If you are like many of your peers you are looking to Google Cloud to reduce overheads, drive innovation, and agility with a wide variety of databases for your Oracle workloads. From Bare Metal Solution to cloud-native databases, Google Cloud offers the infrastructure to meet your needs.

The Benefits

Optimizing your Oracle workloads with Google Cloud solutions has a number of benefits.

Dramatically reduce TCO: Reduce up-front hardware, software, and maintenance costs. Migrating Oracle workloads to Google Cloud could result in up to 78% in TCO savings.

High scale and high availability: Mission-critical applications need dependable databases that scale with your business. Google Cloud offers SLA-backed databases to protect your apps.

Simplify operation: Reduce data center and database management by leveraging managed infrastructure and fully managed database services.

Migration Strategies

Google Cloud provides multiple strategies for your specific migration journey to help you with licensing, maintenance cost, and manageability.

Rehost: To migrate Oracle workloads with specific configurations, Google Cloud offers Bare Metal Solution, where you can simply lift and shift your workloads. Bare Metal Solution provides hardware, hardware support, and integrated billing and support. This infrastructure is connected with a highly resilient interconnect and connects to all native Google Cloud services with less than 2ms latency.

Replatform: Keep your core application code and migrate to compatible platforms to help reduce licensing fees and maintenance costs. You can migrate to an open source database such as Cloud SQL for PostgreSQL that has a certain amount of compatibility with PL/SQL and stored procedure. You can also offload data analytics-centric workloads to SQL-compatible data warehousing solutions like BigQuery.

Rewrite: Rewrite your application to take full advantage of cloud-native databases. If your application requires a relational database with global scalability, you can migrate to Cloud Spanner, which provides scalability with industry-leading high availability of 99.999% SLA.

At Precognitive, we were able to start with a blank technology slate to support our fraud detection software products. When we started building the initial version of our platform in 2017, we had some decisions to make: What coding language to use? What cloud infrastructure provider to choose? What database to use? The majority of the decisions were straightforward, but we struggled to decide upon a database. We had plenty of collective experience with relational databases, but not with a wide-column database like Cloud Bigtable—which we knew we’d need to scale our behavior and device workloads. At launch, our products were supported by a self-managed database, but we quickly migrated to Cloud Bigtable, and we love it.  

To efficiently support our bursty, real-time fraud detection workloads, we needed a cloud database that could satisfy the following key requirements:

• Stability to keep up with increased adoption of our products
• Intelligent scaling that avoids bottlenecks
• Native integrations with BigQuery and Cloud Dataproc
• Managed services that free up our engineers’ time to work on our products

Adding Cloud Bigtable as our performance database

As we scaled our services and added customers, our data collection services for our Device Intelligence and Behavioral Analytics products were seeing thousands of events per second. Cloud Bigtable provided a stable managed database that could handle the volume we were receiving during peak hours. We weren’t always able to handle this scale, as an early version of our product utilized a self-managed database.

Every month, two or three engineers spent hours managing the database instances. Whenever the instances crashed, it would cost at least one engineer a day or two of productivity attempting to restore the instances and recovering any data from our backup database. Managing this database internally was taking precious time away from product development.

We circled back to Cloud Bigtable. After two weeks of R&D, we decided to switch the Device Intelligence and Behavioral Analytics services to Cloud Bigtable.

Cloud Bigtable solved our scaling issues. Cloud Bigtable had been attractive to us from the start because it was fully managed, and offered regional replication and other features we were lacking in our own managed instances. Cloud Bigtable provides horizontal scaling and automatically rebalances row keys (equivalent to a shard key) over time to prevent “hot” nodes. In addition, Cloud Bigtable provides a connector to BigQuery and Cloud Dataproc that allows us to analyze the terabytes of data we are processing and use that data for unsupervised machine learning.

The perks of using Cloud Bigtable

After the migration to Cloud Bigtable, we noticed a number of additional benefits: improved I/O performance, a significant cost reduction, and a sizable decrease in hours spent on database maintenance.

We measured some of our typical metrics before and after implementing Cloud Bigtable. Our request latency dropped by about 30 ms on average (to sub-10 ms) for API requests. Prior to the change, we were seeing latencies of 40+ ms on average. This latency drop on our Behavioral Analytics and Device Intelligence products allowed us to trim about an additional 10 to 15 ms off our average response time across all dependent services.

Before we moved to Cloud Bigtable, we had to scale our database instances every time a new customer was onboarded. We were over-scaling in an attempt to avoid constantly resizing our database servers. By sunsetting our self-managed database and switching to Cloud Bigtable, we cut database infrastructure costs by approximately 35% and can now scale as needed, with a couple of clicks, during onboarding.

