All public clouds offer flexible computing, networking and storage. And you’re likely using at least one public cloud, if not two or more. But if you’re thinking about moving some of your mission-critical applications to the cloud—such as SAP or Oracle workloads—how do you decide which is the best cloud for your needs?
The decision these days isn’t so much around cost; it’s usually much more strategic. And your decision could (and should) be influenced by what you want to do with your data after the migration to Google Cloud. With Google Cloud, for instance, you can modernize and accelerate your use of data and analytics, or build out your machine learning capabilities.
“Google Cloud has an analytics and machine learning technical advantage over the competition. So if a CIO or CTO says that insights from their data is or will be a major contributor to their business strategy, Google Cloud is their best bet.”,” says Aric Bandy, Executive Vice President of CloudOps & Corporate Development with Pythian.
Google’s edge in analytics and machine learning
Google offers advanced machine learning (via its AI Platform), batch and real-time data processing (Dataflow, Pub/Sub, Dataproc) and visual analytics (Google Data Studio). Its analytics solutions are serverless, which removes the complexity of building and maintaining a data analytics system.
Forrester Research named Google a leader in streaming analytics, pointing to Dataflow for its strengths in data sequencing, advanced analytics, performance and high availability. According to the report: “Google Dataflow’s sweet spot is for enterprises that have a preponderance of real-time data generated on Google Cloud Platform or wish to simplify all data processing by using a single platform that unifies both streaming and batch jobs.”
If you’re looking to get the most out of Google Cloud after the migration, consider:
- Analytics: In combination with Pythian’s Advanced Analytics Services, you can accelerate and fine-tune your cloud analytics program from ingestion and data preparation to storage and real-time analysis. This can help you establish an end-to-end view of your customer for better product development and brand loyalty.
- Machine learning: You can deliver valuable insights from raw data by establishing Machine Learning Operations (MLOps) tools and practices, from tracking experiments to post-implementation performance monitoring. This can help you automate manual activities, as well as detect anomalies and find business insights, to achieve maximum return on your Google Cloud investment.
- Enterprise Data Platform (EDP): With Pythian’s EDP services, you can put your data to work by enabling machine learning, self-service analytics, visualizations and BYOA (bring your own analytics tools)—enabling richer, more relevant and timely reporting, as well as data science exploration. Our EDP Quickstart offering gets your data platform up and running with three data sources and your first use case in 90 days or less.
Gaining a true competitive advantage—after a cloud migration
As an example, Pythian worked with a major U.S. online content network to migrate an on-premise Teradata data warehouse to the public cloud—and, ultimately, to improve their analytics capabilities. They chose Google Cloud as the basis for their new data hub and Google BigQuery as their analytics engine.
Pythian designed and rolled out a data pipeline to integrate data from approximately 60 sources, including MySQL database and Oracle database—as well as a number of marketing and web analytics sources, such as Neilsen ratings and viewing data.
In BigQuery, the cost of storage and compute are kept separate, and on-demand pricing allows the customer to pay for only the storage and compute they use. Query response time is also faster with BigQuery, and it can adapt to any data type or format, plus convert formats, without additional charges. The move to cloud was more than a migration—the customer now has a 360-degree view of the customer experience, delivering a true competitive advantage.
See if you’re ready with a readiness assessment
Whether you’re looking for a cloud-native analytics platform or want to take machine learning to the next level, a readiness assessment can provide clarity into your current technical and business environment, areas of concern and opportunity, as well as the pros, cons and costs associated with moving to a Google Cloud architecture.
An assessment can also provide recommendations regarding your future state, such as the tooling required to help you on your path to better business outcomes after your Google Cloud migration.
Find out how to tap into our brain trust with Pythian’s Data, Analytics and Cloud) Advisory Service or bring on our team of data scientists and data engineers to bring advanced analytics complete with MLOps to your cloud migration by scheduling a consultation today.
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