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Latency refers to the delay in processing data. It is the time delay between a request for data and the response from the system providing that data. In computing, latency can occur in different stages of the data processing and transmission process, such as input/output (I/O) operations, network delays, and storage retrieval times. Essentially, it is the time it takes for data to travel from one point to another.
Data transfers between different systems are not instant, and the time of transfer can vary according to the system's load, network bandwidth, and storage type. As data is processed, it moves through several components, and each component introduces a delay, adding to the overall latency.
Latency can occur in different stages of data transmission and data processing, such as the time taken to load data into memory, look up values from an index, and more. The impact of latency can be significant for data processing as it can affect the time-sensitive operations that businesses rely on.
Low latency is critical for businesses working with real-time data such as stock trading, gaming, and IoT applications. High latency can cause delays in data processing, negatively impacting the speed of operations and the overall business's performance. Reducing latency is critical for efficient processing of data, which ultimately leads to faster and more accurate decision-making.
Latency is essential to understand for use cases that rely on real-time and high-speed data processing, such as:
Other terms related to latency include:
Dremio users would be interested in latency because it can impact the speed and accuracy of data processing, which is essential when working with large datasets. Low latency can help Dremio users access and process data faster, leading to more effective decision-making and improved business performance.
Traditional data warehousing involves storing data in a centralized location. This can lead to high latency as data must be transferred between storage and compute resources. Dremio's data lakehouse architecture eliminates this latency by allowing for data processing and storage within the same environment.
In-memory computing is a technology that stores data in RAM, which can reduce latency. However, this approach can be costly and may not be suitable for all workloads. Dremio's distributed architecture allows for data to be processed and stored across multiple nodes, reducing latency without the high costs of in-memory computing.