Pachyderm vs Apache Spark: What are the differences?
Pachyderm: MapReduce without Hadoop. Analyze massive datasets with Docker. Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations; Apache Spark: Fast and general engine for large-scale data processing. 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.
Pachyderm and Apache Spark can be categorized as "Big Data" tools.
Some of the features offered by Pachyderm are:
- Git-like File System
- Dockerized MapReduce
- Microservice Architecture
On the other hand, Apache Spark provides the following key features:
- Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk
- Write applications quickly in Java, Scala or Python
- Combine SQL, streaming, and complex analytics
Pachyderm and Apache Spark are both open source tools. Apache Spark with 22.3K GitHub stars and 19.3K forks on GitHub appears to be more popular than Pachyderm with 3.78K GitHub stars and 364 GitHub forks.
What is Pachyderm?
What is Apache Spark?
Want advice about which of these to choose?Ask the StackShare community!
What are the cons of using Pachyderm?
What tools integrate with Apache Spark?
Spark is good at parallel data processing management. We wrote a neat program to handle the TBs data we get everyday.