Data Engineering Fundamentals
The course teaches core data engineering concepts, covering architecture patterns, ETL/ELT pipelines, batch and stream processing, and data quality testing, enabling engineers to design reliable, scalable data systems.
Who Should Take This
It is ideal for data engineers, analytics engineers, or platform developers with 1–3 years of experience who want to deepen their design‑pattern knowledge and make informed tool choices. Learners aim to build robust pipelines, evaluate trade‑offs, and apply systematic testing to ensure data integrity.
What's Included in AccelaStudy® AI
Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats
Course Outline
61 learning goals
1
Data Architecture Patterns
6 topics
Describe data architecture concepts including data warehouses, data lakes, data lakehouses, and data mesh and explain the evolution from centralized to decentralized data architectures
Describe the medallion architecture including bronze, silver, and gold layers and explain how each layer serves different data quality and consumption requirements
Describe dimensional modeling including star schemas, snowflake schemas, fact tables, dimension tables, slowly changing dimensions, and the role of surrogate keys
Apply data modeling techniques for analytical workloads including denormalization strategies, wide tables, aggregate tables, and materialized views for query performance
Analyze data architecture decisions including when to use a warehouse versus lake versus lakehouse based on data variety, query patterns, cost, and organizational maturity
Describe data mesh principles including domain ownership, data as a product, self-serve data platform, and federated computational governance and explain how they decentralize data architecture
2
ETL and ELT Pipelines
6 topics
Describe ETL and ELT patterns including extract, transform, and load phases, the distinction between ETL and ELT, and when each approach is preferred based on compute and storage trade-offs
Apply data extraction techniques including full extraction, incremental extraction via timestamps and change data capture, and API-based extraction with pagination and rate limiting
Apply data transformation patterns including type casting, deduplication, null handling, joining datasets, pivoting, and business rule application in transformation pipelines
Apply data loading strategies including full refresh, incremental append, upsert via merge operations, and partitioned writes for efficient data warehouse loading
Analyze ETL pipeline design including idempotency, exactly-once processing guarantees, error handling strategies, and dead letter queues for handling malformed records
Apply schema evolution strategies including backward and forward compatibility, schema registry management, and handling breaking changes in data pipeline schemas without downtime
3
Batch Processing
6 topics
Describe batch processing concepts including MapReduce paradigm, distributed computing, data partitioning, shuffling, and the role of batch processing in large-scale data analysis
Describe Apache Spark architecture including driver, executors, RDDs, DataFrames, Catalyst optimizer, and Tungsten execution engine for distributed data processing
Apply Spark DataFrame operations including filtering, grouping, joining, window functions, and UDFs for transforming large-scale datasets in distributed computing environments
Apply Spark performance optimization including partition management, broadcast joins, caching, adaptive query execution, and skew handling for efficient distributed processing
Analyze batch processing architecture decisions including Spark versus other engines, cluster sizing, spot instance strategies, and cost-performance trade-offs for periodic workloads
Apply SQL-on-Spark processing including Spark SQL, temporary views, catalog management, and when to use SQL versus DataFrame API for batch data transformations
4
Stream Processing
6 topics
Describe stream processing concepts including event time versus processing time, windowing, watermarks, exactly-once semantics, and the distinction from micro-batch processing
Describe Apache Kafka architecture including topics, partitions, consumer groups, offset management, and how Kafka provides durable, ordered, replayable event streaming
Apply stream processing frameworks including Kafka Streams, Apache Flink, and Spark Structured Streaming for real-time aggregation, enrichment, and alerting pipelines
Apply change data capture patterns including log-based CDC with Debezium, outbox pattern, and dual-write avoidance for keeping analytical systems synchronized with operational databases
Analyze the Lambda and Kappa architecture patterns including when to combine batch and streaming, when streaming-only suffices, and the operational complexity trade-offs of each approach
Apply event-driven architecture patterns including event sourcing, CQRS, and how event-driven data pipelines enable decoupled, scalable data processing systems
5
Data Quality and Testing
5 topics
Describe data quality dimensions including accuracy, completeness, consistency, timeliness, uniqueness, and validity and explain how poor data quality impacts downstream analytics and ML
Apply data quality testing using frameworks including Great Expectations, dbt tests, and Soda to define expectations, validate data, and generate quality reports
Apply data profiling and anomaly detection including statistical profiling, distribution monitoring, freshness checks, and volume anomaly detection for proactive quality management
Analyze data quality strategy design including data contracts between producers and consumers, quality SLAs, remediation workflows, and the cost of quality versus cost of poor quality
Apply data observability platforms including Monte Carlo, Bigeye, and elementary and explain how automated data quality monitoring reduces time to detect and resolve data incidents
6
Pipeline Orchestration
5 topics
Describe workflow orchestration concepts including DAG-based scheduling, task dependencies, retries, backfills, and the distinction between orchestration and execution engines
Apply orchestration tools including Apache Airflow, Dagster, and Prefect to define, schedule, and monitor data pipelines with dependency management and error handling
Apply dbt for analytics engineering including model definition, testing, documentation, incremental materialization, and how dbt fits into the modern data stack
