Google Cloud Platform

Key Takeaways: 

  1. There is no doubt that the future of the modern enterprise will be powered by data and AI.  
  1. Thanks to digital transformation, business leaders now have access to massive amounts of data. The question is: how can they garner intelligence to drive decision-making from both strategic and operational data? 
  1. The answer revolves around how organizations can modernise their data operations so “time-to-insights” gets reduced. It is also important that enterprises opt for a cloud-based BI solution, so it’s both efficient and cost-effective. 
  1. In this blog, we talk about why Google Cloud Platform (GCP) is a powerful, next-generation solution to power your Business Intelligence (BI) stack. 

Accenture, a global IT organization, had moved to a cloud-based solution to power its internal BI and analytics operations. However, within three years of building the cloud data platform, it had become obsolete. The platform was becoming extremely expensive – both in terms of storage and maintenance.  

Accenture’s IT leaders soon realised they unable to were upgrade their data stack and their developers were experiencing a high learning curve. As a result, instead of leveraging insights to grow the business, much of their time was spent managing technical operations, troubleshooting, and maintenance. 

In May 2019, the company decided to move to Google’s Cloud Platform (GCP). This enabled the company to modernise its data capabilities, make it cost-effective and efficient to drive both innovation and scale.  

GCP: More than just cloud infrastructure for Business Intelligence Operations

Google Cloud Platform (or GCP) is a service that allows organisations to build and maintain business applications that can be published from hyperscale data center facilities via the Web. The computing resources available on GCP are designed for developing, deploying, and operating highly scalable web, mobile and data applications.  

But that’s not all. GCP’s Data Cloud is one of the most powerful platforms to unify both data analytics and AI/ML solutions. It makes it easy to streamline the entire data lifecycle – across data warehouses and data lakes making it ideal for building BI applications at scale.  

Powering the entire data lifecycle with Google Cloud Platform

Some key data and analytics features of GCP that make it ideal for forward-looking enterprises include: 

  • Big Data Architecture: In the past, data was stored in data swamps that made it inaccessible for analytics. GCP’s architecture lets tools store petabytes and exabytes of data economically and in the right architecture.  
  • Data Ingestion: Cloud Storage API enables integrating Google Cloud Storage with other data pipelines to create data lakes and ingest data of different volumes from different sources including IoT, OLTP, and website clickstream activities, among others. 
  • Processing and Analytics: The data thus ingested and stored is made accessible for analysis through focused data marts with a highly organised schema that can make in-place querying easy.  
  • Google Cloud Workflows: An orchestrated data pipeline helps to ensure data marts and currency remain updated and relevant. The raw data ingested in this pipeline is transformed into a format used by downstream consumers and should be created based on some standards, including: 
    • Combined ETL and SQL for ingesting data into BigQuery warehouses and use SQL for querying data.
    • Hadoop being used for batch analytics with Dataproc to run queries on transformed data in Cloud Storage. 
    • BigQuery to facilitate real-time analytics by creating an SQL-based pipeline with stream processing using Dataflow and Pub/Sub with Beam. 

Choosing from Google Cloud Platform’s range of analytics services and tools for a bespoke package 

Business Intelligence leaders can choose relevant tools or services from GCP based on their specific requirements. The most popular tools include:  

#1 – BigQuery: An enterprise-grade data warehouse which facilitates fast SQL queries leveraging Google’s infrastructure. The platform can handle large volumes of datasets that can be moved to BigQuery without users losing control over access to data. Some of its key features include:  

  • ML capabilities: Standard SQL can be used to build and deploy machine learning models in BigQuery (for both structured and semi-structured data). 
  • BigQuery BI Engine: This service enables quick, interactive in-memory analysis of big and complex data sets, with query response times in sub-seconds and a high level of concurrency. 
  • Connected Sheets: This makes it possible to analyze billions of rows of live BigQuery data without using SQL and, instead, using charts, formulas, and pivot tables in Google Sheets.  
  • Data QnA: A natural language interface, it facilitates running analytics on petabytes of data on BigQuery and federated data sources. Data QA can be integrated with existing tools such as Google Sheets, chatbots, and BI solutions, allowing users to use natural human language for analytics. The tool is often used to improve productivity and access to data. 
  • BigQuery Omni: Users can leverage this fully-managed and flexible multi-cloud solution to analyse data on varied cloud environments. Questions can be answered quickly and shared across datasets using SQL in BigQuery’s interface. 

#2 – Dataflow: A solution for fully-managed streaming analysis, its batch processing and autoscaling capabilities help in lowering costs, processing time, and latency. Its features include a streaming engine that improves data latency and autoscaling. 

Dataflow SQL is also used for building real-time dashboards using Google Sheets and other BI solutions.  

#3 Dataprep Cloud: Cleaning, preparing, and exploration of unstructured and structured data is facilitated by Dataprep, a serverless solution that provides intelligent data visualization capabilities.  

More importantly, it enables data transformation and data quality validation with little to no coding.  

#4 Dataproc: This tool simplifies the deployment of Apache Hadoop and Apache Spark clusters in GCP. It makes cluster optimisation and high-availability possible by allowing users to choose the required resources for each cluster node. Dataproc is an important tool for autoscaling.  

#5 – Stream Analytics: Today, enterprises are looking to analyse data in real-time. They want to automate the process of converting data to intelligence and therefore real-time ingestion, processing, and analysis of event streams (data) have come extremely important.  

GCP’s Stream Analytics capabilities is a truly next-generation, allowing enterprises to gather real-time insights.  

# 6 – Marketing Analytics: By applying Google Cloud’s machine learning models on your data, this tool provides a holistic view of customer behavior, allows generation of customer journey maps, and helps to forecast marketing outcomes. This plays a key role in delivering personalised marketing experiences, a lot of which can be automated.  

#7 Data Catalog: This is a serverless, metadata management solution that allows you to scale according to your needs, has a built-in cloud DLP integration for simple data governance, and features for advanced structured searching.  

GCP also plans to offer an ‘Analytics Hub’ for the safe and secure exchange of analytics assets in its next release. The Vertex AI Workbench is a notebook interface that will help manage data, analytics and machine learning workflows on Google’s automated machine learning platform.  

BigSearch with BigQuery will facilitate finding specific data elements across automated logs through fully managed text indexes. 

Merit Group’s expertise in cloud BI  

At Merit Group, we work with some of the world’s leading B2B intelligence companies like Wilmington, Dow Jones, Glenigan, and Haymarket. Our data and engineering teams work closely with our clients to build data products and business intelligence tools. Our work directly impacts business growth by helping our clients to identify high-growth opportunities.  

Our specific services include high-volume data collection, data transformation using AI and ML, web watching, BI, and customized application development.  

We’re experts in Cloud BI, helping companies streamline and migrate to a truly next-generation BI stack.  

Our team also brings to the table deep expertise in building real-time data streaming and data processing applications. Our expertise in data engineering is especially useful in this context. Our data engineering team brings to fore specific expertise in a wide range of data tools including Airflow, Kafka, Python, PostgreSQL, MongoDB, Apache Spark, Snowflake, Redshift, Athena, Looker, and BigQuery.  

If you’d like to learn more about our service offerings or speak to a GCP expert, please contact us here: 

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