Week 12 — My Journey into Data Analytics — DA Minidegree Review — CXL Institute

Some of the Benefits of using Bigquery

  1. Google Analytics connected to BigQuery enables you to answer more, deeper business questions in more detail.
  2. Analyzing massive amounts of traffic data will be done in the cloud. The Data is unsampled, unlike Standard GA.
  3. Many native platforms limit the amount of historical data you can access. For example, Google Search Console offers six months of historical data within its native interface. But with BigQuery you can use it to store all your past data, giving a complete overview of your historical performance.
  4. It is easy to import custom data sets and use them with your Google Analytics data.
  5. With hit-based data, you can analyze what is happening on your website on a very granular level (second by second, filtering by dimensions…), sequence of interactions down to a particular session level.
  6. With BigQuery you can access individual user’s hits (anonymized data), which can help you personalize your website for the next time they visit it.

How to Get started

  • the python-based tool that can access BigQuery from the command line
  • RESTful API to access BigQuery programmatically
  • Requires authorization by OAuth2
  • Google client libraries for Python, JavaScript, PHP, etc.
  • Visualization and Statistical Tools tools like Tableau, QlikView, R, etc.

Big query Structure

  • Native tables: tables backed by native BigQuery storage
  • External tables: tables backed by storage external to BigQuery
  • Views: virtual tables defined by a SQL query
  • Load: load data into a table
  • Query: run a query against BigQuery data •
  • Extract: export a BigQuery table to Google Cloud Storage
  • Copy: copy an existing table into another new or existing table
via https://cloud.google.com/blog/products/data-analytics/new-blog-series-bigquery-explained-overview

The BigQuery interface

  • Query history. Queries you’ve run previously. It’s especially useful when you run tests but forget to register the best queries you might need later.
  • Saved query. Where to find your registered queries. Name them clearly so you can find them quickly later.
  • Job history. The history of what happened in BigQuery — imports, exports, task history, etc.
  • Transfers. Where you see and configure Data Transfers, a Google service to import Google data (e.g. Ads, Play, YouTube) into BigQuery.
  • Scheduled queries. Register queries and runs them every hour/day/week, etc.
  • BI engines. A new feature that integrates with familiar Google tools like Google Data Studio to accelerate data exploration and analysis. A BigQuery enhancer.
  • Resources. Pin a project to see it at the top of the list. It’s super useful when you have lots of projects but work often on just a few. You can also add a public dataset to play with data if you want to learn the tool but don’t have your own.

How to Query Data?

Via https://infotrust.com/

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