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Pachyderm
Pachyderm

7
12
+ 1
2
s3-lambda
s3-lambda

3
19
+ 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.

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.
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          What tools integrate with Pachyderm?
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          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.
          Amazon Athena
          Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.
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