Amazon Redshift Spectrum vs Pachyderm

Overview

Pachyderm

Stacks24

Followers95

Votes5

Amazon Redshift Spectrum

Stacks99

Followers147

Votes3

Amazon Redshift Spectrum vs Pachyderm: What are the differences?

# Introduction

Amazon Redshift Spectrum and Pachyderm are two popular tools used in data analytics and processing. Here are some key differences between the two technologies:

1. **Architecture**: Amazon Redshift Spectrum is a feature of Amazon Redshift that allows you to run queries on data stored in Amazon S3 without the need to load or transform the data. On the other hand, Pachyderm is a data versioning and pipeline management tool that ensures reproducibility and traceability in data processing workflows. Pachyderm uses containerized data pipelines to process data and track changes automatically.

2. **Data Processing Capabilities**: Amazon Redshift Spectrum is primarily focused on enabling ad-hoc queries on large amounts of data stored in Amazon S3, providing a SQL interface for data analysis. In contrast, Pachyderm is designed for versioning data and building data pipelines for end-to-end data processing tasks, including data cleaning, transformation, model training, and deployment in a containerized environment.

3. **Cost Model**: Amazon Redshift Spectrum charges users based on the amount of data scanned during query execution, as well as the number of queries run. On the other hand, Pachyderm is an open-source tool that can be deployed on any Kubernetes cluster, making it a cost-effective solution for managing data workflows without incurring additional fees for data processing or pipeline management.

4. **Data Storage Integration**: Amazon Redshift Spectrum integrates seamlessly with data stored in Amazon S3, allowing users to query data directly from the object store without the need for data movement. In contrast, Pachyderm supports data versioning and storage in various storage systems, including S3, Google Cloud Storage, and network-attached storage (NAS), providing flexibility in choosing storage options for different use cases.

5. **Scalability and Performance**: Amazon Redshift Spectrum leverages the underlying Redshift cluster's compute resources for query processing, offering scalable performance for complex analytical queries. Pachyderm, on the other hand, provides scalability through containerized processing pipelines that can be distributed across multiple nodes in a Kubernetes cluster, allowing for parallel processing of data to improve performance.

6. **Workflow Orchestration**: While Amazon Redshift Spectrum focuses on query execution and data analysis, Pachyderm emphasizes workflow orchestration and data versioning in a containerized environment, providing tools for managing data pipelines, monitoring job execution, and ensuring reproducibility in data processing tasks.

In Summary, Amazon Redshift Spectrum is optimized for ad-hoc queries on data stored in Amazon S3, while Pachyderm focuses on data versioning and pipeline management for end-to-end data processing workflows in a containerized environment.

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CLI (Node.js)

Manual

Detailed Comparison

Pachyderm	Amazon Redshift Spectrum
Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations.	With Redshift Spectrum, you can extend the analytic power of Amazon Redshift beyond data stored on local disks in your data warehouse to query vast amounts of unstructured data in your Amazon S3 “data lake” -- without having to load or transform any data.
Git-like File System;Dockerized MapReduce;Microservice Architecture;Deployed with CoreOS	-
Statistics
Stacks 24	Stacks 99
Followers 95	Followers 147
Votes 5	Votes 3
Pros & Cons
Pros 3 Containers 1 Can run on GCP or AWS 1 Versioning Cons 1 Recently acquired by HPE, uncertain future.	Pros 1 Great Documentation 1 Good Performance 1 Economical
Integrations
Docker Amazon EC2 Google Compute Engine Vagrant	Amazon S3 Amazon Redshift

What are some alternatives to Pachyderm, Amazon Redshift Spectrum?

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.

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.

Related Comparisons

Amazon Redshift Spectrum vs Pachyderm: What are the differences?

# Introduction

Amazon Redshift Spectrum and Pachyderm are two popular tools used in data analytics and processing. Here are some key differences between the two technologies:

1. **Architecture**: Amazon Redshift Spectrum is a feature of Amazon Redshift that allows you to run queries on data stored in Amazon S3 without the need to load or transform the data. On the other hand, Pachyderm is a data versioning and pipeline management tool that ensures reproducibility and traceability in data processing workflows. Pachyderm uses containerized data pipelines to process data and track changes automatically.

2. **Data Processing Capabilities**: Amazon Redshift Spectrum is primarily focused on enabling ad-hoc queries on large amounts of data stored in Amazon S3, providing a SQL interface for data analysis. In contrast, Pachyderm is designed for versioning data and building data pipelines for end-to-end data processing tasks, including data cleaning, transformation, model training, and deployment in a containerized environment.

3. **Cost Model**: Amazon Redshift Spectrum charges users based on the amount of data scanned during query execution, as well as the number of queries run. On the other hand, Pachyderm is an open-source tool that can be deployed on any Kubernetes cluster, making it a cost-effective solution for managing data workflows without incurring additional fees for data processing or pipeline management.

4. **Data Storage Integration**: Amazon Redshift Spectrum integrates seamlessly with data stored in Amazon S3, allowing users to query data directly from the object store without the need for data movement. In contrast, Pachyderm supports data versioning and storage in various storage systems, including S3, Google Cloud Storage, and network-attached storage (NAS), providing flexibility in choosing storage options for different use cases.

5. **Scalability and Performance**: Amazon Redshift Spectrum leverages the underlying Redshift cluster's compute resources for query processing, offering scalable performance for complex analytical queries. Pachyderm, on the other hand, provides scalability through containerized processing pipelines that can be distributed across multiple nodes in a Kubernetes cluster, allowing for parallel processing of data to improve performance.

6. **Workflow Orchestration**: While Amazon Redshift Spectrum focuses on query execution and data analysis, Pachyderm emphasizes workflow orchestration and data versioning in a containerized environment, providing tools for managing data pipelines, monitoring job execution, and ensuring reproducibility in data processing tasks.

Amazon Redshift Spectrum vs Pachyderm

Overview