Datasets so large, fast-moving, or varied that traditional tools struggle to store and process them efficiently.
Big data is commonly described using the three Vs: volume (huge amounts), velocity (data arriving quickly, sometimes in real time), and variety (structured tables mixed with text, images, logs, and sensor readings). Some definitions add veracity and value.
Examples include clickstream data from a popular website, readings from thousands of IoT sensors, or transaction records from a national payment network. Handling this scale led to distributed tools such as Hadoop and Apache Spark, and later to cloud data warehouses and data lakes. The term has become less fashionable as these technologies have matured, but the underlying challenges of storing, cleaning, governing, and extracting insight from large datasets remain central to analytics and AI.
A central repository that stores large volumes of raw data in its original format until it is needed for analysis.
Data VisualizationPresenting data as charts, graphs, maps, or other visuals so patterns, trends, and outliers are easier to understand.
Unsupervised LearningA machine learning approach that finds patterns and structure in data that has no labels or predefined answers.
ZettabyteA unit of digital information equal to one sextillion (10 to the power of 21) bytes, or one billion terabytes.
A plain-English guide to big data: what the 3 Vs mean, real examples, how big data is processed and a simple test for whether your data is actually big enough to matter.
What dbt does, how models, tests and docs work, a simple model example you can follow, what changed with dbt v2 and the Fivetran merger, and how to start.
What structured, unstructured and semi-structured data are, with real business examples, a side-by-side comparison table and how AI is turning unused documents and recordings into usable data.