Table of Contents
Big data
Return to Massive Data, Huge Data, Big Data Topics, Data science platforms, EmEditor for Big Data Editing (Data Editing)
Big data refers to extremely large datasets that are beyond the capability of traditional database management systems and processing software to capture, store, manage, and analyze efficiently. These datasets can come from various sources such as social media, business transactions, online videos, sensors, and more, encompassing a wide variety of types including structured, unstructured, and semi-structured data. The concept of big data is often summarized by the three Vs: Volume, representing the massive amount of data generated; Velocity, indicating the high speed at which new data is produced and needs to be processed; and Variety, referring to the different types of data sources. Big data technologies, including Hadoop, Spark, and NoSQL databases, are used to handle these challenges, enabling insights that can lead to better decision-making, operational efficiencies, and new product development.
Data Science: Fundamentals of Data Science, DataOps, Big Data, Data Science IDEs (Jupyter Notebook, JetBrains DataGrip, Google Colab, JetBrains DataSpell, SQL Server Management Studio, MySQL Workbench, Oracle SQL Developer, SQLiteStudio), Data Science Tools (SQL, Apache Arrow, Pandas, NumPy, Dask, Spark, Kafka); Data Science Programming Languages (Python Data Science, NumPy Data Science, R Data Science, Java Data Science, C Plus Plus Data Science | C++ Data Science, MATLAB Data Science, Scala Data Science, Julia Data Science, Excel Data Science (Excel is the most popular "programming language") - Google Sheets, SAS Data Science, C Sharp Data Science | Data Science, Golang Data Science, JavaScript Data Science, Kotlin Data Science, Ruby Data Science, Rust Data Science, Swift Data Science, TypeScript Data Science, Bash Data Science); Databases, Data (computing) | Data, Data augmentation | Augmentation, Data analysis | Analysis, Data analytics | Analytics, Data archaeology | Archaeology, Data cleansing | Cleansing, Data collection | Collection, Data compression | Compression, Data corruption | Corruption, Data curation | Curation, Data degradation | Degradation, Data editing | Editing (EmEditor), Data engineering, Extract, transform, load | ETL/Extract, load, transform | ELT (Data extraction | Extract-Data transformation | Transform-Data loading | Load), Data farming | Farming, Data format management | Format management, Data fusion | Fusion, Data integration | Integration, Data integrity | Integrity, Data lake | Lake, Data library | Library, Data loss | Loss, Data management | Management, Data migration | Migration, Data mining | Mining, Data pre-processing | Pre-processing, Data preservation | Preservation, Information privacy | Protection (privacy), Data recovery | Recovery, Data reduction | Reduction, Data retention | Retention, Data quality | Quality, Data science | Science, Data scraping | Scraping, Data scrubbing | Scrubbing, Data security | Security, Data steward | Stewardship, Data storage | Storage, Data validation | Validation, Data warehouse | Warehouse, Data wrangling | Wrangling/munging. ML-DL - MLOps. Data science history, Data Science Bibliography, Manning Data Science Series, Data science Glossary, Data science topics, Data science courses, Data science libraries, Data science frameworks, Data science GitHub, Data Science Awesome list. (navbar_datascience - see also navbar_python, navbar_numpy, navbar_data_engineering and navbar_database)
External sites
- Big data
Cloud Monk is Retired ( for now). Buddha with you. © 2025 and Beginningless Time - Present Moment - Three Times: The Buddhas or Fair Use. Disclaimers
SYI LU SENG E MU CHYWE YE. NAN. WEI LA YE. WEI LA YE. SA WA HE.