Your product teams might ask – “Why does it take so long to build a feature or application?” Building applications is a heavy lift due to the technical complexity, which includes the complexity of backend services that are used to manage and store data. Every moment focused on this technical complexity is a distraction from  delivering on core business value. Firestore alters this by having Google Cloud manage your backend complexity through a complete backend-as-a-service! 

Firestore is a serverless, NoSQL document database that unlocks application innovation with simplicity, speed and confidence. 

It acts as a glue that intelligently brings together the complete Google Cloud backend ecosystem, in-app services from Firebase and core UI frameworks & OS from Google.

What is Firestore?

Firestore is a serverless, fully managed NoSQL document database that scales from zero to global scale without configuration or downtime. Here’s what makes Firestore unique:

• Ideal for rapid, flexible and scalable web and mobile development with direct connectivity to the database.
• Supports effortless real time data synchronization with changes in your database as they happen. 
• Robust support for offline mode, so your users can keep interacting with your app even when the internet isn’t available or is unreliable.
• Fully customizable security and data validation rules to ensure the data is always protected
• Built-in strong consistency, elastic scaling, high performance & best in class 99.999% availability 
• Integration with Firebase and Google Cloud services like Cloud Functions and BigQuery, serverless data warehouse.
• In addition to a rich set of Google Cloud service integrations, Firestore also offers deep one-click integrations with a growing set of 3rd party partners via Firebase Extensions to help you even more rapidly build applications.

Document-model database

Firestore is a Document-model database. All of your data is stored in “documents” and then “collections”.  You can think of a document as a JSON object. It’s a dictionary with a set of key-value mappings, where the values can be several different supported data types including strings, numbers or binary values..

These documents are stored in collections. Documents can’t directly contain other documents, but they can point to subcollections that contain other documents, which can point to subcollections, and so on. This structure brings with it a number of advantages. For starters, all queries that you make are shallow, meaning that you can grab a document without worrying about grabbing all the data underneath it. And this means that you can structure your data hierarchically in a way that makes sense to you logically, without having to worry about grabbing tons of unnecessary data. 

How to use Firestore?

Firestore can be used in two modes:

• Firestore in Native Mode:  This mode is differentiated by its ability to directly connect your web & mobile app to Firestore. Native Mode supports up to 10K writes per second, and over a million connections. 
• Firestore in Datastore Mode: This mode supports only server-side usage of Firestore, but supports unlimited scaling, including writes. 

Conclusion

Whatever your application use case may be, if you want to build a feature or an application quickly using Firestore backend-as-a-service. For a more in-depth look into Firestore check out the documentation. https://www.youtube.com/embed/moglAjmwmUQ?enablejsapi=1&

For more #GCPSketchnote, follow the GitHub repo. For similar cloud content follow me on Twitter @pvergadia and keep an eye out on thecloudgirl.dev.

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.

Backup and DR data is a valuable business asset, and ensuring that it’s safe and accessible is essential. In particular, you want to be able to monitor your backups to ensure that the data is indeed protected and that you can quickly recover it in the event of a disaster, user error, or failed upgrade.

Users of Google Cloud’s Backup and DR service often ask:

1. What are the best practices for monitoring my backup and recovery jobs?
2. How can I proactively identify issues and troubleshoot? 
3. How can I create alerts and be notified about important events? 

The good news is that Google Cloud Backup and DR now integrates with Cloud Logging and Cloud Monitoring tools. Now, you can monitor backup events, jobs, appliance health, resource consumption and user actions in the same way you monitor other Google Cloud workloads and services. 

