BigQuery Reviews

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.

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.

I usually need to catalog. In my case, it's more related to data governance. I need to catalog information from BigQuery. I want to ensure the data quality tool is in sync with BigQuery, so I go to BigQuery and do queries to make sure it was synced with Atlan, for example, for data quality tools. I create validation rules and need to write the rule in BigQuery to create a query there, see how long it takes to run, and evaluate its performance in a data quality tool.

What I have seen is that they are using BigQuery as a central repository. They bring dispersed information to BigQuery, which facilitates exploring the data and gaining insights. Consequently, it improves operations, response time, and the business overall.

As a user, I have liked using BigQuery to create queries. They have a table explorer feature that allows you to select a table, choose fields, and generate queries easily, which significantly facilitates my workflow. I also appreciate the lineage feature, which shows how tables relate to each other and enables end-to-end usage visualization. 

Furthermore, its integration with other tools like Atlan through a Google Chrome extension is highly beneficial. Using BigQuery's central repository brings dispersed information together, which facilitates exploring the data and gaining insights. Consequently, it improves operations, response time, and the business overall.

There are integration challenges, particularly with performance when exporting data to BigQuery from other tools like Qualitics. It can be slower and more problematic compared to other platforms such as Snowflake.

I have been working with BigQuery for one year.

I have not seen a lot of problems, so I would say BigQuery is quite stable.

In my opinion, BigQuery is very scalable yet has some limitations regarding performance that are not always as required.

I don't have direct contact with BigQuery's support team. Our organization manages this through internal communication, and I contact my company’s team when issues arise.

Positive

I would recommend using BigQuery because it's a very good tool, easy to manage, and similar to other databases. Those familiar with SQL Server or Oracle can adapt to BigQuery easily. It's a scalable cloud solution. 

Overall, I would rate BigQuery as nine 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.  

We use BigQuery to perform data warehouse migration for clients willing to move to GCP from their on-premise solution.

The solution's pricing is really competitive compared to other peers. The most valuable features of BigQuery is that it supports standard SQL and provides good performance.

I have been using BigQuery for three years.

I rate BigQuery a nine out of ten for stability.

Around 30 to 40 users use BigQuery in our organization.

I rate BigQuery ten out of ten for scalability.

I previously worked with Microsoft SQL Server.

The solution’s initial setup is very easy. You just have to spin up a data set and start using it.

I rate BigQuery ten out of ten for the ease of its initial setup.

The solution can be deployed by one person in a few minutes.

The solution's pricing is cheaper compared to other solutions. On a scale from one to ten, where one is cheap, and ten is expensive, I rate the solution's pricing a two or three out of ten.

Potential users can trust BigQuery without any second thoughts. The solution's pricing is great compared to other solutions. The solution provides more flexibility and supports standard SQL, and anyone coming out from a different platform would not face any challenges adopting BigQuery.

Overall, I rate BigQuery a nine out of ten.

We basically used it to store server data and generate reports for enterprise architects. It was a valuable tool for our enterprise design architect.

Everything they advertised or listed worked exactly as promised. That was advantageous to us. 

In future releases, I would like to see more pre-defined aggregated forms. After using BigQuery, we need to use the data in an enterprise architecture dimensional data model. So, having pre-defined aggregated forms would be helpful. 

Additionally, I would like to see version-based implementation and a fallback arrangement for data stored in BigQuery storage. These are some features I'm interested in.

I have experience with BigQuery. 

When I joined the company, BigQuery was already implemented by our team.

It is a cheap solution. 

I would recommend getting a clear understanding of BigQuery's functionalities and what it's best suited for. If your needs align with its capabilities, then you should definitely proceed. 

BigQuery offers fantastic features, but it's important to understand its purpose beforehand. Otherwise, you might face difficulties later on.

Overall, I would rate the solution an eight 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.

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.