Likelihood to Recommend Google BigQuery really shines in scenarios requiring real-time analytics on large data streams and predictive analytics with its machine learning integration. Teams have been using it extensively all over. However, it may not be the best fit for organizations dealing with small datasets because of the higher costs. And also, it might not be the best fit for highly complex data transformations, where simpler or more specialized solutions could be more appropriate.
Read full review From my own perspective and the tasks that I perform on a daily basis, MySQL is perfect. It has a reasonable footprint, is fast enough and offers the security and flexibility I need. Everyone has their preferred applications and, no doubt, for larger data warehouses or more intensive applications, MySQL may have its limits, but for the area that I operate in, it's a great match.
Read full review Pros Its serverless architecture and underlying Dremel technology are incredibly fast even on complex datasets. I can get answers to my questions almost instantly, without waiting hours for traditional data warehouses to churn through the data. Previously, our data was scattered across various databases and spreadsheets and getting a holistic view was pretty difficult. Google BigQuery acts as a central repository and consolidates everything in one place to join data sets and find hidden patterns. Running reports on our old systems used to take forever. Google BigQuery's crazy fast query speed lets us get insights from massive datasets in seconds. Read full review Security: is embedded at each level in MySQL. Authentication mechanisms are in place for configuring user access and even service account access to applications. MySQL is secure enough under the hood to store your sensitive information. Also, additional plugins are available that sit on top of MySQL for even tighter security. Widely adopted: MySQL is used across the industry and is trusted the most. Therefore, if you face any problems, simply Google it and you shall land in plenty of forums. This is a great relief as when you are in a need of help, you can find it right in your browser. Lightweight application: MySQL is not a heavy application. However, the data you store in the database can get heavy with time, but as in the configuration and MySql application files, those are not very heavy and can easily be installed on legacy systems as well. Read full review Cons It is challenging to predict costs due to BigQuery's pay-per-query pricing model. User-friendly cost estimation tools, along with improved budget alerting features, could help users better manage and predict expenses. The BigQuery interface is less intuitive. A more user-friendly interface, enhanced documentation, and built-in tutorial systems could make BigQuery more accessible to a broader audience. Read full review Although you can add the data you require as more and more data is added, the fixity of it becomes more critical. As the demand, size, and use of the system increase, you may also need to change or acquire more equipment on your servers, although this is an internal inconvenience for the company. Read full review Likelihood to Renew 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.
Read full review For teaching Databases and SQL, I would definitely continue to use MySQL. It provides a good, solid foundation to learn about databases. Also to learn about the SQL language and how it works with the creation, insertion, deletion, updating, and manipulation of data, tables, and databases. This SQL language is a foundation and can be used to learn many other database related concepts.
Read full review Usability web UI is easy and convenient. Many RDBMS clients such as aqua data studio, Dbeaver data grid, and others connect. Range of well-documented APIs available. The range of features keeps expanding, increasing similar features to traditional RDBMS such as Oracle and DB2
Read full review I give MySQL a 9/10 overall because I really like it but I feel like there are a lot of tech people who would hate it if I gave it a 10/10. I've never had any problems with it or reached any of its limitations but I know a few people who have so I can't give it a 10/10 based on those complaints.
Read full review Support Rating 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.
Read full review The support staff is friendly, knowledgeable, and efficient. I only had to get part way through my explanations before they had a solution. They will walk you through a fix or actually connect in and fix the problem for you--or would if you can allow it. I've done it both ways with them. They are always forthcoming with 'how to do this if it happens again' information. I love working with MySQL support.
Read full review Implementation Rating 1. Estimate your data size. 2. Test, test, and test.
Read full review Alternatives Considered I have used
Snowflake and
DataGrip for data retrieval as well as Google BigQuery and can say that all these tools compete for head to head. It is very difficult to say which is better than the other but some features provided by Google BigQuery give it an edge over the others. For example, the reliability of Google is unmatchable by others. One thing that I really like is the ability to integrate Data Studio so easily with Google BigQuery.
Read full review MongoDB has a dynamic schema for how data is stored in 'documents' whereas MySQL is more structured with tables, columns, and rows.
MongoDB was built for high availability whereas MySQL can be a challenge when it comes to replication of the data and making everything redundant in the event of a DR or outage.
Read full review Contract Terms and Pricing Model None so far. Very satisfied with the transparency on contract terms and pricing model.
Read full review Professional Services 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.
Read full review Return on Investment Pricing has been very reasonable for us. The first 10 GB of storage is free each month and costs start at 2 cents per GB per month after that. For example, if you store 1 terabyte (TB) for a month, then the cost would be $20. Streaming data inserts start at 1 cent per 200 megabytes (MBs). The first 1 TB of queries is free, with additional analysis at $5 per TB thereafter. Meta data operations are free. Big Query helps reduce the bar for data analytics, ML and AI. BQ takes care of mundane tasks and streamlines for easy data processing, consumption. The most impressive thing is the ML and AI integration as SQL functions, so the need for moving data around is minimized. The visuals of ML models is very helpful to fine tune training, model building and prediction, etc. Read full review As it is an open source solution through community solution, we can use it in a multitude of projects without cost license The acquisition by Oracle makes you need to contract support for the enterprise version If you have knowledge about oracle databases, you can get more out of the enterprise version Read full review ScreenShots Google BigQuery Screenshots