BigQuery Reviews

My primary use case for the solution is as a powerful tool for handling and analyzing large datasets. The transition to GA4, which uses an event-based measurement framework, necessitated a more robust solution for detailed reporting and data analysis. It serves as both the storage and querying framework for this data.

The platform has significantly improved the organization's ability to analyze detailed and scalable data. It efficiently handles large volumes of data, crucial for timely decision-making and in-depth analytics. However, the shift from free reporting tools to a pay-for-use model has introduced additional costs.

The product's most valuable features include its scalability and the ability to handle complex queries on large datasets. The system's capacity to dynamically allocate resources based on data size and query complexity ensures efficient performance.

The product could benefit from improvements in user-friendliness, particularly in terms of the user interface. An easier, more intuitive graphical user interface (GUI) with drag-and-drop functionality for creating reports and segments would enhance usability.

I have been using BigQuery for approximately five to six years. My usage has increased recently, especially after the launch of GA4 (Google Analytics 4).

The platform's stability is commendable. As part of Google's infrastructure, it benefits from robust reliability and failover mechanisms, ensuring consistent performance and data integrity.

This solution is highly scalable and can efficiently handle vast amounts of data and complex queries. Its dynamic resource allocation ensures that performance scales with data size and query demands.

Google offers limited customer service and support. For detailed assistance, users may need to consult external experts or partners. Support primarily directs users to documentation and community resources.

Negative

Before using BigQuery, I relied on native analytics tools, which offered less detailed reporting. The switch was driven by the need for more comprehensive and scalable reporting capabilities.

The initial setup is relatively straightforward as it is a SaaS offering. However, preparing data for import and setting up queries can require considerable effort and technical knowledge.

Implementation was handled in-house. The expertise required for effectively using this platform often involves extensive reading and self-learning, as the process is quite technical.

The product operates on a pay-for-use model. Costs include storage and query execution, which can accumulate based on data volume and complexity.

While exploring options, I considered various analytics platforms and frameworks, but BigQuery was selected due to its integration with Google's ecosystem and its robust handling of large datasets.

While BigQuery offers powerful capabilities, managing costs effectively and considering the investment required to use the platform at scale is crucial. Additionally, investing in training or consulting services may be necessary to maximize the solution's benefits.

I rate it a ten out of ten.

The primary use case of BigQuery is within banking applications in the CDP. The front-end system pushes data, specifically mobile and net banking data, into BigQuery for processing and analysis. It involves significant data and requires specialized tools to utilize it fully. For example, we use AMS reports, breaking the data into various layers rather than using it in a single database.

At our company, the adoption is still in progress at various layers, but it was recently restarted and put into production. There are less than 200 users currently, but we need to figure out why we even have all this data we send out and whether we should rely on vendor-based databases.

We would want a great database for any new products we develop or if we need to send out an application from one store to another.

The integrated data storage features are good. Altogether, it provides the required functionality.

BigQuery is a single platform that can support different use cases and data bandwidths, whereas other platforms may require additional data platforms for each use case.

There are some limitations in the query latency compared to what it was three years ago. Despite this, BigQuery still provides the necessary functionality as compared to the other platforms.

An additional feature I would like is the one available in AWS, where you have a framework to onboard past services and start building analytical models and data design. The framework makes it easier for any new organization to adopt cloud computing quickly.

I have been working with BigQuery for nine-plus years.

There is a lot of room for improvement in stability.

So they're quickly catching up with the business and marketing needs. I know Google BigQuery started very late in the game, and they covered a lot. However, there is room to improve a lot on that. I rate the stability a seven out of ten.

It is a very scalable solution. I would rate it a ten out of ten.

Sometimes the tickets take time to go through. I would rate it an eight out of ten.

Positive

Our organization has a multi-cloud strategy, and we use different data transmission and storage tools depending on the cloud provider. For example, in Azure, we use Databricks for data transmission and Sines for storing data. In AWS, we use DynamoDB for specific use cases. Regarding Google, we use the CDP platform, specifically BigQuery, for their data storage and analysis needs. 

We have various tools across the different platforms to meet the specific use case needs. We use BigQuery within the Google CDP platform for their data storage and analysis needs. It varies from use case to use case, and we use different platforms accordingly.

The initial setup is very simple.

The costing model is a bit expensive as compared to its equivalent partners. If they can optimize the cost, it would be much better. Otherwise, people would step back.

I would rate it a seven on a scale of one to ten, where ten is for the cheapest, and one is for being high priced.

