If you’re looking to migrate and modernize your Microsoft and Windows workloads, Google Cloud is your premiere destination. No matter what migration strategy you’ve selected or what value you’re looking to achieve, with Google Cloud you’re able to:

• simplify your migration and modernization journey
• reduce your on-prem footprint and increase agility
• optimize license usage to reduce costs
• modernize to reduce single-vendor dependencies
• rely on enterprise-class support backed by Microsoft

Whether you’re looking to migrate applications running on Windows virtual machines, adopt Windows containers in Google Kubernetes Engine (GKE), convert SQL databases to Cloud SQL, or something else, Google Cloud offers you the first-class experience you need. 

But we don’t want you to take our word for it. Try it out yourself with our new online Microsoft and Windows on Google Cloud Demo Center without any commitment or friction.

The demo center uses hands-on guided simulations to walk you through several scenarios for solving business critical challenges with Google Cloud’s Microsoft and Windows solutions. Because these are all simulated, you’ll see how it works without any deployment, configuration, or commitment. It’s a seamless way for you to see exactly how Google Cloud can help you.

Run dedicated hardware and optimize with sole tenant

Sometimes you might want to run your workloads on dedicated hardware (with oversubscription options) for compliance, licensing, and management. Google Cloud provides sole-tenant nodes that allow you to easily deploy your virtual machines onto dedicated machines to avoid “noisy neighbor” issues, address regulatory or licensing constraints, and optimize inter-VM communications.

Plus, the CPU Overcommit option allows oversubscribing sole-tenant node resources by up to 2x, therefore helping save on per-physical core licensing for many licensed workloads like SQL Server.

Learn how to set up a sole tenant group and node.

Optimize license costs with premium images & custom VMs

One of the easiest ways to optimize your cloud experience with virtual machines (VM) is to pick the right VM image. Google Cloud provides premium license-included VM images that are thoroughly tested and optimized, including SQL Server options with pay-as-you-go licensing. These are great for workloads that don’t need to run all the time or when you do not have spare licenses for bring your own license (BYOL). 

Explore some Windows & SQL images and learn how CPU/Memory options can help optimize deployment and save on licensing.

Modernize your databases with Managed SQL Server

Sometimes you need to manage your SQL Server instance to achieve certain business or operational goals. But more often, managing SQL Server deployments can be undifferentiated: backups, high-availability, updates, and patching are just some of the many things you have to take care of when going the do-it-yourself route. One way to modernize your database tier is to migrate to a managed service like Cloud SQL, which is a fully managed Relational Database service for SQL. 

Explore the process of creating an instance in just a few clicks!

Extract apps from VMs and move to containers in GKE with Migrate for Anthos

Many Windows workloads running on virtual machines such as Internet Information Services (IIS) are ideal candidates for migrating to containers without major changes like rewriting or rearchitecting. However, doing this migration manually can be tedious, which is why Migrate for Anthos can help easily re-platform a .NET app running on IIS into a container-based app. 

Simulate intelligently extracting, migrating, and modernizing applications to run natively on containers in GKE and Anthos clusters.

Move .NET applications to GKE on Windows without code changes

When you’re looking to go fully cloud native, you can leverage Windows containers in GKE without rewriting your .NET applications. Simply create clusters with Windows nodes and deploy containerized Windows workloads in a few clicks, even alongside Linux containers. These deployments reduce operational overhead with features such as auto-upgrade, auto-repair, and release channels. 

Learn  how easy it is to build a GKE cluster with a Windows node and deploy an app. 

Now that you’ve gotten a feel for what’s available, go check out the Demo Center. You can also visit us at Windows and Microsoft on Google Cloud to learn more.

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: 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.

Example Data Warehouse Migration Architecture

• Setup foundational elements. In GCP these include, IAM for authorization and access, cloud resource hierarchy, billing, 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.

Patients about to undergo a healthcare procedure understandably want the best medical professionals they can get. But how can they know which doctors have had the most experience and the best outcomes with that particular procedure? How can they make an informed decision about which doctor to select when the information they have is limited to the doctor’s practice area and subjective reviews from other patients?

MD Insider is working to solve that problem using machine learning (ML) to objectively analyze doctor performance. By analyzing data from thousands of institutions and millions of doctor-patient interactions and medical events, MD Insider identifies physician performance insights based on their experience and outcomes. Insights are then integrated into a triage engine, that enables consumers to search for and schedule appointments with providers who meet their clinical criteria and convenience preferences, such as insurances accepted, office hours, locations, and language.

