BigQuery Helps Insurance Firms Leverage Previous Storm Data for Better Pricing Insights - Build What's Next
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BigQuery Helps Insurance Firms Leverage Previous Storm Data for Better Pricing Insights

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With Google Cloud Public Datasets, insurers can use over 100 high-demand public datasets on past storms events in different states, cities, counties, and storm types to track common risks that help unveil insights to drive outcome-based pricing.

It may be surprising to know that U.S. natural catastrophe economic losses totaled $119 billion in 2020, and 75% (or $89.4B) of those economic losses were caused by severe storms and cyclones. In the insurance industry, data is everything. Insurers use data to influence underwriting, rating, pricing, forms, marketing, and even claims handling. When fueled by good data, risk assessments become more accurate and produce better business results. To make this possible, the industry is increasingly turning to predictive analytics, which uses data, statistical algorithms, and machine learning (ML) techniques to predict future outcomes based on historical data. Insurance firms also integrate external data sources with their own existing data to generate more insight into claimants and damages. Google Cloud Public Datasets offers more than 100 high-demand public datasets through BigQuery that helps insurers in these sorts of data “mashups.” 

One particular dataset that insurers find very useful is Severe Storm Event Details from the U.S. National Oceanic and Atmospheric Administration (NOAA). As part of the Google Cloud Public Datasets program and NOAA’s Public Data Program, this severe storm data contains various types of storm reports by state, county, and event type—from 1950 to the present—with regular updates. Similar NOAA datasets within the Google Cloud Public Datasets program include the Significant Earthquake DatabaseGlobal Hurricane Tracks, and the Global Historical Tsunami Database.  

In this post, we’ll explore how to apply storm event data for insurance pricing purposes using a few common data science tools—Python Notebook and BigQuery—to drive better insights for insurers.

Predicting outcomes with severe storm datasets

For property insurers, common determinants of insurance pricing include home condition, assessor and neighborhood data, and cost-to-replace. But macro forces such as natural disasters—like regional hurricanes, flash floods, and thunderstorms—can also significantly contribute to the risk profile of the insured. Insurance companies can leverage severe weather data for dynamic pricing of premiums by analyzing the severity of those events in terms of past damage done to property and crops, for example. 

It’s important to set the premium correctly, however, considering the risks involved. Insurance companies now run sophisticated statistical models, taking into account various factors—many of which can change over time. After all, without accurate data, poor predictions can lead to business losses, particularly at scale.  

The Severe Storm Event Details database includes information about a storm event’s location, azimuth (an angle measurement used in celestial coordination), distance, impact, and severity, including the cost of damages to property and crops. It documents:

  • The occurrence of storms and other significant weather events of sufficient intensity to cause loss of life, injuries, significant property damage, and/or disruption to commerce.
  • Rare, unusual weather events that generate media attention, such as snow flurries in South Florida or the San Diego coastal area.
  • Other significant weather events, such as record maximum or minimum temperatures or precipitation that occur in connection with another event.

Data about a specific event is added to the dataset within 120 days to allow time for damage assessments and other analysis.

Damage caused by the storms.jpg
Damage caused by the storms in the past five years by state

Driving business insights with BigQuery and notebooks

Google Cloud’s BigQuery provides easy access to this data in multiple ways. For example, you can query directly within BigQuery and perform analysis using SQL. 

Another popular option in the data science and analyst community is to access BigQuery from within the Notebook environment to intersperse Python code and SQL text, and then perform ad hoc experimentation. This uses the powerful BigQuery compute to query and process huge amounts of data without having to perform the complex transformations within the memory in Pandas, for example.

In this Python notebook, we have shown how the severe storm data can be used to generate risk profiles of various zip codes based on the severity of those events as measured by the damage incurred. The severe storm dataset is queried to retrieve a smaller dataset into the notebook, which is then explored and visualized using Python. Here’s a look at the risk profiles of the zip codes:

Clusters of Zip codes.jpg
Clusters of Zip codes by number of storms and damage cost.

Another Google Cloud resource for insurers is BigQuery ML, which allows them to create and execute machine learning models on their data using standard SQL queries. In this notebook, with a K-Means Clustering algorithm, we have used BigQuery ML to generate different clusters of zip codes in the top five states impacted by severe storms. These clusters show different levels of impact by the storms, indicating different risk groups. 

