Google BigQuery And Its Benefits

Google BigQuery is an enterprise data warehouse built using BigTable and Google Cloud Platform. It’s serverless, completely managed and a part of Google cloud development services. It works great with most sizes of data, from a 100 row Excel spreadsheet to several Petabytes of data. Most importantly, this can execute a complex query on those data within a few seconds.

An Introduction To BigQuery

1. Querying massive datasets 
2. Secure Access Control 
3. Single view of your data points
4. Super-fast SQL-like queries 
5. Google Analytics data in BigQuery 

Benefits of BigQuery

1. Scales in Petabytes 
2. Input/Output of TBs in seconds 
3. 100,000 rows/sec per table Streaming API 
4. Simple data ingest from GCS or Hadoop 
5. Connect to R, Pandas, Hadoop, Dataflow, etc. 
6. Row-level security and data expiration 

The Architecture of BigQuery

1. It is based on Dremel, a technology pioneered by Google & extensively used within the organisation. 
2. Dremel is a querying service which allows you to execute SQL queries against huge datasets (think hundreds of millions of rows)
3. It uses multi-level execution trees to achieve interactive performance for queries against petabyte datasets.
4. It’s performance advantage comes from its parallel processing architecture. 
5. The query is executed by thousands of servers in a multi-level execution tree structure, with the final results aggregated at the root server.
6. Data structured in BigQuery are in below format:
   1. Datasets 
   2. Tables 
   3. Rows 
   4. Columns 
7.  It is a publicly available implementation of Dremel which is available as an IaaS.

Ways To Interact With BigQuery

1. Loading and exporting data
2. Querying and viewing data
3. Managing data

To perform these interactions, you can use:

1. The BigQuery web UI in the Cloud Console
2. The BigQuery classic web UI
3. The BigQuery command-line tool
4. The BigQuery REST API or client libraries

Loading and exporting data in BIgQuery

In some cases, you load data into BigQuery storage. When you want to get the data back out of BigQuery, you can export the data.

Otherwise, you can set up a table as an external data source, which allows you to query data stored outside of BigQuery.

Querying And Viewing Data

After you load your data into BigQuery, you can use a query or view the data in your tables directly. For example, you can perform the following tasks:

1. Run interactive queries
2. Run batch queries
3. Create a view, which is a virtual table defined by a SQL query
4. Use partitioned tables to query a subset of your data

Managing data

You can handle data in BigQuery in the following ways besides querying and displaying the data:

1. Listing projects, jobs, datasets, and tables
2. Getting information about jobs, datasets, and tables
3. Defining, updating, or patching datasets and tables
4. Deleting datasets and tables
5. Managing table partitions

Sample and Use cases of Big-Query

1. To get unsampled custom funnels with added benefits 
   1. No Backfilling 
   2. Historical Information 
   3. Apply filters 
   4. Unlimited steps 
2. To get the last interaction (event) that the user performed before landing on a given page 
3. To get all the sessions with transactions wherein particular events were performed by users and funnels generated after when a particular event has been performed.

We are a cloud app development company that specializes in using Google Cloud Platform (GCP) to build scalable enterprise applications. Our development team is skilled at using Google BigQuery to address various Big Data challenges, augment data security, and facilitate seamless data accessibility.