Demand for spoken audio content such as podcasts remains robust despite the proliferation of video services and other entertainment options for consumers. Shibin Li, Co-founder of Castbox, credits growth of the global podcast platform to the following: speed and availability, market-leading features, the proliferation of smart devices to deliver audio content, and activities — such as driving and working around the house — that make consuming video difficult.

Founded in 2016 and headquartered in Beijing, China, Castbox enables users to locate, access, and create spoken audio content. Available on iOS and Android, Castbox supports 50 million podcasts, on-demand radio programs, and audiobooks in 70 languages from 175 countries. The platform hosts about 2 million users per day and is the largest podcast platform on Android.

Castbox includes a range of features that build on its core service to provide a high-quality user experience. These features include curated podcast recommendations and in-audio search.

A combination of services

At its inception, Castbox relied on a combination of a multinational cloud services, as well as Google Cloud Platform (GCP) services, including the Google BigQuery analytics data warehouse and Cloud APIs to provide programmatic interfaces with Google Cloud services, and a range of services from the Google mobile development platform Firebase.

However, as Castbox matured and its user base expanded, the business increased its reliance on Google Cloud Platform and Firebase.

“We needed to access stable cloud services as we could not tolerate long periods of downtime that would compromise the user experience,” says Li. “Furthermore, we had to support up to 50,000 concurrent connections, and potentially more in future, without disruption.”

“Based on our analysis of the data in Google BigQuery, we can determine what type of content users are listening to, how long they like to listen to it, and when they like to listen to it. This allows us to recommend similar podcasts to each user based on the preferences he or she expressed, encouraging activity on and return visits to our platform.”

—Shibin Li, Co-Founder, Castbox

Competitive differentiation

Castbox also found machine learning-powered Google Cloud Platform APIs could help deliver features, such as in-audio search, that differentiate the podcast platform from its competitors. In addition, Firebase SDKs and Firebase A/B Testing would enable Castbox to create and analyze new applications, as well as make adjustments based on user feedback. Firebase Realtime Database would allow the business to support tens of thousands of concurrent user connections.

The diligence of the Google Cloud team in advising Li and her team about forthcoming products and services also swayed Castbox towards Google technologies. The business gained the opportunity with Google to join several programs that offered early access to Google innovations.

Signature in-audio search service

Castbox now uses Google Cloud Platform services in the Tokyo, Japan, and U.S. East regions. Cloud Speech-to-Text API plays a key role in delivering Castbox’s signature in-audio search service. This service enables users to search transcriptions of audio content on the platform for words or phrases. The search results incorporate the title of the podcast and the search term in context (for example, within the sentence or sentence excerpt in which it appears). Each use of the word or phrase is time-stamped so it can easily be found. The API enables Castbox developers to apply neural network algorithms to achieve audio-to-text conversion accuracy rates of greater than 96%, while search queries typically experience latency of just 50 milliseconds.

In addition, the latency of comparison data, converting audio to text, is only about 250 milliseconds, contributing to the processing of about 12 minutes worth of audio to text in just 10 minutes. “We can process about 20 hours of audio files in one day,” Li says. “This enables us to transcribe and index all the new episodes of a podcast in that period.”

50,000 concurrent connections

With Firebase Realtime Database, Castbox now supports up to 50,000 concurrent connections to its platform with an average latency per connection of just 10 milliseconds. “Firebase Realtime Database also allows us to continue operating in offline mode, which is extremely helpful if we experience any network disruptions,” Li explains. “When we come back online again, any data is simply synchronized with the database.”

Google BigQuery and the analytics capabilities of Firebase SDKs also enable Castbox to monitor and analyze user behaviors. “Based on our analysis of the data in Google BigQuery, we can determine what type of content users are listening to, how long they like to listen to it, and when they like to listen to it,” Li explains. “This allows us to recommend similar podcasts to each user based on the preferences he or she expressed, encouraging activity on and return visits to our platform.”

“Given our queries may span up to 40 days of data, we may be analyzing up to 1,200 GB at one time. We have no problem doing this with Google BigQuery.”

—Shibin Li, Co-Founder, Castbox

Castbox also uses its analyses of Google BigQuery data to amend banners and summaries on its platform to encourage users to listen to additional content. Furthermore, the service is prepared to make surprise recommendations of content to users based on the preferences and reactions of users with similar tastes.

“We analyze a pool of data growing at up to 30 GB per day,” Li explains. “Given our queries may span up to 40 days of data, we may be analyzing up to 1,200 GB at one time. We have no problem doing this with Google BigQuery.” These analyses also support Castbox’s decision to start creating original content, such as finance and economic news, for its platform.

Checking weekly changes

Castbox does not rely only on analyzing user data to deliver a high-quality experience. The business aggregates user feedback from emails and Google Play reviews to make weekly changes to its platform. It then uses Firebase A/B Testing to check whether these changes are met with a positive user response.