The example notebook is a reference guide to enable analysts to easily incorporate and leverage public datasets to augment their analysis and streamline the journey to business insights. Instead of having to figure out how to access and use this data yourself, the public datasets, coupled with BigQuery and other solutions, provide a well-lit path to insights, leaving you more time to focus on your own business solutions.

Making an impact with big data

Google Cloud’s Public Datasets is just one resource within the broader Google Cloud ecosystem that provides data science teams within the financial services with flexible tools to gather deeper insights for growth. The severe storm dataset is a part of our environmental, social, and governance (ESG) efforts to organize information about our planet and make it actionable through technology, helping people make a positive impact together. 

To learn more about this public dataset collaboration between Google Cloud and NOAA, attend the Dynamic Pricing in Insurance: Leveraging Datasets To Predict Risk and Price session at the Google Cloud Financial Services Summit on May 27. You can also check out our recent blog and explore more about BigQuery and BigQuery ML.

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Google Cloud Celebrates Journey of 3 Inspiring Founders for the Asian Pacific American Heritage Month

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Google Cloud celebrates diversity in leadership by recognizing the journey of the 3 founders of the Asia and Pacific Island (AAPI) heritage in building their business and leveraging Google's technology. Learn more.

May is Asian Pacific American Heritage Month —a time for us to come together to celebrate and remember the important people and history of Asian and Pacific Island heritage. This feature highlights three AAPI founders from the Google For Startups community.

Read on to learn how these three founders built their businesses, leverage Google tech, and suggestion they have for aspiring entrepreneurs.

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CultivatePeople 

Founder: Lola Han

Description: CultivatePeople’s compensation software, Kamsa, provides global market pay rate data and helps companies make data-driven salary decisions so they can attract and retain their most valuable asset: employees. 

Why GCP: “CultivatePeople began using GCP when integrating SSO/SAML authentication after  our SaaS product became a high priority. We were able to exceed our clients’ expectations and increase their level of trust in our platform’s capabilities. We’re currently investigating additional machine learning products, such as Cloud AutoML, that will allow us to quickly deliver exciting features.” 

Note from the Founder: “I grew up with immigrant parents who value stability and are risk-averse. My parents discouraged me from starting my own company because they didn’t want to see me struggle financially or see my health suffer (due to stress). I felt strongly about what CultivatePeople could do, so I started the company as a sole founder in 2017 and watched it double in size year over year since. While the ones I love most may not have cheered me on initially, it was important for me to hang on to the encouraging words of former bosses, executives, and founders to keep me focused on my mission. 

My advice for other AAPI founders is to be a “silent assassin” and believe in the mission and values of your organization. Always remember to stop along the way and: 

 1) Enjoy the journey by celebrating wins and giving yourself credit;

2) Follow your intuition—it’s (almost) always right; 

3) Recognize and invest in your people regularly (ie. give increases more than once a year, if warranted);

4) Give regular words of affirmation to employees on even small achievements.”

swit

Swit

Founder: Josh Lee and Max Lim

Description: Swit is a team collaboration platform that seamlessly combines team chat with task management by allowing teams to turn their conversations into trackable tasks and share tasks to chat with simple drag-and-drop functionality, ensuring everyone is on the same page and projects get done faster.

Why GCP: “Swit is a cross-category hybrid work tool for chat and tasks. This functionality requires more complicated and heavier architecture for performance. So, configuring and managing virtual machines was really challenging to scale up our systems, while handling occasional unexpected traffic surges and frequent updates. Eventually we divided our monolithic architecture into 35 microservices when we launched our official product. The migration to GKE took around one month, and it turned out to be well worth the effort—our systems became able to offer high scalability and enough resilience to keep its uptime no matter what happened. Now we’re operating 84 workloads and 252 microservices with high stability with remarkably low downtime – less than 0.00001%/year.”

Note from the Founder: “As an AAPI founder based in Silicon Valley, I feel proud of the work ethics and diligence fellow Korean American entrepreneurs and professionals have long demonstrated here. Especially with K-pop breaking into the mainstream, I feel even more proud of our culture that strives toward an absolute perfection molded through years of training and dedication. The mission-driven culture of Silicon Valley coupled with Google’s edging technology and creativity really helped us build a product that not only encompasses verticals but also transcends cultures. Swit is growing at an unprecedented rate, and we hope  to join the long list of successful AAPI entrepreneurs here. Swit’s close network with the AAPI community wouldn’t have been possible without Google support. We are grateful for this collaborative environment, and we hope to become the next-generation ambassador for collaboration after Google.” 

