Hadoop vs Pachyderm

Overview

Hadoop

Stacks2.7K

Followers2.3K

Votes56

GitHub Stars15.3K

Forks9.1K

Pachyderm

Stacks24

Followers95

Votes5

Hadoop vs Pachyderm: What are the differences?

Data Processing Approach: Hadoop uses a batch-processing approach for handling large volumes of data, where data is stored in HDFS (Hadoop Distributed File System) and processed using MapReduce. On the other hand, Pachyderm employs a data lineage approach, enabling data versioning and reproducibility by treating data as a series of immutable versions.
Scalability: Hadoop is known for its horizontal scalability by adding more nodes to a cluster to handle increasing data volumes and processing requirements. In contrast, Pachyderm provides a different scalability model based on containerization and Kubernetes, allowing users to scale data pipelines independently of underlying storage.
Data Versioning and Lineage: Pachyderm excels at data versioning and lineage tracking, maintaining a detailed history of changes made to data and enabling users to trace back to previous versions easily. In contrast, Hadoop does not inherently focus on data versioning and lineage management, which can be challenging in some use cases.
Processing Flexibility: Hadoop is primarily focused on batch processing workloads, while Pachyderm provides more flexibility by supporting batch, streaming, and machine learning workloads within the same platform. This versatility allows users to handle diverse data processing requirements efficiently.
Metadata Management: Hadoop requires additional tools or frameworks for metadata management, such as Apache Hive or Apache HBase, to handle metadata associated with data processing. In contrast, Pachyderm integrates metadata management within its platform, simplifying the process of organizing and querying metadata related to data operations.
Concurrency Handling: Pachyderm offers better support for concurrency by enabling multiple users to work collaboratively on different data pipelines without conflicts, thanks to its containerized approach and versioning capabilities. In comparison, Hadoop may face challenges with concurrent data processing tasks that require careful coordination to avoid data inconsistencies.

In Summary, Hadoop relies on batch processing with HDFS and MapReduce, while Pachyderm emphasizes data versioning, scalability with containers, and processing flexibility.

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Advice on Hadoop, Pachyderm

pionell

Sep 16, 2020

Needs adviceon

MariaDB

I have a lot of data that's currently sitting in a MariaDB database, a lot of tables that weigh 200gb with indexes. Most of the large tables have a date column which is always filtered, but there are usually 4-6 additional columns that are filtered and used for statistics. I'm trying to figure out the best tool for storing and analyzing large amounts of data. Preferably self-hosted or a cheap solution. The current problem I'm running into is speed. Even with pretty good indexes, if I'm trying to load a large dataset, it's pretty slow.

159k views159k

Comments

Detailed Comparison

Hadoop	Pachyderm
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.	Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations.
-	Git-like File System;Dockerized MapReduce;Microservice Architecture;Deployed with CoreOS
Statistics
GitHub Stars 15.3K	GitHub Stars -
GitHub Forks 9.1K	GitHub Forks -
Stacks 2.7K	Stacks 24
Followers 2.3K	Followers 95
Votes 56	Votes 5
Pros & Cons
Pros 39 Great ecosystem 11 One stack to rule them all 4 Great load balancer 1 Java syntax 1 Amazon aws	Pros 3 Containers 1 Can run on GCP or AWS 1 Versioning Cons 1 Recently acquired by HPE, uncertain future.
Integrations
No integrations available	Docker Amazon EC2 Google Compute Engine Vagrant

What are some alternatives to Hadoop, Pachyderm?

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

Related Comparisons

Hadoop vs Pachyderm: What are the differences?

Data Processing Approach: Hadoop uses a batch-processing approach for handling large volumes of data, where data is stored in HDFS (Hadoop Distributed File System) and processed using MapReduce. On the other hand, Pachyderm employs a data lineage approach, enabling data versioning and reproducibility by treating data as a series of immutable versions.
Scalability: Hadoop is known for its horizontal scalability by adding more nodes to a cluster to handle increasing data volumes and processing requirements. In contrast, Pachyderm provides a different scalability model based on containerization and Kubernetes, allowing users to scale data pipelines independently of underlying storage.
Data Versioning and Lineage: Pachyderm excels at data versioning and lineage tracking, maintaining a detailed history of changes made to data and enabling users to trace back to previous versions easily. In contrast, Hadoop does not inherently focus on data versioning and lineage management, which can be challenging in some use cases.
Processing Flexibility: Hadoop is primarily focused on batch processing workloads, while Pachyderm provides more flexibility by supporting batch, streaming, and machine learning workloads within the same platform. This versatility allows users to handle diverse data processing requirements efficiently.
Metadata Management: Hadoop requires additional tools or frameworks for metadata management, such as Apache Hive or Apache HBase, to handle metadata associated with data processing. In contrast, Pachyderm integrates metadata management within its platform, simplifying the process of organizing and querying metadata related to data operations.
Concurrency Handling: Pachyderm offers better support for concurrency by enabling multiple users to work collaboratively on different data pipelines without conflicts, thanks to its containerized approach and versioning capabilities. In comparison, Hadoop may face challenges with concurrent data processing tasks that require careful coordination to avoid data inconsistencies.

In Summary, Hadoop relies on batch processing with HDFS and MapReduce, while Pachyderm emphasizes data versioning, scalability with containers, and processing flexibility.

Hadoop vs Pachyderm

Overview