Our organization has solutions independent of the cloud-native solution, and Microsoft encourages that. For instance, Database is one of the tools which can be deployed across different clouds.

In terms of storing data, we prefer to go with the table as compared to Synapse as the database. Then, in terms of enabling the porter on Databricks, which is much faster compared to any other database in the current industry.

BigQuery scores pretty well for trusting the larger as well as broader data. Across all the 99 security queries, the benchmark can be pretty impressive. And that is the only reason we eventually did the Databricks with Azure. The partnership with Databricks and Azure was great.

BigQuery is a tool wherein it can support your structured, unstructured, secured, and unsecured data, and it can support the server if you use any right-level services from BigQuery.

However, data encryption and integration could be difficult if you want to transfer data to another cloud. For example, when I have data from the other cloud, it would be difficult to bring that data into the data systems for me. Even if I consider doing it, it will cost me and might be expensive.

When you try to import data from one vendor to another, it also results in additional data transfer costs and data integration issues.

If you keep the solution in the same platform and the same data fabric level, then the data from that level get joined and maintained locally to that cloud. And if you're sending some data across the cloud, only use the basics to connect the data. That way it'll detect the fabric. So if you go with the native tool, that is the limitation we'll have. Cloud diagnostics does get you out of it.

When it comes to BigQuery, it is deployed in one cloud. It is native to Google and can only stay on Google; that is the only drawback.

Overall, I would rate it a seven out of ten.

We use Cloud SQL for our web applications. Previously, we used Microsoft Cloud, but we transitioned due to cost benefits. We find Google Cloud Platform (GCP) to be more cost-effective. For BigQuery, we store data in a message queue similar to Kafka, and when an event occurs, that data is triggered to be inserted into a BigQuery table through subscriptions.

We have seen significant improvements in data management processes, particularly with integration capabilities that allow us to easily retrieve and manipulate data through simple queries. This enhances our workflow significantly.

BigQuery allows for very fast access, and it is efficient in handling large datasets compared to other SQL databases. It integrates well with other GCP products, and creating subscriptions in the UI is straightforward. The whole ecosystem of GCP products makes BigQuery beneficial for our data-handling tasks. Additionally, it is more cost-effective compared to alternatives like AWS.

There is a limitation when copying data directly from BigQuery; it only supports up to ten MB when copying data to the clipboard. For larger data, we have to download it as JSON or Excel files. This limitation could be addressed for better usability.

I have been working with Google Cloud SQL for over one year.

I have not experienced any downtime issues with the solution; it has been stable.

There are some limitations in scalability, particularly when dealing with very large datasets. However, the cost savings we gain often balance this out.

We haven't had much interaction with technical support outside of accessing documentation available online.

Positive

Before using BigQuery, we were utilizing Azure Warehouse. We switched to BigQuery as GCP services were slightly cheaper and more cost-effective. The cost savings were a significant factor in our decision.

The initial setup process for BigQuery was straightforward. By using Terraform, we were able to manage the entire setup efficiently and keep track of who is making changes.

We have a team of developers who manage the platform. Infrastructure changes are tracked, and project owners approve updates.

I can't provide concrete documentation on ROI, but GCP's evolving services have been more cost-effective compared to AWS.

AWS has a large number of users and has built a model with high costs, whereas GCP offers cost-effective solutions.

AWS was another option considered, but due to cost considerations, we opted for GCP.

I recommend BigQuery, especially if you're already using GCP products, as the integration with other Google services is seamless.

I'd rate the solution nine out of ten.

BigQuery allows you to quickly analyze logs from your systems to identify the severity of issues. It integrates well with other Google Cloud services, such as Cloud Logging, where you can easily manipulate various data types and analyze all logs.

BigQuery excels at data analysis. It processes vast amounts of information using its advanced architecture and sophisticated querying capabilities, making it crucial for critical insights and safe for handling sensitive data. 

BigQuery should integrate with other tools, such as Cloud Logging and Local Studio, to enhance its capabilities further and enable powerful and innovative analyses.

I have been using BigQuery for two years.

I have opted for Fireye, Elasticsearch, and Alcon. One principal difference is that BigQuery starts with machine learning and WAN implementations, while you can implement VMware or other active boxes. Therefore, it is recommended that cloud VMs be used for BigQuery processes. You can execute jobs in the cloud, such as VMware.