With this integration, you can now:

• Monitor events related to backup and restore – With detailed Google Cloud Backup and DR service event logs in Cloud Logging, you can now use log based alerts to create a customisable notification, in near-real time. This lets you monitor important events such as  backup job failures, jobs that did not run, local backup storage saturation, and network failures.
• Configure fine grained alerting – You can write custom event queries on a wide variety of dimensions such as event severity, application type, application name, job type, job name, error message and many more. This gives a lot of flexibility to define alerts for specific events or conditions within a system, thus reducing noise and helping you identify and fix issues easily. 
• Get notified on your preferred notification channel – Google Cloud offers seven pre-built notification channels that let you receive customisable notifications directly over email, SMS, Slack or the Google Cloud mobile app. Alternatively, you can integrate with your own monitoring and event management tools using webhooks or Pub/Sub. 
• Derive useful insights to troubleshoot issues – With Log Analytics, you can use BigQuery to query your data using SQL queries and generate operational insights, which can help you reduce time spent troubleshooting. For example, you can analyze the key reasons that backup and restore jobs fail for a given application or application type. 

The integration also lets you proactively discover deeper issues related to backup and recovery, such as capacity-related issues or certain job failures that happen periodically, by monitoring recurring events in the logs over time. With log-based metrics, you can: 

• Create trend charts to track important metrics such as the number of backup or restore jobs that failed during a day/week/month 
• Receive a notification when the number of occurrences crosses a threshold, for example receive a notification if a snapshot pool saturates more than five times a week 
• Monitor trends in data, such as latency values in logs, and receive a notification if the values change in an unacceptable way.

Getting started

The time to learn that your backup failed should never be when you go to restore. By integrating our Backup and DR service with Cloud Monitoring and Cloud Logging, you can get valuable assurances about the health of your backup and business continuity processes with the same tools that you use to manage other Google Cloud workloads. To get started, check out the Backup and DR event logs page. You can also watch a video that shows you how to set up and use the service and configure some custom alerts.

Businesses around the globe are realizing the benefits of replacing legacy data silos with cloud-based enterprise data warehouses, including easier collaboration across business units and access to insights within their data that were previously unseen. However, bringing data from numerous disparate data sources into a single data warehouse requires you to develop pipelines that ingest data from these various sources into your enterprise data warehouse. Historically, this has meant that data engineering teams across the organization procure and implement various tools to do so. But this adds significant complexity to managing and maintaining all these pipelines and makes it much harder to effectively scale these efforts across the organization. Developing enterprise-grade, cloud-native pipelines to bring data into your data warehouse can alleviate many of these challenges. But, if done incorrectly, these pipelines can present new challenges that your teams will have to spend their time and energy addressing. 

Developing cloud-based data ingestion pipelines that replicate data from various sources into your cloud data warehouse can be a massive undertaking that requires significant investment of staffing resources. Such a large project can seem overwhelming and it can be difficult to identify where to begin planning such a project. We have defined the following principles for data pipeline planning to begin the process. These principles are intended to help you answer key business questions about your effort and begin to build data pipelines that address your business and technical needs. Each section below details a principle of data pipelines and certain factors your teams should consider as they begin developing their pipelines.

Principle 1: Clarify your objectives

The first principle to consider for pipeline development is clarify your objectives. This can be broadly defined as taking a holistic approach to pipeline development that encompasses requirements from several perspectives: technical teams, regulatory or policy requirements, desired outcomes, business goals, key timelines, available teams and their skill sets, and downstream data users. Clarifying your objectives clearly identifies and defines requirements from each key stakeholder at the beginning of the process and continually checks development against these requirements to ensure the pipelines built will meet these requirements.This is done by first clearly defining the desired end state for each project in a way that addresses a demonstrated business need of downstream data users. Remember that data pipelines are almost always the means to accomplish your end state, rather than the end state itself. An example of an effectively defined end-state is “enabling teams to gain a better understanding of our customers by providing access to our CRM data within our cloud data warehouse” rather than “move data from our CRM to our cloud data warehouse”. This may seem like a merely semantic difference, but framing the problem in terms of business needs helps your teams make technical decisions that will best meet these needs. 