Analyze orchestration architecture decisions including task granularity, idempotency requirements, SLA management, and scaling strategies for pipelines with hundreds of tasks
Apply data pipeline testing strategies including unit tests for transformations, integration tests for pipeline stages, and end-to-end tests with fixture data for orchestrated workflows
7
Data Storage and Formats
5 topics
Describe data storage formats including Parquet, ORC, Avro, JSON, and CSV and explain the trade-offs between columnar and row-based formats for analytical versus transactional workloads
Describe table formats including Delta Lake, Apache Iceberg, and Apache Hudi and explain how they add ACID transactions, time travel, and schema evolution to data lake storage
Apply partitioning and bucketing strategies including date-based partitioning, hash bucketing, Z-ordering, and compaction to optimize query performance on large datasets
Analyze storage layer design including choosing between object storage, HDFS, and cloud-native storage, data lifecycle management, tiered storage, and cost optimization strategies
Apply data lake organization patterns including directory structures, naming conventions, metadata tagging, and how consistent layout enables discoverability across teams and tools
8
Data Governance
5 topics
Describe data governance concepts including data catalogs, metadata management, data lineage, ownership, and the role of governance in enabling data democratization while ensuring compliance
Apply data catalog and metadata management using tools including Apache Atlas, DataHub, and Amundsen to enable data discovery, impact analysis, and governance workflows
Apply data privacy and compliance techniques including PII detection, data masking, tokenization, role-based access control, and audit logging for GDPR and CCPA compliance
Analyze data governance strategy including organizational models for data ownership, cross-team data sharing agreements, and balancing governance overhead with data accessibility
Apply data quality scorecards and reporting including automated quality dashboards, trend tracking, and communicating data quality status to data consumers and leadership
9
Cloud Data Services
5 topics
Describe cloud data services including managed databases, serverless query engines, cloud data warehouses, and how cloud providers offer integrated data platform solutions
Apply cloud data warehouse concepts including Snowflake, BigQuery, and Redshift including separation of compute and storage, auto-scaling, and query optimization techniques
Apply serverless data processing including AWS Glue, Azure Data Factory, and Dataflow for managed ETL without infrastructure provisioning or cluster management
Analyze cloud data platform selection including vendor lock-in considerations, multi-cloud data strategies, egress costs, and the total cost of ownership for different cloud data architectures
Apply data lakehouse concepts including unified batch and streaming on a single storage layer, open table formats with cloud warehousing capabilities, and the convergence of lakes and warehouses
10
Pipeline Observability
5 topics
Describe data pipeline observability including logging, metrics, tracing, and alerting and explain how observability differs from monitoring for complex data systems
Apply data pipeline monitoring including SLA tracking, freshness monitoring, row count anomaly detection, and cost tracking dashboards for production data pipelines
Apply incident response for data pipelines including root cause analysis, impact assessment, communication protocols, and postmortem practices for data quality incidents
Analyze observability platform design including centralized versus distributed logging, metric aggregation strategies, and the balance between observability depth and operational cost
Apply data pipeline cost monitoring including compute cost attribution per pipeline, storage growth tracking, and optimization recommendations for reducing cloud data platform spend
11
Data Team Practices
7 topics
Describe analytics engineering practices including the role of analytics engineers, the modern data stack, and how dbt, version control, and CI/CD professionalize data transformation work
Apply data pipeline development practices including code review for SQL and pipeline code, testing strategies, documentation standards, and environment management for data teams
Analyze data team organizational patterns including centralized, embedded, and federated data teams and evaluate how organizational structure affects data engineering effectiveness
Apply data engineering interview concepts including system design for data-intensive applications, SQL optimization problems, and distributed systems trade-off analysis for career preparation
Describe the evolution of data engineering from ETL scripting to modern platform engineering and explain how the analytics engineering movement redefined roles and responsibilities in data teams
Analyze build versus buy decisions for data infrastructure including evaluating open-source tools, managed services, and commercial platforms based on team size, budget, and technical requirements
Apply data product design including defining data SLAs, consumer interfaces, documentation standards, and how treating datasets as products improves cross-team data consumption
Hands-On Labs
15 labs
~420 min total
Console Simulator
Code Sandbox
Practice in a simulated cloud console or Python code sandbox — no account needed. Each lab runs entirely in your browser.
Scope
Included Topics
- Data architecture (warehouses, lakes, lakehouses, mesh), ETL/ELT pipeline design, batch processing (Spark), stream processing (Kafka, Flink), data quality and testing, pipeline orchestration (Airflow, Dagster, dbt), storage formats (Parquet, Delta Lake, Iceberg), data governance and compliance, cloud data services, pipeline observability, analytics engineering practices
Not Covered
- Specific cloud certification exam objectives (covered in cert-specific domains)
- Machine learning model training and feature engineering (covered in ML/MLOps domains)
- Database administration and performance tuning internals
- Business intelligence dashboard design and visualization
- Data science statistical analysis and hypothesis testing
Ready to master Data Engineering Fundamentals?
Adaptive learning that maps your knowledge and closes your gaps.
Subscribe to Access