Check out more from Swit in their founder story.

wisy

WISY 

Founder: Min Chen

Description: Wisy develops technology to bring digital efficiency into the physical world, supporting consumer products businesses and making them thrive in the new economy. All of us have a bad experience when we can’t find the product we want to buy. That is a $1.9T problem in the consumer-packaged goods industry that Wisy is solving with AI and analytics to help manufacturers and retailers sell more by reducing out-of-stocks and waste at a global scale.

Why GCP: “GCP has an intuitive, easy to use interface, was lower cost, and offered preemptible instances with flexible compute options. Some of the reasons why Wisy decided to use GCP include instance and payment configurability, privacy and traffic security, cost-efficiency, and Machine Learning.

Wisy has been able to advance quickly with product development, as well as collaborate better and iterate faster in the creation of our AI models, while reducing costs by 40%. At Wisy, we are solving a problem that affects everyone who shops at a store.“

Note from the Founder: “Two years ago, I moved to San Francisco to expand my second startup, Wisy. This is when I learned that my name ‘Min’ stands for ‘minority.’ I was born in China, raised in a Black community in Panama, received scholarships to attend both Carnegie Mellon and UC Berkeley. I worked for 20 years in several countries, but I have never felt so discriminated against due to my race, ethnicity, gender and age than during my time in Silicon Valley. However, this is also the place I learned that my diverse life experience is my competitive advantage. My background enables me to recruit and relate to people in different countries, create scalable and flexible products for multinational customers, and run global operations efficiently.

My recommendation to AAPI founders is to find strength in their multicultural background. Don’t hide what makes you unique, do not limit yourselves, and do not let others limit you. You will lose your edge when trading authenticity for validation. Be proud and own your story.”

If you want to learn more about how Google Cloud can help your startup, visit our Startup Program application page here and sign up for our monthly startup newsletter to get a peek at our community activities, digital events, special offers, and more.To learn more about how you can help #StopAsianHate during Asian Pacific American Heritage Month and beyond, visit their website here.

Case Study

There are 2 Key Traits You Need to Battle the Slowdown. FM Logistic Know How to Enable Them

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As companies move forward slowly to recover, many find that two key traits can help accelerate them: Openness and mobility. With over 26,000 employees worldwide, FM Logistic found a neat trick to enable it. And it did not take long to implement.

FM Logistic provides its international customers with complete logistics solutions that cover everything from warehousing and handling, to transport and distribution, co-packing and co-manufacturing, and supply chain optimization. Operating in 14 countries including France, Russia, Poland, India, Vietnam, Brazil, and China, FM Logistic supports its clients by offering specialized services across a number of markets including consumer goods, retail, cosmetics, and health.

“We are in the process of transforming all our tools and integrating new solutions to be able to work faster and more efficiently. Two main IT priorities are mobility and openness, in terms of working from anywhere and being able to interact with other information systems.”

– Communication Manager, FM Logistic

As a business that has existed since 1967 and in 2018 achieved a turnover of €1.178 billion with 9.5% annual growth, FM Logistic is always looking to help secure the company’s position within an evolving sector. To better serve its customers, FM Logistic decided to launch an innovation project in 2017 to transform the internal digital tools of the company and replace its intranet, email, and productivity software. The company looked for an integrated solution that would enable more collaborative ways of working and bring its international operations closer together. The company found that implementing G Suite and LumApps social intranet was the perfect combination.

“We are in the process of transforming all our tools and integrating new solutions to be able to work faster and more efficiently,” says the Communication Manager at FM Logistic. “Two main IT priorities are mobility and openness, in terms of working from anywhere and being able to interact with other information systems.”

A global transformation

For large international companies with global operations, implementing a single integrated solution to enable collaboration across regions while respecting regional variations can be a real challenge.

“We have 26,000 employees spread across a broad geography, with a variation in cultures and technological maturity. There was an aspiration to work in collaboration, but the necessary tools were not in place,” says FM Logistic’s Communication Manager. FM Logistic looked for a solution to enable new, more collaborative work practices, which were flexible in terms of usage and that, most importantly, would work as part of an integrated solution.

