Foundations of Forward Deployed Engineering & Customer Discovery

• The FDE Blueprint & Customer-Centric Engineering
  • Core Concepts: Anatomy of the FDE role; shifting from isolated SWE sprint cycles to iterative client delivery loops; navigating the friction between scalable product roadmaps and bespoke client requirements
  • Comparative Dynamics: Structural differences, boundaries, and collaboration models between SWE, Solutions Engineering (SE), Product Management (PM), and FDEs
  • Key Capabilities for Customer-Facing Engineers: End-to-end customer engagement lifecycle, Balancing technical excellence with business outcomes, Managing ambiguity in enterprise environments, Real-world FDE case studies
  • Hands-on:
    • Analyze multiple customer implementation scenarios
    • Map business challenges to technical solutions
    • Evaluate successful and unsuccessful deployment examples
    • Create a stakeholder value analysis
  • Discovery, Requirements Gathering & Stakeholder Mapping
    • Discovery & Requirements Elicitation: discovery methodologies, business process analysis, identifying hidden requirements, translating business needs into technical specifications
    • Stakeholder Mapping & Interviewing: identifying stakeholders, conducting technical and non‑technical interviews, managing conflicting priorities
    • Defining Success & Validation Criteria: establishing measurable success metrics and ensuring alignment across stakeholders
    • Hands-on: Live role-play mock customer interviews simulating defensive IT administrators and non-technical business executives
  • Communication, Consulting & Customer Engagement
    • Executive & Technical Communication: fundamentals, technical storytelling, delivering difficult recommendations
    • Customer Meeting & Engagement Skills: running customer meetings, expectation management, escalation handling, building stakeholder trust
    • Consulting & Advisory Practices: status reporting, framing recommendations, guiding customers through complex decisions
    • Hands-on: Executive briefing simulations, customer presentation practice, technical proposal review

