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Google BigQuery VS Steampipe

Compare Google BigQuery VS Steampipe and see what are their differences

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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

Google BigQuery logo Google BigQuery

A fully managed data warehouse for large-scale data analytics.

Steampipe logo Steampipe

Steampipe: select * from cloud; The extensible SQL interface to your favorite cloud APIs select * from AWS, Azure, GCP, Github, Slack etc.
  • Google BigQuery Landing page
    Landing page //
    2023-10-03
  • Steampipe Landing page
    Landing page //
    2023-09-30

Google BigQuery features and specs

  • Scalability
    BigQuery can effortlessly scale to handle large volumes of data due to its serverless architecture, thereby reducing the operational overhead of managing infrastructure.
  • Speed
    It leverages Google's infrastructure to provide high-speed data processing, making it possible to run complex queries on massive datasets in a matter of seconds.
  • Integrations
    BigQuery easily integrates with various Google Cloud Platform services, as well as other popular data tools like Looker, Tableau, and Power BI.
  • Automatic Optimization
    Features like automatic data partitioning and clustering help to optimize query performance without requiring manual tuning.
  • Security
    BigQuery provides robust security features including IAM roles, customer-managed encryption keys, and detailed audit logging.
  • Cost Efficiency
    The pricing model is based on the amount of data processed, which can be cost-effective for many use cases when compared to traditional data warehouses.
  • Managed Service
    Being fully managed, BigQuery takes care of database administration tasks such as scaling, backups, and patch management, allowing users to focus on their data and queries.

Possible disadvantages of Google BigQuery

  • Cost Predictability
    While the pay-per-use model can be cost-efficient, it can also make cost forecasting difficult. Unexpected large queries could lead to higher-than-anticipated costs.
  • Complexity
    The learning curve can be steep for those who are not already familiar with SQL or Google Cloud Platform, potentially requiring training and education.
  • Limited Updates
    BigQuery is optimized for read-heavy operations, and it can be less efficient for scenarios that require frequent updates or deletions of data.
  • Query Pricing
    Costs are based on the amount of data processed by each query, which may not be suitable for use cases that require frequent analysis of large datasets.
  • Data Transfer Costs
    While internal data movement within Google Cloud can be cost-effective, transferring data to or from other services or on-premises systems can incur additional costs.
  • Dependency on Google Cloud
    Organizations heavily invested in multi-cloud or hybrid-cloud strategies may find the dependency on Google Cloud limiting.
  • Cold Data Performance
    Query performance might be slower for so-called 'cold data,' or data that has not been queried recently, affecting the responsiveness for some workloads.

Steampipe features and specs

  • Unified Interface
    Steampipe provides a unified SQL-based interface to query data from various cloud services and APIs, simplifying data access.
  • Open Source
    Being open source, Steampipe allows for community contributions, transparency, and flexibility in adapting the tool to specific needs.
  • Plugin Ecosystem
    Steampipe has a growing ecosystem of plugins that enable easy integration with numerous services, enhancing its versatility.
  • Real-Time Data Access
    It facilitates real-time querying of data from live APIs, which is beneficial for up-to-date insights and monitoring.
  • Cross-Platform Compatibility
    Steampipe is designed to work on multiple platforms, including Windows, MacOS, and Linux, making it accessible to a wide range of users.

Possible disadvantages of Steampipe

  • Complex Setup
    Initial setup and configuration can be complex, requiring a good understanding of SQL and the specific APIs being used.
  • Performance Overhead
    Query performance may be impacted due to the abstraction layer and real-time consolidation of data from multiple sources.
  • Limited Community Support
    As a relatively new tool, Steampipe may have limited community support and fewer resources compared to more established alternatives.
  • Resource Intensive
    Running multiple queries against APIs and cloud services can become resource intensive, potentially increasing costs and load on systems.
  • Learning Curve
    Users unfamiliar with SQL may face a learning curve in effectively utilizing Steampipe for querying different data sources.

Analysis of Google BigQuery

Overall verdict

  • Google BigQuery is a powerful and flexible data warehouse solution that suits a wide range of data analytics needs. Its ability to handle large volumes of data quickly makes it a preferred choice for organizations looking to leverage their data effectively.