To do that, FM Logistic worked with Google Partner Devoteam G Cloud to implement G Suite alongside the LumApps intranet portal. It took six months to complete the migration of 7,000 accounts, with employees accessing the Business or Basic G Suite edition according to their needs. “Before, with our physical infrastructure we found ourselves buying additional hard drives as we ran out of storage,” says the Technical Project Manager at FM Logistic. “That’s no longer a problem, and we can tailor access depending on whether or not employees need unlimited storage .”

“It’s a real advantage to be able to access your account from any device and work from anywhere. Hangouts Meet has been really transformational in that respect: it has reduced the need for travel significantly, even in the few months we’ve been using it so far.”

– Communication Manager, FM Logistic

“We followed Google’s G Suite migration advice and had early adopter ambassadors in every country. By the time we got around to the final country, very little input was needed as they were ready to go!” says the Technical Project Manager. “Many of them were already familiar with the product, so it was very intuitive. Our feedback surveys gave a satisfaction rating of almost 4.5 out of 5 in relation to the transition. We also had significant executive support, with two members of the executive committee on the steering board. That really helped the project to move quickly.”

As a result, employees were quick to understand the benefits of the new system. “It’s a real advantage to be able to access your account from any device and work from anywhere. That was clear from the start,” says the Communication Manager. “Hangouts Meet has been really transformational in that respect: it has reduced the need for travel significantly, even in the few months we’ve been using it so far.”

LumApps: a fully-integrated enterprise hub

One of FM Logistic’s main reasons for choosing G Suite was the seamless integration with LumApps’ intranet portal. LumApps adds value to G Suite, thereby boosting and sustaining employee adoption. FM Logistic chose LumApps to create a hub for its enterprise, where everything is centralized from internal communications to business apps.

“We didn’t want a patchwork of solutions, we needed a platform that worked as an integral whole,” says the Technical Project Manager. “With LumApps, employees access the intranet using their Google authentication and can access all their G Suite tools through the intranet. Moreover, LumApps is Google native, so the integration is seamless and we know that it will evolve to accommodate any changes that might take place.”

“The main advantages we see are communication and knowledge sharing,” says the Communication Manager. “With LumApps, all 26,000 of our employees are now able to access the portal in their own language and access local news.”

For the next step, FM Logistic is considering integrating social communities so that employees can express themselves and be more engaged in the corporate culture.

Supporting digital maturity

Thanks to Drive, employees can now work from wherever they are, and are able to work more efficiently and more collaboratively. “From an administrative point of view, users can benefit from automatic updates, so there is less pressure on the IT department,” says the Technical Project Manager. “And we’re seeing many innovative uses of the tools to work in more efficient ways: many services have gone paperless with Forms, so less time is wasted; commercial agents are using Drive to work together on tenders simultaneously; and with Sheets, tasks are automatically sent from the team manager’s file to employee’s Calendar.”

“G Suite and LumApps are the first step of our digital transformation, in terms of collaboration and moving into the cloud. We have real confidence in our partners LumApps and Google, and we’re investing in those relationships for the long-term benefit of the company.”

– Communication Manager, FM Logistic

Now, FM Logistic wants to further support its employees in exploring all the tools G Suite has to offer. “We are embarking on a second phase to give our employees the skills they need for advanced uses of Docs, Sheets, and Forms,” says the Communication Manager. “It’s a learning curve, but it’s key to our goal of transforming our everyday processes.”

“G Suite and LumApps are the first step of our digital transformation, in terms of collaboration and moving into the cloud. We have real confidence in our partners LumApps and Google, and we’re investing in those relationships for the long-term benefit of the company.”

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Case Study

A Winner’s Story of Optimizing IT Operations for Informed Business Decisions with Google Cloud

An award-winning leader in the enterprise software industry, Broadcom Software is known for modernizing, securing and optimizing the most complex and largest hybrid environments. It’s broad portfolio of industry-leading, enterprise infrastructure and security software built on different tech stack and vision of tech leaders, are run on on-prem, private and public cloud and operated differently. To have a unified standard platform to run their wide range of products, Broadcom Software needed infrastructure as a service and Kubernetes orchestration layer, standard DevOps and security practices as well as common set of DevSecOps tools. Although the company knew there was no common ground to run all their products, migrating products into container-based architecture, they discovered many common strands that they could leverage for efficiency. Watch to learn the role played by Google Cloud in the Broadcom’s transformation!