After clearly defining the business problem you are trying to solve, you should facilitate requirement gathering from each stakeholder and use these requirements to guide the technical development and implementation of your ingestion pipelines. We recommend gathering stakeholders from each team, including downstream data users, prior to development to gather requirements for the technical implementation of the data pipeline. These will include critical timelines, uptime requirements, data update frequency, data transformation, DevOps needs, and security, policy, or regulatory requirements by which a data pipeline must meet.

Principle 2: Build your team

The second principle to consider for pipeline development is build your team. This means ensuring you have the right people with the right skills available in the right places to develop, deploy, and maintain your data pipelines. After you have gathered your pipeline requirements, you can begin to develop a summary architecture that will be used to build and deploy your data pipelines. This will help you identify the human talent you will need to successfully build, deploy, and manage these data pipelines and identify any potential shortfalls that would require additional support from either third-party partners or new team members.

Not only do you need to ensure you have the right people and skill sets available in aggregate, but these individuals need to be effectively structured to empower them to maximize their abilities. This means developing team structures that are optimized for each team’s responsibilities and their ability to support adjacent teams as needed.

This also means developing processes that prevent blockers to technical development whenever possible, such as ensuring that teams have all of the appropriate permissions they need to move data from the original source to your cloud data warehouse without violating the concept of least privilege. Developers need access to the original data source (depending on your requirements and architecture) in addition to the destination data warehouse. Examples of this are ensuring that developers have access to develop and/or connect to a Salesforce Connected App or read access to specific Search Ads 360 data fields.

Principle 3: Minimize time to value

The third principle to consider for pipeline development is minimize time to value. This means considering the long-term maintenance burden of a data pipeline prior to developing and deploying it in addition to being able to deploy a minimum viable pipeline as quickly as possible. Generally speaking, we recommend the following approach to building data pipelines to minimize their maintenance burden: Write as little code as possible. Functionally, this can be implemented by:

1. Leveraging interface-based data ingestion products whenever possible. These products minimize the amount of code that requires ongoing maintenance and empower users who aren’t software developers to build data pipelines. They can also reduce development time for data pipelines, allowing them to be deployed and updated more quickly. 

• Products like Google Data Transfer Service and Fivetran allow for managed data ingestion pipelines by any user to centralize data from SaaS applications, databases, file systems, and other tooling. With little to no code required, these managed services enable you to connect your data warehouse to your sources quickly and easily.
• For workloads managed by ETL developers and data engineers, tools like Google Cloud’s Data Fusion provide an easy-to-use visual interface for designing, managing and monitoring advanced pipelines with complex transformations.

2. Whenever interface-based products or data connectors are insufficient, use pre-existing code templates. Examples of this include templates available for Dataflow that allow users to define variables and run pipelines for common data ingestion use cases, and the Public Datasets pipeline architecture that our Datasets team uses for onboarding.

3. If neither of these options are sufficient, utilize managed services to deploy code for your pipelines. Managed services, such as Dataflow or Dataproc, eliminate the operational overhead of managing pipeline configuration by automatically scaling pipeline instances within predefined parameters.

Principle 4: Increase data trust and transparency

The fourth principle to consider for pipeline development is increase data trust and transparency. For the purposes of this document, we define this as the process of overseeing and managing data pipelines across all tools. Numerous data ingestion pipelines that each leverage different tools or are not developed under a coordinated management plan can result in “tech sprawl”, which significantly increases the management overhead of data ingestion pipelines as the quantity of data pipelines increases. This becomes especially cumbersome if you are subject to service-level agreements, or legal, regulatory, or policy requirements for overseeing data pipelines. Preventing tech sprawl is, by far, the best strategy for dealing with it by developing streamlined pipeline management processes that automate reporting. Although this can theoretically be achieved by building all of your data pipelines using a single cloud-based product, we do not recommend doing so because it prevents you from taking advantage of features and cost optimizations that come with choosing the best product for your use case. 

