Apache Spark vs Pachyderm

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Pachyderm

Stacks24

Followers95

Votes5

Apache Spark vs Pachyderm: What are the differences?

Architecture: Apache Spark is designed for in-memory processing, making it faster for iterative algorithms and interactive data queries. On the other hand, Pachyderm is built on a content-addressed file system, enabling version control and tracking changes to data over time.
Use cases: Apache Spark is commonly used for processing large volumes of data using its distributed computing framework, making it suitable for data analytics and machine learning tasks. Pachyderm, on the other hand, is ideal for managing data pipelines, enabling data lineage tracking and reproducibility in data processing workflows.
Scalability: Apache Spark is known for its scalability and ability to handle large-scale data processing tasks by distributing computations across multiple nodes in a cluster. Pachyderm, on the other hand, focuses on scalability in terms of managing data pipelines and ensuring the reproducibility of data processing steps.
Data Processing Model: Apache Spark follows a batch and stream processing model, allowing for real-time data processing and analytics. Pachyderm, on the other hand, emphasizes a version-controlled data processing model, enabling users to track changes to data and reproduce results consistently.
Ease of Use: Apache Spark provides a user-friendly API for data processing tasks, making it easier for developers and data scientists to work with large datasets. Pachyderm, on the other hand, requires a steeper learning curve due to its emphasis on version control and managing data pipelines.
Community Support: Apache Spark has a large and active community of developers and contributors, ensuring continuous development and support for the platform. Pachyderm, being a newer tool, has a smaller community, which may impact the availability of resources and support for users.

In Summary, Apache Spark and Pachyderm differ in terms of their architecture, use cases, scalability, data processing model, ease of use, and community support.

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Advice on Apache Spark, Pachyderm

Krishna Chaitanya

Head of Technology at Adonmo

Jun 27, 2021

Review

For such a more realtime-focused, data-centered application like an exchange, it's not the frontend or backend that matter much. In fact for that, they can do away with any of the popular frameworks like React/Vue/Angular for the frontend and Go/Python for the backend. For example uniswap's frontend (although much simpler than binance) is built in React. The main interesting part here would be how they are able to handle updating data so quickly. In my opinion, they might be heavily reliant on realtime processing systems like Kafka+Kafka Streams, Apache Flink or Apache Spark Stream or similar. For more processing heavy but not so real-time processing, they might be relying on OLAP and/or warehousing tools like Cassandra/Redshift. They could have also optimized few high frequent queries using NoSQL stores like mongodb (for persistance) and in-memory cache like Redis (for further perfomance boost to get millisecond latencies).

53.8k views53.8k

Comments

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

576k views576k

Comments

Detailed Comparison

Apache Spark	Pachyderm
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 is an open source MapReduce engine that uses Docker containers for distributed computations.
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;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3	Git-like File System;Dockerized MapReduce;Microservice Architecture;Deployed with CoreOS
Statistics
GitHub Stars 42.2K	GitHub Stars -
GitHub Forks 28.9K	GitHub Forks -
Stacks 3.1K	Stacks 24
Followers 3.5K	Followers 95
Votes 140	Votes 5
Pros & Cons
Pros 61 Open-source 48 Fast and Flexible 8 One platform for every big data problem 8 Great for distributed SQL like applications 6 Easy to install and to use Cons 4 Speed	Pros 3 Containers 1 Versioning 1 Can run on GCP or AWS Cons 1 Recently acquired by HPE, uncertain future.
Integrations
No integrations available	Docker Amazon EC2 Google Compute Engine Vagrant

What are some alternatives to Apache Spark, Pachyderm?

Presto

Distributed SQL Query Engine for Big Data

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.

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Related Comparisons

Apache Spark vs Pachyderm: What are the differences?

Architecture: Apache Spark is designed for in-memory processing, making it faster for iterative algorithms and interactive data queries. On the other hand, Pachyderm is built on a content-addressed file system, enabling version control and tracking changes to data over time.
Use cases: Apache Spark is commonly used for processing large volumes of data using its distributed computing framework, making it suitable for data analytics and machine learning tasks. Pachyderm, on the other hand, is ideal for managing data pipelines, enabling data lineage tracking and reproducibility in data processing workflows.
Scalability: Apache Spark is known for its scalability and ability to handle large-scale data processing tasks by distributing computations across multiple nodes in a cluster. Pachyderm, on the other hand, focuses on scalability in terms of managing data pipelines and ensuring the reproducibility of data processing steps.
Data Processing Model: Apache Spark follows a batch and stream processing model, allowing for real-time data processing and analytics. Pachyderm, on the other hand, emphasizes a version-controlled data processing model, enabling users to track changes to data and reproduce results consistently.
Ease of Use: Apache Spark provides a user-friendly API for data processing tasks, making it easier for developers and data scientists to work with large datasets. Pachyderm, on the other hand, requires a steeper learning curve due to its emphasis on version control and managing data pipelines.
Community Support: Apache Spark has a large and active community of developers and contributors, ensuring continuous development and support for the platform. Pachyderm, being a newer tool, has a smaller community, which may impact the availability of resources and support for users.

In Summary, Apache Spark and Pachyderm differ in terms of their architecture, use cases, scalability, data processing model, ease of use, and community support.

Apache Spark vs Pachyderm

Overview