Data Engineering Roadmap

Purpose: To gather data from different sources.
Sources:
- Databases (SQL, NoSQL)
- Files (CSV, JSON, XML)
- APIs and Web Services
- Third-party systems (CRM, ERP)
Tools and Techniques:
- SQL Queries
- Web Scraping
- Data Extraction Tools (e.g., Apache Nifi, Talend)

Purpose: To convert the extracted data into a format suitable for analysis or loading.
Common Transformations:
Data Cleaning: Removing duplicates, handling missing values, correcting errors.
Data Integration: Combining data from different sources.
Data Aggregation: Summarizing data (e.g., computing averages, sums).
Data Enrichment: Adding additional information to data.
Normalization and Denormalization: Structuring data to reduce redundancy or to flatten data structures.
Data Type Conversion: Changing data types (e.g., string to date).
Tools and Techniques:

Purpose: To load the transformed data into a target database or data warehouse.
Targets:
Data Warehouses (e.g., Amazon Redshift, Google BigQuery)
Databases (e.g., MySQL, PostgreSQL)
Data Lakes (e.g., Hadoop, Amazon S3)
Loading Techniques:
Full Load: Loading all the data at once.
Incremental Load: Loading only the new or updated data since the last load.
Batch Processing: Loading data in batches at scheduled intervals.
Real-Time Processing: Continuously loading data in real-time.
Tools and Techniques:
ETL Tools (e.g., Apache Nifi, Talend, Informatica)
Data Load Scripts (SQL, Python)
Streaming Platforms (e.g., Apache Kafka)

Data Quality: Ensuring the data is accurate, complete, and reliable throughout the ETL process.
Scalability: Designing ETL processes that can handle increasing volumes of data efficiently.
Error Handling and Logging: Implementing mechanisms to capture errors and log activities for monitoring and debugging.
Automation and Scheduling: Using tools to automate ETL workflows and schedule them to run at specific times.
Documentation: Keeping thorough documentation of ETL processes, data sources, transformation logic, and data mappings.

Informatica PowerCenter: A widely used enterprise data integration tool.
Talend: An open-source ETL tool with a rich set of features for data integration.
Apache Nifi: A data integration tool that supports data flow automation.
Apache Spark: A powerful data processing engine that can be used for ETL tasks.
Pentaho Data Integration (PDI): An open-source ETL tool also known as Kettle.
Microsoft SQL Server Integration Services (SSIS): A data integration tool provided by Microsoft.