A monitoring service such as Google Cloud Monitoring Service or Splunk that automates metrics, events, and metadata collection from various products, including those hosted in on-premise and hybrid computing environments, can help you centralize reporting and monitoring of your data pipelines. A metadata management tool such as Google Cloud’s Data Catalog or Informatica’s Enterprise Data Catalog can help you better communicate the nuances of your data so users better understand which data resources are best fit for a given use case. This significantly reduces your pipeline’s governance burden by eliminating manual reporting processes that often result in inaccuracies or lagging updates.

Principle 5: Manage costs

The fifth principle to consider for pipeline development is manage costs. This encompasses both the cost of cloud resources and the staffing costs necessary to design, develop, deploy, and maintain your cloud resources. We believe that your goal should not necessarily be to minimize cost, but rather maximizing the value of your investment. This means maximizing the impact of every dollar spent by minimizing waste in cloud resource utilization and human time. There are several factors to consider when it comes to managing costs:

• Use the right tool for the job – Different data ingestion pipelines will have different requirements for latency, uptime, transformations, etc. Similarly, different data pipeline tools have different strengths and weaknesses. Choosing the right tool for each data pipeline can help your pipelines operate significantly more efficiently. This can reduce your overall cost, free up staffing time to focus on the most impactful projects, and make your pipelines much more efficient.
• Standardize resource labeling –  Implement and utilize a consistent labeling schema across all tools and platforms to have the most comprehensive view of your organization’s spending. One example is requiring all resources to be labeled by the cost center or team at time of creation. Consistent labeling allows you to monitor your spend across different teams and calculate the overall value of your cloud spending.
• Implement cost controls – If available, leverage cost controls to prevent errors that result in unexpectedly large bills. 
• Capture cloud spend – Capture your spend on all cloud resource utilization for internal analysis using a cloud data warehouse and a data visualization tool. Without it, you won’t understand the context of changes in cloud spend and how they correlate with changes in business.
• Make cost management everyone’s job – Managing costs should be part of the responsibilities of everyone who can create or utilize cloud resources. To do this well, we recommend making cloud spend reporting more transparent internally and/or implementing chargebacks to internal cost centers based on utilization.

Long-term, the increased granularity in cost reporting available within Google Cloud can help you better measure your key performance indicators. You can shift from cost-based reporting (i.e. – “We spent $X on BigQuery storage last month”) to value-based reporting (i.e. – “It costs $X to serve customers who bring in $Y revenue”). 

To learn more about managing costs, check out Google Cloud’s “Understanding the principles of cost optimization” white paper.

Principle 6: Leverage continually improving services

The sixth principle is leverage continually improving services. Cloud services are consistently improving their performance and stability, even if some of these improvements are not obvious to users. These improvements can help your pipelines run faster, cheaper, and more consistently over time. You can take advantage of the benefits of these improvements by:

• Automating both your pipelines and pipeline management: Not only should data pipelines be automated, but almost all aspects of managing your pipelines can also be automated. This includes pipeline/data lineage tracking, monitoring, cost management, scheduling, access management and more. This helps reduce long-term operational costs of each data pipeline that can significantly alter your value proposition and prevent any manual configurations from negating the benefits of later product improvements.
• Minimizing pipeline complexity whenever possible: While ingestion pipelines are relatively easy to develop using UI-based or managed services, they also require continued maintenance as long as they are in use. The most easily maintained data ingestion pipelines are typically the ones that minimize complexity and leverage automatic optimization capabilities. Any transformation in a data ingestion pipeline is a manual optimization of the pipeline that may struggle to adapt or scale as the underlying services improve. You can minimize the need for such transformations by building ELT (extract, load, transform) pipelines rather than ETL (extract, transform, load) pipelines. This pushes transformations down to the data warehouse that is use a specifically optimized query engine to transform your data rather than manually configured pipelines.

Next steps

If you’re looking for more information about developing your cloud-based data platform, check out our Build a modern, unified analytics data platform whitepaper. You can also visit our data integration site to learn more and find ways to get started with your data integration journey.

