Perfect for projects where Elasticsearch makes sense: if you decide to employ ES in a project, then you will almost inevitably use LogStash, and you should anyways. Such projects would include: 1. Data Science (reading, recording or measure web-based Analytics, Metrics) 2. Web Scraping (which was one of our earlier projects involving LogStash) 3. Syslog-ng Management: While I did point out that it can be a bit of an electric boo-ga-loo in finding an errant configuration item, it is still worth it to implement Syslog-ng management via LogStash: being able to fine-tune your log messages and then pipe them to other sources, depending on the data being read in, is incredibly powerful, and I would say is exemplar of what modern Computer Science looks like: Less Specialization in mathematics, and more specialization in storing and recording data (i.e. Less Engineering, and more Design).
In the current lot of hundreds of SIEM solutions out there in the market, ArcSight ESM is fairly less expensive with strong fundamentals in place. The log ingestion, correlation are very well performing and totally worth ROI. However, the tool has lost its way when it comes to staying abreast with current feature curve of SIEM technology and the evolution has not been done by MicroFocus. Search times are high and there is no major plug-in that has been introduced as part of the product life cycle.
Logstash design is definitely perfect for the use case of ELK. Logstash has "drivers" using which it can inject from virtually any source. This takes the headache from source to implement those "drivers" to store data to ES.
Logstash is fast, very fast. As per my observance, you don't need more than 1 or 2 servers for even big size projects.
Data in different shape, size, and formats? No worries, Logstash can handle it. It lets you write simple rules to programmatically take decisions real-time on data.
You can change your data on the fly! This is the CORE power of Logstash. The concept is similar to Kafka streams, the difference being the source and destination are application and ES respectively.
Integration with smart logger and ESM to create rules and easy management of the same.
Easy integration with all end point security management tool(IPS/IDS, Firewall, Anti-Virus) and their consolidated output at a single place to effectively rectifying true and false positives.
Since it's a Java product, JVM tuning must be done for handling high-load.
The persistent queue feature is nice, but I feel like most companies would want to use Kafka as a general storage location for persistent messages for all consumers to use. Using some pipeline of "Kafka input -> filter plugins -> Kafka output" seems like a good solution for data enrichment without needing to maintain a custom Kafka consumer to accomplish a similar feature.
I would like to see more documentation around creating a distributed Logstash cluster because I imagine for high ingestion use cases, that would be necessary.
Overall, it is a good investment in order for an organization to stay compliant and stay secure from all the wild things happening. It is definitely a cost effective tool with some good features including correlation, log storage, reporting and dashboards. If a customer is looking for advanced set of features, then I would highly not recommend this.
I personally haven't reached the support team, however, the engineers never complained about the Arcsight support team. We had some issues with the tool in the past but every time we reached the support, all issues were resolved in a timely manner.
MongoDB and Azure SQL Database are just that: Databases, and they allow you to pipe data into a database, which means that alot of the log filtering becomes a simple exercise of querying information from a DBMS. However, LogStash was chosen for it's ease of integration into our choice of using ELK Elasticsearch is an obvious inclusion: Using Logstash with it's native DevOps stack its really rational
Multiple platforms are already supported by Arcsight. Support is good. Scripts can be used to get data from multiple threat intel sources & the same can be used in correlation rules to detect any suspicious activity. Reporting features are good & you can check any backdated information within new clicks.
Positive: Learning curve was relatively easy for our team. We were up and running within a sprint.
Positive: Managing Logstash has generally been easy. We configure it, and usually, don't have to worry about misbehavior.
Negative: Updating/Rehydrating Logstash servers have been little challenging. We sometimes even loose data while Logstash is down. It requires more in-depth research and experiments to figure the fine-grained details.
Negative: This is now one more application/skill/server to manage. Like any other servers, it requires proper grooming or else you will get in trouble. This is also a single point of failure which can have the ability to make other servers useless if it is not running.
Logger helps us to decrease incident response times.
It also decreased our project times with the man/day calculations. Before this solution, it may take up to 10 men/days to do something. After this, it becomes nearly half of the time.