Case Study

How TapClicks’ Google Cloud Migration Makes Life Easy for Marketers

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TapClicks, a smart marketing cloud, migrated its core applications to Google Cloud to reduce costs, address data-sharing concerns for its customers and explore new possibilities. Learn how this managed their customers' marketing infrastructure.

Editor’s note: In this blog post we learn how TapClicks migrated to Google Cloud to offer their marketing customers a unified platform for data management, operations, insights, and analysis.

TapClicks is a smart marketing cloud, powered by data, that unifies our customer’s marketing. By choosing to migrate our core applications last year to Google Cloud, we cut costs, solved data-sharing concerns for our customers, and opened our stack up to a new ecosystem of possibilities. 

The core problem that we’re solving for our customers is how to manage their marketing infrastructures data and operations. Life isn’t easy for marketers now. There are 7,000 different vendors servicing this space today – creating much complexity between digital agencies, media, and brands. Marketers face challenges in navigating all of these systems, logging in and out, understanding pacing goals, and managing the flow of marketing data so they can analyze and report internally as well as to their clients at scale.

We unify omnichannel campaign data (250 API connectors and 6000 Smart Connectors ™ ) from a plethora of marketing sources on an automated data warehousing solution, creating simplicity for organizations. Over 4,000 agencies, media companies, and brands use our Marketing Operations and Data Management Platform, which imports data at scale and creates an automatic data warehouse on Google Cloud. Teams can also leverage TapClicks, like our world class Facebook connector, to import data directly into Google Data Studios.  Beyond importing and storing, we also provide data exporting to other Google solutions like Google Data Studio and Google Sheets.  We also create interactive dashboards that let stakeholders and clients analyze their data, as well as automated, multi-channel reports that go out to clients at specified times. So channel comparisons, optimizations, attribution, and calculations are easily performed.  Some of our customers are able to generate hundreds of thousands of individual reports and dashboards for their clients.

Although we may be best known for our reporting and analytics, we also empower teams managing the marketing operations workflow from customers and internal stakeholders, especially at scale. Our user-friendly, configurable system helps manage their orders and campaigns. Through automation of this process, we deliver tremendous amounts of efficiency, time saving, cost savings, and reduction of errors. The combination of these solutions makes up our unified platform, with additional capabilities like marketing intelligence that offers competitive and brand-level analysis. This is a disruptive solution in use by all leading media companies, agencies and many brands.

Partnering for possibilities

We faced a few challenges with our original tech stack, which included a mix of the leader in web services revenue, leaders in high performance data warehousing, as well as vendors on bare metal servers. 

  • One challenge was around costs, which were growing. 
  • Second, many of our customers work with multiple brands, and are very hesitant to share their data with the leader in web services, who’s often viewed as their competitor. 
  • Third, these vendors are more focused on their own revenue rather than a true long term partnership that would enable their customers to enjoy similar success as they have experienced.

When looking at other cloud providers, Google Cloud emerged for us as the front runner. They were competitive on costs, and their native Kubernetes support was superior— a big selling point for our DevOps team. There’s also a movement in the marketing and advertising industry away from AWS toward Google Cloud because of the data-sharing concern. Finally, most of our customers are already using Google Cloud tools, so there’s brand recognition and familiarity there, and easier integrations with their own systems.

Migrating to Google Cloud

Our migration, which took about five months, involved moving a significant chunk of our infrastructure, including our core applications, using Google Kubernetes Engine (GKE). In our legacy architecture, each of our clients was assigned to one of our virtual machines (VMs), and there was a lot of unused capacity because we had to provision for the max usage. We appreciated GKE’s cloud native capabilities, especially autoscaling, a huge benefit for our web application. We have varying usage patterns during the day, and though our application is mostly used during business hours, there are also days in the month of higher usage, and autoscaling saves us time and costs. GKE also makes deployments much easier, and we anticipate a lot of benefits there for our developer environments. We’ve moved some of our microservices into GKE and plan to move more in the future. All in all, we were able to migrate our core products and the bulk of our AWS spend successfully to Google Cloud. 

We also moved from our other vendors Relational Database Service (RDS) to running MySQL on our own VMs on Google Cloud, which gives us more flexibility in terms of settings and fine tuning. We’re still trying to find the best mix as we’re modernizing our infrastructure, and we took this opportunity to migrate from MySQL 5.7 to 8.0.  

