Visual roadmaps.
Click-to-track.
Structured learning paths for every major data engineering stack — each rendered as an interactive graph you can zoom, pan, and progress-track in your browser.
Data Engineer — Junior to Senior
Storage, compute, orchestration, streaming, quality, cloud, and system design — everything needed to go from junior to senior data engineer.
Apache Spark
Master Spark's execution model, Catalyst optimizer, AQE, joins, Structured Streaming, and Kubernetes deployment. Goes beyond syntax — covers the why behind every performance decision.
Apache Kafka
Deep dive into Kafka's storage model, consumer groups, replication, exactly-once semantics, Kafka Streams, ksqlDB, and production operations at scale.
SQL & Data Modeling
Window functions, recursive CTEs, query optimization, star/snowflake schema, SCDs, Data Vault, and modern Iceberg/Delta modeling.
Python for Data Engineers
Type hints, async I/O, Pandas, Polars, DuckDB, Airflow DAGs, dbt Python models, testing with pytest, and packaging for production.
System Design for Data
CAP theorem, consistency models, Lambda/Kappa/Lakehouse architectures, data mesh, event-driven systems, and real-world large-scale case studies.
Docker
Master Docker from images and containers to multi-service orchestration with Compose. Covers networking, volumes, multi-stage builds, registries, and production hardening.
Kubernetes
Complete Kubernetes roadmap covering architecture, workloads, networking, storage, security, and operations. Goes beyond kubectl to cover the control plane internals that matter for production.
Terraform
Master Terraform HCL, providers, state management, modules, and CI/CD integration. Covers real-world patterns for managing multi-cloud infrastructure safely at scale.
Apache Airflow
Master Airflow from DAG basics to production-scale deployments. Covers operators, sensors, executors, dynamic DAGs, XComs, and Kubernetes integration for modern data engineering.
dbt Core
Learn dbt from project setup to advanced incremental models, snapshots, and packages. The standard for modern analytics engineering workflows where data transformation happens inside the warehouse.
Apache Flink
Master Flink from DataStream API to Table API/SQL, windowing, exactly-once checkpointing, and production deployment on Kubernetes and YARN.
Snowflake
Master Snowflake's unique architecture, data loading patterns, SQL extensions, performance tuning, and data sharing capabilities. The leading cloud data warehouse for modern data teams.
Databricks
Master Databricks from workspace basics to Delta Lake, Unity Catalog, MLflow, and production Spark deployments. The leading platform for lakehouse architecture implementation.
AWS Data Stack
Master AWS data services: S3, Glue, Athena, Redshift, Kinesis, EMR, and Lake Formation. Build production-grade data lakes and pipelines using AWS-native tools.
Google Cloud Data
Master Google Cloud data services: BigQuery, Dataflow, Pub/Sub, Dataproc, Cloud Storage, and Vertex AI. Build serverless analytics pipelines on GCP's best-in-class data platform.
Azure Data Platform
Master Azure data engineering with Synapse Analytics, Azure Data Factory, ADLS Gen2, Event Hubs, and Databricks. The enterprise data platform for Microsoft-first organizations.
PostgreSQL
Master PostgreSQL from SQL fundamentals to index internals, MVCC, query planning, advanced SQL features, and production administration. The world's most advanced open-source relational database.
MongoDB
Master MongoDB document modeling, CRUD, aggregation pipeline, indexes, replica sets, and sharding. The world's most popular document database for flexible schema data.
Redis
Master Redis data structures, persistence, pub/sub, Streams, Cluster, and production patterns. The most popular in-memory data store for caching, sessions, and real-time leaderboards.
Elasticsearch
Master Elasticsearch indexing, mapping, query DSL, aggregations, performance tuning, and production operations. The backbone of search, log analytics, and observability platforms.
Apache Cassandra
Master Cassandra data modeling, CQL, partitioning, consistency levels, and operations. The go-to database for IoT, time-series, and globally distributed workloads requiring linear scalability.
Neo4j
Master Neo4j graph modeling, Cypher query language, data import, performance tuning, and real-world applications in fraud detection, recommendations, and knowledge graphs.
ClickHouse
Master ClickHouse column-oriented storage, MergeTree engine family, SQL analytics, materialized views, performance tuning, and production operations for sub-second analytics at scale.
