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Custom Data Engineering Learning Programs
Finding a consultative data learning partner to design and execute effective learning strategies shouldn't be so hard.
Let’s Chat Strategy
Current Data Engineering Landscape
As technology continues to evolve, data engineers must update their skills to stay relevant in a rapidly growing field. Proficiency in programming languages such as SQL, Python, and Java, along with frameworks like Apache, Hadoop, and Spark, is essential for efficiently managing and analyzing large datasets.
In the United States, the average salary for a data engineer is approximately $115,000 annually. This compensation reflects the specialized skill set required in a field that is rapidly growing. As advancements in cloud computing and machine learning continue, ongoing education and adaptation will be crucial for data engineers to thrive.
of employees would stay longer in companies that are willing to invest in their professional development.
of employees prefer to learn or train in the workplace to grow their skills.
of tech budgets are spent on optimizing existing business capabilities and augmenting capabilities with new capabilities.
Our Philosophy
CONSUMABLE
ILT is delivered in consumable chunks within daily workflows
SUPPORTED
Self-paced, pre and post-work and office hours through the experience. No one is left behind
CUSTOMIZED
Every program is customized to your team, the stack, and the desired outcome
REAL WORLD
Practical learning through real-world projects, capstones, and live coding increase adoption cycle
MEASUREABLE
True measurement before and after the program to determine skill gain and where additional support is needed
Data Engineering Learning Sample Topics
Mastering Data Quality and Governance
The Essentials of Data Modeling for Scalable Systems
Implementing Real-Time Data Streaming
Advanced ETL Processes and Tools
Securing Your Data Infrastructure
NoSQL Databases: When to Use and Why
Sample Program: Data Engineering
PROGRAM OVERVIEW
The Data Engineering for Developers customBILT program transitions existing software developers into skilled data engineers by mastering data modeling, ETL, warehousing, and real-time processing, reinforced by hands-on labs and projects.
Audience
Existing Python developers needing to transition into Data Engineering.
Intended Outcome
Equip the learners with a comprehensive understanding and practical skills in various aspects of data engineering.
ASSESSMENT
CHECK
CHECK
ASSESSMENT
FOUNDATIONS
- OVERVIEW OF DATA ENGINEERING
- PRINCIPLES OF DATA MODELING;
NORMALIZATION AND DEMORALIZATION;
DESIGNING SCHEMAS - ETL VS. ELT; DESIGNING ETL PIPELINES
ADVANCED TOPICS
- INTRODUCTION TO DATA WAREHOUSES
- DIFFERENCES BETWEEN DATA LAKES AND WAREHOUSES
- STREAMING DATA FUNDAMENTALS; KAFKA BASICS
REAL-WORLD APPLICATION
- INTRODUCTION TO WORKFLOW MANAGEMENT
- OVERVIEW OF CLOUD PROVIDERS
- DATA GOVERNANCE FRAMEWORKS
- PROJECT BRIEFING AND TEAM FORMATION
- PROJECT AND WRAP-UP
Easy path to get started.
1. Tell us about your project needs
Meet with us and we'll review your overall team needs.
2. We design a custom learning solution
Working with your stakeholders, we'll collaborate on a win-win solution.
3. Approve the custom, blended learning solution
After approving the solution, we'll decide on a mutually beneficial timeframe.
4. We resource, build and manage
Get matched with a technical solution expert to build the entire solution.
5. We pilot the solution
We pilot and test the solution before the broad rollout.
6. We adjust and improve
The goal is continuously improving the impact and ROI over the transformation effort.