David Regalado
VP of Engineering at a Stealth Startup. Passionate about all things data!
Lima, Peru
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David is a distinguished leader in the field of data engineering, currently serving as the VP of Engineering at a Stealth Startup. With over 16 years of extensive experience elevating projects that require an in-depth understanding of data warehousing, automation, pipelines, and cloud architecture. He has helped financial services, telecommunications, consumer credit reporting agencies, cyber security, ad-tech, and consulting firms create systems to extract and ingest data by applying advanced manipulation, normalization, and optimization skills, enabling data scientists to be more efficient and focused on delivering value to the business. David is a data architect and technical advisor who has worked extensively with Google Cloud Platform, Apache Beam, Terraform, and database management systems like MySQL and PostgreSQL. David has consistently demonstrated a profound impact on the industry.
As the founder of the Data Engineering Latam community and an AI Time Journal Ambassador, David has fostered knowledge sharing and innovation in the data space. His teaching stint at the University of Chicago, where he imparted Python for Data Science, highlights his dedication to education and mentorship.
A graduate with top honors from the Technological University of Peru (UTP) in Systems Engineering, David further honed his skills with studies in Creative Thinking at Imperial College London and Cognitive Technologies at Deloitte University Press. His academic and professional journey is marked by specializations from prestigious institutions such as:
- Data Science from Johns Hopkins University.
- Data Warehousing for Business Intelligence from Colorado University.
- Data Science and Big Data Analytics from MIT.
- Data Governance from University of Buenos Aires
David holds numerous industry certifications including:
- Google Cloud Professional Cloud Architect
- Google Cloud Professional Data Engineer
- Google Cloud Associate Cloud Engineer
- Google Cloud Digital Leader
- Databricks Lakehouse Fundamentals
- Scrum[.]org Professional Scrum Master
His expertise is further validated by completing more than 10 specializations from Google Cloud, spanning Digital Transformation, Data Engineering, Big Data, and Machine Learning:
- Digital Transformation using AI/ML with Google Cloud.
- Data Engineering, Big Data, and Machine Learning on GCP
- Architecting Hybrid Cloud Infrastructure with Anthos
- Cloud Architecture with Google Cloud
- Architecting with Google Kubernetes Engine
- Architecting with Google Compute Engine
- Networking in Google Cloud
- Security in Google Cloud Platform
- Serverless Data Processing with Dataflow
- Developing Applications with Google Cloud Platform
- Data Engineering on Google Cloud Platform
- Cloud Engineering with Google Cloud
With a robust portfolio of 65 digital badges from CloudSkillsboost and certifications in Python and R programming, David is recognized by Google as a Google Cloud Champion Innovator.
Committed to driving innovation and fostering diversity, David continues to be a trailblazer in the data industry, advocating for Latinx inclusion and mentoring the next generation of technology leaders.
Other areas of interest: Neuro-Linguistic Programming, Emotional Intelligence, Creative Thinking, Upskilling, Mentoring, Public Speaking, Storytelling, Social Media, Photography, Physics.
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A Gentle Introduction to Apache Beam
This technical talk will provide an overview of Apache Beam, an open-source, unified programming model for batch and streaming data processing pipelines that simplify large-scale data processing dynamics. Thousands of organizations around the world choose Apache Beam due to its unique data processing features, proven scale, and powerful yet extensible capabilities.
A data engineer can select a programming language from a variety of language SDKs: Java, Python, Go, SQL, TypeScript, Scala (via Scio), or leverage multi-language capabilities to empower every team member to write transforms in their favorite programming language and use them together in one robust, multi-language pipeline. Apache Beam eliminates the skill set dependency and helps avoid becoming tied to a specific technology skill set and stack.
The talk will be useful for those interested in learning about Apache Beam and its capabilities for batch and streaming data processing jobs.
Multimodal search: LLMs with vision change businesses
In this session, we will introduce how to use Google Cloud AI tools to quickly harness the power of Large Language Models to understand and organize millions of images, building a next-generation search and recommendation experience.
Why are Embeddings So Cool?
Embeddings unlock a ton of exciting possibilities:
- Recommendation Powerhouse: Netflix suggesting movies you'll love? Spotify curating your perfect playlist? That's embeddings at work, matching your taste to similar items.
- Search Beyond Keywords: Ever searched Google for images using another image? That's reverse image search, powered by image embeddings. Embeddings let us find similar items even if they don't share exact keywords.
- Chatbots That Get You: Embeddings help chatbots understand the intent behind your words, enabling more natural and meaningful conversations.
-And So Much More: Embeddings are used in fraud detection, anomaly detection, even predicting when machine learning models become outdated.
Predict Bike Trip Duration with a Regression Model in BQML
BigQuery ML offers several advantages over other approaches to using ML or AI with a cloud-based data warehouse:
BigQuery ML democratizes the use of ML and AI by empowering data analysts, the primary data warehouse users, to build and run models using existing business intelligence tools and spreadsheets. Predictive analytics can guide business decision-making across the organization.
You don't need to program an ML or AI solution using Python or Java. You train models and access AI resources by using SQL—a language that's familiar to data analysts.
BigQuery ML increases the speed of model development and innovation by removing the need to move data from the data warehouse. Instead, BigQuery ML brings ML to the data, which offers the following advantages:
- Reduced complexity because fewer tools are required.
- Increased speed to production because moving and formatting large amounts of data for Python-based ML frameworks isn't required to train a model in BigQuery.
GDG DevFest UK & Ireland Sessionize Event
David Regalado
VP of Engineering at a Stealth Startup. Passionate about all things data!
Lima, Peru
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