Working with Google BigQuery
Do you want to use Google BigQuery? Let us know some basics about it and how you use BigQuery. Statistics show that every day the world creates roughly 2.5 quintillion bytes of data. The growing concern for all organizations is how to store these large volumes of data effectively. The hardware that can support such volumes of data is costly and complex to maintain.
Google came up with a solution to such problems with Google’s own enterprise data warehouse solution – Google BigQuery. It is google cloud’s fully managed solution within acceptable expenses and scalable to very high volumes. Google mentions it to be petabyte-scale.
Google cloud platform has many tools with various functionalities. BigQuery is one of them. It is a cloud data warehouse which can store a considerable amount of data and enables users to run analytics almost near real-time.
Security is of prime importance, and hence the services such as multiple layers of security offered hold value. Project owners efficiently manage access Control.
Why Google BigQuery?
BigQuery lets users use SQL like syntax which is a big add on. The infrastructure requirements are almost nothing. Many people recommend data lakes in BigQuery since it’s very easy and faster. Google provides the solution as an end to end one. From fast data integration tools to scalable storage to quick visualizations, it is also the ease of using ML tools on top of this data.
In recent times, many companies are offering integration with BigQuery via a somewhat generic architecture example given below. The source, structure, formats of the data getting in can be of a vast type. The Integration methods are also categorized into single-use vs batch processing to handle expected large volumes of data. Google drive, google sheets etc. also can be used to input data.
Road Only – Storage is similar to storing in a big table in columnar format. Indexing cannot be used. A single query execution happens across all the machines on the google platform.
Google offers multi-usage by offering various services. The pricing services are on-demand based and very flexible. Based on Projects and usage, pricing can be managed.
Open-source API is one of them which can be used to give it a try. It is like using a sandbox system with limited functionality, but it provides a picture of the services’ capabilities.
- On the Go analytics – easy and fast.
- Prerequisites are minimal.
- Infrastructure setup is not required.
- Data Storage and Transformation, Calculations are executed separately.
- No redundant data storage.
- Building and scaling tables are comfortable.
- Access control at the project, dataset and field level.
- The most complex query fetching terabytes of data can be executed in seconds.
- More recommended service uptime since deployment is across the platform.
Limitations with Google BigQuery:
- Workflow is complicated for a new user.
- Modify/Update Delete – not supported by Google BigQuery
- Issues occur when serial operations are in scope.
- There is a daily destination table update limit set as 1,000 updates per table per day.
- There are a few more limitations such as concurrent queries, max run time etc. set by default.
Selecting Google BigQuery:
- Consolidating data and web statistical analytics.
- Google or Facebook Ads data analytics.
- In depth data mining requirements.
- Storage of sensitive data where data grain level access control is required.
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