Glossary

Correlation Search refers to a data analysis technique based on SAF Language and used to identify and explore relationships, connections, or patterns between multiple variables or datasets. This method involves examining how changes in one variable are associated with changes in another, aiming to uncover dependencies or interactions that might not be immediately apparent.

In correlation searches, statistical measures such as correlation coefficients are often employed to quantify the strength and direction of relationships between variables. Positive correlation indicates that as one variable increases, the other tends to increase as well, while negative correlation suggests that as one variable increases, the other tends to decrease.

This technique is commonly used in various fields, including statistics, economics, social sciences, and data analytics, to uncover hidden insights, forecast trends, and make informed decisions based on the detected relationships between different data points. Correlation searches can be a valuable tool for understanding complex systems and making data-driven conclusions.

Machine data refers to the valuable information generated by machines and systems, including logs, events, and metrics, as well as user activity across IT systems. By analyzing this data, businesses can gain real-time visibility into their systems, detect anomalies, troubleshoot issues, and make data-driven decisions to optimize operations, enhance performance, improve customer experiences, and drive business growth.

Operational Storage refers to a specialized type of data storage system designed for efficient data indexing and frequent searches. Its primary purpose is to support quick access to information, particularly for time-sensitive operations or inquiries that are typically performed within a short timeframe, usually not exceeding 7 days. As a result of this emphasis on rapid retrieval, Operational Storage demands higher computational resources to handle data processing effectively. This storage solution is tailored to scenarios where data needs to be readily available and searchable, and where timely decision-making and analysis are crucial components of the operational process.

Retrospective Storagerefers to a specialized data storage system that prioritizes data retention and archiving over immediate indexing of incoming data streams. In contrast to operational storage, retrospective storage does not actively index the data stream but instead focuses on storing historical information for the purpose of retrospective analysis. Searches or queries on retrospective storage are conducted less frequently compared to operational storage, as they are typically performed for historical analysis, long-term trend identification, or deep insights.

One of the primary benefits of retrospective storage is its resource efficiency. By not expending computational resources on continuous indexing and frequent searches, it conserves CPU, RAM, and disk resources. This makes it a cost-effective solution for storing and managing large volumes of historical data, especially when real-time access and rapid querying are not immediate priorities. Retrospective storage caters to use cases where the emphasis is on long-term data preservation, infrequent but comprehensive analysis, and resource optimization.

SAF Language is a type of "Search Processing Language," refers to a specialized programming language that serves as the core component of the SAF (Search Anywhere Framework). This language is designed to facilitate the execution of correlation searches and various other advanced search and analysis tasks within the framework. It offers a range of functions, operators, and SQL and UNIX-pipe syntax that enable users to express intricate relationships between variables and datasets.

Specifically tailored for correlation searches, the SAF Language empowers users to identify, quantify, and visualize connections between different data elements. It supports the creation of queries that delve into large volumes of data to discover meaningful relationships, trends, and anomalies. By leveraging the capabilities of the SAF Language, analysts and researchers can perform sophisticated data analysis without the need for extensive programming expertise.

Ultimately, the SAF Language plays a vital role in unlocking the potential of the SAF framework, enabling users to conduct effective correlation searches and gain valuable insights from their data.

Search Unit is a computational entity within the SAF cluster, designed to facilitate data loading and analysis tasks within operational and retrospective storage. This unit is optimized to handle significant data processing operations with efficiency.

In the context of operational storage, a Search Unit possesses the capability to efficiently load and analyze up to 1 TB of data per day. It excels in conducting quick searches and analysis tasks on data stored within the operational storage, contributing to the timely retrieval of information for time-sensitive decision-making processes.

Furthermore, in the context of retrospective storage, a Search Unit plays a pivotal role in scalability. It enables the expansion of retrospective storage by accommodating up to 5 TB of data with a single data replica. This function allows the retrospective storage system to manage larger volumes of historical data, facilitating in-depth analysis and comprehensive insights over extended time periods.

Overall, the Search Unit is a crucial component of the SAF cluster, supporting both operational and retrospective storage needs by ensuring efficient data processing, retrieval, and scalability.