We have spent zero hours managing a Cloud Bigtable database since launch, and we put the time we are saving every month toward product development.

Moving forward with Cloud Bigtable

As an engineering team, we love working with Cloud Bigtable. We are not only seeing improved developer experience and reduced latency, which keeps the engineers happy, but also reduced costs, which keeps the business happy. We’re able to build more product, too, with the time we’ve saved by switching to Cloud Bigtable. Stay tuned to our engineering blog for more on the lessons we’ve learned and our contributions to the wider Cloud Bigtable community.

As Google Cloud has become a choice for more startups, I’ve experienced an increase in founders asking how they should think about cloud services. Though each startup is different and requirements may vary across industries and regions, I’ve seen a few core best practices that help startups to succeed—as well as several traps to avoid. 

For example, If you go with a public cloud provider, you’re ideally not starting from ground zero like you would running your own data center, but it’s important not to introduce similar complexity in a virtualized environment. Just because you’re using public cloud infrastructure doesn’t mean you want to manage it.

Instead, you want to leverage platforms that abstract away complexity so your team can focus on delivering value to customers. That’s where serverless comes in. Serverless platforms are fully managed by the provider, offering automatic scaling for workloads, as well as provisioning, configuring, patching, and management of servers and clusters. Freed from these resource-intensive tasks, your technical talent can focus on the things that differentiate your business, not on IT curation. 

This applies to not only running stateless applications, but also managing and analyzing data. You should be collecting data points about which features are most popular on your platform, what people are buying, the types of activities that help your customers get their jobs done, and so on. This information is essential to building and executing on a product roadmap that will serve your customers. However, it’s not enough to collect this data, you need to make your data accessible, secure, and easy for your team to analyze—so where do you run and host it? 

On Google Cloud, this is where options like Spanner and Cloud SQL can play a large role, as can BigQuery for analysis. You won’t have to worry about standing up infrastructure or patching servers—you can just stream your data to our data management platforms, where it’s available whenever someone needs to run a query. By leveraging a serverless architecture, your startup can be data-driven without having to invest in the traditional complexity of database administration and management—and that can significantly change your playing field. 

Serverless is just one of the factors you should consider as you build out your tech stack. To hear my thoughts on a range of other topics relevant to startups — such as security, cloud credits, and the differences among managed services — check out the below video or visit our Build and Grow page.

https://www.youtube.com/embed/gn4RjLbs8Hc?enablejsapi=1&

Editor’s note: In Today’s guest post we hear from beauty leader L’Oréal about their approach to building a modern data platform on fully managed services: managing the ingest of diverse datasets into BigQuery with Cloud Run, and orchestrating transformations into relevant business domain representations for stakeholders across the organization. Learn more about how businesses have benefited from Cloud Run in Forrester’s report on Total Economic Impact.

L’Oréal was born out of science. For over 100 years, we have always shaped the future of beauty, and taken its eternal quest to new horizons. This has earned us our current position as the world’s uncontested beauty leader (~€ 32 B annual sales in 2021), present in 150 countries with over 85,000 employees.

Today, with the power of our game-changing science, multiplied by cutting-edge technologies, we continue our lifelong journey of shaping the future of beauty.

As a Beauty Tech company, we leverage our decades-long heritage of rich data assets to empower our decision-making with instant, sophisticated analysis.

Because we oversee global brands, which must adapt to local requirements, we need to maintain a deep understanding of what a brands’ data represents, while managing disparate legal and regulatory requirements for different countries. Our end goal is to run a safe, compliant and sustainable data warehouse as efficiently and effectively as possible.

We sync and aggregate internal and external data from a wide variety of sources across organizations and retail stores. This made the management of our data warehouse infrastructure used to be very complex and hard to manage before Google Cloud. L’Oréal’s footprint was so large that we once found it impossible to have a standardized method to handle data. Every process was vendor-specific, and the infrastructure was brittle. We went looking for a solution to our complex data infrastructure needs, and defined the following non-negotiable principles:

