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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Google BigQuery

    Score8.8 out of 10
    N/AGoogle's BigQuery is part of the Google Cloud Platform, a database-as-a-service (DBaaS) supporting the querying and rapid analysis of enterprise data.

    $6.25

    per TiB (after the 1st 1 TiB per month, which is free)

    Zabbix

    Score8.5 out of 10
    N/AZabbix is an open-source network performance monitoring software. It includes prebuilt official and community-developed templates for integrating with networks, applications, and endpoints, and can automate some monitoring processes.N/A
    Pricing
    Google BigQueryZabbix
    Editions & Modules
    Standard edition
    $0.04 / slot hour
    Enterprise edition
    $0.06 / slot hour
    Enterprise Plus edition
    $0.10 / slot hour
    No answers on this topic
    Offerings
    Pricing Offerings
    Google BigQueryZabbix
    Free Trial
    YesNo
    Free/Freemium Version
    YesNo
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details——
    More Pricing Information
    Features
    Google BigQueryZabbix
    Database-as-a-Service
    Comparison of Database-as-a-Service features of Google BigQuery and Zabbix
    Feature
    Google BigQuery
    8.5
    80 Ratings
    1% above category average
    Zabbix
    -
    Ratings
    Automatic software patching8.017 Ratings00 Ratings
    Database scalability9.079 Ratings00 Ratings
    Automated backups8.524 Ratings00 Ratings
    Database security provisions8.873 Ratings00 Ratings
    Monitoring and metrics8.675 Ratings00 Ratings
    Automatic host deployment8.013 Ratings00 Ratings
    Best Alternatives
    Google BigQueryZabbix
    Small Businesses
    MongoDB Atlas
    Score7.8 out of 10
    No answers on this topic
    Medium-sized Companies
    Azure Database
    Score8.8 out of 10
    Icinga
    Score9 out of 10
    Enterprises
    Google Cloud SQL
    Score8.7 out of 10
    HPE OneView
    Score6 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    Google BigQueryZabbix
    Likelihood to Recommend
    9.0
    (79 ratings)
    8.3
    (28 ratings)
    Likelihood to Renew
    8.1
    (5 ratings)
    9.0
    (3 ratings)
    Usability
    6.6
    (6 ratings)
    8.6
    (4 ratings)
    Availability
    7.3
    (1 ratings)
    -
    (0 ratings)
    Performance
    6.4
    (1 ratings)
    -
    (0 ratings)
    Support Rating
    4.8
    (11 ratings)
    5.0
    (5 ratings)
    Implementation Rating
    -
    (0 ratings)
    8.0
    (2 ratings)
    Configurability
    6.4
    (1 ratings)
    -
    (0 ratings)
    Contract Terms and Pricing Model
    10.0
    (1 ratings)
    -
    (0 ratings)
    Ease of integration
    7.3
    (1 ratings)
    -
    (0 ratings)
    Product Scalability
    7.3
    (1 ratings)
    -
    (0 ratings)
    Professional Services
    8.2
    (2 ratings)
    -
    (0 ratings)
    User Testimonials
    Google BigQueryZabbix
    Likelihood to Recommend
    Google
    Event-based data can be captured seamlessly from our data layers (and exported to Google BigQuery). When events like page-views, clicks, add-to-cart are tracked, Google BigQuery can help efficiently with running queries to observe patterns in user behaviour. That intermediate step of trying to "untangle" event data is resolved by Google BigQuery. A scenario where it could possibly be less appropriate is when analysing "granular" details (like small changes to a database happening very frequently).
    Incentivized
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    Zabbix
    Zabbix is great for monitoring your servers and seeing alerts when the system uses too much CPU or memory. This allowed the system Engineer to be proactive and add resources to these systems to avoid interrupting the services. Especially servers running operations applications and services. This is one of the best usages for Zabbix.
    Incentivized
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    Pros
    Google
    • Realtime integration with Google Sheets.
    • GSheet data can be linked to a BigQuery table and the data in that sheet is ingested in realtime into BigQuery. It's a live 'sync' which means it supports insertions, deletions, and alterations. The only limitation here is the schema'; this remains static once the table is created.
    • Seamless integration with other GCP products.
    • A simple pipeline might look like this:-
    • GForms -> GSheets -> BigQuery -> Looker
    • It all links up really well and with ease.
    • One instance holds many projects.
    • Separating data into datamarts or datameshes is really easy in BigQuery, since one BigQuery instance can hold multiple projects; which are isolated collections of datasets.
    Incentivized
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    Zabbix
    • Collecting hardware data - CPU, Memory, Network, and Disk Metrics are collected and reported on.
    • Flexible design - It is very easy to build out even very large environments via the templating system. You can also start where you are - network monitoring, server monitoring, etc. and then build it out from there as time and resources permit.
    • Provides a "plugin architecture" (via XML templates) to allow end users to extend it to monitor all kinds of equipment, software, or other metrics that are not already added into the software already.
    • Very complete documentation. Almost every aspect of Zabbix has been documented and reported on.
    • Cost - Zabbix is FOSS software and always free. Support is reasonably priced and readily available.
    Incentivized
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    Cons
    Google
    • Please expand the availability of documentation, tutorials, and community forums to provide developers with comprehensive support and guidance on using Google BigQuery effectively for their projects.
    • If possible, simplify the pricing model and provide clearer cost breakdowns to help users understand and plan for expenses when using Google BigQuery. Also, some cost reduction is welcome.
    • It still misses the process of importing data into Google BigQuery. Probably, by improving compatibility with different data formats and sources and reducing the complexity of data ingestion workflows, it can be made to work.
    Incentivized
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    Zabbix
    • Creating an alert & its trigger can be made easier.
    • More VM-based data collection counters should be introduced to have better VM monitoring.
    • The raw counters collection agent in every node is relatively weak. It goes down often, which needs more stability.
    Incentivized
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    Likelihood to Renew
    Google
    We have to use this product as its a 3rd party supplier choice to utilise this product for their data side backend so will not be likely we will move away from this product in the future unless the 3rd party supplier decides to change data vendors.
