BigQuery  Google  Data Analysis  
   Technology    Programming

Beginners Guide to Start Using Google's BigQuery for Big Data Analysis

 DECEMBER 31, 2018   by Dulari

BigQuery is Google Cloud Platform's fully managed data warehouse which let you sparingly query substantial volumes of data at speed anyone can expect from Google. It provides Pay as you go strategy which offers Google’s pricing benefits and the scalability and security of Google's world-class infrastructure to boost your business visions.

Bigquery by Google

Features of BigQuery

Following are some of the useful features of BigQuery:

1. Fully Managed, Serverless Insight

GCP that is Google cloud platform excels the industry in the ability to let you analyze data at the scale of the entire web, with the awareness of SQL and in a fully managed, serverless architecture where backend infrastructure is fully handled on behalf of you. One of the wonderful features of Google's big data analytics products is that they are able to scale automatically while you focus only on the business insight you want to uncover.

2. Fast Queries on Petabyte-scale Datasets

BigQuery is Cloud Platform's fully managed data warehouse that lets you frugally query massive volumes of data at a speed which anyone would expect from Google. Google does not charge daily but you have to pay as you go. Google provides pricing benefits and the scalability and security of Google's best infrastructure to power your business insights.

3. Unified Batch and Stream Processing

Google Cloud has Dataflow which is an innovative, fully managed service for developing and executing a huge range of data processing patterns which includes ETL, batch computation, and stream analytics. You can express your computation with no switching cost as you use a single tool and programming model for both batch and continuous stream processing flows.

4. Spark and Hadoop in the Cloud

Nowadays companies are standardizing on abundant open source tools which include Spark, Hadoop, MapReduce, Hive, and Pig, but this will soon see a natural transition to Cloud Dataproc. One reason for this would be while using Dataproc you should not worry about your data pipelines outgrowing clusters as it allows you to create and resize clusters quickly at any given point in time.

5. Managed Databases, Object Storage and Archival

Specific business questions which you may encounter in the future cannot be predicted in prior but can be solved if you have relevant data in hand when they occur. One should always preserve events and valuable metadata related to your business environment, by storing it economically to analyze later. You can choose from a variety of globally available storage products for your data, from managed SQL to NoSQL options, including Google's category-defining archival product.

6. The Next Stage of Machine Intelligence

In today's era, most of the companies are shifting towards big data analytics. Companies are willing to apply Google's heritage of machine learning and analytics at web-scale to real-world data relevant to their business. Cloud Platform enables modest-sized teams to aggregate and run machine learning workloads on a huge amount of data to do predictive analytics.

7. Tap Into Innovation

Google has excelled the industry with innovations in data science technologies such as MapReduce, BigTable, and Dremel and now Google is making the latest generation of its data science tools available to everyone, including market-leading programming tools and programming models.

How to use Bigquery for your enterprise?

BigQuery provides the query results in a few seconds even if your data size is in Terabytes. After knowing the amazing features of Bigquery listed above, one would definitely like to register for its free trial. So to start with Bigquery follow the following steps:

Step 1: Do you have a Google Account?

You should have a Google mail account. If you don't have an account, register on Google to get a new one, and if you already have one then login to your account.

Step 2: Open Google Bigquery

Open a new tab and type following URL:

Click on Try it for Free button to start your trial access. Follow the registration process.

Then you will see the following screen. Click on View Console

google bigquery getting started guide

After clicking the View Console button you will see the following screen:

google bigquery getting started guide view console screen

Step 3: Select a Table to run a test query

There are many databases which are openly available under the Resources tab.

google BigQuery getting started guide - Run a query

You can select any table and view its details, preview the table and check the schema of the table.