For instance, you can compute analytics for email, apply filters, and manipulate weather data. It provides higher efficiency, though exact benchmarks are unclear. Additionally, starting the query flow login request can also be advantageous.

The initial setup is automatic. It requires one person. You need to log in to the Google Cloud platform, import the necessary package into your query, and then you can start querying your data. 

If you need a solid CRM solution integrated with Azure, you'll need knowledgeable people to support it. Three individuals can form a strong CRM team connected to Azure, leveraging BigQuery.

You can use BigQuery to generate and manage large datasets efficiently. Whether using a flexible integrated environment like Dataflow or a local studio, BigQuery provides powerful tools for querying and analyzing data.

The product is free of cost.

Setting up BigQuery on GCP is crucial. When creating a service account, you define the permissions required for project identification or access monitoring systems.

You configure policies using IAM roles to manage access permissions effectively within GCP. These roles govern the service accounts created for specific tasks such as data processing, system monitoring, or other service integrations. When you activate these policies, a JSON token is generated. This token can authenticate and authorize access to Google services like BigQuery or other third-party applications.

Moreover, by configuring VMs to match data processing requirements, you ensure that the data is securely handled by the applications associated with the service accounts. This setup enables seamless communication between your applications and Google services, facilitating efficient data acquisition and processing.

Overall, I rate the solution a seven out of ten.

It is a pivotal component in enterprise data architecture, and crucial in data lake operations, whether supporting data warehouses or functioning as part of a broader data lake ecosystem.

One of the most significant advantages lies in the decoupling of storage and compute which allows to independently scale storage and compute resources, with the added benefit of extremely cost-effective storage akin to object storage solutions. Its unique architecture not only provides robust enterprise data warehouse capabilities but also seamlessly integrates with data lake functionalities.

The main challenges are in the areas of performance and cost optimizations. Achieving optimal results demands a certain level of familiarity with the platform's internals. The key point for improvement lies in the performance optimization.

I have been working with it for three months.

It exhibits a high level of stability and security, there are no notable issues in these aspects. I would rate it nine out of ten.

It is designed to seamlessly scale with the growing demands of data processing, there are no issues with it. I would rate it nine out of ten.

The technical support is commendable. However, there is room for improvement in the availability of resources and documentation from a technological standpoint. I would rate it seven out of ten.

Neutral

In the landscape of enterprise data warehouses, BigQuery stands out as a superior choice when compared to alternatives like Azure Synapse, AWS Redshift, and Snowflake. While Snowflake is known for its higher costs, and Redshift is perceived as both complex and expensive, Azure Synapse presents its own set of constraints with its MPP architecture and reliance on an RDBMS in-between. BigQuery, on the other hand, has a distinct edge with its seamless migration process, vast capabilities, and a harmonious balance of storage, computing, cost-effectiveness, and performance efficiency. This is particularly evident as organizations and professionals, including myself, have experienced ease in migrating from other vendors to BigQuery. Drawing from my extensive experience working across various cloud platforms such as AWS, Azure, and Snowflake, BigQuery consistently emerges as a robust and preferable solution.

The initial setup is straightforward.

Its cost structure operates on a pay-as-you-go model. I would rate it seven out of ten.

Whether for small, medium, or large enterprises, it is a recommendable choice. Its pricing model makes it accessible and manageable based on your usage. Given that many individuals and businesses already have Gmail accounts and utilize Google Cloud workspaces, incorporating BigQuery into operations is seamless. Moreover, a complimentary reporting tool, Looker Studio, is available for free, enhancing the reporting capabilities on BigQuery or via Google Sheets. Overall, I would rate it eight out of ten.

In the current landscape where organizations prioritize cloud solutions like Google Cloud, BigQuery plays a pivotal role in delivering scalability, flexibility, and numerous benefits for data management and analysis for our clients.

BigQuery's managed nature ensures that it's always up-to-date and maintained by Google on its cloud platform. This aspect makes it an ideal choice for organizations seeking cloud-based solutions instead of on-premises ones.

It allows our customers to adapt to various data types, including unstructured and flat data sets. BigQuery excels at structuring data, performing predictions, and conducting insightful analyses and it leverages machine learning and artificial intelligence capabilities, powered by Google's Duarte AI. It seamlessly integrates with Duarte AI, enabling the use of simple SQL queries to access Vertex AI foundation models directly within BigQuery. This unique capability is especially valuable for text-processing tasks, such as sentiment analysis. It provides a unified interface for all data practitioners, making it versatile for both traditional and sentiment analysis tasks. It's particularly adept at extracting specific entities from large datasets without the need for specialized models. Another notable aspect of BigQuery is its serverless architecture, which means there's no need for dedicated servers which is a great benefit.

