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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    HBase

    Score7.3 out of 10
    N/AThe 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

    Score5 out of 10
    N/AQubole is a NoSQL database offering from the California-based company of the same name.N/A
    Pricing
    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
    HBaseQubole
    NoSQL Databases
    Comparison of NoSQL Databases features of Apache HBase and Qubole
    Feature
    Apache HBase
    7.7
    5 Ratings
    14% 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
    HBaseQubole
    Small Businesses
    Redis Software
    Score8.3 out of 10
    Redis Software
    Score8.3 out of 10
    Medium-sized Companies
    IBM Cloudant
    Score7.4 out of 10
    IBM Cloudant
    Score7.4 out of 10
    Enterprises
    IBM Cloudant
    Score7.4 out of 10
    IBM Cloudant
    Score7.4 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    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
    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.
    Incentivized
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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.
    Incentivized
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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.
    Incentivized
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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.
    Incentivized
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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.
    Incentivized
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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.
    Incentivized
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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.
    Incentivized
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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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