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AtScale vs Pig

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AtScale
25
83
+ 1
0
Pig
59
111
+ 1
5
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Pig vs AtScale: What are the differences?

Pig: Platform for analyzing large data sets. Pig is a dataflow programming environment for processing very large files. Pig's language is called Pig Latin. A Pig Latin program consists of a directed acyclic graph where each node represents an operation that transforms data Operations are of two flavors: (1) relational-algebra style operations such as join, filter, project; (2) functional-programming style operators such as map, reduce. ; AtScale: The virtual data warehouse for the modern enterprise. Its Virtual Data Warehouse delivers performance, security and agility to exceed the demands of modern-day operational analytics.

Pig and AtScale belong to "Big Data Tools" category of the tech stack.

Pig is an open source tool with 580 GitHub stars and 448 GitHub forks. Here's a link to Pig's open source repository on GitHub.

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Pros of AtScale
Pros of Pig
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      Finer-grained control on parallelization
    • 1
      Proven at Petabyte scale
    • 1
      Open-source
    • 1
      Join optimizations for highly skewed data

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    - No public GitHub repository available -

    What is AtScale?

    Its Virtual Data Warehouse delivers performance, security and agility to exceed the demands of modern-day operational analytics.

    What is Pig?

    Pig is a dataflow programming environment for processing very large files. Pig's language is called Pig Latin. A Pig Latin program consists of a directed acyclic graph where each node represents an operation that transforms data. Operations are of two flavors: (1) relational-algebra style operations such as join, filter, project; (2) functional-programming style operators such as map, reduce.

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    Jobs that mention AtScale and Pig as a desired skillset
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    What companies use AtScale?
    What companies use Pig?
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    What tools integrate with AtScale?
    What tools integrate with Pig?

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    What are some alternatives to AtScale and Pig?
    Denodo
    It is the leader in data virtualization providing data access, data governance and data delivery capabilities across the broadest range of enterprise, cloud, big data, and unstructured data sources without moving the data from their original repositories.
    Apache Impala
    Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.
    Druid
    Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.
    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.
    Looker
    We've built a unique data modeling language, connections to today's fastest analytical databases, and a service that you can deploy on any infrastructure, and explore on any device. Plus, we'll help you every step of the way.
    See all alternatives

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