Simple understanding of BigQuery, SQL and the power of big data
By Astrid Hall on December 4, 2018
Data is an incredible thing. It can enhance every aspect of our lives, from our health to our work. It influences the way we buy things, the things we do; it even has the potential to decide who we are friends with through social networks and apps.
If you’re not embracing data, then you need to do some serious reconsideration. But if you’re a data scientist, then it’s definite that you need no more convincing of the power of data.
SQL is a programming language primarily used with databases and it’s an important tool for any data scientist. And BigQuery is a database which lets you use SQL with very large data sets.
It’s important to understand as much as possible about using SQL and BigQuery. A very useful post by ‘DanB’ gives and introductory guide on using the software.
As a web service from Google, BigQuery is used for handling or analysing big data. It also offers users the ability to manage data using fast SQL-like queries for real-time analytics. Big data is a term which refers to data sets so large or complex that traditional data-processing application software can’t deal with it sufficiently
Harnessing big data and using BigQuery and SQL to process it can be a highly useful functionality. It can allow you to run big data analysis on marketing and business data to improve processes.
The media depicts personal data as something which will give us a lack of privacy because of software like smart tech, but it’s really quite the opposite. BigQuery has a variety of powerful and useful benefits.
It’s a sophisticated service with 12 user-facing components. These include:
1. Opinionated Storage Engine: BigQuery has the storage engine that optimizes and evolves the storage without any disruptions.
2. Jupiter Network: It is the internal data center network that allows BigQuery to separate storage and compute.
3. Standard SQL and Dremel Execution Engine: Dremel allows smart scheduling and pipeline execution.
4. Serverless Service Model: Serverless model helps in the highest level of abstraction, automation, and manageability.
5. Enterprise-Grade Data Sharing: Sharing of Exabyte scaled datasets is possible with BigQuery due to the separation of computing and storage. You can even share data with other organizations, and you pay for the storage while they pay on per query basis.
6. Federated Query Engine: If the data is in GCS, Google Drive or Bigtable, you can query data from BigQuery with no data movement. This is called Federated Query Engine.
7. IAM, Audit Logs and Authentication: BigQuery allows organizations the high granularity control and role for users. OAuth and Service Accounts are the two modes of authentication used for access controls.
8. Datasets: It supports Public, Commercial, Marketing, and Free pricing tier.
9. UX, CLI, SDK, ODBC/JDBC, and API: It is an access pattern where everything is wrapped around REST API.
10. Pay-Per-Query AND Flat Rate Pricing: The two pricing models as per user need.
11. Streaming Ingest: Capability of processing millions of rows at a time.
12. Batch Ingest: Processing capacity of millions of data, however not as fast as streaming processing.
Whether you’re a software developer learning more about BigQuery and SQL, or a business looking to harness big data and analytics, we can help support you in whatever your needs might be.
To find out more, get in touch by emailing email@example.com.