MD Insider also offers robust data APIs to help health systems, health plans, and employers reduce costs and improve quality of care. Payers use the APIs to curate high-quality provider networks and manage provider directories. Examples of MD Insider’s data APIs include Provider Experience and Share of Practice metrics, Provider Quality and Outcomes, Network Modeling, Expert Clinical Search Taxonomy, Find a Provider, Acute-Care Hospital Quality, and Provider and Facility Metadata.

“We use Google Cloud Platform the way the cloud was supposed to be used. By comparison, other cloud providers feel like you’re renting somebody else’s data center.”

—Ed Holsinger, Lead Data Engineer and Head of Data Science, MD Insider

MD Insider is continuously ingesting the latest performance data about physicians and analyzing billions of rows of data. Requiring constant scalability, the company was born in the cloud; however, it had difficulty configuring server instances for the optimal balance of memory and CPU, and its Hadoop cluster had to be kept running 24/7. Network performance was often slow for no apparent reason. As a result, failure rates from node timeouts increased, and costs grew along with the data. MD Insider had to estimate its usage and pay up front, and received little financial benefit from sustained use commitments.

Knowing that data would continue to grow, MD Insider decided to move its data services — the most demanding and complex portion of its infrastructure — to Google Cloud Platform (GCP), and took advantage of GCP managed services for container management and big data analytics.

“One of the reasons we decided to move to Google Cloud Platform is because it feels like a unified, well-designed cloud architecture and pricing model,” says Ed Holsinger, Lead Data Engineer and Head of Data Science at MD Insider. “We use Google Cloud Platform the way the cloud was supposed to be used. By comparison, other cloud providers feel like you’re renting somebody else’s data center.”

“Moving to Google Cloud Platform and using Kubernetes Engine gave us 5x the processing power for analyzing physician performance at 75% less cost. Our data scientists have more power than ever before to generate insights for our customers.”

—Ed Holsinger, Lead Data Engineer and Head of Data Science, MD Insider

5x the performance, 75% less cost

MD Insider now uses Kubernetes Engine to automate container management and deploy clusters in minutes with just a few clicks. When hundreds of machines are required to analyze a large dataset, automation in Kubernetes Engine deploys ML models as containers, each of which manages the full lifecycle of its task, including scaling up resources, deploying results, and scaling back down when the task is finished. It’s easy for MD Insider to specify exactly how much CPU and memory each container needs, helping maximize performance while reducing costs.

“Moving to Google Cloud Platform and Kubernetes Engine gave us 5x the processing power for analyzing physician performance at 75% less cost,” says Ed. “Our data scientists have more power than ever before to generate insights for our customers. Data scoring jobs that used to take three business days now take four hours.”

Adds Galen Meurer, Senior Software Engineer at MD Insider: “Even if all Google Cloud Platform had to offer was Kubernetes Engine, I would still want to use it. Previously we spent up to 30% of our time managing our container infrastructure, which we can now use for product development.”

A foundation for data science

MD Insider was happy to find that GCP offers a wide variety of managed services. For example, the company is supplementing its Kubernetes Engine clusters with BigQuery for its big data masters and selection jobs, enabling scientists to analyze new and different types of data as well as analyze larger datasets in less time. MD Insider also uses Cloud Storage for big data staging and Cloud Dataproc to run managed Apache Spark clusters for data processing.

“Google Cloud Platform gives us an incredibly powerful cloud architecture for data engineering and data science,” says Eric Wilson, CEO of MD Insider. “That gives our scientists independence, they can do what they need to do without waiting and with no contention between them.”

A developer-friendly platform

Migrating its data services was such a success that MD Insider decided to move the rest of its infrastructure to GCP, including the front end for its web application. Since the migration, MD Insider has experienced no unplanned downtime on GCP, allowing it to easily meet the 99.5% uptime SLA it promises to customers. It’s also taking advantage of Build Triggers in Kubernetes Engine to automate container builds and reduce build times by more than 40%. Production code can be updated in seconds, with no impact to end users other than making new features available.

“GCP has simplified our workflow in so many ways, from intelligent load balancing to content delivery and automating builds,” says Matthew Frey, Software Engineer. “Everything on GCP is cohesive and developer friendly, with a superior UI and better network performance than other cloud providers.”

Ryan Beaini, Senior Software Engineer at MD Insider, agrees: “Since we moved to GCP, our developers are definitely happier. The pain and the headaches we experienced because of the limitations of our previous toolset all went away.”

“We’re a small company, but what we’re doing is incredibly important. We’re helping people make decisions about healthcare providers that could impact their lives and even be life-saving. Google Cloud Platform is helping us make our mission bigger, better, and brighter.”

