Introduction

The client enables organisation to make the move to Cloud based solutions based on a detailed analysis of their existing systems, combined with a thorough understanding of the business drivers. This includes planning, analytics, risk-assessment and security auditing, guaranteeing that expectations are clearly defined and confirms the suitability of each and every decision.

Why We Chose Pluto7

Pluto7, a Google Cloud Premier Partner, specializes in deploying accelerated solutions and custom applications in smart analytics, machine learning, and AI. The client realized that we can add immense value by leveraging our knowledge of machine learning and integrating that with current Google Cloud Platform tools such as BigQuery, Natural Language, and Google Kubernetes Engine to create an integrated solution that allows them to leverage their industry experience to build a compelling, data-driven product for many SMBs and middle market customers.

Solution

Google Cloud Premier Partner Pluto7 was instrumental during their migration project for our expertise on the Google cloud platform. Our solutions for building their production systems around Google Cloud Platform products helped them both solve the problem at hand and reduce the costs significantly. Pluto7 also helped educate the team on all GCP functionality. 

Cloud Dataflow was used to build pipelines to import data between their on-prem cluster and the Google Cloud Platform. Data is stored in BigQuery at different levels, divided into different staging layers. With the Data Flow models working between the data movement in between the staging layers.

There were Cloud Functions which are used to trigger the above mentioned Dataflow workflows. Cloud functions are written as python scripts which were initially triggered when there is data submission in the source database. So whenever there is ingress in the source database a cloud function is triggered and the cloud dataflow takes place.

Results

Reaching this problem of the absence of any automating mechanism in the Google Cloud Platform and considering that the team did not want to automate the workflows using cron jobs. They switched to Google Cloud Composer and we provided our expertise on the same. We automated workflows using Google Cloud Composer to orchestrate the workflows and built a system that would also alert the users once there is a workflow failure.

BigQuery was used as a Datawarehouse and we helped them with several cases where they could not find any solution to the problems that they faced. They needed our help in creating external avro tables by supplying the schema inline. They faced an error that is logged on as a major bug and we gave them a solid solution to solve their problems while using BigQuery for their advantage.

 

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Challenges

  • Replicating their exact failover scenarios and building generic solutions over them so that their is a minimum effort on the client side for building the full scale Data pipeline.
  • Experimentation of various out of the box solutions to solve out-of the box use-cases.
  • Educating and training a fairly new team to onboard the Google Cloud Platform and build solutions on top of that.
  • Remotely handling all the requests and solving them in record time.

Results

  • Successful implementations of various new components and use-cases.
  • Implementation of Cloud Dataflow to migrate data between the on-prem sources and the Google Cloud Platform.
  • Cloud Function to trigger the automated pipeline jobs.
  • Cloud Composer to orchestrate all the Components across the Google Cloud Platform.
  • Enabled BigQuery to act as an omni-channel data repository

Products Used

  • Google Cloud Platform 
  • BigQuery 
  • Cloud Composer
  • Cloud Functions
  • Cloud Dataflow

Customer Success Stories

  • Success Story: Pluto7 helped a leading Oil & Gas company accurately predict the drill bits for site

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  • Success Story: Pluto7 optimized demand forecasting process for a leading data storage solutions provider

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  • Success Story: Pluto7 helped a leading American department store retail chain to accurately forecast demand patterns

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