SQL queries remain a preferred choice for many IT database administrators, and BigQuery's ability to handle SQL queries efficiently enhances its appeal. However, there's a challenge when it comes to integrating BigQuery with homegrown database solutions, which some medium and small-sized clients rely on. While it's possible to test database integration with it using a sandbox environment, achieving seamless integration can be complex, especially for open data solutions. For greater flexibility and ease of use, it would be beneficial if BigQuery offered more third-party add-ons and connectors, particularly for databases that don't have built-in integration options.

In my previous roles at different organizations, I had around three to four years of experience with GCP products. During the last five months, my engagement has focused on BigQuery specifically.

All GCP products, including BigQuery, are known for their stability and reliability. In instances where issues arise, such as product bugs or challenges, Google steps in with its robust support and maintenance services. They provide a direct helpline for organizations, allowing clients to reach out to Google and swiftly address their queries. The product itself has reached a level of maturity where most challenges have been addressed.

It provides impressive scalability capabilities.

Google's support services, particularly for GCP (Google Cloud Platform) products, are known for their agility and effectiveness. As a partner, we place a significant reliance on Google's support system, which is highly responsive and adaptable. Certain challenges can still surface, particularly in the realm of integration. Issues may arise if there's a mismatch in languages, systems, or configurations within the integration layer. These technical challenges can be addressed through thorough investigation and resolution. It's worth noting that not only does Google offer comprehensive support, but partners also contribute to providing excellent support and managed services for BigQuery and other GCP products.

Positive

In my previous organization, I had experience working with IBM's data warehouse solution, specifically IBM Db2 on Cloud. However, it's important to note that IBM's solution was primarily a database service, whereas BigQuery serves a different purpose. Users find it exceptionally user-friendly, allowing them to request data in plain language, with Google's machine learning and artificial intelligence taking care of the technical aspects. BigQuery also offers robust integration options. It seamlessly connects with various data sources and tools, including Google Cloud Storage, Google Sheets, Google Data Studios, and third-party BI tools like Tableau and Looker.

To acquire and use BigQuery, the typical process involves obtaining a GCP (Google Cloud Platform) license specific to the product. The initial setup of the product is relatively straightforward and static. Typically, it takes around one to two weeks to integrate BigQuery into your existing architecture.

BigQuery stands out as an attractive option for organizations seeking a hassle-free, plug-and-play solution. It's a robust choice that delivers strong returns on investment and addresses various needs efficiently.

The pricing is adaptable, ensuring that organizations can tailor their usage and costs based on their specific requirements and configurations within the Google Cloud Platform. You don't need multiple licenses; a single GCP BigQuery license suffices. Once you have this license in place, you will be billed according to your chosen pricing model. Google offers flexibility in pricing models to accommodate the unique needs of different customers, making it a versatile and customer-centric solution.

When it comes to evaluating competitors in the data warehouse and analytics space, it's essential to consider the strengths and differences among major players, especially Google, Amazon, and Microsoft. Google's BigQuery, Amazon's Redshift, and Microsoft's Azure Synapse Analytics are three prominent contenders in this market. Redshift is a robust database and analytics platform known for its scalability and tight integration with AWS services. BigQuery shares several strengths with Amazon Redshift and Microsoft Azure Synapse Analytics. All three are scalable and capable of handling large datasets. However, where Google shines is in its integration capabilities and architectural design, which many users find straightforward and user-friendly.

My advice would be to first understand your client's weak points, the challenges they face, their ambitions, vision, and data-related dreams. It's crucial to identify their desired analytical capabilities for informed decision-making within their organization. Once these critical aspects are on the table, the choice between BigQuery or any other data warehouse and analytical platform can be made. Through this approach, clients will gradually build their understanding of how BigQuery can serve as a database house and analytical platform within their architecture. It empowers them to efficiently store, analyze, and query large datasets, making it an ideal choice for organizations dealing with substantial data volumes and the need for rapid, data-driven decision-making. I would rate it nine out of ten.

I use it to deal a lot with marketing, specifically Google Ads, YouTube, and Google Analytics. But mostly, I utilize it for its capabilities to sync directly up with Google ads transfers.