    Incentivized
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    Zabbix
    It is free. It didn't cost anything to implement (other than my time and the cost incurred for it) and it is filling a badly needed gap in our IT infrastructure. Support is available if we have issues and can be done annually or paid for on a per incident basis as needed. Expansion, updates, and all other future lifecycle activities are likewise free of cost, so as long as someone is able to implement/maintain the software (and the OSS project is maintained) then I imagine the company will never leave it.
    Incentivized
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    Usability
    Google
    I think overall it is easy to use. I haven't done anything from the development side but an more of an end user of reporting tables built in Google BigQuery. I connect data visualization tools like Tableau or Power BI to the BigQuery reporting tables to analyze trends and create complex dashboards.
    Incentivized
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    Zabbix
    I think every organization, especially the IT department, needs a tool like this. I know of another product like Zabbix that gives a similar or the same solution, but its range makes it very useful. You can see almost all the device info in one place: disk usage, disk space, network usage, etc.
    Incentivized
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    Reliability and Availability
    Google
    I have never had any significant issues with Google Big Query. It always seems to be up and running properly when I need it. I cannot recall any times where I received any kind of application errors or unplanned outages. If there were any they were resolved quickly by my IT team so I didn't notice them.
    Incentivized
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    Zabbix
    No answers on this topic
    Performance
    Google
    I think Google Big Query's performance is in the acceptable range. Sometimes larger datasets are somewhat sluggish to load but for most of our applications it performs at a reasonable speed. We do have some reports that include a lot of complex calculations and others that run on granular store level data that so sometimes take a bit longer to load which can be frustrating.
    Incentivized
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    Zabbix
    No answers on this topic
    Support Rating
    Google
    BigQuery can be difficult to support because it is so solid as a product. Many of the issues you will see are related to your own data sets, however you may see issues importing data and managing jobs. If this occurs, it can be a challenge to get to speak to the correct person who can help you.
    Incentivized
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    Zabbix
    The setup is the most time-consuming portion of using zabbix. It takes a lot of effort to shape it into a usable format and even then it can get very messy. It's not exactly intuitive and as mentioned the UI seems a bit antiquated. If I was to roll out a monitoring solution from scratch, I'd probably look for alternatives which are easier to use and maintain.
    Incentivized
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    Implementation Rating
    Google
    No answers on this topic
    Zabbix
    We are a mainly Windows environment, so it would be useful if we could have used Active Directory to deploy agents. As of version 4.2, Zabbix has announced a new agent MSI file to allow exactly that. Unfortunately, we didn't have that option. Also, for Linux and MAC deployments, there is no simple way to deploy that. Using remote scripts you may be able to create something, but most places will opt for either SNMP (agentless) or manual installation of agents to add to Zabbix. A way of deploying agents via discovery would go a long way to helping in the adoption of the tool.
    Incentivized
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    Alternatives Considered
    Google
    PowerBI can connect to GA4 for example but the data processing is more complicated and it takes longer to create dashboards. Azure is great once the data import has been configured but it's not an easy task for small businesses as it is with BigQuery.
    Incentivized
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    Zabbix
    We're using the Solarwinds suite as our global monitoring standard, but it is very complex and its licensing model makes it difficult to monitor a wide range of technologies. So, we're using Zabbix as a complement on our monitoring process. Zabbix is a way more flexible and has free integrations to a wide range of technologies. It is also more 'user friendly' and easy to manage.
    Incentivized
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    Contract Terms and Pricing Model
    Google
    None so far. Very satisfied with the transparency on contract terms and pricing model.
    Read full review
    Zabbix
    No answers on this topic
    Scalability
    Google
    We have continued to expand out use of Google Big Query over the years. I'd say its flexibility and scalability is actually quite good. It also integrates well with other tools like Tableau and Power BI. It has served the needs of multiple data sources across multiple departments within my company.
    Incentivized
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    Zabbix
    No answers on this topic
    Professional Services
    Google
    Google Support has kindly provide individual support and consultants to assist with the integration work. In the circumstance where the consultants are not present to support with the work, Google Support Helpline will always be available to answer to the queries without having to wait for more than 3 days.
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    Zabbix
    No answers on this topic
    Return on Investment
    Google
    • Previously, running complex queries on our on-premise data warehouse could take hours. Google BigQuery processes the same queries in minutes. We estimate it saves our team at least 25% of their time.
    • We can target our marketing campaigns very easily and understand our customer behaviour. It lets us personalize marketing campaigns and product recommendations and experience at least a 20% improvement in overall campaign performance.
    • Now, we only pay for the resources we use. Saved $1 million annually on data infrastructure and data storage costs compared to our previous solution.
    Incentivized
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    Zabbix
    • Good ROI when trying to get ahead of network related problems instead of waiting for users to complain
    • Good ROI when doing upgrades or exploratory work on a neglected network
    • Good ROI when showing usage trends to management showing higher actual usage versus what was thought to be happening
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    ScreenShots

