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Google BigQuery VS MicroStrategy

Compare Google BigQuery VS MicroStrategy and see what are their differences

Google BigQuery logo Google BigQuery

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

MicroStrategy logo MicroStrategy

MicroStrategy is a cloud-based platform providing business intelligence, mobile intelligence and network applications.
  • Google BigQuery Landing page
    Landing page //
    2023-10-03
  • MicroStrategy Landing page
    Landing page //
    2023-05-07

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.

MicroStrategy features and specs

  • Robust Analytics
    MicroStrategy provides powerful analytics and business intelligence capabilities, allowing users to create complex reports and dashboards with a wide variety of data sources.
  • Scalability
    MicroStrategy is designed to handle large volumes of data, making it suitable for large enterprises that require high-performance analytics.
  • Data Connectivity
    The platform supports a vast array of data connectors, making it easy to integrate with various databases, cloud services, and other data sources.
  • Mobile Capabilities
    MicroStrategy offers robust mobile analytics applications, enabling users to access reports and dashboards from their mobile devices with ease.
  • Security Features
    The platform provides strong security features, including role-based access control, data encryption, and rigorous authentication processes.

Possible disadvantages of MicroStrategy

  • Complexity
    MicroStrategy can be complex to implement and requires significant technical expertise to fully leverage its capabilities, which may necessitate specialized training.
  • Cost
    The platform can be expensive, particularly for small to mid-sized organizations, as it involves licensing fees and costs associated with training and implementation.
  • Learning Curve
    New users might experience a steep learning curve due to the comprehensive and advanced features offered by the platform.
  • Customization Limitations
    While MicroStrategy is highly configurable, there can be limitations when it comes to customizing certain aspects to meet unique organizational needs or preferences.
  • Performance Issues
    Some users report performance issues, particularly when dealing with extremely large datasets or during peak load times, which can impact the user experience.

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 MicroStrategy

Overall verdict

  • MicroStrategy is a solid choice for organizations that require a high-performance, scalable business intelligence solution. Its extensive features and strong security make it suitable for large enterprises with complex data needs.

Why this product is good

  • MicroStrategy is a well-established business intelligence platform known for its robust analytics capabilities, scalability, and comprehensive reporting features. It offers powerful data visualization tools, a wide range of data connectors, and supports mobile intelligence for users on the go. The platform is also recognized for its strong security features and the ability to handle large datasets efficiently, making it a solid choice for enterprises seeking in-depth data insights.

Recommended for

  • Large enterprises needing robust data analytics and reporting
  • Organizations with significant data security requirements
  • Companies looking for high scalability and performance
  • Businesses seeking a comprehensive platform that supports dynamic dashboards and mobile intelligence solutions

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

MicroStrategy videos

Overview of MicroStrategy Desktop 10.10

More videos:

  • Review - Introducing MicroStrategy 2019

Category Popularity

0-100% (relative to Google BigQuery and MicroStrategy)
Data Dashboard
68 68%
32% 32
Big Data
100 100%
0% 0
Business Intelligence
0 0%
100% 100
Data Warehousing
100 100%
0% 0

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 MicroStrategy

Google BigQuery Reviews

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 BigQuery’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 “heavy” queries that operate using a large set of data. This means it’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
16 Top Big Data Analytics Tools You Should Know About
Google BigQuery is a fully-managed, serverless data warehouse that enables scalable analysis over petabytes of data. It is a Platform as a Service that supports querying using ANSI SQL. It also has built-in machine learning capabilities.

MicroStrategy Reviews

10 Best Alternatives to Looker in 2024
MicroStrategy: MicroStrategy delivers a comprehensive enterprise analytics platform that supports advanced data analysis and mobile applications, offering powerful insights into a wide array of business metrics.

Social recommendations and mentions

Based on our record, Google BigQuery seems to be more popular. It has been mentiond 42 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 (42)

  • Every Database Will Support Iceberg — Here's Why
    This isn’t hypothetical. It’s already happening. Snowflake supports reading and writing Iceberg. Databricks added Iceberg interoperability via Unity Catalog. Redshift and BigQuery are working toward it. - Source: dev.to / about 1 month ago
  • RisingWave Turns Four: Our Journey Beyond Democratizing Stream Processing
    Many of these companies first tried achieving real-time results with batch systems like Snowflake or BigQuery. But they quickly found that even five-minute batch intervals weren't fast enough for today's event-driven needs. They turn to RisingWave for its simplicity, low operational burden, and easy integration with their existing PostgreSQL-based infrastructure. - Source: dev.to / about 2 months ago
  • How to Pitch Your Boss to Adopt Apache Iceberg?
    If your team is managing large volumes of historical data using platforms like Snowflake, Amazon Redshift, or Google BigQuery, you’ve probably noticed a shift happening in the data engineering world. A new generation of data infrastructure is forming — one that prioritizes openness, interoperability, and cost-efficiency. At the center of that shift is Apache Iceberg. - Source: dev.to / about 2 months ago
  • Study Notes 2.2.7: Managing Schedules and Backfills with BigQuery in Kestra
    BigQuery Documentation: Google Cloud BigQuery. - Source: dev.to / 4 months ago
  • Docker vs. Kubernetes: Which Is Right for Your DevOps Pipeline?
    Pro Tip: Use Kubernetes operators to extend its functionality for specific cloud services like AWS RDS or GCP BigQuery. - Source: dev.to / 7 months ago
View more

MicroStrategy mentions (0)

We have not tracked any mentions of MicroStrategy yet. Tracking of MicroStrategy recommendations started around Mar 2021.

What are some alternatives?

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

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

Domo - Domo: business intelligence, data visualization, dashboards and reporting all together. Simplify your big data and improve your business with Domo's agile and mobile-ready platform.

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

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.

Sisense - The BI & Dashboard Software to handle multiple, large data sets.

Presto DB - Distributed SQL Query Engine for Big Data (by Facebook)