Once you’re ready to begin building your data ingestion pipelines, learn more about how Cloud Data Fusion and Fivetran can help you make sure your pipelines address these principles.

The last few years have underscored the importance of speed in bringing new drugs and medical devices to market, while ensuring safety and efficacy. Over this time, healthcare and life sciences organizations have transformed the way they research, develop, and deliver patient care by embracing agility and innovation.

Now, the industry is set to reap the benefits of cloud technology and overcome the existing barriers to innovation.

What’s holding back innovation?

Costly clinical trials: The process of trialing and developing new drugs and devices is still long and costly, with more than 1 in 5 clinical trials failing due to a lack of funding.1 The high failure rate comes as no surprise when you consider the average clinical trial costs $19 million and takes 10-15 years (through all 3 phases) to be approved.2

Stringent security requirements: Pre-clinical R&D and clinical trials use large volumes of highly sensitive patient data – making the life sciences industry one of the top sectors targeted by hackers.3 On top of this, the FDA and other regulatory bodies have strict requirements for medical device cybersecurity.

Unpredictable supply chains: Global supply chains are becoming increasingly complex and unpredictable. This can be brought on by anything from supply shortages, to geo-political events, and even bad weather. Making things worse is the lack of visibility into medical shipment disruptions – so when disaster strikes you’re often caught off guard.

Google Cloud for life sciences

At Alphabet, we’ve made significant investments in healthcare and life sciences, helping to tackle the world’s biggest healthcare problems, from chronic disease management, to precision medicine, to protein folding.

Together with Google, you can transform your life sciences organization and deliver secure, data-driven innovation across the value chain.

• Accelerate clinical trials to deliver life-saving treatments faster and at less cost. Clinical trials require relevant and equitable patient cohorts that can produce clinically valid data. Solutions like DocAI can enable optimal patient matching for clinical trials, helping organizations optimize clinical trial selection and increase time to value. How that patient data is collected is also important. Collection in a physician’s office captures a snapshot of the participant’s data at one point in time and doesn’t necessarily account for daily lifestyle variables. Fitbit, used in more than 1,500 published studies–more than any other wearable device–can enrich clinical trial endpoints with new insights from longitudinal lifestyle data, which can help improve patient retention and compliance with study protocols. We have introduced Device Connect for Fitbit, which empowers healthcare and life sciences enterprises with accelerated analytics and insights to help people live healthier lives. We are able to empower organizations to improve clinical trials in key ways:

1. Enable clinical trial managers to quickly create and launch mobile and web RWE collection mechanism for patient reported outcomes
2. Enable privacy controls with Cloud Healthcare Consent API and, as needed, remove PHI using Cloud Healthcare De-identification API
3. Ingest RWE and data into BigQuery for analysis
4. Leverage Looker to enable quick visualization and powerful analysis of a study’s progress and results

• Ensure security and privacy for a safe, coordinated, and compliant approach to digital transformation. Google Cloud offers customers a comprehensive set of services including pioneering capabilities such as BeyondCorp Enterprise for Zero Trust and VirusTotal for malicious content and software vulnerabilities; Chronicle’s security analytics and automation coupled with services such as Security Command Center to help organizations detect and protect themselves from cyber threats; as well as expertise from Google Cloud’s Cybersecurity Action Team. Google Cloud also recently acquired Mandiant, a leader in dynamic cyber defense, threat intelligence and incident response services.

• Optimize supply chains and enhance your data to prepare for the unpredictable. With a digital supply chain platform, we can empower supply chain professionals to solve problems in real time including visibility and advanced analytics, alert-based event management, collaboration between teams and partners, and AI-driven optimization and simulation.

Ready to learn more? We’ll be taking a deep dive into each of the challenges outlined above in our life sciences video series. Stay tuned.

1. National Library of Medicine
2. How much does a clinical trial cost?
3. Life Sciences Industry Becomes Latest Arena in Hackers’ Digital Warfare