    Google BigQuery Screenshots

    Screenshot of Migrating data warehouses to BigQuery - Features a streamlined migration path from Netezza, Oracle, Redshift, Teradata, or Snowflake to BigQuery using the fully managed BigQuery Migration Service.Screenshot of bringing any data into BigQuery - Data files can be uploaded from local sources, Google Drive, or Cloud Storage buckets, using BigQuery Data Transfer Service (DTS), Cloud Data Fusion plugins, by replicating data from relational databases with Datastream for BigQuery, or by leveraging Google's data integration partnerships.Screenshot of generative AI use cases with BigQuery and Gemini models - Data pipelines that blend structured data, unstructured data and generative AI models together can be built to create a new class of analytical applications. BigQuery integrates with Gemini 1.0 Pro using Vertex AI. The Gemini 1.0 Pro model is designed for higher input/output scale and better result quality across a wide range of tasks like text summarization and sentiment analysis. It can be accessed using simple SQL statements or BigQuery’s embedded DataFrame API from right inside the BigQuery console.Screenshot of insights derived from images, documents, and audio files, combined with structured data - Unstructured data represents a large portion of untapped enterprise data. However, it can be challenging to interpret, making it difficult to extract meaningful insights from it. Leveraging the power of BigLake, users can derive insights from images, documents, and audio files using a broad range of AI models including Vertex AI’s vision, document processing, and speech-to-text APIs, open-source TensorFlow Hub models, or custom models.Screenshot of event-driven analysis - Built-in streaming capabilities automatically ingest streaming data and make it immediately available to query. This allows users to make business decisions based on the freshest data. Or Dataflow can be used to enable simplified streaming data pipelines.Screenshot of predicting business outcomes AI/ML - Predictive analytics can be used to streamline operations, boost revenue, and mitigate risk. BigQuery ML democratizes the use of ML by empowering data analysts to build and run models using existing business intelligence tools and spreadsheets.