Instead of having to go directly into the platform, pull various reports after and save those reports, port them over into Google Sheets, and then import ranges and queries. Then, having to transform the data to my needs, I can build a SQL script that is to my needs directly within the platform so that when the data comes out at the platform, it's already essentially punched into the format that I needed.

Its SQL editor is very easy to use and very easy to conceptualize. The way that it breaks data down into silos is easily discernible. So, I guess that's really it.

There is a good amount of documentation out there, but they're consistently making changes to the platform, and, like, their literature hasn't been updated on some plans.

I have been using BigQuery for a little over a year.

It's pretty stable. It's fast, and it is able to go through large quantities of data pretty quickly.

I think that it's easy to scale. For instance, when I need the data for a new client, I just ask to have their account added to my MCC, and the MCC deploys through basically, rolls out all the accounts available really quickly.

I am the sole user of the solution in my company.

I've tried getting in touch with the support, and that's actually the difficult part. So, unless you're using a higher-tiered version of the platform, getting support can be problematic.

I got into Google Big Query since it met my needs.

Regarding the deployment model, I work in its native GUI. I'm not sure what the SaaS version is, so I just utilize it with Google Cloud's native console.

Regarding the deployment process, I would have to create your own instance within Google Cloud. You create a project, that project. Then, you start nesting your data streams into that project. And then we do have to backfill some of the data because it'll only start grabbing data from the date that you tell it to in thirty days before. So if you need data that is previous to thirty days, then you've got it going to backfill it. After that, I found that it was a pretty easy and quick deployment.

Speaking about the time for deployment, I would say that having the knowledge I have now, it wouldn't take me even an afternoon. But at the time, because I didn't know what I was doing, it took about two-three days.

I did the deployment myself.

Price-wise, I think that is very reasonable. Like, I don't use a ton of computing when it comes to the platform, so I haven't ever really had to pay when it comes to the product. I really don't have to pay from month to month.

I did not go through other solutions.

I would tell those planning to use the solution to just go out and utilize as much information as possible. There's a ton of great information on the platform and how it can be best utilized.

The solution doesn't necessarily require maintenance.

It's a great platform. It's pretty easy to use. You do have to have some skill and uptake when it comes to actually writing SQL and writing queries. But then it does need better support capabilities. But aside from that, it's a pretty good platform.

I rate the solution a seven out of ten.

We have a cloud solution that runs in a centralized mode for a few hundred senior managers who require diverse reports, ranging from daily operational details to more substantial analyses, such as sales trends, movie ticket sales clustering, and reporting.

The flexibility of its serverless architecture is advantageous in handling the variable nature of our workloads. Instead of relying on a fixed database cluster with constant costs, it allows you to pay for the resources you consume during peak times. This on-demand pricing model appears to be more cost-effective, particularly when dealing with occasional heavy queries that involve analyzing billions of data points, such as ticket sales for millions of movies. The ability to scale internally using Kubernetes adds another layer of flexibility to our setup, allowing us to adapt to varying demands efficiently. Its fast response times during peak usage make it a suitable choice for our dynamic and variable data processing needs. I appreciate its impressive optimization and automation features, observed during small-scale tests. It stands out in efficiently handling internal actions without the need for manual intervention in tasks like building cubes and defining final dimensions.

The primary hurdle in this migration lies in the initial phase of moving substantial volumes of data to cloud-based platforms. This becomes even more pronounced when dealing with terabytes of data. Uploading data to cloud services requires careful consideration and optimization to ensure a smooth and efficient migration, especially when dealing with large datasets.

I started using it recently.

It inherently manages scalability with its auto-scaling capabilities. The ability to dynamically adjust resources based on demand is a key factor in optimizing performance and ensuring that our system can handle varying workloads efficiently. We operate as a small company with a modest business scale, handling a few medium-sized projects each year.

The current bottleneck in our migration process primarily revolves around bandwidth issues, especially during the initial data ingestion phase.

The deployment process itself is straightforward and not a source of concern. The real challenge lies in the bandwidth limitations and the time-consuming nature of data uploading. While a comprehensive evaluation is still pending, it's anticipated that the data upload alone might take up to a week or more.

The pricing appears to be competitive for the intended usage scenarios we have in mind.

In my evaluation of alternative solutions, I'm exploring Hydra, a columnar version of Postgres with partitioning capabilities. While I'm still learning about its features and performance, it seems promising. Additionally, I'm considering ClickHouse, which has shown exceptional benchmark results. I've completed an initial installation to assess its functionality.

Overall, I would rate it eight out of ten.