Logstash vs. Qlik Talend Cloud

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).

Incentivized

This tool fits all kinds of organizations and helps to integrate data between many applications. We can use this tool as data integration is a key feature for all organizations. It is also available in the cloud, which makes the integration more seamless. The firm can opt for the required tools when there are no data integration needs.

Incentivized

• 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.

Incentivized

• Talend Data Integration allows us to quickly build data integrations without a tremendous amount of custom coding (some Java and JavaScript knowledge is still required).
• I like the UI and it's very intuitive. Jobs are visual, allowing the team members to see the flow of the data, without having to read through the Java code that is generated.
• Dynamically table creation from new source.

• It is heavy i.e., intensive as of now. Need to reduce overhead to save CPU/RAM consumption
• Need to be more Kubernetes-friendly. Should support auto-scaling and K8s observability
• Initial configuration is still complex. A seamless config procedure is still required

• Pricing for sure can be the area for improvement.
• Real time processing is slow as compared to other tools like Abinitio.
• While developing batches, it crashes a lot. It may be the issue with me, but I wanted to highlight it.

Incentivized

As I said earlier, for a production-grade OpenStack Telco cloud, Logstash brings high value in flexibility, compliance, and troubleshooting efficiency. However, this brings a higher infra & ops cost on resources, but that is not a problem in big datacenters because there is no resource crunch in terms of servers or CPU/RAM

We use Talend Data Integration day in and day out. It is the best and easiest tool to jump on to and use. We can build a basic integration super-fast. We could build basic integrations as fast as within the hour. It is also easy to build transformations and use Java to perform some operations.

Incentivized

Good support, specially when it relates to PROD environment. The support team has access to the product development team. Things are internally escalated to development team if there is a bug encountered. This helps the customer to get quick fix or patch designed for problem exceptions. I have also seen support showing their willingness to help develop custom connector for a newly available cloud based big data solution

Incentivized

Logstash can be compared to other ETL frameworks or tools, but it is also complementary to several, for example, Kafka. I would not only suggest using Logstash when the rest of the ELK stack is available, but also for a self-hosted event collection pipeline for various searching systems such as Solr or Graylog, or even monitoring solutions built on top of Graphite or OpenTSDB.

Incentivized

In comparison with the other ETLs I used, Talend is more flexible than Data Services (where you cannot create complex commands). It is similar to Datastage speaking about commands and interfaces. It is more user-friendly than ODI, which has a metadata point of view on its own, while Talend is more classic. It has both on-prem and cloud approaches, while Matillion is only cloud-based.

Incentivized

• Positive: LogStash is OpenSource. While this should not be directly construed as Free, it's a great start towards Free. OpenSource means that while it's free to download, there are no regular patch schedules, no support from a company, no engineer you can get on the phone / email to solve a problem. You are your own Engineer. You are your own Phone Call. You are your own ticketing system.
• Negative: Since Logstash's features are so extensive, you will often find yourself saying "I can just solve this problem better going further down / up the Stack!". This is not a BAD quality, necessarily and it really only depends on what Your Project's Aim is.
• Positive: LogStash is a dream to configure and run. A few hours of work, and you are on your way to collecting and shipping logs to their required addresses!

Incentivized

• It’s only been a positive RoI with Talend given we’ve interfaced large datasets between critical on-Prem and cloud-native apps to efficiently run our business operations.
• 40K+ plots data, covering 1K+ crop varieties.
• 3K+ Customer & their credit data, 3K+ product inventory & pricing.

Incentivized