Google Cloud Platform Big Data Tools – Improve Data Analytics

This is the second part of a series on Google Cloud Platform. Part II will discuss GCP data analytics.
Google is a long-standing leader in data analytics. They continue to improve their data analytics offerings. Google Cloud Platform (GCP) allows you to store, process, store and analyze all your data in one location. This allows you to shift your focus from infrastructure to business analytics that informs business decisions. You can use GCP Big Data tools with other cloud-native or open-source solutions to suit your needs. Here is a brief overview of GCP Big Data Tools, and how you can use them to improve your analytics.
BigQuery
BigQuery is one of the most powerful Big Data tools available to companies of any size. It is a managed, serverless enterprise data warehouse that uses SQL. You can use it to analyze data in real-time, but you can also bring in data from spreadsheets or object storage. According to Nicholas Harteau (VP of engineering at Spotify), companies like Spotify have chosen BigQuery over other GCP products, as Google’s products are more advanced that other cloud providers’ tools.
BigQuery allows you and your employees to focus on providing insights that will improve your bottom line. Motorola, for example, increased their data collection capabilities after switching to BigQuery/App Engine. This allowed them to provide more information to their customers to help with product troubleshooting.
BigQuery is affordable, which is a great thing for the bottom line. You don’t have to pay extra for resources you don’t use. To further reduce costs, Google gives you 1TB of analyzed information and 10GB of storage each month for free. Your data is protected by BigQuery, which encrypts your data at rest and in transit. It also has granular access controls that ensure your data remains secure. BigQuery can also be used with StackDriver to monitor and log.
Google’s BigQuery Data Transfer Service makes it easy for you to move your data into BigQuery. This managed service allows you to move data from YouTube, AdWords and DoubleClick, as well as other SaaS applications.
Cloud DataProc
Cloud DataProc may be a better choice for you if you are using Hadoop/Spark/Beam. Cloud DataProc makes it easy to set up and configure Apache Hadoop services with Spark, Hive and Pig in less than two minutes. It is a managed service that simplifies the initial setup and allows you to get to work immediately. It is fully managed and highly automated. However, you can still use manual controls. It is easy to scale, highly available, and offers multiple methods to manage your cluster, including an interface on the Web and a REST API. It keeps costs low by allowing pre-emptible virtual machines instances to be used. These instances can be up to 80% cheaper than traditional virtual machines. You can also pay per-second for the resources that you use, so that you only pay what you need.
It can be difficult to transfer data between different data formats or services. This sort of work is often handled by Extract-Transform-Load (ETL) services that extract data from one data source, parse it and transform it in the manner required, and then finally load it into another data structure. Cloud DataFlow, a fully managed, serverless ETL-like ETL service, can not only work on data in real-time but also can handle large quantities of data. Cloud DataFlow is a serverless ETL-like service. Resource management and allocation are easy.
Cloud DataFlow works by using a series of user-supplied cloud functions (referred to as transformations). Cloud DataFlow can be used to create and manage cloud.