Elastic Stack

Elastic Cloud on Kubernetes for best scalability

Comments: Organizing chat data to be searchable and log management to proactively fix issues.

Pros:

One of the best features I like is that Elastic built their own kubernetes operator to extend the k8s orchestration and make it easy to deploy, scale, change, secure and configure hot-warm infrastructures. Their operator saves a ton of time during configuration. I have deployed stacks on different k8s architectures like Azure Kubernetes Service, Amazon Elastic Kubernetes Service and small on prem clusters with microk8s without issues. When we reach performance thresholds we add more elastic nodes and ECK secures and joins it to the cluster and in minutes we can leverage the extra compute. A lot of changes that are done after going to PROD are non-disruptive since ECK is aware of the main node and makes sure to pass the master role before the main one is re-deployed. I have also migrated Elastic Cloud Enterprise deployments running on bare metal and the stability of ECK is unmatched.

Cons:

Currently it is not recommended or supported for a PROD cluster to do its own self monitoring so you have to deploy a monitoring cluster. In cloud scenarios this adds costs and extra complexity so it will be great to have this feature supported.

A very good stack to store and visualize data.

Comments: Overall I am happy with my decision to choose Elastic Stack. It is completely fulfilling my requirements without any issues.Kibana works very well even with a lot of data.

Pros:

I mostly like the Elastic Search queries. Those follow a certain standard and work very well. Data retrieval is very easy and fast in Elastic Search.Visualizing data thru Kibana is very smooth and straightforward. It supports a couple of inbuilt dashboards to represent the data nicely.

Cons:

Nothing to dislike about Elastic Stack. The only thing is configuration and setup should be correct. It could take some time for beginners.

This powerful tool allows you to take data from any source and format to search and analyze.

Pros:

It is a super fast and efficient data extraction tool. Recommended for medium-sized projects. Handles large amounts of data, is scalable.

Cons:

Usable from any device, however these must be state-of-the-art and offer great calculation speeds and ram storage.

Elastic Stack - A Complete Package for Big Data Visualizations and Fast Data Query!

Comments: Elastic Stack is a powerful platform which allows you to quickly search and query on the data even if the data is in huge volume, thanks to its distributed computing and storage. it has enabled me to develop an application which fetches results from TBs of data in seconds.

Pros:

1.Allows Faster searching and query operations 2.Provides with easy data visualization for analysis 3.Support for multiple data sources 4.Good SDK support for quick integration with application 5.Scalable as per the requirement with support of kubernetes

Cons:

1.UI is simple ,could be made more robust and dynamic 2.Calculations and processing speed can be further improved 3.Proper usage knowledge is required when using it on scalable platforms

The perfect searching allied to a RDB

Comments: We've been pairing Elasticsearch with a traditional RDB in many projects with great results. This way we don't compromise our data reliability and searching speed is blazing fast.

Pros:

Searching is where elasticsearch is second to none, either in terms, n-grams or full-text. Latest releases have greatly improved the aggregation performance, so it's also a great fit for analytics workloads. The customizable sharding and replica configurations make is very reliable too.

Cons:

Searching and joining different documents has room for improvement, it's usualy not as fast as we would like it to be, so most of the times we end up un-normalizing documents and en-richening their data to boost searching performance.