Big Data Concepts Cheat Sheet
Explains the core big data concepts, Hadoop/Spark ecosystem components, and columnar storage formats used to process datasets at distributed scale.
2 PagesIntermediateMar 18, 2026
Distributed Processing with PySpark
Aggregate a large dataset across a cluster.
python
from pyspark.sql import SparkSessionfrom pyspark.sql import functions as Fspark = SparkSession.builder.appName("sales-analysis").getOrCreate()df = spark.read.parquet("s3://bucket/sales/")result = ( df.filter(F.col("amount") > 0) .groupBy("region") .agg(F.sum("amount").alias("total_sales"), F.count("*").alias("num_orders")) .orderBy(F.desc("total_sales")))result.write.mode("overwrite").parquet("s3://bucket/output/")spark.stop()
Core Concepts (The Vs)
What distinguishes 'big data' from a regular dataset.
- Volume- Scale of data, often terabytes to petabytes, exceeding single-machine storage/processing
- Velocity- Speed at which data is generated and must be processed (batch vs. streaming)
- Variety- Mix of structured, semi-structured (JSON, XML), and unstructured data (text, images)
- Veracity- Data quality and trustworthiness; noisy or inconsistent data undermines analysis
- Horizontal scaling- Adding more commodity machines to a cluster rather than upgrading a single machine
- Data locality- Moving computation to where the data resides rather than moving data across the network
Ecosystem Components
The building blocks of a typical big data stack.
- HDFS- Hadoop Distributed File System; splits files into blocks replicated across cluster nodes
- MapReduce- Programming model that processes data in parallel Map (transform) then Reduce (aggregate) phases
- Apache Spark- In-memory distributed processing engine, much faster than MapReduce for iterative workloads
- YARN- Yet Another Resource Negotiator; manages cluster resources and job scheduling in Hadoop
- Apache Kafka- Distributed event streaming platform for high-throughput publish/subscribe messaging
- Data lake vs. data warehouse- Data lakes store raw data in any format; warehouses store structured, modeled data
Pro Tip
Prefer columnar formats like Parquet over CSV/JSON for analytical workloads - reading only the columns you query, combined with predicate pushdown, can cut I/O by an order of magnitude.
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