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Google Cloud Monitoring VS Google BigQuery

Compare Google Cloud Monitoring VS Google BigQuery and see what are their differences

Grapple
Do-It-Yourself Data Analytics & Business Intelligence, Powered by AI featured
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Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Google Cloud Monitoring logo Google Cloud Monitoring

Gain visibility into the performance, uptime, and overall health of cloud-powered apps on Google Cloud and other cloud or on-premises environments.

Google BigQuery logo Google BigQuery

A fully managed data warehouse for large-scale data analytics.
  • Google Cloud Monitoring Landing page
    Landing page //
    2023-08-01
  • Google BigQuery Landing page
    Landing page //
    2023-10-03

Google Cloud Monitoring features and specs

  • Integration with Google Cloud Platform
    Google Cloud Monitoring offers seamless integration with other Google Cloud services, allowing for unified monitoring and easy setup for users already using Google Cloud products.
  • Comprehensive Metrics and Alerts
    It provides a wide range of metrics and customizable alerts, enabling users to closely monitor their applications and systems for any issues and take proactive measures.
  • Custom Dashboards
    Users can create custom dashboards to visualize important metrics in a way that makes sense for their specific needs, enhancing the ability to quickly spot trends and anomalies.
  • Scalability
    The platform is designed to scale easily, handling the needs of both small applications and large, complex systems without a loss in performance or manageability.
  • Built-In Logging and Tracing
    Google Cloud Monitoring integrates with Google Cloud Logging and Cloud Trace, providing deeper insights by correlating logs and traces with metrics.

Possible disadvantages of Google Cloud Monitoring

  • Complexity for New Users
    For users who are not already familiar with Google Cloud Platform, the initial learning curve can be quite steep due to the vast array of features and settings.
  • Cost
    While there is a free tier, the pricing can become expensive as usage grows, especially for larger deployments that require advanced features and more data processed.
  • Limited Third-Party Integrations
    Compared to some dedicated monitoring solutions, Google Cloud Monitoring may have fewer integrations with third-party services and tools, which can be a limitation for some users.
  • Regional Availability
    Some features may have limited availability depending on the region, which could affect users operating in locations where certain services are not fully supported.
  • Potential for Information Overload
    Due to the comprehensive range of metrics and notifications, users may find themselves overwhelmed with information if dashboards and alerts are not carefully configured.

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.

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 Cloud Monitoring videos

+ Add

Cloud Monitoring in a minute

More videos:

  • Tutorial - How to Get Started with Google Cloud Monitoring

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

Category Popularity

0-100% (relative to Google Cloud Monitoring and Google BigQuery)

Google Cloud Monitoring

Google BigQuery

Dev Ops
100 100%
Dev Ops
0% 0
Data Dashboard
0 0%
Data Dashboard
100% 100
Monitoring Tools
100 100%
Monitoring Tools
0% 0
Big Data
0 0%
Big Data
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 Cloud Monitoring and Google BigQuery

Google Cloud Monitoring Reviews

We have no reviews of Google Cloud Monitoring yet.
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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...
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
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.
Source: www.analytixlabs.co.in

Social recommendations and mentions

Based on our record, Google BigQuery should be more popular than Google Cloud Monitoring. 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 Cloud Monitoring mentions (7)

View more

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 / 5 months 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 / 6 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 / 6 months ago
  • Study Notes 2.2.7: Managing Schedules and Backfills with BigQuery in Kestra
    BigQuery Documentation: Google Cloud BigQuery. - Source: dev.to / 8 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 / 11 months ago
View more

What are some alternatives?

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

Amazon CloudWatch - Amazon CloudWatch is a monitoring service for AWS cloud resources and the applications you run on AWS.

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

Cortex Project - Horizontally scalable, highly available, multi-tenant, long term Prometheus.

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.

Google Cloud Functions - A serverless platform for building event-based microservices.

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

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