Likelihood to Recommend Hbase is well suited for large organizations with millions of operations performing on tables, real-time lookup of records in a table, range queries, random reads and writes and online analytics operations. Hbase cannot be replaced for traditional databases as it cannot support all the features, CPU and memory intensive. Observed increased latency when using with MapReduce job joins.
Read full review 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.
Read full review Pros Scalability. HBase can scale to trillions of records. Fast. HBase is extremely fast to scan values or retrieve individual records by key. HBase can be accessed by standard SQL via Apache Phoenix. Integrated. I can easily store and retrieve data from HBase using Apache Spark. It is easy to set up DR and backups. Ingest. It is easy to ingest data into HBase via shell, Java, Apache NiFi, Storm, Spark, Flink, Python and other means. Read full review 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. Read full review Cons There are very few commands in HBase. Stored procedures functionality is not available so it should be implemented. HBase is CPU and Memory intensive with large sequential input or output access while as Map Reduce jobs are primarily input or output bound with fixed memory. HBase integrated with Map-reduce jobs will result in random latencies. Read full review 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 Read full review Likelihood to Renew There's really not anything else out there that I've seen comparable for my use cases. HBase has never proven me wrong. Some companies align their whole business on HBase and are moving all of their infrastructure from other database engines to HBase. It's also open source and has a very collaborative community.
Read full review We're pretty heavily invested in ElasticSearch at this point, and there aren't any obvious negatives that would make us reconsider this decision.
Read full review Usability 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.
Read full review Support Rating 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.
Read full review Implementation Rating Do not mix data and master roles. Dedicate at least 3 nodes just for Master
Read full review Alternatives Considered Cassandra os great for writes. But with large datasets, depending, not as great as HBASE.
Cassandra does support parquet now. HBase still performance issues.
Cassandra has use cases of being used as time series. HBase, it fails miserably. GeoSpatial data, Hbase does work to an extent. HA between the two are almost the same.
Read full review 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.
Read full review Return on Investment As Hbase is a noSql database, here we don't have transaction support and we cannot do many operations on the data. Not having the feature of primary or a composite primary key is an issue as the architecture to be defined cannot be the same legacy type. Also the transaction concept is not applicable here. The way data is printed on console is not so user-friendly. So we had to use some abstraction over HBase (eg apache phoenix) which means there is one new component to handle. Read full review 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. Read full review ScreenShots