DuckDB
Master DuckDB's columnar engine, rich SQL extensions, zero-copy Parquet/Arrow integration, and use as an embedded analytics engine in Python, R, and production pipelines.
Trino
Master Trino's architecture, SQL dialect, connector ecosystem, cost-based optimizer, and production deployment for federated analytics across data lakes and databases.
Apache Iceberg
Master Apache Iceberg's table format specification, schema and partition evolution, catalog integration, row-level DML, and engine integration with Spark, Flink, and Trino.
Delta Lake
Master Delta Lake's transaction log, schema enforcement, DML operations, OPTIMIZE/Z-ORDER, Delta Sharing, and integration with Spark, Flink, and dbt for production lakehouse architectures.
Machine Learning
Comprehensive ML roadmap covering math prerequisites, data preprocessing, core algorithms, model evaluation, feature engineering, and MLOps deployment. Built around scikit-learn with production best practices.
PyTorch & Deep Learning
Build and train neural networks from mathematical foundations through production deployment. Covers CNNs, RNNs, Transformers, distributed training, and Hugging Face fine-tuning.
MLflow & MLOps
Manage the full ML lifecycle with MLflow: experiment tracking, model registry, project packaging, and CI/CD pipelines for reliable model deployment.
LangChain & LLM Apps
Build production LLM applications with LangChain and LangGraph: RAG pipelines, agents, tool use, memory, and observability with LangSmith.
Vector Databases
Store, index, and query high-dimensional embeddings for semantic search, RAG, and recommendation systems using Pinecone, Qdrant, Weaviate, and pgvector.
Pandas & NumPy
Master data wrangling and numerical computing with Pandas and NumPy — the foundational Python libraries for every data engineering and data science workflow.
TypeScript
Build type-safe applications at scale with TypeScript's structural type system, generics, utility types, and modern framework integrations.
Go (Golang)
Write concurrent, high-performance systems in Go: goroutines, channels, REST APIs, gRPC, and cloud-native tooling with a minimal runtime footprint.
Linux & Bash
Master the Linux command line and Bash scripting for system administration, automation, and DevOps — the universal foundation for server-side engineering.
Git & GitHub
Version control from commits and branches through advanced rebase, GitHub Actions CI/CD, and monorepo tooling — essential for every software engineer.
Prometheus & Grafana
Implement full-stack observability with Prometheus metrics collection, PromQL queries, Alertmanager routing, and Grafana dashboards for production systems.
FastAPI
Build high-performance async REST APIs in Python with FastAPI, Pydantic validation, dependency injection, SQLAlchemy, and production deployment patterns.
Apache Hadoop
Understand Hadoop's distributed ecosystem — HDFS, YARN, MapReduce, Hive, and HBase — and how modern cloud lakehouses evolved from these foundations.
GraphQL
Design and build flexible GraphQL APIs with type-safe schemas, efficient resolvers, Apollo Federation for microservices, and production security patterns.
Scala
Write type-safe functional programs and Spark jobs in Scala — combining JVM performance with FP abstractions, effect systems, and reactive streaming.
Apache Superset
Deploy Apache Superset for self-service analytics: connect 40+ databases, build interactive dashboards, and secure with RBAC and row-level security.
Feature Engineering
Transform raw data into powerful ML features: numerical scaling, categorical encoding, text embeddings, time series lags, feature selection, and sklearn pipelines.
Data Visualization
Communicate data insights effectively with the right chart types, color theory, Matplotlib, Seaborn, Plotly, Dash, Tableau, Power BI, and D3.js.
Great Expectations
Implement data quality pipelines with Great Expectations: write expectation suites, run checkpoint validations, generate Data Docs, and integrate with Airflow and dbt.
Apache NiFi
Design and operate Apache NiFi data flows: ingest from any source, route and transform FlowFiles, version flows with NiFi Registry, and monitor in production.
Explore the graph
Each roadmap is a zoomable, pannable D3 canvas. Sections and topics are visually connected so you see the full path at a glance.
Click any topic
Clicking a node opens a detail panel with a description, numbered key concepts, and curated resources — docs, books, tutorials.
Track your progress
Mark topics as done, in progress, or skipped. Progress is saved in localStorage — no account needed. Pick up where you left off.