Why this product is good

  • Google BigQuery is a fully-managed data warehouse that simplifies the analysis of large datasets. It is known for its scalability, speed, and integration with other Google Cloud services. It supports standard SQL, has built-in machine learning capabilities, and allows for seamless data integration from various sources. The serverless architecture means that users don't need to worry about infrastructure management, and its pay-as-you-go model provides cost efficiency.

Recommended for

  • Businesses requiring fast processing of large datasets
  • Organizations that already utilize Google Cloud services
  • Companies looking for a cost-effective, scalable analytics solution
  • Teams interested in using SQL for data analysis
  • Data scientists integrating machine learning with their data workflows

Google BigQuery videos

+ Add

Cloud Dataprep Tutorial - Getting Started 101

More videos:

  • Review - Advanced Data Cleanup Techniques using Cloud Dataprep (Cloud Next '19)
  • Demo - Google Cloud Dataprep Premium product demo

Steampipe videos

+ Add

Superbooth 2023: Erica Synths - Steampipe

More videos:

  • Review - BEST SYNTHS @ SUPERBOOTH23: PWM Mantis, UDO Super Gemini, Erica Synths STEAMPIPEโ€ฆ and more
  • Review - Erica Synths STEAMPIPE The Synth with no oscillators!

Category Popularity

0-100% (relative to Google BigQuery and Steampipe)

Google BigQuery

Steampipe

Data Dashboard
100 100%
Data Dashboard
0% 0
Big Data
82 82%
Big Data
18% 18
Cloud Infrastructure
0 0%
Cloud Infrastructure
100% 100
Big Data Analytics
100 100%
Big Data Analytics
0% 0

User comments

Share your experience with using Google BigQuery and Steampipe. For example, how are they different and which one is better?
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Reviews

These are some of the external sources and on-site user reviews we've used to compare Google BigQuery and Steampipe

Google BigQuery Reviews

Database for Data Analytics
Processing typeDescriptionUse casesCommon databasesProcessing typesProcesses data in scheduled intervals (hours, days). High-latency but cost-efficient for large datasets.Financial reporting, trend analysis, historical analyticsSnowflake, Amazon Redshift, Google BigQueryContinuously ingests and processes data with minimal latency for real-time decision-making.Fraud...
Source: blog.devart.com
Data Warehouse Tools
Google BigQuery: Similar to Snowflake, BigQuery offers a pay-per-use model with separate charges for storage and queries. Storage costs start around $0.01 per GB per month, while on-demand queries are billed at $5 per TB processed.
Source: peliqan.io
Top 6 Cloud Data Warehouses in 2023
You can also use BigQueryโ€™s columnar and ANSI SQL databases to analyze petabytes of data at a fast speed. Its capabilities extend enough to accommodate spatial analysis using SQL and BigQuery GIS. Also, you can quickly create and run machine learning (ML) models on semi or large-scale structured data using simple SQL and BigQuery ML. Also, enjoy a real-time interactive...
Source: geekflare.com
Top 5 Cloud Data Warehouses in 2023
Google BigQuery is an incredible platform for enterprises that want to run complex analytical queries or โ€œheavyโ€ queries that operate using a large set of data. This means itโ€™s not ideal for running queries that are doing simple filtering or aggregation. So if your cloud data warehousing needs lightning-fast performance on a big set of data, Google BigQuery might be a great...
Source: www.shipyardapp.com
Top 5 BigQuery Alternatives: A Challenge of Complexity
BigQuery's emergence as an attractive analytics and data warehouse platform was a significant win, helping to drive a 45% increase in Google Cloud revenue in the last quarter. The company plans to maintain this momentum by focusing on a multi-cloud future where BigQuery advances the cause of democratized analytics.
Source: blog.panoply.io

Steampipe Reviews

We have no reviews of Steampipe yet.
Be the first one to post

Social recommendations and mentions

Google BigQuery might be a bit more popular than Steampipe. We know about 47 links to it since March 2021 and only 42 links to Steampipe. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Google BigQuery mentions (47)

