Software Alternatives, Accelerators & Startups

Google BigQuery VS PostHog

Compare Google BigQuery VS PostHog and see what are their differences

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Google BigQuery logo Google BigQuery

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

PostHog logo PostHog

An open source suite of product and data tools including product analytics, feature flags, session replay, A/B testing, surveys, and more.
  • Google BigQuery Landing page
    Landing page //
    2023-10-03
  • PostHog Landing page
    Landing page //
    2024-07-05

For developers just starting out, PostHog is a free way to understand how your product is being used, without having to send any data to 3rd parties.

For enterprise customers, one data security becomes a key concern, or B2C businesses where using a SaaS solution is unaffordable, it's typical to see teams hosting an event capture platform, a data lake, and sophisticated analytics tools. The end result is that data scientists are needed and most developers don't have easy access to product intel. PostHog solves that gap - it lets everyone understand how your product is being used, without having to send data to 3rd parties, even once you have scaled to millions of visitors.

It has a JS snippet that can autocapture events, and pre-built libraries to push backend data to. Build up full user histories, visualize product trends, funnels, and run experiments with new features.

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.

PostHog features and specs

  • Self-Hosting Option
    PostHog can be self-hosted, allowing you to maintain control over your data and ensuring compliance with strict data privacy regulations.
  • Complete Analytics Suite
    Provides a complete suite of product analytics tools including feature flags, session recordings, and heatmaps, enabling comprehensive user behavior analysis.
  • Open-Source
    Being open-source, PostHog allows for high customizability and the potential to contribute to the codebase, fostering a community-driven development approach.
  • Privacy-Focused
    Designed with privacy in mind, PostHog globally complies with GDPR, CCPA, and other privacy laws, reducing the risk of legal complications.
  • Event-Driven Architecture
    Its event-driven architecture provides high flexibility in tracking custom events, allowing for more detailed and tailored analytics.
  • Integrations
    PostHog integrates with a variety of tools and services such as Slack, GitHub, and Zapier, streamlining workflows and enhancing productivity.

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

Analysis of PostHog

Overall verdict

  • Yes, PostHog is a robust and versatile analytics tool. Its open-source nature, coupled with a rich feature set comparable to major analytics platforms, makes it an excellent choice for teams looking for an in-depth and customizable analytics solution.

Why this product is good

  • PostHog is a full-featured analytics platform that provides powerful tools for product teams to understand user behavior without sending data to third parties. It offers features such as event tracking, session recording, feature flags, and heatmaps, making it a comprehensive solution for product analytics. The platform is open-source, allowing for customization and self-hosting, which is a significant advantage for teams with specific needs or concerns about data privacy.

Recommended for

    PostHog is particularly well-suited for product teams, developers, and startups that require deep insights into user interactions and need the flexibility of a self-hosted solution. It is also a good fit for organizations that prioritize data privacy and want to maintain full control over their data.

Google BigQuery videos

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

PostHog videos

PostHog Walk Through

More videos:

  • Review - Open Source Product Analytics With PostHog

Category Popularity

0-100% (relative to Google BigQuery and PostHog)
Data Dashboard
100 100%
0% 0
Analytics
0 0%
100% 100
Big Data
100 100%
0% 0
Web Analytics
0 0%
100% 100

User comments

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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 PostHog

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...
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

PostHog Reviews

The best Hotjar alternatives & competitors, compared
According to BuiltWith, as of February 2024, PostHog is used on 5,169 (0.52%) of the top 1 million websites. Hotjar is used by 72,048 of the top 1 million websites. Typical PostHog users are engineers and product managers at startups and mid-size companies, such as Webshare, AssemblyAI, and Purplewave.
Source: posthog.com
The 8 best free and open-source feature flag services
BlogBackSign inBlogThe 8 best free and open-source feature flag servicesPosted byThe best open-source feature flag tools1. PostHogWhat is PostHog?Supported librariesHow much does it cost?2. UnleashWhat is Unleash?Supported SDKsHow much does it cost?3. GrowthBookWhat is GrowthBook?Supported SDKsHow much does it cost?4. FlagsmithWhat is Flagsmith?Supported SDKsHow much does it...
Source: posthog.com

Social recommendations and mentions

Based on our record, PostHog should be more popular than Google BigQuery. It has been mentiond 73 times since March 2021. 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 / 4 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 / 5 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 / 6 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 / 8 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 / 9 months ago
View more

PostHog mentions (73)

  • I built an AI IDE that shows you exactly what it sends to the model
    What it deliberately does not have: accounts, cloud sync, a marketplace, Or any paywall. Telemetry is opt-in, off by default, anonymous, and collects Zero content โ€” the endpoint is configurable if you'd rather self-host PostHog. It's free software, and it will stay that way. - Source: dev.to / 18 days ago
  • Opus vs GPT on Real Ops, Part 2: One Drove, One Was Driven
    Opus, zero nudges. Realised on its own that an abandoned signup never fires identify, triangulated the anonymous session from time, platform and registration events, decoded the PostHog replay blobs, confirmed the duplicate account in Supabase, proved the reset email never sent, and pulled the root cause out of an unmasked DOM field. One prompt in; root cause out. - Source: dev.to / about 1 month ago
  • Why We Open-Sourced Our Audit Logging Instead of Using Splunk
    This is the same model that PostHog, Supabase, and dozens of other developer tools use. Open core, with a managed offering on top. - Source: dev.to / 5 months ago
  • What Is Web3 User Analytics? A Complete Guide to Driving Growth
    Offchain: Website traffic, in-app behaviour, marketing channels, growth campaigns (Google Analytics or PostHog). - Source: dev.to / 4 months ago
  • Validating Your Startup Idea with a Landing Page, Waitlist, and Stripe Test Mode in One Weekend
    --- Title: "Validate Your Startup Idea in One Weekend: Next.js + PostHog + Stripe Test Mode" Published: true Description: "A step-by-step workshop for wiring up a landing page with analytics, a waitlist, and Stripe test-mode checkout to measure real willingness-to-pay before writing product code." Tags: typescript, api, architecture, cloud Canonical_url:... - Source: dev.to / 4 months ago
View more

What are some alternatives?

When comparing Google BigQuery and PostHog, 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?

Mixpanel - Mixpanel is the most advanced analytics platform in the world for mobile & web.

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.

Amplitude - Chart Your Path to Growth with Digital Analytics

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.

Plausible.io - Plausible Analytics is a simple, open-source, lightweight (< 1 KB) and privacy-friendly web analytics alternative to Google Analytics. Made and hosted in the EU, powered by European-owned cloud infrastructure ๐Ÿ‡ช๐Ÿ‡บ