Get Started Free
No time limit - totally free - just the way you like it.Sign Up Now
Data latency refers to the delay or lag between when data is created or updated and when it becomes available for use in data processing or analysis. It is the time it takes for data to travel from its source to the destination, such as a data lakehouse or a data warehouse. Data latency can vary depending on factors like the volume of data, the complexity of data processing, and the infrastructure used to transmit and store data.
Data latency can occur due to various reasons, including network congestion, data transmission delays, data processing time, or data storage and retrieval time. For example, in a traditional data warehouse setup, data is typically batch-loaded at regular intervals, causing a delay in making the data available for analysis. On the other hand, in a real-time streaming data pipeline, data is processed and made available for analysis almost instantaneously, resulting in low data latency.
Data latency plays a crucial role in data-driven decision-making processes and analytics. By reducing data latency, businesses can access and analyze near real-time data, enabling them to make more timely and accurate decisions. Lower data latency also allows organizations to detect and respond to critical events or anomalies quickly, which is particularly beneficial in industries like finance, healthcare, and e-commerce.
Data latency is essential in various use cases, including:
There are several technologies and terms related to data latency:
Dremio is a data lakehouse platform that combines data lake and data warehouse capabilities, allowing users to access and analyze data in real-time. By understanding data latency and its impact, Dremio users can optimize their data pipelines, reduce data latency, and gain real-time insights from their data lakehouse. Data latency-awareness can help Dremio users make more informed decisions when designing their data processing and analytics workflows.