Elasticsearch is an enterprise search tool from Elastic in Mountain View, California.
$16
per month
MySQL
Score 8.3 out of 10
N/A
MySQL is a popular open-source relational and embedded database, now owned by Oracle.
N/A
SAP HANA Cloud
Score 8.9 out of 10
N/A
SAP HANA is an application that uses in-memory database technology to process very large amounts of real-time data from relational databases, both SAP and non-SAP, in a very short time. The in-memory computing engine allows HANA to process data stored in RAM as opposed to reading it from a disk which means that the data can be accessed in real time by the applications using HANA. The product is sold both as an appliance and as a cloud-based software solution.
Even when sphinx base code is on c++ and they obtain a great performance from it, even when they have a set of plugins that allow to integrate with common database systems like MySQL, Elasticsearch is on top of license and all their experience on search. It also provides a long …
When we first evaluated Elasticsearch, we compared it with alternatives like traditional RDBMS products (Postgres, MySQL) as well as other noSQL solutions like Cassandra & MongoDB. For our use case, Elasticsearch delivered on two fronts. First, we got a world-class search …
All database systems have things they are good at, and things they aren't as good at. Riak/SOLR is great as a K/V store, but SOLR cannot handle requests as fast as ElasticSearch. In fact, SOLR is the reason we had to migrate to ElasticSearch. Redis is great at SET operations …
Solr is the only other alternative product I've used. Elasticsearch in comparison is a much better product. The query language in elasticsearch along with the cluster management and sharding makes Elasticsearch a clear winner.
Even though Lucene is very powerful it is not easy to implement Lucene as a search provider. Lucene is the core of Elasticsearch and they made implementation very easy.
Ability to support JSON queries, Percolator, ease to set up and custom routing were some of the reasons why we decided to use Elasticsearch instead of Solr.
SQLite - Is the goto DB for Mobile/Desktop Apps. Its not as elaborate as Mysql but since its a RDBMS it provides all the basic features and its lite. We use mysql at the backend and for desktop app we use SQLite
postgres - Its a formidable opponent. It is fast and reliable and …
If you are looking for a relational database (depending on your app), MySQL is a good place to start. MongoDB and Cassandra are NoSQL options (very powerful). I am more inclined towards PostgreSQL as it's more scalable over time. MySQL was bought by Oracle and the community …
- SAP HANA has much better performance as it offers the power of in-memory computing. - SAP HANA is very well suited for analytics and data warehousing; we also have SAP BW on HANA. - MySQL is not suited for data warehousing and analytics; doesn't offer the power of in-memory …
Really, it is appropriate for any kind of company. For everyone to be successful and make wise business decisions, effective data analytics are essential. Any research analytics firm can benefit greatly from it. It won't be of much use to a modeling agency; instead, a business …
This has helped to keep a close eye on our entire data silos to get intelligent analytics and other indicators. During its use, we had also gone for other options but we realize that this is most suitable for us that's why we permanently picked this.
As SAP HANA is built into memeory, the proformance of the product is hard to beat against it's rivals but that comes with a price. You can get away with using another product but you'll be losing out else where. For example in time/multiple systems and efforts to relplicate …
Similar to other big DBMS, but better or equal at stability and technical maintenance. Better or equal at documentation. There is room for improvement at SQL path analyzing.
We compared Microsoft BI with SAP HANA. The reasons to go with SAP HANA were - 1. ability to ingest data into HANA from a non SAP database 2. in-memory database resulting in faster real time analytics 3. ability to scale up 4. ability to replicate data real time 5. very solid …
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.
MySQL is best suited for applications on platform like high-traffic content-driven websites, small-scale web apps, data warehouses which regards light analytical workloads. However its less suited for areas like enterprise data warehouse, OLAP cubes, large-scale reporting, applications requiring flexible or semi-structured data like event logging systems, product configurations, dynamic forms.
I think if you have a large organization, it's probably the product and the marketplace to go to. We're a large management consulting firm operating in four to seven countries. And generally speaking, I think that's the size and the scope where it scales best. I can't speak to smaller companies, but I can't see smaller companies leveraging the benefits as much as a larger organization can.
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.
Real-time reporting and analytics on data: because of its in-memory architecture, it is perfect for businesses that need to make quick decisions based on current information.
Managing workload with complex data: it can handle a vast range of data types, including relational, documental, geospatial, graph, vector, and time series data.
Developing and deploying intelligent data applications: it provides various tools for such applications and can be used for machine learning and artificial intelligence to automate tasks, gain insights from data, and make predictions.
Learning curve: is big. Newbies will face problems in understanding the platform initially. However, with plenty of online resources, one can easily find solutions to problems and learn on the go.
Backup and restore: MySQL is not very seamless. Although the data is never ruptured or missed, the process involved is not very much user-friendly. Maybe, a new command-line interface for only the backup-restore functionality shall be set up again to make this very important step much easier to perform and maintain.
Requires higher processing power, otherwise it won't fly. How ever computing costs are lower. Incase you are migrating to cloud please do not select the highest config available in that series . Upgrading it later against a reserved instance can cost you dearly with a series change
Lack of clarity on licensing is one major challenge
Unless S/4 with additional features are enabled mere migration HANA DB is not a rewarding journey. Power is in S/4
For teaching Databases and SQL, I would definitely continue to use MySQL. It provides a good, solid foundation to learn about databases. Also to learn about the SQL language and how it works with the creation, insertion, deletion, updating, and manipulation of data, tables, and databases. This SQL language is a foundation and can be used to learn many other database related concepts.
We would rate our likelihood of renewing at 9/10. SAP HANA Cloud has proven to be a highly reliable and scalable data platform that consistently delivers strong performance. Its seamless integration with our overall SAP landscape, combined with improved analytics and real-time data capabilities, makes it a core part of our long-term technology strategy.
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.
I give MySQL a 9/10 overall because I really like it but I feel like there are a lot of tech people who would hate it if I gave it a 10/10. I've never had any problems with it or reached any of its limitations but I know a few people who have so I can't give it a 10/10 based on those complaints.
It is very useful solution which provides you speedier data processing, real-time analytics. It helps you manage diverse data types. It also offers you excellent disaster management. It has user friendly interface which helps you navigate system and transactions easily and perform task smoothly.
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.
We have never contacted MySQL enterprise support team for any issues related to MySQL. This is because we have been using primarily the MySQL Server community edition and have been using the MySQL support forums for any questions and practical guidance that we needed before and during the technical implementations. Overall, the support community has been very helpful and allowed us to make the most out of the community edition.
However, I am not the right person to answer this as we have another department to handle support and contact the service provider for any support required. Although i will say that they are the quick respondent and knows how to handle querry of the customers and provide quick and better support.
Professional GIS people are some of the most risk-averse there are, and it's difficult to get them to move to HANA in one step. Start with small projects building to 80% use of HANA spatial over time.
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.
MongoDB has a dynamic schema for how data is stored in 'documents' whereas MySQL is more structured with tables, columns, and rows. MongoDB was built for high availability whereas MySQL can be a challenge when it comes to replication of the data and making everything redundant in the event of a DR or outage.
I have deep knowledge of other disk based DBMSs. They are venerable technology, but the attempts to extend them to current architectures belie the fact they are built on 40 year old technology. There are some good columnar in-memory databases but they lack the completeness of capability present in the HANA platform.
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.
