{"id":66711,"date":"2018-07-05T09:34:02","date_gmt":"2018-07-05T09:34:02","guid":{"rendered":"https:\/\/www.whizlabs.com\/blog\/?p=66711"},"modified":"2021-03-16T12:19:45","modified_gmt":"2021-03-16T12:19:45","slug":"google-bigquery","status":"publish","type":"post","link":"https:\/\/www.whizlabs.com\/blog\/google-bigquery\/","title":{"rendered":"Google BigQuery and Its Role in Big Data Analytics"},"content":{"rendered":"

Big data is for the processing of massive chunk of formatted and unformatted data; the purpose of such big data analysis is marketing and business analysis. There are numerous data warehousing supports available in the market and not to mention, big data support through cloud platform is the recent craze and best solution. <\/span><\/p>\n

Among the three Cloud competitors in the market, Google is investing a lot in Cloud platform offering to gain ground<\/span>.<\/span> Google BigQuery is Google\u2019s fully managed, serverless data warehouse solution that has invaded the big data analysis field currently.<\/span><\/p>\n

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Recommended Reading:<\/strong> Why is Big Data Analytics<\/a> so important?<\/p>\n<\/blockquote>\n

Google BigQuery is a highly scalable and fast data warehouse for enterprises that assist\u00a0the data analysts in Big data analytics at all scales. Furthermore, Google BigQuery is a low-cost warehouse that allows data analysts to become more productive. There is no infrastructure that needs to be managed, and you can always focus on meaningful insights derived from data analysis.<\/span><\/p>\n

In the next sections, we will discuss Google Big query as a technology with its features, analytics capabilities, and how it performs when compared with other similar technologies in the market.<\/span><\/p>\n

Know More about Google BigQuery and Its Components<\/span><\/h2>\n

We have already mentioned that Google BigQuery is a low-cost data warehouse solution. In addition to that, Big Query allows enterprises to set up the warehouse in a few seconds, and one can query the data immediately. It uses SQL to provide JDBC and ODBC drivers for quick data integration. Also, using Google BigQuery, you can scale seamlessly with huge reach and capacity. Moreover, as a user to analyze data, you can perform parallel execution along with performance optimization. <\/span><\/p>\n

\"Google<\/p>\n

BigQuery is a sophisticated service with 12 user-facing components:<\/span><\/p>\n

1. Opinionated Storage Engine:<\/strong> BigQuery has the storage engine that optimizes and evolves the storage without any disruptions.<\/span><\/p>\n

2. Jupiter Network:<\/strong> It is the internal data center network that allows BigQuery to separate storage and compute.<\/span><\/p>\n

3. Standard SQL and Dremel Execution Engine<\/b>: Dremel allows smart scheduling and pipeline execution.<\/span><\/p>\n

4. Serverless Service Model:<\/b> Serverless model helps in the highest level of abstraction, automation, and manageability.\u00a0<\/span><\/p>\n

5. Enterprise-Grade Data Sharing:<\/b> 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. <\/span><\/p>\n

6. Federated Query Engine:<\/b> 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.<\/span><\/p>\n

7. IAM, Audit Logs and Authentication:<\/b> 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. <\/span><\/p>\n

8. Datasets: <\/b>It supports Public, Commercial, Marketing, and Free pricing tier.<\/span><\/p>\n

9. UX, CLI, SDK, ODBC\/JDBC, and API: <\/b>It is an access pattern where everything is wrapped around REST API.<\/p>\n

10. Pay-Per-Query AND Flat Rate Pricing:<\/b> The two pricing models as per user need.<\/p>\n

11. Streaming Ingest:<\/b> Capability of processing millions of rows at a time.<\/span><\/p>\n

12. Batch Ingest: <\/b>Processing capacity of millions of data, however not as fast as streaming processing.<\/p>\n

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New in the world of Big Data. Start from the basics, read our previous blog All You Should Know About Big Data and learn Big Data.<\/a><\/p>\n<\/blockquote>\n

Comprehensive List of Google BigQuery Features<\/span><\/h2>\n

There are a large number of features of Google BigQuery. Let’s have a look over the important Google BigQuery features.<\/p>\n