Microsoft Power BI is a visualization and data discovery tool from Microsoft. It allows users to convert data into visuals and graphics, visually explore and analyze data, collaborate on interactive dashboards and reports, and scale across their organization with built-in governance and security.
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 operations we use the tool for many different topics, from factory quality systems to high level reviews. We have created kind of an internal "App Store" based on Power BI where you have a lot of different dashboards for different solutions (cost, cash, health and safety, sales, factories, distribution centers...) and you as an user just need to get in that "App Store" and enter in whatever tool can be useful for you. It is open to all the operations employees and can use on demand. Also it has raised the imagination of our colleagues, as they are not only working by themselves creating new reports, but also raising fantastic ideas that can be extended for the usage of all the community.
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.
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.
The desktop app is great but needs a lot of performance improvements
No MacOS Version for the Desktop app, this is a big limitation for business since executives prefer Macs
Premium Cloud Version of Power BI is awfully expensive
On-Premise Version of the Power BI Reports Server is bundled only with SQL Server Enterprise License and cannot be purchased separately and requires Software Assurance Subscription
On-Premise Power BI Report Server doesn't support ADFS, AzureAD or any Claims-Based authentication platform, a sad disadvantage for enterprises
At this point, I think we all know who has taken the lead in the business intelligence and analytics market worldwide. With fresh new updates every other day on top of an already robustly built product with all features that one can dream of is a no brainer, I feel. Microsoft will invariably be synonymous with quality and professionalism.
I can't really speak to the support overall, [but] I will say that in the almost three years I have used the system, I have only needed to contact their support team once. I think the team was helpful, but it did take some time for us to resolve the issues/ request that they had. I guess the good news is that the system is pretty stable, and I personally have rarely needed to contact their technical support team.
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
[Microsoft] Power BI is practical and effective, like a hammer for a nail, it is easy to use and produces very quickly the results that in most cases are urgently required by clients (nice reports to share on the web). To start using [Microsoft] Power BI you need a business email address, with that you create an account in Power BI Service and in less than 1 hour you will have installed Power BI Desktop, a report will have been created and it will have been published on the web .
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.
