Introduction to Google BigQuery and Google Datalab
Google BigQuery is a cloud-based Infrastructure as a Service model designed by Google which is used for storing and processing massive data sets with the help of several SQL queries. It can be said that BigQuery is a type of database which is different from the transactional databases like MySQL and MongoDB. Although we can use BigQuery as a transactional database the only problem we will be facing would be that it would take more time for executing the query. Google Datalab is a powerful interactive tool which is created for visualizing, exploring and transforming the data and to build several machine learning models on Google Cloud Platform or simply GCP. It easily connects to multiple cloud services to provide the main focus on data science tasks only.
Why Choose BigQuery?
One of the main perks of using Google Cloud Platform (GCP) is having google BigQuery. So, the great thing about BigQuery is that with the help of BigQuery we can quickly scan millions and billions of rows of a table in some seconds only. Apart from the speed of executing billions of rows, it also takes care of infrastructure management. Queries which used to take several hours for executing can be completed in some seconds only. This is all happened with the help of Google BigQuery only. If looking at the market, there are an infinite number of solutions for the same thing, but which one is the best it depends on the use cases. There are several needs of an organization due to which they use BigQuery. Some of them are as follows - Provides a Managed Solutions - Here managed solutions meant to be a managed infrastructure which is entirely provided by Google BigQuery. So, the prime focus would be building a product, making it properly and building it fast. It stops spending a significant amount of engineering effort and time. Keeps the Cost Contained - As it is a fact that none of the organization wants to pay more than it is necessary. As, it is a cloud-based service, so the user has to pay only that amount of money that is in use, i.e., a pay per use basis or we can say that it provides flexible pricing. Ability to Greatly Scale up and Manageable in a Small Amount of Time - In BigQuery, fully manageable means that there is no need to take care of the Infrastructure and the database administration by the developer. There is also no need to think about the deployment of the clusters and while scaling the data and even not to think about how to configure compression or how to set up disk while scaling. All these things are taken care of by the BigQuery itself. Availability and Reliability - General purpose of using any service is that data should always be available. So, in BigQuery data is still available as it is replicated on multiple data centers. BigQuery itself replicates the data between several zones to maintain the proper availability of data. It not only replicates the data to different data centers but also provide load balancing among various data centers.
Why Choose Google DataLab?
How to Use BigQuery?
With the help of web UI in the (Google Cloud Platform) GCP console, we can easily use BigQuery as a visual interface for running the tasks like implementing the queries, importing data and exporting the data. For implementing BigQuery in different ways, we have to follow the steps mentioned below - Query a Public Dataset - For querying in a public dataset, the web UI provides an interface to query tables which also includes the public dataset which is offered by the BigQuery itself. For querying data in the public dataset, we have to perform the following steps - i. Go to the BigQuery web UI in the GCP console. Below is the link for the BigQuery UI. https://console.cloud.google.com/BigQuery?p=BigQuery-public-data&page=project ii. Click on compose new query button. iii. Write the query which you want to implement. iv. After writing the query validate the query by the query validator. v. If the checkmark becomes green, it means there is no error and then runs the query. Creating a Dataset - We can create the dataset in the web UI for storing the data, and for creating the dataset, follow the given steps - i. Again goto the web UI in the GCP console. Below is the link for the BigQuery UI. https://console.cloud.google.com/BigQuery?p=BigQuery-public-data&page=project ii. In the navigation panel, go to the resource section and create a new project and give the name to it. iii. After that create the original dataset by going to the details section and click on the create dataset button. By clicking to the create dataset button, it will ask for several fields. iv. While creating a dataset, enter proper dataset ID and data location. Currently, the public datasets are stored in US location. v. After that leave all the fields as default and click on create a dataset, and the dataset will be created.
Combining BigQuery and DataLab
Google BigQuery is like a helping hand for querying on a large data set. It is fast when performing SQL queries on a large dataset. We can either use Datalab and BigQuery for the faster database queries with the help of SQL like syntax. For using the google datalab and BigQuery, make sure that -
You must have signed in to a google account.
Google compute engine virtual machine has been created and currently active.
APIs related to machine learning and dataflow has been enabled.
Must have an active project and an active notebook.
By combining both the Datalab and BigQuery, we can even create a predictive ML model. Here datalab connects to a cloud virtual machine to a python notebook along with the connection to the BigQuery dataset, and to do this follow the steps mentioned below - Open the Cloud Shell and click on activate cloud shell button. On clocking it, a shell session will appear to launch the Datalab instance. Access BigQuery from Datalab, the data in BigQuery will automatically be available in the Datalab notebook. Create a model, and it means that creating a new cell in the notebook and run the code, making sure that you have given your table name. Evaluate the performance and predict.
A Centralized Approach
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