As Stephen Marsland once said, “if data had mass, the earth would be a black hole.” A vast part of the immense amount of structured and unstructured data that we call “Big Data” is nothing but machine-originated log data. Logs are generated for a lot of different purposes – from security to debugging and troubleshooting. They constitute a gold mine of useful information and actionable insights if properly stored, managed, and analyzed.
It comes as no surprise that recent surveys show that log management is used by more than half of the digital companies that have deployed Big Data projects. For nearly 60% of them, log management is a priority. We hope yours is one of the wiser companies as well!
LOG MANAGEMENT AS A USE CASE FOR BIG DATA
Logs are a perfect example of large volume, high-velocity data sets where information comes in a lot of different varieties. If you didn’t guess it already, we’re talking about the “three Vs” of Big Data here, showing how the logging is one of the most known use cases for Big Data. When the sheer number and variety of formats is properly handled through Big Data solutions, log data is suitable to perform complex security and auditing tasks.
Needless to say, effective log management is the cornerstone of every robust security strategy. But when large numbers of security events need to be processed every day (sometimes petabytes), it could take days or weeks before all the data is compiled to be analyzed with a traditional approach. Logs may come from countless endpoints on the sensor grid, with different log structures for each type. Then add machine data captured from the network and Big Data is the only affordable and scalable approach to handle the three Vs.
Log management provides an instant overview of the overall health of hardware and software systems by visually monitoring them, and improves business intelligence by providing real-time information on the efficiency of applications and servers. But log management and Big Data open a world of opportunities beyond just continual tuning, especially for business analytics.
For example, many e-commerce sites capture behavioral data to understand their customers, such as checking where users clicked in their websites, what they searched for, what products they bought, and what their shopping experience looked like. That’s a useful source of downstream information, indeed, but now let’s imagine if all that data is captured, aggregated and analyzed in real-time. Log data aggregating, parsing, and processing tools such as Graylog can be combined with trend visualization and analysis software to provide a seamless log analytics process. Employing Big Data solutions in tandem with robust log management provides organizations with an endless flow of upstream business insights that can be used to build better prediction models or to improve user experience.
BIG DATA AND LOG MANAGEMENT
Merging and manipulating complex data is a very hard task. Although it is theoretically possible to find anything by performing a series of transformations on your data, data analysts know very well that you need a complete plan before engaging in even the timidest search.
LOG MANAGEMENT TOOL FOR LARGE VOLUMES OF DATA – ENTER GRAYLOG
Many log management tools require a certain degree of precision to define a search and find something meaningful. That’s okay if you’re looking for something in particular. But when you’re scavenging through Big Data, most of the time you don’t know what you’re looking for before creating the query. You’re just looking for a potential threat – you don’t know the answer already.
Graylog is an amazingly flexible tool that provides analysts with all the freedom they need to explore the data without such a detailed plan. With Graylog, you can keep revealing new information as you explore, and dive deeper into the search results step by step until you find the answers you’re looking for. Our log management software doesn’t need extended training and experience to be used, and literally, any user can make sense of its simple and intuitive interface almost immediately. Your operators can focus their efforts on becoming better investigators rather than just “experts in the Graylog language.”
CONCLUSION
From business intelligence to security, system optimization and IT operations, the applications of Big Data in log management are countless. But in order to harness the full potential of the immense data lakes, your organization need the best tools. From noise to knowledge, Graylog is the tool you need to become the Master of Big Data.
