to offer decisions to leaders.

Mastering organizational skills in data helps you offer an easy playground for future analytics.

Let's begin finding how we can organize your data, document the undocumented, and start saving more time.

Your data prep, joining, and cleaning of the data is completed? Now it's time to support your solution. Hopefully you have documentation available or excited to document the undocumented. Data warehouse support is fun because it usually requires learning a beautiful puzzle.

Helping develop new solutions is always fun and glamours but the key to adoption is support and easy remediation. Tyler loves troubleshooting errors, reverse engineering other SQL processes into new warehouses, consolidating micro service databases, fixing your front-end solution with quick remediation to your warehouse, and many other tools.

Tyler has experience developing ETL solutions with business units in tandem with IT.

If you're not building for tomorrow, what are you building? Data warehouses need to be built to scale because the CEO is going to be pissed if everything sucks next year. Analytics automation solutions offer scalability based on your needs, skills, and budget but sometimes it's not always necessary.

Tyler has experience scaling data warehouse solutions across every ETL product, loves helping teams solve together, and knows the complexities data warehouses have in the future.

Tyler will help you automate it, optimize it down, and build a solution that is flexible to change.

Automating data discovery is a fun task to complete. Finding correlations in your data warehouse will help you find predictive variables you never considered. Automating finding the best join, data type, and even automating SQL can help you discover new things about your database.

You can use your data warehouse to be a hub of answers, suggestions about your data, solutions to attempt, and predictions about the future.

It may be easy to find your predictive model usage in your current data warehouse. But your team will begin enriching the data with new sources, web scraped data, and finding correlations in public sources will help you quickly discovery new insights.

All your hard work pays off when your data comes back valid and 100% perfect. As a technical expert, you know nothing is perfect, applications have error handling but if your SQL or code isn't perfect - things break! ETL has a high chance of failure depending on how you develop your data pipelines.

Tyler has experience inventing custom ETL applications, offering data warehousing with instant access points to non-technical users.

Offering the business, management, or product owners access to make easy changes to micro services and strategically fix or update reports.