• No Ops: The job of a developer at L’Oréal is not to manage servers. We need an elastic infrastructure that scales on demand, so that our developers can focus on delivering customized and inclusive beauty experiences to all consumers, rather than focusing on managing servers.
• Secure: We have strict security and compliance requirements which vary by country, and we employ a zero-trust security strategy. We must keep both our own internal data and customer data safe and encrypted.
• Sustainable : Our data lives in multiple environments, including on-prem data centers and public cloud services. We must be able to securely access and analyze this data while minimizing the complexity and environmental impact of moving and duplicating data.
• End-to-end supervision: Because developers shouldn’t be managing servers, we need a “single pane of glass” dashboard to monitor and triage the system if something goes wrong.
• Easy-to-deploy: Deploying code safely should not compromise velocity. We are constantly developing innovations that push the boundaries of science and reinvent beauty rituals. We need integrated tools to make our code deployment process seamless and safe.
• Event-driven architecture: Our data is used globally by research, product, business and engineering teams with high expectations on data quality and timeliness. Many of our internal processes and analysis are based on near real-time data.
• Data products delivered “as a service”: We want to empower our employees to drive business value at record speed. To that end, we need solutions that enable us to remove the developers from the critical path of solution delivery as much as possible.
• Extract-load-transform (ELT): Our goal is to implement the pattern to load data as soon as possible into the data warehouse to take advantage of SQL transformations.

After considering multiple vendors on the market, with these principles in mind, we landed on end-to-end Google Cloud serverless and data tooling. We were already using Google Cloud for a few processes, including BigQuery, and loved the experience.

We’ve now expanded our use of Google Cloud to fully support the L’Oréal Beauty Tech Data Platform.

L’Oréal’s Beauty Tech Data Platform incorporates data from two types of sources: directly via API, which is data that adapts easily to our schema and is inserted directly into BigQuery, and bulk data from integrations, which require event-driven transformations using Eventarc mechanisms. These transformations are performed in Cloud Run and Cloud Functions (2nd gen), or directly in SQL. With Google Cloud, we can adapt very quickly.

Today, we currently have 8500 flows for ~5000 users using the native zero-trust capabilities offered by Google Cloud. Indeed, the flows come from Google Cloud and other third-party services.

BigQuery enabled us to adopt standard SQL as our universal language in our data warehouse and meet all expectations for queries and reporting. We were also able to load original data using features like federated queries, and efficiently transitioned from ETL to ELT data ingestion by handling semi-structured data with SQL. This approach of loading original data from sources into BigQuery with non-destructive transformations allows us to reprocess data for new use-cases easily, directly within BigQuery.

Our applications are hosted on multiple environments – on-premises, in Google Cloud, and in other public clouds. This made it difficult for our data engineers and analysts to natively analyze data across clouds until we started using BigQuery Omni. This capability of BigQuery allowed us to globally access and analyze data across clouds through a single pane of glass using the native BigQuery user interface itself. Without BigQuery Omni, it would’ve been impossible for our teams to natively do cross-cloud analytics. Moreover, it eliminated the need for us to move sensitive data, which is not only expensive because of local tax and subsea transport, but also incredibly risky – sometimes even forbidden – because of local regulations.

Today Google Cloud powers our Beauty Tech Data Platform, which stores 100TB of production data in BigQuery and processes 20TB of data each month. We have more than 8000 governed datasets, and 2 millions of BigQuery tables coming from multiple data sources such as Salesforce, SAP, Microsoft, and Google Ads.

For more complex transformations where custom and specific libraries are required, Cloud Workflows help us to manage the complexity very efficiently by orchestrating steps in containers through Cloud Run, Cloud Functions and even BigQuery jobs — the most used way to transform and add value to the L’Oréal data.

Additionally, by using BigQuery and Google Cloud’s serverless compute for API ingestion, bulk data loading, and post-loading transformations, we can keep the entire system in a single boundary of trust at a fraction of the cost. With ingest, queries, and transformations all being fully elastic and on-demand, we no longer have to perform capacity planning for either the compute or analytics components of the system. And of course these services’ pay-as-you-go model perfectly aligns with L’Oréal’s strategy of only paying for something when you use it.

Google Cloud fulfilled the requirements of our Beauty Tech Data Platform. And as if offering us a no-ops, secure, easy-to-deploy, custom-development free, event-based platform with end-to-end supervision wasn’t enough, Google Cloud also helped us with our sustainability efforts.

Being able to measure and understand the environmental footprint of our public cloud usage is also a key part of our sustainable tech roadmap. With Google Cloud Carbon Footprint, we can easily see the impact of our sustainable infrastructure approach and architecture principles. Our Beauty Tech platform is a strategic ambition for L’Oréal: inventing the beauty products of the future while becoming the company of the future.

Sustainable tech is an imperative and a very important step towards this ambition of creating responsible beauty for our consumers, and sustainable-by-design tech services for our employees. We all have a role to play, and by joining forces, we can have a positive impact.

Google Cloud’s data ecosystem and serverless tools are highly complementary, and made it possible to build a next-generation data analytics platform that met all our needs.

Get started using serverless and BigQuery together on Google Cloud today.