Elasticsearch vs. SAS Visual Analytics

Elasticsearch is a really scalable solution that can fit a lot of needs, but the bigger and/or those needs become, the more understanding & infrastructure you will need for your instance to be running correctly. Elasticsearch is not problem-free - you can get yourself in a lot of trouble if you are not following good practices and/or if are not managing the cluster correctly. Licensing is a big decision point here as Elasticsearch is a middleware component - be sure to read the licensing agreement of the version you want to try before you commit to it. Same goes for long-term support - be sure to keep yourself in the know for this aspect you may end up stuck with an unpatched version for years.

Incentivized

I was in a meeting with the client and there I have to show them some analytic data to them. But I was confused about how I will manage to show big data to clients with accuracy. But then the SAS Visual Analytics software helps me in presenting accurate data at the moment and it was very presentable and through that, I got the deal for that business.

Incentivized

• As I mentioned before, Elasticsearch's flexible data model is unparalleled. You can nest fields as deeply as you want, have as many fields as you want, but whatever you want in those fields (as long as it stays the same type), and all of it will be searchable and you don't need to even declare a schema beforehand!
• Elastic, the company behind Elasticsearch, is super strong financially and they have a great team of devs and product managers working on Elasticsearch. When I first started using ES 3 years ago, I was 90% impressed and knew it would be a good fit. 3 years later, I am 200% impressed and blown away by how far it has come and gotten even better. If there are features that are missing or you don't think it's fast enough right now, I bet it'll be suitable next year because the team behind it is so dang fast!
• Elasticsearch is really, really stable. It takes a lot to bring down a cluster. It's self-balancing algorithms, leader-election system, self-healing properties are state of the art. We've never seen network failures or hard-drive corruption or CPU bugs bring down an ES cluster.

Incentivized

• Provides the flexibility to the end user to slice and dice the data.
• Anyone can make predictive models with the help of in-built algorithms without the need to write a single line of code or knowledge of what's under the hood of algorithms.
• The feature to simply ask a question related to data and getting a response in form of text, chart or graph is amazing.

Incentivized

• Joining data requires duplicate de-normalized documents that make parent child relationships. It is hard and requires a lot of synchronizations
• Tracking errors in the data in the logs can be hard, and sometimes recurring errors blow up the error logs
• Schema changes require complete reindexing of an index

Incentivized

• SAS is relatively expensive when compared to other BI tools and requires a large amount of upfront fee which becomes an issue for smaller organizations.
• UI for the dashboards looks a little date in comparison to competitors like Tableau and Microstrategy.
• Integration with other open source software like Python needs to be built in.

Incentivized

We're pretty heavily invested in ElasticSearch at this point, and there aren't any obvious negatives that would make us reconsider this decision.

Incentivized

SAS really is the cutting edge in Business Intelligence. That is all they do! They are constantly coming out with new products, product upgrades, and their tech support is second to none. In addition, their support of Education has made our ability to acquire their product possible.

To get started with Elasticsearch, you don't have to get very involved in configuring what really is an incredibly complex system under the hood. You simply install the package, run the service, and you're immediately able to begin using it. You don't need to learn any sort of query language to add data to Elasticsearch or perform some basic searching. If you're used to any sort of RESTful API, getting started with Elasticsearch is a breeze. If you've never interacted with a RESTful API directly, the journey may be a little more bumpy. Overall, though, it's incredibly simple to use for what it's doing under the covers.

Incentivized

SAS BI is good for creating reports and dashboards and then sharing it with the users. It also has ability to manage access to the reports and dashboards but somehow with most of the world moving to open source languages R, Python and Julia, SAS BI feels to be archaic in terms of feature set and integrations it allow[s]. Also, comparing it with other Business Intelligence tools like Tableau and Microsoft BI, the functionality of SAS BI is very limited and doesn't justify the pricing.

Incentivized

We've only used it as an opensource tooling. We did not purchase any additional support to roll out the elasticsearch software. When rolling out the application on our platform we've used the documentation which was available online. During our test phases we did not experience any bugs or issues so we did not rely on support at all.

Incentivized

When you call tech support, you are immediately routed to a person who can answer your question. Often they can answer on the spot. However, if they cannot, you are given a track number and then followed up with. There have been times when I have had multiple track numbers open and they will actually TRACK YOU DOWN to ensure that your problem has been resolved. Issues do not fall into black holes with SAS. They are also willing to do a WebEx with you to diagnose the problem by seeing your environment, which is always helpful.

Do not mix data and master roles. Dedicate at least 3 nodes just for Master

Incentivized

As far as we are concerned, Elasticsearch is the gold standard and we have barely evaluated any alternatives. You could consider it an alternative to a relational or NoSQL database, so in cases where those suffice, you don't need Elasticsearch. But if you want powerful text-based search capabilities across large data sets, Elasticsearch is the way to go.

Incentivized

I have used Crystal Reports, Jaspersoft and SQL Server Reporting Services (SSRS). I would recommended Business Intelligence over SSRS and Crystal Reports. SSRS is very SQL-centric and Crystal Reports is more of an end-user tool. I would recommend Jaspersoft over Business Intelligence for developing a seamless web-based reporting interface but I highly recommend Business Intelligence for end-user ad-hoc reporting.

Incentivized

• We have had great luck with implementing Elasticsearch for our search and analytics use cases.
• While the operational burden is not minimal, operating a cluster of servers, using a custom query language, writing Elasticsearch-specific bulk insert code, the performance and the relative operational ease of Elasticsearch are unparalleled.
• We've easily saved hundreds of thousands of dollars implementing Elasticsearch vs. RDBMS vs. other no-SQL solutions for our specific set of problems.

Incentivized

• SAS Visual Analytics licensing cost should be reduced to enable users to use it in mass scale.
• More statistical and mathematical equations and theories should be converted into SAS functions for reusing.
• Because of enriched ETL and reporting capabilities SAS Visual Analytics is most preferred by business users.

Incentivized