Software Engineering, Enterprise Data Fundamentals & Distributed Systems

• Python Programming: Fundamentals
  • Python Environment & Execution Basics: Jupyter setup, Python shell, running scripts, working with strings and print statements
  • Core Data Types & Structures: integers, lists, dictionaries, tuples, sets, files, and mutable vs. immutable type
  • Control Flow & Comprehensions: if/elif/else, boolean, membership tests, for/while loops, list/dict comprehensions
  • Functions & Scope: defining functions, arguments, local vs. global scope, and basic polymorphism concepts
  • Modules & Imports: creating modules, import patterns, using dir()/help(), and key built‑in modules
  • Object‑Oriented Programming: classes, objects, inheritance, and polymorphism (function/operator overloading)
  • Error & Exception Handling: structured exception handling patterns for production‑grade code
  • Pydantic for Structured Data: typed models, fields, validators, and schema‑driven inputs/outputs for tools and workflows
  • AsyncIO Fundamentals: event loops, coroutines, tasks, concurrency primitives, and parallel API/tool calls
  • Hands-on: applied exercises across Python fundamentals, data structures, OOP, Pydantic, and AsyncIO
• Collaborative Development & Git Workflows
  • Git Fundamentals & Repository Hygiene: initializing repos, understanding commits, staging, branching, tagging, and maintaining clean commit history
  • Branching Strategies for Teams: Git Flow, trunk‑based development, feature branches, release branches, and choosing the right model for enterprise environments
  • Pull Request Workflows: creating PRs, writing effective descriptions, linking issues, using templates, and ensuring traceability
  • Code Review Best Practices: reviewing for correctness, readability, security, and maintainability; giving constructive feedback; reviewer/author responsibilities
  • Rebasing, Merging & Conflict Resolution: when to rebase vs. merge, interactive rebasing, resolving conflicts cleanly, and avoiding history pollution
  • Release & Version Management: semantic versioning, tagging releases, generating changelogs, and coordinating multiteam release cycles
  • Team Collaboration & Engineering Culture: communication norms, pairing strategies, shared ownership, documentation expectations, and aligning on coding standards
  • Hands-on:
    • Multi‑Developer Repository Simulation: branching, committing, and merging in a shared repo with realistic team workflows
    • Pull Request Review Exercises: practicing structured reviews, identifying issues, and writing actionable feedback
    • Merge Conflict & Rebase Drills: resolving conflicts, rewriting history safely, and practicing clean merges under time pressure
  • Data Fundamentals for Customer Solutions
    • Relational Databases:
      • Relational Database & SQL Foundations: core relational concepts (tables, rows, keys, relationships), understanding schemas, and how relational models support enterprise customer solutions
      • SQL Querying Essentials: writing SELECT queries, filtering, sorting, joining tables, grouping/aggregations, and applying predefined SQL functions for analytics and reporting
      • Working with Tables, Indexes & Performance: creating and modifying tables, understanding primary/foreign keys, using indexes for query optimization, and recognizing common performance pitfalls
      • Integrating SQL with Python: connecting Python applications to SQL databases, executing queries programmatically, handling result sets, and using libraries like sqlite3 or SQL Alchemy patterns relevant to FDE workflows
      • Hands-on:
        • SQL Querying & Table Operations: writing queries, creating tables, experimenting with indexes, and analyzing performance differences
        • Python‑to‑SQL Integration Exercises: connecting Python code to a live SQL database, executing queries, and building simple data‑driven workflows
      • NoSQL Databases
        • Relational vs. Non‑Relational Databases: key differences in data modeling, scalability, schema flexibility, and when to choose NoSQL over traditional relational systems in customer solution architectures
        • Introduction to NoSQL: core concepts, distributed storage principles, horizontal scaling, and schema‑less design trade‑offs
        • Types of NoSQL Databases: document stores, key‑value stores, column‑family databases, and graph databases with real‑world use cases
        • Document Datastore: MongoDB
          • MongoDB Fundamentals: architecture, collections vs tables, documents vs rows, BSON format, and where MongoDB fits in distributed systems
          • CRUD Operations & Querying: creating documents, reading/filtering collections, updating nested structures, deleting documents, and using the MongoDB query language
          • Document Modeling & Schema Design: structuring documents, embedding vs referencing, schema evolution, and performance‑oriented modeling
