Big Data-related terms

 

• AI and Machine Learning - Both artificial intelligence and machine learning remain central to Big Data processing and predictive modeling. Subcategories like deep learning and neural networks help in analyzing complex data and creating human-like insights.
• Data Governance & Privacy - With increasing concerns over data privacy, terms like data governance, data access control, and tokenization are frequently highlighted as they address how data is stored, accessed, and anonymized.
• Cloud and Distributed Computing - Cloud computing platforms (e.g., Amazon Web Services, Google Cloud) and frameworks such as Hadoop and MapReduce are crucial for handling large datasets across distributed systems.
• Data Democratization and Data Literacy - These buzzwords emphasize the importance of making data accessible to a wider range of users within organizations, alongside the need for data literacy among employees who may not be data experts.
• DataOps and Data Mesh - Concepts like DataOps (data operations) and data mesh are gaining traction as companies seek to improve collaboration and decentralize data handling to reduce bottlenecks.
• Internet of Things (IoT) - As more devices connect and communicate data, IoT has become a frequent topic in Big Data conversations, especially as companies integrate more sensors and devices into their data ecosystems.
• Predictive and Prescriptive Analytics - These analytics approaches help businesses use past data to forecast future trends and recommend actions, often relying on machine learning algorithms for accurate predictions.
• Structured vs. Unstructured Data - Handling unstructured data is one of the most significant challenges in Big Data. While structured data fits into databases, unstructured data requires specialized tools for organization and analysis.
• NoSQL Databases - These databases, including Cassandra and MongoDB, support the storage and processing of large-scale, unstructured data, which is essential for many Big Data applications.