Google BigData Platform

Posted by Ram Narasimhan on Mar 23, 2021 12:20:20 PM

The birth of big data occurred simultaneously with the web and internet when there was a creation of multiple avenues to model unique data collection methods, which analyze and store the massive amounts of structured and unstructured data and extract useful information from it. However, as time passed, data from web-based content was dwarfed by data generated by mobile devices and most recently, the Internet of Things. A record of 40 zettabytes of data was created by 2020 and the market is growing at a rate of 45% annually.

About Google BigData Platform

The Big Data Platforms hence combined the various big data specific applications and utilities into a single enterprise-level solution. Google Cloud as a Big Data Platform emerged to be a scalable, flexible, cost-effective, and secure place to store, process, and analyze big data. These features are especially essential when your data is growing exponentially (the Big Data market is forecasted to double its size by 2027) so that you get the most value out of your data. IBM believes successful enterprises are 166% more likely to make decisions based on data. So, to satiate your big data needs, there are several services provided by the Google Cloud Platform. These services are being constantly updated to accommodate the complexities that come with handling big data. 

How to utilize Google's Big Data Platform?

To adequately utilize the big data platform, you can use their ever-evolving services including- 

  • Google Cloud PubSub: Google Cloud PubSub is an asynchronous messaging service. With its highly reliable communication layer, it allows applications to exchange messages quickly and securely. The general flow of messages is from publisher to PubSub topic, and from topic to PubSub subscriptions where the message is stored. The subscriber application can read the message from the subscriptions. Apart from managing communication among different applications, it also serves as the backbone for the stream analytics pipeline. The high capacity of Google PubSub proves to be its greatest advantage.
  • Google Cloud Dataproc: Google Cloud Dataproc is a fully managed service for Apache Hadoop and Spark. Batch processing, streaming, querying and machine learning are some of the operations of Dataproc. It is a faster, cost-effective, and simpler way to create clusters of instances. You can dynamically change and configure a cluster. It is autoscaled, and hence you do not compromise data pipelines. The fast deployment acts as an advantage for Dataproc. 
  • Google BigQuery: Google BigQuery is a data warehouse that allows you to store massive datasets, close to the range of petabytes(as people generate 2.5 quintillion bytes of data each day), and query them. The interface of BigQuery is familiar to relational databases, it uses SQL queries, has a table structure, and supports streaming and batch writing into the database. It can load, export, copy and query complex data. It can even capture streaming data and examine it for real-time analytics. It is also integrated with all the other Google Cloud Platform Services. The advantage of BigQuery is that it is serverless and you can share datasets between different projects. It is best used for offline analytics and interactive queueing. 
  • Google Cloud BigTable: Google Cloud BigTable is also a managed service designed to tackle massive operational pressure and deliver high performance. It is a low latency storage stack with a very big capacity of close to a terabyte. It is good to use in operational and analytical applications. It is a NoSQL database with the advantage of cluster resizing with zero downtime. 
  • Google Cloud Dataflow: Google Cloud Dataflow is a managed service for building data pipelines to analyze data in the cloud. It is capable of serverless batch and stream processing. It has the advantage of dynamic work rebalancing and can display a wide range of data processing patterns with very fast deployment. 
  • Google Cloud Composure: Google Cloud Composure is an orchestration tool to manage data processing in a workflow. It can create clusters, perform transformations on extracted data and upload results. It essentially fills the gaps of other Google Cloud Solutions like Dataproc. The best thing about Cloud Composure is that it is built on the Apache Airflow project, and therefore, inherits all the benefits of Airflow. 
  • Google Cloud DataLab: Google Cloud DataLab is built on Jupyter. It is a way to visualize and explore your data. It is essentially a large-scale data tool that supports SQL, Python, and Javascript. 
  • Google Cloud DataFusion: Google Cloud DataFusion is used in "extract, transform and load" data pipelines to build and manage operations as a part of a data integration service. The visual interface is simple and anyone can prepare, transfer and transform data in the pipeline. 
  • Google Cloud DataPrep: Google Cloud DataPrep acts as a tool for preparing, exploring, and visualizing data you are working with. It provides a simple and intelligible web interface to build ETL pipelines. This ends up automating a lot of manual work for data engineers. 
  • Google Cloud IoT Core: Google Cloud IoT Core is used to provide a connection between devices and the Google Big Data Platform so that messages can be exchanged through safe HTTPS and MQTT transfer protocols. The biggest advantage of IoT core is secure connectivity and good device management. 

Thus, utilizing the services of Google's Big Data Platform, you could manage your Big Data more efficiently since 95% of businesses need to manage unstructured data in some form. With the help of the products in Google Cloud, you have a wide range of containers, data analytics tools, databases, developer tools, etc. to make the most of your Big Data and even notice a 10% reduction in overall cost. For security, Google's Big Data Platform ensures binary authorization, transparency in access, data loss prevention measures, risk protection programs, and so on. 83% of enterprise executives pursue big data services to gain a competitive advantage. You can also explore all the usage options to build the architecture to reach your needed business goals while the platform ensures the scalability, maintainability, and reliability of your framework.

Topics: Big Data & Data Science