Our next stage is exploring more of the capabilities and services of Google Cloud, including BigQuery, which we’re considering for our own data warehouse. The fact that we could also run Snowflake on Google Cloud, if needed, was another selling point for our migration. 

We’re especially interested in BigQuery ML’s machine learning and natural language processing capabilities, which enabled better predictive insights. Our customers want insights from their campaigns— which are working, which are paying off, where should they invest next? Using our platform, they’re looking not only to generate reporting, but also identify opportunities to improve campaign performance. We plan to use AI and ML to improve those capabilities, so that our customers can seamlessly unlock insight and intelligence from their marketing data and campaigns.

Double-clicking on Google Cloud

For us, being able to deeply leverage and partner with Google Cloud to deliver those solutions on a single stack is critical, and we think our customers will love it. We see TapClicks and Google Cloud partnering at a level beyond what you typically see in a cloud provider relationship. Already, fifty percent of our company is working with various Google Cloud solutions, and we envision TapClicks and Google Cloud as extensions of each other, providing a single, powerful platform solution. 

Google Cloud understands the partnership concept, and their team was able to shine a light on their services and what they could bring to the table. Compared to our previous experiences, dealing with the Google Cloud team has been a true pleasure. Now that we’ve migrated, we’re ready to take our next steps into the services available to us in the Google Cloud ecosystem, and the problems we’ll continue to solve for our customers. Learn more about TapClicks and BigQuery ML.

How-to

Developers and Practitioners’ Guide for Moving On-prem Data Warehouse to BigQuery

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Google Cloud's BigQuery is a serverless, scalable and cost-effective solution for handling EDW use cases. Read to ease your migration journey of on-prem EDW to BigQuery along with examples and considerations for a successful data migration strategy!

Data teams across companies have continuous challenges of consolidating data, processing it and making it useful. They deal with challenges such as a mixture of multiple ETL jobs, long ETL windows capacity-bound on-premise data warehouses and ever-increasing demands from users. They also need to make sure that the downstream requirements of ML, reporting and analytics are met with the data processing. And, they need to plan for the future – how will more data be handled and how new downstream teams will be supported?

Checkout how Independence Health Group is addressing their enterprise data warehouse (EDW) migration in the video above.

Why BigQuery?

On-premises data warehouses become difficult to scale so most companies’ biggest goal is to create a forward looking system to store data that is secure, scalable and cost effective. GCP’s BigQuery is serverless, highly scalable, and cost-effective and is a great technical fit for the EDW use-case. It’s a multicloud data warehouse designed for business agility. But, migrating a large, highly-integrated data warehouse from on-premise to BigQuery is not a flip-a-switch kinda migration. You need to make sure your downstream systems dont break due to inconsistent results in migrating datasets, both during and after the migration. So..you have to plan your migration. 

Data warehouse migration strategy

 The following steps are typical for a successful migration: 

  • Assessment and planning: Find the scope in advance to plan the migration of the legacy data warehouse 
    • Identify data groupings, application access patterns and capacities
    • Use tools and utilities to identify unknown complexities and dependencies 
    • Identify required application conversions and testing
    • Determine initial processing and storage capacity for budget forecasting and capacity planning 
    • Consider growth and changes anticipated during the migration period 
    • Develop a future state strategy and vision to guide design
  • Migration: Establish GCP foundation and begin migration
    • As the cloud foundation is being set up, consider running focused POCs to validate data migration processes and timelines
    • Look for automated utilities to help with any required code migration
    • Plan to maintain data synchronization between legacy and target EDW during the duration of the migration. This becomes a critical business process to keep the project on schedule.
    • Plan to integrate some enterprise tooling to help existing teams span both environments
    • Consider current data access patterns among EDW user communities and how they will map to similar controls available in Big Query. 
    • Key scope includes code integration and data model conversions
    • Expect to refine capacity forecasts and refine allocation design. In Big Query there are many options to balance cost and performance to maximize business value. For example, you can use either on-demand or flat-rate slot pricing or a combination of both. 
  • Validation and testing
    •  Look for tools to allow automated, intelligent data validation 
    • Scope must include both schema and data validation
    • Ideally solutions will allow continuous validation from source to target system during migration
    • Testing complexity and duration will be driven by number and complexity of applications consuming data from the EDW and rate of change of those applications 

A key to successful migration is finding Google Cloud partners with experience migrating EDW workloads. For example, our Google Cloud partner Datametica offers services and specialized Migration Accelerators for each of these migration stages to make it more efficient to plan and execute migrations.