          • Indexes & Performance Optimization: creating indexes, compound indexes, understanding query plans, and optimizing read/write patterns
          • Working with Data Types: arrays, nested objects, dates, ObjectIds, and other BSON types
          • MongoDB Compass: visually exploring collections, running queries, analyzing indexes, and understanding data distribution
          • Integrating MongoDB with Apache Spark: using the MongoDB Spark Connector, loading collections into Spark DataFrames, and performing distributed transformations
          • Hands-on:
            • CRUD & Querying Labs: inserting, querying, updating, and deleting documents across realistic datasets
            • Schema Design & Indexing Exercises: modeling documents for customer scenarios, creating indexes, and analyzing performance impacts
            • MongoDB Compass Exploration: navigating collections visually, running queries, and inspecting data structures
            • Python + MongoDB Integration Lab: connecting Python applications to MongoDB, executing CRUD operations, and building small end‑to‑end workflows
          • Distributed Data Processing
            • Big Data Overview
              • Big Data Foundations: history of Hadoop, the 5 V’s of Big Data, and major industry distributions such as Cloudera, Hortonworks, EMR, and Databricks
              • Evolution of the Big Data Ecosystem: pre‑Spark tools like MapReduce, Hive, Pig, and HBase contrasted with the post‑Spark ecosystem built around in‑memory, unified analytics
              • Apache Spark Architecture: comparison of MapReduce vs Spark, understanding Spark clusters, drivers, executors, and how distributed execution work
            • Getting Started with Apache Spark & Spark SQL
              • Spark Components and Architecture: Spark Core, SQL, Streaming, MLlib, GraphX, SparkSession, SparkContext, and how distributed execution works
              • PySpark Fundamentals: writing distributed code in Python, running transformations and actions, and using Jupyter Notebook for interactive development
              • Data I/O with PySpark: reading and writing structured and unstructured data, working with common formats, and integrating with HDFS
              • Spark SQL Essentials: SQL engine, Catalyst optimizer, DataFrame‑backed SQL execution, and performing filtering, projections, joins, and aggregations
              • Managing Tables and Catalogs: creating and managing tables, external tables, views, and understanding Spark’s catalog system
              • Spark SQL Functions: using built‑in string, date, numeric, and aggregation functions for analytical workloads
            • Apache Spark Data Structures: RDD
              • RDD Fundamentals: distributed collections, partitions, and the low‑level programming model
              • Lineage and Lazy Evaluation: how Spark tracks transformations and optimizes execution
              • RDD Transformations: map, filter, flatMap, reduceByKey, and other functional operations
              • RDD Actions: collect, count, take, save, and trigger execution
              • RDD Persistence and Caching: memory vs disk storage levels and performance considerations
            • Apache Spark Data Structures: DataFrames
              • DataFrame Fundamentals: schema‑aware distributed data structures built on Spark SQL, offering optimized execution through the Catalyst optimizer
              • Reading and Writing Data: loading CSV, JSON, Parquet, ORC, and other formats, inferring schemas, and writing DataFrames back to storage systems
              • Schema Management: creating explicit schemas, using DDL strings, handling schema evolution, and validating data types
              • Core Transformations: select, filter, with Column, drop, groupBy, order By, joins, and other essential DataFrame operations
              • Aggregation Operations: sum, mean, min, max, count, multi‑column aggregations, and window‑based analytical functions
              • Handling Nested and Complex Data: working with arrays, structs, maps, and nested fields commonly found in semi‑structured data
              • Handling Corrupt or Bad Records: permissive vs fail‑fast modes, dealing with malformed rows, and enforcing schema consistency
              • Performance Considerations: avoiding unnecessary shuffles, minimizing UDF usage, caching and persistence strategies, and understanding DataFrame do’s and don’ts
              • Spark Lifecycle and Spark UI: understanding jobs, stages, tasks, DAG visualization, and using the Spark UI for debugging and performance tuning
              • Building Data Transformation Pipelines: chaining transformations, modularizing ETL logic, and preparing DataFrames for downstream analytics or ML workflow
            • Hands-on: Multiple labs covering these topics for Distributed Processing

