StackShareStackShare
Follow on
StackShare

Discover and share technology stacks from companies around the world.

Follow on

© 2025 StackShare. All rights reserved.

Product

  • Stacks
  • Tools
  • Feed

Company

  • About
  • Contact

Legal

  • Privacy Policy
  • Terms of Service
  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data As A Service
  5. Google BigQuery vs Pachyderm

Google BigQuery vs Pachyderm

OverviewComparisonAlternatives

Overview

Google BigQuery
Google BigQuery
Stacks1.8K
Followers1.5K
Votes152
Pachyderm
Pachyderm
Stacks24
Followers95
Votes5

Google BigQuery vs Pachyderm: What are the differences?

### Key Differences between Google BigQuery and Pachyderm

Google BigQuery and Pachyderm are both powerful tools for handling big data, but they have some key differences that set them apart. 

1. **Scalability**: Google BigQuery is a fully managed scalable data warehouse while Pachyderm is a scalable container-based computation platform. BigQuery is designed for large-scale data analysis and offers automatic scaling capabilities, making it suitable for handling huge datasets and complex analysis tasks. On the other hand, Pachyderm focuses on scalable data versioning and lineage, allowing users to track and manage changes to their data over time.

2. **Data Processing Approach**: Google BigQuery uses a columnar storage format and executes SQL-like queries on structured data, making it ideal for data analysis and manipulation. In contrast, Pachyderm processes data in a containerized environment using a version-controlled data pipeline, providing a seamless way to work with versioned data and reproduce results. This difference in data processing approach makes Pachyderm more efficient for data-driven workflows that require reproducibility and data lineage tracking.

3. **Ecosystem Integration**: Google BigQuery seamlessly integrates with other Google Cloud services, such as Cloud Storage, Dataflow, and Dataproc, making it easy to build end-to-end data pipelines and analytical solutions within the Google Cloud ecosystem. Pachyderm, on the other hand, can be integrated with different cloud providers and on-premises environments, offering more flexibility in terms of infrastructure and deployment options.

4. **Data Versioning and Lineage**: Pachyderm provides built-in tools for data versioning and lineage tracking, allowing users to monitor changes to their data and ensure the reproducibility of their processes. Google BigQuery, on the other hand, lacks native support for data versioning and lineage, making it less suitable for workflows that require strict data governance and auditability.

5. **Programming Language Support**: While both Google BigQuery and Pachyderm support multiple programming languages for data processing tasks, BigQuery primarily relies on SQL for querying and analysis, whereas Pachyderm provides more flexibility in terms of programming languages and libraries, allowing users to implement custom data processing pipelines using tools like Python, R, and Java.

6. **Cost Structure**: Google BigQuery operates on a pay-as-you-go pricing model based on data processed and storage used, with a free tier for experimentation. In contrast, Pachyderm offers flexible pricing options based on resources consumed and the number of pipelines deployed, providing cost-effective solutions for different usage scenarios.

In Summary, Google BigQuery and Pachyderm differ in scalability, data processing approach, ecosystem integration, data versioning and lineage capabilities, programming language support, and cost structure, catering to different needs in the big data analytics and processing landscape.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs
CLI (Node.js)
or
Manual

Detailed Comparison

Google BigQuery
Google BigQuery
Pachyderm
Pachyderm

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

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

All behind the scenes- Your queries can execute asynchronously in the background, and can be polled for status.;Import data with ease- Bulk load your data using Google Cloud Storage or stream it in bursts of up to 1,000 rows per second.;Affordable big data- The first Terabyte of data processed each month is free.;The right interface- Separate interfaces for administration and developers will make sure that you have access to the tools you need.
Git-like File System;Dockerized MapReduce;Microservice Architecture;Deployed with CoreOS
Statistics
Stacks
1.8K
Stacks
24
Followers
1.5K
Followers
95
Votes
152
Votes
5
Pros & Cons
Pros
  • 28
    High Performance
  • 25
    Easy to use
  • 22
    Fully managed service
  • 19
    Cheap Pricing
  • 16
    Process hundreds of GB in seconds
Cons
  • 1
    You can't unit test changes in BQ data
  • 0
    Sdas
Pros
  • 3
    Containers
  • 1
    Versioning
  • 1
    Can run on GCP or AWS
Cons
  • 1
    Recently acquired by HPE, uncertain future.
Integrations
Xplenty
Xplenty
Fluentd
Fluentd
Looker
Looker
Chartio
Chartio
Treasure Data
Treasure Data
Docker
Docker
Amazon EC2
Amazon EC2
Google Compute Engine
Google Compute Engine
Vagrant
Vagrant

What are some alternatives to Google BigQuery, Pachyderm?

Apache Spark

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.

Amazon Redshift

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Qubole

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon EMR

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Amazon Athena

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

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

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

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.

Altiscale

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

Related Comparisons

Bootstrap
Materialize

Bootstrap vs Materialize

Laravel
Django

Django vs Laravel vs Node.js

Bootstrap
Foundation

Bootstrap vs Foundation vs Material UI

Node.js
Spring Boot

Node.js vs Spring-Boot

Liquibase
Flyway

Flyway vs Liquibase