BigQuery Reviews

Right now, we are downloading raw Google Analytics four data to BigQuery and then manipulating it. It's mostly used for behavioral data in online datasets.

It has not impacted our operational costs and productivity much; however, it offers a valuable solution for our use case.

BigQuery's querying capabilities are very optimized for large datasets. It generally works faster, making it effective for large data analytics projects.

It would be beneficial if BigQuery could be made more affordable.

I have been familiar with BigQuery for at least four years, probably around six.

I would rate the stability of BigQuery as seven out of ten.

Scalability is easier with BigQuery, and I would rate it as ten out fo ten.

We haven't really needed to use Google's technical support.

Positive

We previously worked with Redshift. We found BigQuery to be the only option for our current use case. Redshift is mostly cheaper, especially if you want to manage your own data warehouse. 

We had some issues with BigQuery, which, when combined with cost, influenced our decision to move back to Redshift as our main data warehouse.

No cloud solution is straightforward. We faced difficulties in configuration, permissions, etc. All cloud providers use their own terminology, and while documented, adapting to their terminology takes time.

We have internal people for maintenance, so we don't pay for additional support.

We are above the free threshold, so we are paying around 40 euros per month for BigQuery. It is generally low cost.

In addition to working with Redshift earlier, we also had integration with Alooma as an ETL solution. However, Alooma doesn't exist anymore.

I would recommend BigQuery to others. However, making it more affordable would be appreciated.

I'd rate the solution 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.

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.

We use the product as a data warehouse to store metrics data.

At the enterprise level, the pricing for storing data in BigQuery is practically free.

They could enhance the platform's user accessibility. Currently, the structure of BigQuery leans more towards catering to hard-code developers, making it less user-friendly for data analysts or non-technical users.

We have been using BigQuery for four years.

It is a very stable product.

It is a scalable platform.

The decision to use BigQuery was likely influenced by the new project's alignment with Google Cloud services.

The initial setup process is easy and similar to any other database product.

The platform is inexpensive.

I recommend BigQuery. It offers low costs and is quite easy to use, compared to other options like AWS or Azure. If you're already working within the Google Cloud ecosystem, it's a perfect match. However, if you're primarily focused on data warehousing and need something more accessible, Snowflake might be a better option.

The integration with other tools has greatly enhanced our data visualization capabilities. It's tightly integrated across various components.

The serverless architecture has been immensely beneficial for our projects. We no longer have to concern ourselves with infrastructure management or maintenance, as everything is automated. It makes our team smaller and alleviates worries about infrastructure or downtime.

For a beginner learning to use it for the first time, it's relatively straightforward. 

I rate it an eight 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.

Our company uses the solution as a data warehouse. We have ten to twenty users who consume the solution from reports. 

The feature called calibrating the capacity is valuable.

The solution is easy to use and has quite good performance.

We would like to be able to calibrate the solution to run on top of a raw file. Currently, we have to move raw files from Google storage to the solution and load them for transformation. We shouldn't need to move data first to get an analysis.

I have been using the solution for five years. 

The solution is stable so I rate stability a nine out of ten. 

We have experienced a few glitches in our company only. When we run queries, they take a few to five minutes when they should only take one minute. There is a problem with the services in Indonesia. 

The solution is scalable and has quite good performance. You scale at the same time you execute a user's role and can easily get one to ten million pro. 

I rate scalability a nine out of ten. 

Technical support was quite responsive and handled our issue. 

I rate support an eight out of ten. 

Positive

The setup is quite simple so I rate it a nine out of ten. 

The solution is pretty affordable and quite cheap in comparison to PDP or Cloudera. 

The solution could be less expensive. You have to be careful how you design, query, or partition because it could cost you a lot of money.

I rate pricing an eight out of ten. 

When we decided to move to the cloud, we compared the solution to KWS. We found that the performance of Google Cloud and the solution were better than KWS. The setup and configuration were also simpler.

I rate the solution an eight out of ten. 

We primarily use the solution for data analytics. 

I enjoy the scalability of the solution. Its scalability is very impressive.

It's straightforward to set up.

The solution has been stable.

We'd like to have more integrations with other technologies. We'd like something like CrossCloud - something that can be on AWS and Azure and can be easily integrated.

