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  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data As A Service
  5. GenieDB vs Qubole

GenieDB vs Qubole

OverviewComparisonAlternatives

Overview

Qubole
Qubole
Stacks36
Followers104
Votes67
GenieDB
GenieDB
Stacks0
Followers5
Votes0

GenieDB vs Qubole: What are the differences?

GenieDB: Multi-master, multi-cloud, geo-distributed MySQL database. GenieDB’s DBaaS uses multi-master replication and multi-regional distribution on multiple cloud infrastructures (Amazon AWS, IBM Softlayer, DigitalOcean, Rackspace Cloud, and Google Cloud) to help reduce latency, eliminate downtime, and easily scale your database; Qubole: Prepare, integrate and explore Big Data in the cloud (Hive, MapReduce, Pig, Presto, Spark and Sqoop). Qubole is a cloud based service that makes big data easy for analysts and data engineers.

GenieDB and Qubole are primarily classified as "SQL Database as a Service" and "Big Data as a Service" tools respectively.

Some of the features offered by GenieDB are:

  • Multi-master replication with auto-conflict resolution
  • Geo-distribution across multiple regions and cloud providers using eventual consistency model with geo-compliance
  • Auto-recovery and healing with re-synchronization

On the other hand, Qubole provides the following key features:

  • Intuitive GUI
  • Optimized Hive
  • Improved S3 Performance

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Detailed Comparison

Qubole
Qubole
GenieDB
GenieDB

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

GenieDB’s DBaaS uses multi-master replication and multi-regional distribution on multiple cloud infrastructures (Amazon AWS, IBM Softlayer, DigitalOcean, Rackspace Cloud, and Google Cloud) to help reduce latency, eliminate downtime, and easily scale your database.

Intuitive GUI;Optimized Hive;Improved S3 Performance;Auto Scaling;Spot Instance Pricing;Managed Clusters;Cloud Integration;Cluster Lifecycle Management
Multi-master replication with auto-conflict resolution; Geo-distribution across multiple regions and cloud providers using eventual consistency model with geo-compliance; Auto-recovery and healing with re-synchronization; Data security using industry standard SSL encryption and firewall; Elastic scaling by adding new nodes/clusters or by increasing capacity on existing servers; Rapid database provisioning across multiple cloud providers and regions; Easy MySQL migration that supports MySQL dump
Statistics
Stacks
36
Stacks
0
Followers
104
Followers
5
Votes
67
Votes
0
Pros & Cons
Pros
  • 13
    Simple UI and autoscaling clusters
  • 10
    Feature to use AWS Spot pricing
  • 7
    Optimized Spark, Hive, Presto, Hadoop 2, HBase clusters
  • 7
    Real-time data insights through Spark Notebook
  • 6
    Easy to manage costs
No community feedback yet
Integrations
Google Compute Engine
Google Compute Engine
Microsoft Azure
Microsoft Azure
No integrations available

What are some alternatives to Qubole, GenieDB?

Amazon RDS

Amazon RDS

Amazon RDS gives you access to the capabilities of a familiar MySQL, Oracle or Microsoft SQL Server database engine. This means that the code, applications, and tools you already use today with your existing databases can be used with Amazon RDS. Amazon RDS automatically patches the database software and backs up your database, storing the backups for a user-defined retention period and enabling point-in-time recovery. You benefit from the flexibility of being able to scale the compute resources or storage capacity associated with your Database Instance (DB Instance) via a single API call.

Google BigQuery

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

Amazon Redshift

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Amazon Aurora

Amazon Aurora

Amazon Aurora is a MySQL-compatible, relational database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. Amazon Aurora provides up to five times better performance than MySQL at a price point one tenth that of a commercial database while delivering similar performance and availability.

Amazon EMR

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Google Cloud SQL

Google Cloud SQL

Run the same relational databases you know with their rich extension collections, configuration flags and developer ecosystem, but without the hassle of self management.

ClearDB

ClearDB

ClearDB uses a combination of advanced replication techniques, advanced cluster technology, and layered web services to provide you with a MySQL database that is "smarter" than usual.

Altiscale

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

Snowflake

Snowflake

Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.

Azure SQL Database

Azure SQL Database

It is the intelligent, scalable, cloud database service that provides the broadest SQL Server engine compatibility and up to a 212% return on investment. It is a database service that can quickly and efficiently scale to meet demand, is automatically highly available, and supports a variety of third party software.

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