Cloud Infrastructure, DevOps, and Observability

• DevOps
  • DevOps Foundations: what DevOps is, its goals, benefits, and the cultural shift toward collaboration and shared ownership
  • CI/CD Concepts & Pipelines: introduction to CI/CD, continuous integration workflows, pipeline setup, and using tools like Jenkins and GitHub Actions
  • DevOps Best Practices: automation, versioning, testing strategies, infrastructure as code, and continuous improvement principles
• Containerization and Orchestration
  • Containers with Docker
    • Docker Fundamentals: what containers are, Docker architecture, and core Docker commands
    • Building & Managing Images: writing Dockerfiles, building images, tagging, pushing, and managing container lifecycles
    • Docker Compose: defining multi‑container applications, local development workflows, and integration testing with Compose
  • Container Orchestration with Kubernetes
    • Kubernetes Architecture: clusters, nodes, pods, deployments, services, and control plane components
    • Deploying & Scaling Applications: manifests, deployments, scaling strategies, load balancing, and rolling updates or rollbacks
    • Kubernetes Networking & Service Discovery: cluster networking, services, DNS, ingress, and traffic routing
    • Service Meshes & Observability: introduction to service meshes, plus monitoring and visualization with Prometheus and Grafana
  • Hands-on:
    • Docker Labs: building images, writing Dockerfiles, running containers, and using Docker Compose for multi‑service applications
    • Kubernetes Labs: deploying applications, scaling workloads, managing services, performing rolling updates, and exploring cluster behavior
    • Prometheus & Grafana Labs: collecting metrics, creating dashboards, visualizing cluster health, and monitoring deployed applications
  • Cloud Computing Foundations (GCP)
    • Cloud Computing Overview: core cloud concepts, service models, deployment models, and why enterprises adopt cloud platforms
    • Security with Google Cloud: Google’s secure‑by‑design infrastructure, shared responsibility model, encryption, and global network architecture
    • GCP Resource Hierarchy: organizations, folders, projects, and how hierarchical structure enables governance and access control
    • Identity and Access Management (IAM): principles of IAM, role‑based access, service accounts, and managing permissions at scale
    • Compute Services on GCP: understanding VM instances, serverless compute, containers, and choosing the right compute option for workloads
    • Databases on GCP: working with relational databases (Cloud SQL, Alloy DB) and NoSQL databases (Firestore, Bigtable)
    • BigQuery Data Warehouse: columnar storage, SQL analytics, federated queries, and enterprise‑scale data warehousing
    • Connecting to Google Cloud Platform: authentication methods, CLI usage, Cloud Shell, and accessing GCP resources programmatically
    • Hands-on: Practical labs will be included on selected GCP topics, such as IAM, compute services, databases, and BigQuery, to reinforce real‑world cloud skills
  • Infrastructure as Code with Terraform
    • Introduction to Infrastructure as Code (IaC): principles of declarative infrastructure, automation, repeatability, and version‑controlled environments
    • Terraform and Cloud Fundamentals: Terraform workflow, providers, resources, and how Terraform interacts with cloud APIs
    • Hashicorp Configuration Language (HCL): syntax, variables, outputs, expressions, and writing reusable configuration files
    • Terraform State Management: purpose of state files, remote state backends, locking, and best practices for team collaboration
    • Terraform Modules: creating and using modules, structuring reusable components, and promoting consistency across environments
    • Provisioning Resources on GCP with Terraform: configuring providers, defining GCP resources, and managing infrastructure lifecycles declaratively
    • Hands-on: Practical labs will be included on selected Terraform topics, such as writing HCL, managing state, using modules, and provisioning basic cloud resources

Applied Enterprise AI/ML Solutions & Customer Delivery

• Introduction to AI, Machine Learning, and Deep Learning
  • What is Artificial Intelligence (AI): systems designed to perform tasks that typically require human intelligence such as reasoning, perception, and decision‑making
  • What is Machine Learning (ML): algorithms that learn patterns from data to make predictions or decisions without being explicitly programmed
  • What is Deep Learning (DL): a subset of ML that uses multi‑layer neural networks to learn complex patterns from large datasets
  • What Are Models: mathematical representations that map inputs to outputs based on patterns learned from data
  • What Models Learn: weights, features, and relationships within data that allow them to classify, predict, generate, or recommend
  • Types of Learning: supervised, unsupervised, and reinforcement learning, and when each approach is used
  • AI in the Enterprise: practical applications such as automation, forecasting, anomaly detection, personalization, and intelligent assistants
• Large Language Model (LLM) Fundamentals
  • Generative AI Landscape: evolution of generative models, industry trends, and how enterprises adopt GenAI capabilities
  • Foundation Models: large language models, multimodal models, embeddings, and how foundation models enable broad AI functionality
  • Prompt Engineering: crafting effective prompts, controlling model behavior, iterative refinement, and prompt patterns for enterprise tasks
  • Context Management: grounding models with context, retrieval‑augmented generation (RAG), memory strategies, and managing context windows
  • Model Selection Criteria: choosing between open‑source and proprietary models, evaluating latency, accuracy, cost, and deployment constraints
  • Enterprise AI Use Cases: customer support automation, knowledge retrieval, workflow acceleration, analytics augmentation, and domain‑specific copilots
  • AI Limitations and Risks: hallucinations, bias, data privacy, security concerns, and responsible AI practices