A data lake is a large, centralized repository of data. The data in a data lake is stored in its native form, making it a combination of structured, unstructured, and semi-structured data. Data lakes store full-fidelity data until it is needed.
Data lakes can be an invaluable tool for organizations when they don’t know what data will be used for. Analysts can only provide value to the organization if it exists and is available, and failing to collect data or downsampling to certain fields and features place this at risk. Data lakes ensure that potentially valuable data is available by collecting and storing it in its original form.
Data lakes and data warehouses are both designed to store data for an organization. However, they store data in different formats, and for different purposes.
A data warehouse is designed to store structured data in tables and hierarchical dimensions. This is useful for applications where an organization has already identified features of interest and developed tables based on these. For example, a data warehouse is well-suited to supporting the generation of predefined reports.
Data lakes store data in their native formats, which means that they preserve all of the features of the data. This provides additional context and allows the generation of new reports and analytics that use data that might have been discarded when converting data for storage in a data warehouse.
The architecture of a data lake is commonly flat, using object storage or files to hold data. This is because data lakes are designed to store data in its native format, rather than the tables of a data warehouse. In addition to data storage, a data lake must also be capable of supporting data exploration and analytics activity.
To be effective, a data lake must offer scalable:
Data lakes provide analysts with the infrastructure that they need to store and access unstructured data, which requires scalable infrastructure. Cloud-based solutions with their flexible storage and processing power are ideally suited to data lakes.
Security data lakes can be used to collect and store security data from various systems, applications, and security solutions.
Some of the advantages of a security data lake include:
Some security data is highly structured, making it well-suited to storage and processing by security information and events management (SIEM), extended detection and response (XDR), and similar solutions. However, a security data lake can be invaluable for ensuring that a security team has access to whatever data it needs for incident response, threat hunting, or digital forensics after an event has occurred.
Check Point’s security solutions are designed to integrate, providing centralized visibility and management across an organization’s security architecture. This centralization and integration streamlines SOC operations and enables organizations to more effectively prevent, detect, and respond to potential security incidents.
Horizon Events is Check Point’s security data lake, providing centralized access and efficient searching of security logs for all of Check Point’s solutions. Find out how a security data lake can enhance your organization’s security operations by signing up for a free trial today.