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Panoply
Panoply

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Qubole
Qubole

22
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+ 1
53
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Qubole vs Panoply: What are the differences?

Developers describe Qubole as "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. On the other hand, Panoply is detailed as "Collect, combine, and integrate all your data with any analytics tools". It is the data warehouse built for analysts. Our data management platform automates all three key aspects of the data stack: data collection, management, and query optimization.

Qubole and Panoply belong to "Big Data as a Service" category of the tech stack.

Some of the features offered by Qubole are:

  • Intuitive GUI
  • Optimized Hive
  • Improved S3 Performance

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

  • Data warehouse
  • Business Intelligence
  • Optimized Query Engine
- No public GitHub repository available -
- No public GitHub repository available -

What is Panoply?

It is the data warehouse built for analysts. Our data management platform automates all three key aspects of the data stack: data collection, management, and query optimization.

What is Qubole?

Qubole is a cloud based service that makes big data easy for analysts and data engineers.
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          What are some alternatives to Panoply and Qubole?
          Stitch
          Stitch is a simple, powerful ETL service built for software developers. Stitch evolved out of RJMetrics, a widely used business intelligence platform. When RJMetrics was acquired by Magento in 2016, Stitch was launched as its own company.
          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.
          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.
          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 EMR
          It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.
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          How developers use Panoply and Qubole
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          Pinterest uses QuboleQubole

          We ultimately migrated our Hadoop jobs to Qubole, a rising player in the Hadoop as a Service space. Given that EMR had become unstable at our scale, we had to quickly move to a provider that played well with AWS (specifically, spot instances) and S3. Qubole supported AWS/S3 and was relatively easy to get started on. After vetting Qubole and comparing its performance against alternatives (including managed clusters), we decided to go with Qubole

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