Step 4: Querying the Database

To query the database you should follow the following steps

  1. Go to the BigQuery web UI. (
  2. Click the Compose query button.
  3. Now you have to enter a valid BigQuery SQL query syntax in the New Query text area.
  4. Click Show Options.
  5. In the Destination Table section, click Select Table.
  6. In the Select Destination Table dialog:
    1. For Project, choose the project where the destination table will be created.
    2. For Dataset, choose the dataset that will store the table.
    3. In the Table ID field, enter a table name. The name of the table ID must be distinctive in the destination dataset. The table name can be up to 1024 characters long and can only contain a to z, A to Z, 0 to 9, or _ (underscore).
    4. Click OK.
  7. In the Destination Table section, for Write Preference, choose one of the following:
    1. Write if empty: Writes the query results to the table only if the table is empty.
    2. Append to table: appends the query results to an existing table.
    3. Overwrite table: overwrites an existing table with the same name using the query results.
  8. For Processing Location, click Unspecified and choose your data's location. You can leave the processing location set to unspecified if your data is in the US or EU multi-region location. When your data is in the US or the EU, the processing location is automatically detected.
  9. Click Run Query. This creates a query job that writes the query results to the table you specified.

Step 5: Running Interactive and Batch Query

Now let's learn how we can run interactive and batch queries.

Running Interactive Query

Mainly BigQuery runs interactive queries by default, which means that the query is executed as soon as possible. Interactive queries are counted towards your concurrent rate limit and also your daily limit.

The results of queries are always saved to either a temporary or a permanent table. It is always your choice to decide whether to append or overwrite data in an existing table or to create a completely new table if none exists by that name.

To run any interactive query which writes to a temporary table, we should follow the following steps:

  1. Go to the BigQuery web UI.
  2. Click on Compose query.
  3. In the New Query textarea and enter a valid BigQuery SQL query.
  4. Click Show Options button.
  5. For Processing Location, click Unspecified and choose your data's location.
  6. Click the Run Query button.

running interactive query in BigQuery platform

Running Batch Query

BigQuery also provides batch queries. BigQuery queues each batch query on your behalf and starts the query as soon as idle resources are available, usually, such resources are available within a few minutes. If BigQuery does not start the query within 24 hours, it changes the job priority to interactive. Batch queries don't count towards your concurrent rate limit, which can make it easier to start many queries at once.

To run batch query we should follow the following steps:

  1. Go to the BigQuery web UI.
  2. Click the Compose query button.
  3. In the New Query textarea and provide a valid BigQuery SQL query.
  4. Click the Show Options button.
  5. Select the Batch option in the Query Priority section.
  6. For Processing Location, click Unspecified and choose your data's location.
  7. Click the Run Query button.

Benefits of using Big Query

  1. It takes only a few seconds to query massive data sets.
  2. Google Analytics, which is connected to BigQuery allows you to answer more, deeper business analytics questions.
  3. Google Cloud can analyze massive amounts of traffic data which means you will not need to purchase any extra computer hardware to process this massive data.
  4. Applying the right data mining algorithms plays a vital role in the raw data which can help you to discover patterns of user's behavior that are otherwise difficult to notice in standard reporting.
  5. As a premium user, you are entitled with $500 monthly credit which covers a big portion of the data storage and processing costs which is associated with using BigQuery with Google Analytics Premium membership.
  6. Custom data sets can also be created with your Google Analytics data. Making Google Analytics data richer with your uploaded custom data sets makes it very easy when you bring both together in BigQuery.
  7. The BigQuery API will provide you access to unfiltered hits collected from your custom application based on your uploaded custom data sets. This API will allow you to use the data in your own dashboards, visualizations, analysis, and more. For example, Tableau Software has a connector to BigQuery, making it painless for you to use this powerful data visualization software to understand your granular digital analytics data.
  8. You can easily analyze what is happening on your website on a very granular level (second by second, filtering by dimensions etc), the sequence of interactions down to a particular session level with the help of hit based data.
  9. 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.




Made with by Abhishek Ahlawat

© 2019 Studytonight.   All rights reserved. Protection Status