Apache HBase vs. Qubole

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
HBase
Score 7.3 out of 10
N/A
The Apache HBase project's goal is the hosting of very large tables -- billions of rows X millions of columns -- atop clusters of commodity hardware. Apache HBase is an open-source, distributed, versioned, non-relational database modeled after Google's Bigtable.N/A
Qubole
Score 5.2 out of 10
N/A
Qubole is a NoSQL database offering from the California-based company of the same name.N/A
Pricing
Apache HBaseQubole
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
HBaseQubole
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Features
Apache HBaseQubole
NoSQL Databases
Comparison of NoSQL Databases features of Product A and Product B
Apache HBase
7.7
5 Ratings
13% below category average
Qubole
8.3
1 Ratings
6% below category average
Performance7.15 Ratings7.01 Ratings
Availability7.85 Ratings6.01 Ratings
Concurrency7.05 Ratings8.01 Ratings
Security7.85 Ratings7.01 Ratings
Scalability8.65 Ratings10.01 Ratings
Data model flexibility7.15 Ratings10.01 Ratings
Deployment model flexibility8.25 Ratings10.01 Ratings
Best Alternatives
Apache HBaseQubole
Small Businesses
IBM Cloudant
IBM Cloudant
Score 8.4 out of 10
IBM Cloudant
IBM Cloudant
Score 8.4 out of 10
Medium-sized Companies
IBM Cloudant
IBM Cloudant
Score 8.4 out of 10
IBM Cloudant
IBM Cloudant
Score 8.4 out of 10
Enterprises
IBM Cloudant
IBM Cloudant
Score 8.4 out of 10
IBM Cloudant
IBM Cloudant
Score 8.4 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache HBaseQubole
Likelihood to Recommend
7.7
(10 ratings)
8.0
(1 ratings)
Likelihood to Renew
7.9
(10 ratings)
6.0
(1 ratings)
User Testimonials
Apache HBaseQubole
Likelihood to Recommend
Apache
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.
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Qubole
I find Qubole is well suited for getting started analyzing data in the cloud without being locked in to a specific cloud vendor's tooling other than the underlying filesystem. Since the data itself is not isolated to any Qubole cluster, it can be easily be collected back into a cloud-vendor's specific tools for further analysis, therefore I find it complementary to any offerings such as Amazon EMR or Google DataProc.
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Pros
Apache
  • 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.
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Qubole
  • From a UI perspective, I find Qubole's closest comparison to Cloudera's HUE; it provides a one-stop shop for all data browsing and querying needs.
  • Auto scaling groups and auto-terminating clusters provides cost savings for idle resources.
  • Qubole fits itself well into the open-source data science market by providing a choice of tools that aren't tied to a specific cloud vendor.
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Cons
Apache
  • 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.
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Qubole
  • Providing an open selection of all cloud provider instance types with no explanation as to their ideal use cases causes too much confusion for new users setting up a new cluster. For example, not everyone knows that Amazon's R or X-series models are memory optimized, while the C and M-series are for general computation.
  • I would like to see more ETL tools provided other than DistCP that allow one to move data between Hadoop Filesystems.
  • From the cluster administration side, onboarding of new users for large companies seems troublesome, especially when trying to create individual cluster per team within the company. Having the ability to debug and share code/queries between users of other teams / clusters should also be possible.
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Likelihood to Renew
Apache
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.
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Qubole
Personally, I have no issues using Amazon EMR with Hue and Zeppelin, for example, for data science and exploratory analysis. The benefits to using Qubole are that it offers additional tooling that may not be available in other cloud providers without manual installation and also offers auto-terminating instances and scaling groups.
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Alternatives Considered
Apache
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.
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Qubole
Qubole was decided on by upper management rather than these competitive offerings. I find that Databricks has a better Spark offering compared to Qubole's Zeppelin notebooks.
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Return on Investment
Apache
  • 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.
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Qubole
  • We like to say that Qubole has allowed for "data democratization", meaning that each team is responsible for their own set of tooling and use cases rather than being limited by versions established by products such as Hortonworks HDP or Cloudera CDH
  • One negative impact is that users have over-provisioned clusters without realizing it, and end up paying for it. When setting up a new cluster, there are too many choices to pick from, and data scientists may not understand the instance types or hardware specs for the datasets they need to operate on.
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