—Eric Wilson, CEO, MD Insider

Securing billions of rows of clinical and non-clinical healthcare data

As a healthcare technology company, MD Insider processes billions of rows of clinical and non-clinical healthcare data. To control user access to GCP, it uses Identity & Access Management (IAM) along with Yubico YubiKeys for hardware-based two-factor authentication when logging into Google Workspace. MD Insider takes comfort that GCP encrypts data at rest by default, and encrypts and authenticates data in transit when data moves outside physical boundaries not controlled by Google or on behalf of Google.

“On GCP, everything that we need to be encrypted for compliance purposes is encrypted, which is fantastic,” says Eric. “When I tell our potential clients and partners about the resources that Google has dedicated to security, it gives them the confidence that their data will be protected.”

Transforming how teams work

As a growing company, MD Insider must collaborate seamlessly between offices in California, Colorado, and Illinois. It relies on Google Workspace for communication and productivity, using Docs, Sheets, and Slides to drive the business. Employee and team files are stored in Drive, and meetings are conducted via Google Meet with Chromebox for Meetings videoconferencing hardware kits. Google Workspace also helps MD Insider maintain information security by authenticating email domains with digital signatures in Gmail and scanning outgoing email using Gmail Data Loss Prevention (DLP).

“I use Google Workspace every day, and everyone else here does too,” says Eric. “Team Drives are a big time saver for us. We’ve let our previous office software expire, because there’s no need to pay for those licenses anymore.”

Promoting healthcare transparency

With GCP helping MD Insider increase velocity and momentum, the company is making exciting progress. For example, it has entered into a strategic partnership with Zelis Healthcare, which will use MD Insider’s API to provide insights for a next-gen analytics platform that will give health plans unprecedented transparency around physician performance.

“We’re a small company, but what we’re doing is incredibly important,” says Eric. “We’re helping people make decisions about healthcare providers that could impact their lives and even be life-saving. Google Cloud Platform is helping us make our mission bigger, better, and brighter.”

*Google Workspace was formerly known as G Suite prior to Oct. 6, 2020.

Visualizing trends in your logs is critical when troubleshooting an issue with your application. Using the histogram in Logs Explorer, you can quickly visualize log volumes over time to help spot anomalies, detect when errors started and see a breakdown of log volumes. But static visualizations are not as helpful as having more options for customization during your investigations. 

That’s why we’re excited to announce that we recently added three new query controls along with separate colors for log severity to the histogram. These new features make it even easier to refine and analyze your logs by time range. The new histogram controls help find logs before or after the current period, jump to a specific time range represented in a histogram bar and zoom in/out of the current time window in the histogram.

Histogram colors

The histogram now makes it easier to view the breakdown of logs by severity with the introduction of color coding. For example, the severity colors make it easy to spot an increasing number of errors even when the volume of requests is relatively constant. Looking at the histogram below, the red vs blue shading makes it clear that there has been an increase in overall log volume and provides a visual breakdown of errors within that log volume.

Pan left/right to scroll through time

Sometimes in your troubleshooting journey, you may want to look at the logs directly before or after the current set of logs. Perhaps there was an unexpected spike in errors at the beginning of the time range and you need to see the logs in the time period directly preceding the current time range. Pressing the left arrow on the left side of the histogram shifts the time range earlier while the arrow on the right side of the histogram shifts the time range ahead. Either arrow will refine the time range in the query and rerun the query to return the logs in the new time range.

Zooming in or out 

Zooming in or out from a given time range may be useful to visualize fine-grained details or a broader trend Clicking the zoom in or out icons in the upper right corner of the histogram refines the time range in the query and then reruns the query, returning the logs in the newly defined time range.

Scrolling to time 

If you see a large spike in logs volume in the histogram, it’s useful to quickly review the logs generated during that spike. Clicking on the histogram bar that contains the spike now scrolls you to the logs generated during that time period.

Where to find the histogram 

The histogram is a panel in Logs Explorer that can be displayed or hidden using the controls in the Page Layout menu. When you no longer want to display the histogram, click the “X” button in the upper right corner to quickly close it. To open it again, use the same Page Layout menu to enable the histogram display.

Get started with the histogram

These improvements move the histogram from a utility for visualization to an integral part of the troubleshooting journey. We are continuously working to launch new features that make Cloud Logging the best place to troubleshoot your Google Cloud logs. If you are not already a Cloud Logging user, review this getting started documentation or watch a quick video on troubleshooting services on Google Kubernetes Engine (GKE) to learn more. If you have specific questions or feedback, please join the discussion on our Google Cloud Community, Cloud Operations page.