Data Warehouse Migration Strategy
Click to enlarge

Data warehouse migration: Things to consider

  • Financial benefits of open source: Target moving to ‘Open Source’ where none of the services have license fees. For example BigQuery uses Standard SQL; Cloud Composer is managed Apache Airflow, Dataflow is based on Apache Beam. Taking these as managed services provides the financial benefits of open source, but avoids the burden of maintaining open source platforms internally. 
  • Serverless: Move to “serverless” big data services. The majority of the services used in a recommended GCP data architecture scale on demand allowing more cost effective alignment with needs. Using fully managed services lets you focus engineering time on business roadmap priorities, not building and maintaining infrastructure. 
  • Efficiencies of a Unified platform: Any data warehouse migration involves integration with services that surround the EDW for data ingest and pre-processing and advanced analytics on the data stored in the EDW to maximize business value. A cloud provider like GCP offers a full breadth of integrated and managed ‘big data’ services with built-in machine learning. This can yield significantly reduced long-term TCO by increasing both operational and cost efficiency when compared to EDW-specific point solutions. 
  • Establishing a solid cloud foundation: From the beginning, take the time to design a secure foundation that will serve the business and technical needs for workloads to follow. Key features include: Scalable Resource Hierarchy, Multi-layer security, multi-tiered network and data center strategy and automation using Infrastructure-as-Code. Also allow time to integrate cloud-based services into existing enterprise systems such as CI/CD pipelines, monitoring, alerting, logging, process scheduling, and service request management. 
  • Unlimited expansion capacity: Moving to cloud sounds like a major step, but really look at this as adding more data centers accessible to your teams. Of course, these data centers offer many new services that are very difficult to develop in-house and provide nearly unlimited expansion capacity with minimal up-front financial commitment. . 
  • Patience and interim platforms: Migrating an EDW is typically a long running project. Be ready to design and operate interim platforms for data synchronization, validation and application testing. Consider the impact on up-stream and down-stream systems. It might make sense to migrate and modernize these systems concurrent with the EDW migration since they are probably data sources and sinks and may be facing similar growth challenges. Also be ready to accommodate new business requirements that develop during the migration. Take advantage of the long duration to have existing your operational teams learn new services from the partner leading the deployment so your teams are ready to take over post-migration. 
  • Experienced partner: An EDW migration can be a major undertaking with challenges and risks during migration, but offers tremendous opportunities to reduce costs, simplify operations and offer dramatically improved capacities to internal and external EDW users. Selecting the right partner reduces the technical and financial risks, and allows you to plan for and possibly start leveraging these long-term benefits early in the migration process.
Data Warehouse Migration Architecture
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Example Data Warehouse Migration Architecture

  • Setup foundational elements. In GCP these include, IAM for authorization and access, cloud resource hierarchybilling, networking, code pipelines, Infrastructure as Code using Cloud Build with Terraform ( GCP Foundation Toolkit), Cloud DNS and a dedicated/partner Interconnect to connect to the current data centers.
  • Activate monitoring and security scanning services before real user data is loaded using Cloud Operations for monitoring and logging and Security Command Center for security monitoring. 
  • Extract files from on-premise legacy EDW and move to Cloud Storage and establish on-going synchronization using Big Query Transfer services
  • From Cloud Storage, process the data in Dataflow and Load/Export data to BigQuery. 
  • Validate the export using Datametica’s validation utilities running in a GKE cluster and Cloud SQL for auditing and historical data synchronization as needed. Application teams test against the validated data sets throughout the migration process. 
  • Orchestrate the entire pipeline using Cloud Composer, integrated with on-prem scheduling services as needed to leverage established processes and keep legacy and new systems in sync. 
  • Maintain close coordination with teams/services ingesting new data into the EDW and down-streams analytics teams relying on the EDW data for on-going advanced analytics. 
  • Establish fine-grained access controls to data sets and start making the data in Big Query available to existing reporting, visualization and application consumption tools using BigQuery data connectors for ‘down-stream’ user access and testing. 
  • Incrementally increase Big Query flat-rate processing capacity to provide the most cost-effective utilization of resources during migration. 

To learn more about migrating from on-premises Enterprise Data Warehouses (EDW) to Bigquery and GCP here.

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