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Pachyderm
Pachyderm

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2
TempoDB
TempoDB

2
2
+ 1
0
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Pachyderm vs TempoDB: What are the differences?

What is Pachyderm? MapReduce without Hadoop. Analyze massive datasets with Docker. Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations.

What is TempoDB? Store & analyze time series data from sensors, smart meters, servers & more. TempoDB is the first database service for time series data (ex: measuring thermostat temperatures, network latencies, heart rates). Time series is a unique Big Data problem that breaks traditional databases (MySQL, MongoDB, etc). Today, businesses spend months and millions attempting to build solutions to manage all this data and yet still fail to store as much as they need or analyze it effectively. TempoDB is a purpose-built database service that enables businesses to store and analyze massive streams of time series data, so they can learn from the past, understand the present, and predict the future.

Pachyderm and TempoDB are primarily classified as "Big Data" and "SQL Database as a Service" tools respectively.

Some of the features offered by Pachyderm are:

  • Git-like File System
  • Dockerized MapReduce
  • Microservice Architecture

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

  • Flexible, powerful API
  • Store as much high resolution (1 ms max) time series data as you need for long range historical analysis
  • We guarantee data availability and protect against loss by replicating each live datapoint 3x and using geographically distributed backup environments

Pachyderm is an open source tool with 3.78K GitHub stars and 364 GitHub forks. Here's a link to Pachyderm's open source repository on GitHub.

- No public GitHub repository available -

What is Pachyderm?

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

What is TempoDB?

TempoDB is the first database service for time series data (ex: measuring thermostat temperatures, network latencies, heart rates). Time series is a unique Big Data problem that breaks traditional databases (MySQL, MongoDB, etc). Today, businesses spend months and millions attempting to build solutions to manage all this data and yet still fail to store as much as they need or analyze it effectively. TempoDB is a purpose-built database service that enables businesses to store and analyze massive streams of time series data, so they can learn from the past, understand the present, and predict the future.
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        What tools integrate with Pachyderm?
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        What are some alternatives to Pachyderm and TempoDB?
        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.
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