It would be great if they added data anonymization to their list of features. We'd like to see data compliance and masking so we can enforce things region by region.

I've been using the solution since around 2019.

I haven't seen any tickets relating to trouble with scalability. It seems to be reliable. There are no bugs or glitches. It doesn't crash or freeze. 

The scalability is excellent. It can handle large datasets and scale up pretty easily as the data volume grows. It expands very easily.

We have 80 to 100 people using the solution right now. It's used on a daily basis. 

I haven't used technical support just yet. I haven't come across any problems which would require me to reach out. 

I've used Data Warehouse in the past and am familiar with Teradata and Snowflake.

If I have to compare BigQuery with Teradata in terms of performance, capabilities, ease of use, and integrations, BigQuery scales up better. However, in terms of licensing and paper use, Teradata is quite good.

If we compare it with other things like Snowflake, Snowflake has its own unique architectural advantages. However, I haven't seen Snowflake over on Google Cloud. I have seen Snowflake over on AWS and Azure. The architecture of Snowflake has its own unique advantages and is largely on other clouds.

The initial setup is very simple and straightforward. I'd rate the ease of implementation a four out of five.

We find the pricing reasonable enough for our use cases. However, it's too early to comment on if it will be good in the long run. We have to properly plan data around different tiers, including which to archive where so that we use it in a more optimized fashion. We will need to properly plan everything and we haven't really done that yet.

I'd rate it a four out of five in terms of its competitive pricing. 

I'm an end-user. I'm still new to the company. I'm not sure which version of the solution we're on.

All cloud systems have more or less the same functionality. It's just a matter of choosing one that makes sense for your business.

When it comes to how to leverage analytics, some of the AI and machine learning from Google come ahead of the competition. Other than that, the other analytics options are fairly competitive between Google, AWS, and Microsoft. It's just that,  when it comes to extending the analytics to AI/ML, Google is ahead of the competition there.

I'd recommend the solution to others. 

I would rate it eight out of ten.

BigQuery has got a lot of traditional functionalities. You can store the data. You can process the data.

In Teradata, it's very fast compared to BigQuery. The processing capability and inbuilt MPP architecture support processing millions or billions of records in a few seconds. BigQuery faces challenges in processing and retrieving the same data.

So, the processing capability can be an area of improvement. 

Another area of improvement is in terms of the storage area, as BigQuery does support some limited types of data storage file format. In order to see the data, we need to store the data in a relational database. So, in the future, they should be capable of querying the data from the data lake. 

Before storing it in the RDBMS. At the moment, they don't have this feature for how my raw data looks unless you store the data in tables. Never know what sort of data. 

That's one thing, like, definitely they need to improve because before we model the data to explore what kind of data I'm getting in the raw stage then it's easy to, like model and process the data.

It supports petabytes of data like Teradata. One advantage of using BigQuery is that it's cloud-based. You don't need additional space or nodes to process growing data. It's auto-scalable, eliminating the need to plan and expand infrastructure as your organization's data grows.

We never had any major issues. However, when comparing technical support between Teradata and BigQuery, Teradata has a larger global support team. BigQuery has comparatively less support from the company to the customer.

We haven't experienced major issues or outages, so it's always available. It's multi-region, and if one server goes down, another server in that region takes over.

Positive

BigQuery can be used for any type of company. It has the capability of building applications and storing data. It can be used for OLTP or OLAP. It has many other products within the Google space.

Teradata, on the other hand, mainly focuses on building databases, storing and processing SysTrack data. BigQuery is an analytical platform where you can store and process data, and Google Cloud Platform has different products for other purposes.

You can build your application or organize data, structure, and structure. You can build reporting solutions on the Google Cloud Platform itself. It has everything - storing, processing, integrating, and building solutions, all in one product.

When comparing BigQuery with Azure scenarios, there are differences. It depends on the organization's requirements and use case.

There are two types of pricing: the storage price and the processing price. Storage is very, very cheap compared to Teradata. But processing, it depends, like, how much of an amount of data you are processing. They charge the query you run on the big query.

In terms of the data warehousing, and data analytical platform, BigQuery is one of the products in the Google Cloud platform. So, I would rate it a nine out of ten in terms of data warehousing.