  • Ruby on Rails Performance: 7 Lessons from Scaling FirstPromoter
    We migrated the analytics layer to Google BigQuery. Same queries that timed out in PostgreSQL now run in under 2 seconds. But not everything belongs in BigQuery โ€” we initially moved too aggressively and actually reverted some queries back when the added complexity wasn't justified. Our rule of thumb: if a query scans hundreds of thousands of rows or involves complex time-series aggregations, BigQuery. Everything... - Source: dev.to / 3 months ago
  • How to Analyze 47 Million Hacker News Posts: A Data Scientist's Dream Dataset Just Got Better
    Google BigQuery - For large-scale data processing and SQL-based analysis. - Source: dev.to / 4 months ago
  • What if ML pipelines had a lock file?
    Data Pipelines usually read from tables that change over time. Most of these tables are stored in a data warehouse like Amazon Redshift or Google BigQuery. Rows are added or removed. Backfills happen. A column gets renamed or its meaning changes. Even when teams snapshot data, those snapshots are often implicit, not recorded as part of the pipeline run itself. - Source: dev.to / 5 months ago
  • Best SQL Courses with Certificates for 2026
    SQL endures because it's the non-negotiable interface for relational data. Enterprise data storage still relies heavily on relational databases despite new alternatives. What makes SQL valuable for learners is transferabilityโ€”while dialects differ across PostgreSQL, SQL Server, and BigQuery, the fundamentals stay consistent. - Source: dev.to / 7 months ago
  • Why Your Snowflake Bill is High and How to Fix It with a Hybrid Approach
    Within classic cloud data warehouses, Google BigQuery presents a different pricing model. Its on-demand, per-terabyte-scanned pricing can be cost-effective for sporadic forensic queries. But it carries the risk of a runaway query where a single mistake leads to a massive bill. - Source: dev.to / 8 months ago
View more

Steampipe mentions (42)

  • Build API integrations with SQL and YAML โ€“ no SaaS lock-in, no drag-and-drop UIs
    The request / data fetching is interesting in how "easy" it is to write. I did basic perusal of the examples, but I'd be interested to see what it looks like with rate-limited endpoints and concurrent requests. Another tangentially related project is https://steampipe.io/ though it is for exposing APIs via Postgres tables and the clients are written using Go code and shared through a marketplace. - Source: Hacker News / about 1 year ago
  • Cyphernetes: A Query Language for Kubernetes
    I really really like Steampipe to do this kind of query: https://steampipe.io, which is essentially PostgreSQL (literally) to query many different kind of APIs, which means you have access to all PostgreSQL's SQL language can offer to request data. They have a Kubernetes plugin at https://hub.steampipe.io/plugins/turbot/kubernetes and there are a couple of things I really like: * it's super easy to request... - Source: Hacker News / over 1 year ago
  • DuckDB Doesn't Need Data to Be a Database
    Https://steampipe.io/ showcases some really interesting scenarios for using FDWs in place of regular ETL and API integrations. - Source: Hacker News / about 2 years ago
  • Cloud Tools You Probably Haven't Heard Of
    Steampipe is a tool for querying cloud APIs and other data sources using SQL in a zero-ETL manner. - Source: dev.to / over 2 years ago
  • Osquery: An sqlite3 virtual table exposing operating system data to SQL
    Few projects in the same realm that you should also checkout - [1] Steampipe (https://steampipe.io/) [2] InfraSQL (https://iasql.com/). - Source: Hacker News / over 2 years ago
View more

What are some alternatives?

When comparing Google BigQuery and Steampipe, you can also consider the following products

Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.โ€ŽWhat is Apache Spark?

CloudQuery - CloudQuery enables you to assess, audit, and evaluate the configurations of your cloud assets.

Looker - Looker makes it easy for analysts to create and curate custom data experiencesโ€”so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.

StackQL.io - Query, provision, secure & operate cloud resources using SQL

Jupyter - Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.

Turbot - Turbot's guardrails deliver automated operational, cloud security and cloud compliance controls of AWS deployments and other cloud enterprise infrastructure. Learn more.

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