Unleashing the superpowers of data with Google Cloud
Customers today are in different stages of their Data modernization journey, and they need advice to fully realize the power of data based on their workflows. This article aims to offer a phased approach starting from simple data analytics to complex data workflows, machine learning models and Data Visualization insights using GCP’s Smart Analytics tools. The tool helps customers to accelerate their data adoption strategy and extract meaningful insights based on their data workflows to drive their business forward.
Data is a valuable asset for organizations of all sizes, and the ability to harness the power of data can drive business growth and innovation. Google Cloud offers a range of products and services that can help organizations unlock the full potential of their data.
Overview
One of the key tools for managing and analyzing data on Google Cloud is BigQuery, a fully managed data warehousing and analytics platform.
The Google BigQuery is the power centre of Google Cloud Platform, and it can be chosen as the one stop service for all the customer challenges in the Data world. BigQuery is flexible, open, and intelligent. It replaces a Data Warehouse on prem and helps to create Data Marts by organizing the tables into different datasets as per the business requirement. Most importantly, BigQuery can be used as a Data Lake to load the raw data, and then transform the raw data according to the respective requirements. BigQuery allows organizations to analyze large and complex datasets quickly and cost-effectively, using SQL queries or popular business intelligence tools such as Tableau and Looker. Also, it can be an authorized distributor across your business. With BigQuery, organizations can gain insights into their data in real-time, enabling them to make data-driven decisions and optimize their business processes.
The following phases depicts how to kickstart the customers’ data journey.
Phase 1: Static Analytics
Loading data (csv, json, Google Drive) manually into BigQuery, then query data from BigQuery followed by producing powerful visual dashboards via Looker Studio.
Phase 2: Batch Pipeline Data Analytics
In the second phase, you need to automate and perform parallel data processing with the help of Cloud Dataflow using any types of files (csv, json) from Google Cloud Storage Bucket. Then, you need to upload the Data into analytics engine BigQuery. By using an ELT approach, de-normalize and perform transformations as per customer’s requirement by creating the specific views and tables. Followed by building the powerful dashboards using Looker Studio as the visualization tool. You can have multiple visualization options like Tableau, Qlik on the GCP VM’s and the Google’s native Visualization tool Looker.
Phase 3: Deeper data exploration
In this phase, you can perform interactive Data Exploration, and quick visualization of the Data present in the BigQuery using Vertex AI. You can start this phase by spinning an Vertex AI which allows the user to explore, analyze, transform, and visualize their data from Bigquery in much more depth fueled by the power of Python. The Jupyter Notebook seamlessly connects and interacts with the BigQuery in this phase.
Phase 4: Simple ML Insights
This phase initiates the journey into ML by creating the respective models via BigQuery ML. This helps to predict the next values in the data. The process includes to write SQL like queries into BigQuery to create Machine Learning models, to train the data and predict the next values with respect to the data in the BigQuery.
Phase 5: Realtime Processing
Data pipelines captures Realtime or IoT data, ingested with the help of asynchronous messaging service by Cloud Pub/Sub. From this, you need to run parallel data processing using Dataflow, followed by feeding the data to BigQuery and generating dashboards using Looker Studio. You can save the cost of the BigQuery streaming inserts by another option to use micro batch inserts from the Google Cloud Storage with the help of Google Data Flow.
Phase 6: ETL Processing
Another powerful tool for data management on Google Cloud is Cloud Data Fusion, a fully managed data integration platform and it’s no code ETL tool, that enables organizations to build, orchestrate, and manage data pipelines, regardless of the complexity or variety of their data sources. Cloud Data Fusion makes it easy to wrangle, combine and transform data from multiple sources, enabling organizations to gain a more comprehensive view of their data.
Summary — Data Modernizing Journey
Kickstart the customer’s Data journey with the help of Google Cloud Native tools like-Google BigQuery, Looker Studio, Dataflow, Cloud Pub/Sub, Vertex AI, BigQuery ML and Data Fusion by adopting the above phased approach in a easy and a simple manner. The following snapshot reflects the last stage of the Data Modernization Journey.
All the above-described phases can be demonstrated with respect to the sample data and data pipelines. This makes it easier for customers to understand better on how to get started on their Data cloud journey.
Thanks for going through the blog, see you next time!
You can reach out to me @ LinkedIn if you need any further help on this article or any GCP certifications and implementations!
