Build scalable data pipelines and infrastructure
Data Engineering is the backbone of modern data-driven organizations, focusing on building and maintaining the infrastructure that enables data collection, storage, and processing at scale. As a data engineer, you will design and implement data pipelines, manage databases, work with big data technologies, and ensure data quality and availability. This comprehensive roadmap covers SQL and NoSQL databases, ETL/ELT processes, data warehousing, big data tools like Spark and Kafka, cloud platforms, and orchestration tools like Airflow. You will learn to handle petabytes of data, optimize query performance, and build real-time data streaming systems. Data engineers are in extremely high demand as companies increasingly rely on data for decision-making. The role requires strong programming skills, understanding of distributed systems, database expertise, and knowledge of cloud infrastructure.
8-10 weeks
OOP, error handling, file I/O, libraries
Complex queries, optimization, indexing, transactions
Administration, performance tuning, replication
MongoDB, Redis, Cassandra, use cases
6-8 weeks
Normalization, ER diagrams, foreign keys
Star schema, snowflake schema, fact tables
OLAP vs OLTP, data marts, slowly changing dimensions
Architecture, storage formats (Parquet, Avro)
8-10 weeks
Extract, transform, load processes
DAGs, operators, scheduling, monitoring
Validation, testing, monitoring
Git, CI/CD for data pipelines
10-12 weeks
RDDs, DataFrames, PySpark, optimization
HDFS, MapReduce, Hive, HBase
Apache Kafka, real-time pipelines
Parquet, ORC, Avro, compression
8-10 weeks
S3, Redshift, Glue, EMR, Lambda
BigQuery, Dataflow, Pub/Sub, Cloud Storage
Synapse, Data Factory, Databricks
Terraform, CloudFormation
6-8 weeks
Security, compliance, lineage, cataloging
Query tuning, partitioning, caching
Lambda architecture, Kappa architecture
Automation, monitoring, observability