Google Analytics vs Google BigQuery

Overview

Google Analytics

Stacks128.5K

Followers50.7K

Votes5.1K

Google BigQuery

Stacks1.8K

Followers1.5K

Votes152

Google Analytics vs Google BigQuery: What are the differences?

Key Differences between Google Analytics and Google BigQuery

Data Analysis vs. Data Warehousing: Google Analytics is primarily used for data analysis and tracking website user behavior, providing insights into user demographics, acquisition channels, and website performance. On the other hand, Google BigQuery is a data warehousing solution that enables businesses to store, query, and analyze large volumes of structured and semi-structured data in real-time.
Real-Time vs. Batch Processing: With Google Analytics, data is processed and displayed in near real-time, allowing users to track website activity and metrics as they happen. In contrast, Google BigQuery is optimized for batch processing and analyzing large datasets over extended periods, making it a powerful tool for complex queries and deep analysis.
Data Collection Method: Google Analytics collects data through website tracking codes, where JavaScript is embedded on web pages to capture user interactions. It relies on cookies and client-side tracking mechanisms. In contrast, Google BigQuery receives data from various sources, including Google Analytics, but it can also ingest data from other external systems, cloud storage, streaming data, or data warehouses.
Data Accessibility and Scalability: Google Analytics provides a user-friendly interface and pre-built dashboards for easy access to data analysis and reporting. It offers a limited set of dimensions and metrics, suitable for general web analytics needs. In contrast, Google BigQuery provides more flexibility and scalability, allowing users to run complex SQL queries on vast amounts of data, with the ability to integrate with other data sources and conduct advanced data analysis.
Pricing Model: Google Analytics offers both free and premium versions, with the premium version providing additional features and support. It is mainly aimed at small and medium-sized businesses. On the other hand, Google BigQuery operates on a pay-per-query basis, with separate pricing for storage and data processing. It aligns its pricing with the volume of data stored and the amount of data processed for analysis.
Data Ownership and Integration: When using Google Analytics, the data collected is owned by the website owner, but Google has certain rights to use and analyze the data for its own purposes. Google Analytics data can be integrated with other Google products, such as Google Ads, to provide a holistic view of advertising and website performance. Google BigQuery, being a data warehousing solution, allows integration with various data sources, both internal and external, providing a unified view of large amounts of data.

In Summary, Google Analytics is a powerful tool for real-time web analytics and user behavior analysis, while Google BigQuery is a scalable data warehousing solution for advanced data analysis, querying massive datasets, and integration with multiple data sources.

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Detailed Comparison

Google Analytics	Google BigQuery
Google Analytics lets you measure your advertising ROI as well as track your Flash, video, and social networking sites and applications.	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.
Analysis Tools- Google Analytics is built on a powerful, easy to use, reporting platform, so you can decide what data you want to view and customize your reports, with just a few clicks.;Content Analytics- Content reports help you understand which parts of your website are performing well, which pages are most popular so you can create a better experience for your customers.;Social Analytics- The web is a social place and Google Analytics measures success of your social media programs. You can analyze how visitors interact with sharing features on your site (like the Google +1 button) and engage with your content across social platforms.;Mobile Analytics- Google Analytics helps you measure the impact of mobile on your business. Additionally, if you build mobile apps Google Analytics offers Software Development Kits for iOS and Android so you can measure how people use your app.;Conversion Analytics- Find out how many customers you're attracting, how much you're selling and how users are engaging with your site with Google Analytics' range of analysis features.;Advertising Analytics- Make the most of your advertising by learning how well your social, mobile, search and display ads are working. Link your website activity to your marketing campaigns to get the complete picture and improve your advertising performance.	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.
Statistics
Stacks 128.5K	Stacks 1.8K
Followers 50.7K	Followers 1.5K
Votes 5.1K	Votes 152
Pros & Cons
Pros 1483 Free 927 Easy setup 891 Data visualization 698 Real-time stats 406 Comprehensive feature set Cons 11 Confusing UX/UI 8 Super complex 6 Very hard to build out funnels 4 Poor web performance metrics 3 Very easy to confuse the user of the analytics	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
Integrations
Mad Mimi Hipmob Visual Website Optimizer Squarespace ClickTale CloudFlare Segment Optimizely FreshDesk SnapEngage	Xplenty Fluentd Looker Chartio Treasure Data

What are some alternatives to Google Analytics, Google BigQuery?

Mixpanel

Mixpanel helps companies build better products through data. With our powerful, self-serve product analytics solution, teams can easily analyze how and why people engage, convert, and retain to improve their user experience.

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.

Piwik

Matomo (formerly Piwik) is a full-featured PHP MySQL software program that you download and install on your own webserver. At the end of the five-minute installation process, you will be given a JavaScript code.

Qubole

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

Amazon EMR

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

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.

Snowflake

Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.

Clicky

Clicky Web Analytics gives bloggers and smaller web sites a more personal understanding of their visitors. Clicky has various features that helps stand it apart from the competition specifically Spy and RSS feeds that allow web site owners to get live information about their visitors.

Stitch

Stitch is a simple, powerful ETL service built for software developers. Stitch evolved out of RJMetrics, a widely used business intelligence platform. When RJMetrics was acquired by Magento in 2016, Stitch was launched as its own company.

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.