[Avg. reading time: 4 minutes]
Learning Big Data
Learning Big Data goes beyond just handling large datasets. It involves building a foundational understanding of data types, file formats, processing tools, and cloud platforms used to store, transform, and analyze data at scale.
Types of Files & Formats
- Data File Types: CSV, JSON
- File Formats: CSV, TSV, TXT, Parquet
Linux & File Management Skills
- Essential Linux Commands:
ls,cat,grep,awk,sort,cut,sed, etc. - Useful Libraries & Tools:
awk,jq,csvkit,grep– for filtering, transforming, and managing structured data
Data Manipulation Foundations
- Regular Expressions: For pattern matching and advanced string operations
- SQL / RDBMS: Understanding relational data and query languages
- NoSQL Databases: Working with document, key-value, columnar, and graph stores
Cloud Technologies
- Introduction to major platforms: AWS, Azure, GCP
- Services for data storage, compute, and analytics (e.g., S3, EMR, BigQuery)
Big Data Tools & Frameworks
- Tools like Apache Spark, Flink, Kafka, Dask
- Workflow orchestration (e.g., Airflow, DBT, Databricks Workflows)
Miscellaneous Tools & Libraries
- Visualization:
matplotlib,seaborn,Plotly - Data Engineering:
pandas,pyarrow,sqlalchemy - Streaming & Real-time:
Kafka,Spark Streaming,Flume
Tip: Big Data learning is a multi-disciplinary journey. Start small — explore files and formats — then gradually move into tools, pipelines, cloud platforms, and real-time systems.