Data Processing

Explore Real-time Data Streaming, its benefits to businesses, and its role in data lakehouse environments for data scientists and tech professionals.

Learn about Batch Data Processing, its benefits, use cases, and its role in the context of a data lakehouse environment.

Streaming Ingestion is a data processing technique that enables real-time ingestion and processing of data streams.

Real-time Streaming is a data processing technique that enables the continuous processing and analysis of data as it is generated.

Transaction Log Processing is the analysis and utilization of transaction logs to optimize data processing and analytical workflows.

Serverless Data Processing is a cloud computing model that allows businesses to process and analyze data without the need for managing or provisioning servers.

Near-Real-Time ETL is a data integration process that enables businesses to extract, transform, and load data in near real-time, improving data processing and analytics.

Real-time Data Processing is the practice of processing and analyzing data as it is generated or received, allowing businesses to make immediate and data-driven decisions.

Parallel Querying is a technique that allows for the simultaneous processing of multiple queries in a distributed computing environment.

Concurrency is the ability to run multiple tasks or processes simultaneously, enabling efficient data processing and analytics.

Micro-batch Processing is a data processing technique that operates on small batches of data at regular intervals, providing the benefits of both batch processing and real-time processing.

Distributed Processing is a computing approach that involves dividing tasks across multiple machines to improve performance, scalability, and fault tolerance.

Massively Parallel Processing is a data processing technique that divides tasks into smaller sub-tasks and executes them in parallel across multiple processors or computing nodes.

Data Reconciliation is a process that compares and matches data from different sources to ensure consistency and accuracy.

Data Querying is the process of retrieving specific information from databases or data sources using SQL-like statements for analysis, reporting, and decision-making.