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Pachyderm vs s3-lambda

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Pachyderm
23
94
+ 1
5
s3-lambda
4
64
+ 1
0
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Pachyderm vs s3-lambda: What are the differences?

Developers describe Pachyderm as "MapReduce without Hadoop. Analyze massive datasets with Docker". Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations. On the other hand, s3-lambda is detailed as "Lambda functions over S3 objects: each, map, reduce, filter". s3-lambda enables you to run lambda functions over a context of S3 objects. It has a stateless architecture with concurrency control, allowing you to process a large number of files very quickly. This is useful for quickly prototyping complex data jobs without an infrastructure like Hadoop or Spark.

Pachyderm and s3-lambda can be categorized as "Big Data" tools.

Pachyderm and s3-lambda are both open source tools. Pachyderm with 3.78K GitHub stars and 364 forks on GitHub appears to be more popular than s3-lambda with 1.05K GitHub stars and 43 GitHub forks.

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Pros of Pachyderm
Pros of s3-lambda
  • 3
    Containers
  • 1
    Versioning
  • 1
    Can run on GCP or AWS
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    Cons of Pachyderm
    Cons of s3-lambda
    • 1
      Recently acquired by HPE, uncertain future.
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      - No public GitHub repository available -

      What is Pachyderm?

      Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations.

      What is s3-lambda?

      s3-lambda enables you to run lambda functions over a context of S3 objects. It has a stateless architecture with concurrency control, allowing you to process a large number of files very quickly. This is useful for quickly prototyping complex data jobs without an infrastructure like Hadoop or Spark.

      Need advice about which tool to choose?Ask the StackShare community!

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      What companies use Pachyderm?
      What companies use s3-lambda?
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        What tools integrate with Pachyderm?
        What tools integrate with s3-lambda?
        What are some alternatives to Pachyderm and s3-lambda?
        Hadoop
        The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
        Apache Spark
        Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
        Airflow
        Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
        Kafka
        Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
        DVC
        It is an open-source Version Control System for data science and machine learning projects. It is designed to handle large files, data sets, machine learning models, and metrics as well as code.
        See all alternatives

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