“We are extremely pleased with Google Cloud Platform and Firebase. We have been able to differentiate ourselves from our competitors and provide an attractive option for users at a time when content and entertainment options are exploding. We have a great opportunity with Google to continue to improve the value of our offering to users and build engagement and loyalty.”

—Shibin Li, Co-Founder, Castbox

Castbox’s positive experiences with Google Cloud Platform are encouraging the business to grow its use of the product. “We are trying to move some more services to Google Cloud Platform because it is very stable and scalable,” Li says. The business is keen to explore the capabilities of Cloud Pub/Sub to provide low latency messaging between applications, Cloud Spanner to deliver a distributed relational database service, and Cloud Dataflow to transform and enrich data in stream and batch modes.

“We are extremely pleased with Google Cloud Platform and Firebase,” Li concludes. “We have been able to differentiate ourselves from our competitors and provide an attractive option for users at a time when content and entertainment options are exploding. We have a great opportunity with Google to continue to improve the value of our offering to users and build engagement and loyalty.”

As we become increasingly reliant on technology to make decisions, geospatial data is becoming more critical than ever. It is a powerful resource that can be used to solve a variety of problems, from tracking the movement of goods, identifying interest areas and potential areas of disaster.

Geospatial data incorporates data with a geographic component, such as latitude and longitude coordinates, addresses, postal codes, or place names, and can be obtained from a variety of sources, including satellites, sensors, and surveys, making it an incredibly powerful tool with a broad range of applications.

One of the key features of BigQuery, Google Cloud’s serverless enterprise data warehouse, is its ability to analyze geospatial data. However, oftentimes geospatial data sits in a variety of public clouds, not just Google Cloud. To access it effectively, you need a multi-cloud analytics solution that lets you capitalize on the distinct capabilities of each cloud platform, while extracting insights and value from data sitting across multiple cloud platforms.

BigQuery Omni is a multi-cloud analytics solution that enables the analysis of data stored across public cloud environments, including Google Cloud, Amazon Web Services (AWS) and Microsoft Azure, without the need to transfer the data. With BigQuery Omni, users can employ the same SQL queries and tools used to analyze data in Google Cloud to analyze data in other clouds, making it easier to gain insights from all data, regardless of the storage location. For businesses using multiple clouds, BigQuery Omni is an excellent tool to unify analytics and optimize the value of data.

With BigQuery Omni, organizations can analyze location-based information or geographic components, such as latitude and longitude coordinates, addresses, postal codes, or place names without even copying their data to Google Cloud. For example, you could use BigQuery Omni to analyze data from a fleet of delivery vehicles to track their location and identify potential problems.

BigQuery Omni and geospatial data analysis

If you are working with geospatial data, BigQuery Omni is a powerful tool that can help you to get insights from your data. It is scalable, reliable, and secure, making it a great option for unifying your analytics and getting the most out of your data. 

Here are a few examples of ways organizations might use BigQuery Omni for geospatial data:

• A transportation company could use BigQuery Omni to analyze data from GPS sensors in its vehicles to track the movement of its fleet and identify potential problems.
• A retail company could use BigQuery Omni to analyze data from its point-of-sale systems to track customer behavior and identify trends.
• A government agency could use BigQuery Omni to analyze data from its weather sensors to track the movement of storms and identify areas at risk of flooding.

BigQuery Omni and geospatial data can be used together to gain insights into a variety of business problems. Specifically, some of the advantages of using BigQuery Omni and geospatial data include:

• Access to quality geospatial data: BigQuery supports loading of newline-delimited GeoJSON files and provides built-in support for loading and querying geospatial data. Data from public data sources like BigQuery public datasets, the Earth Engine catalog, and the United States Geological Survey (USGS) can be easily integrated into your BigQuery environment. Earth Engine has an integrated data catalog with a comprehensive collection of analysis-ready datasets, including satellite imagery and climate data. This data can be combined with proprietary data sources such as SAP, Oracle, Esri ArcGIS Server, Carto, and QGIS.
• Loading and preprocessing of geospatial data: BigQuery has built-in support for loading and querying geospatial data types, and you can use partner solutions such as FME Spatial ETL to load data.
• Working with different geospatial data types and formats: BigQuery supports a variety of file types and formats including WKT, WKB, CSV and GeoJSON.
• Coordinate reference systems: BigQuery’s geography data type is globally consistent. That means that your data is registered to the WGS84 reference system and your analyses can span a city block or multiple continents.

Overall, geospatial analytics with BigQuery Omni provides a wide range of technical capabilities for processing and analyzing geospatial data, making it a powerful tool for businesses that need to work with location-based data.

Analyzing geospatial data with BigQuery Omni

Imagine a retailer who has a large chain of department stores with locations all over the country. They are looking to expand their business and want to identify areas with high sales potential. They want a way to get a better understanding of their sales volume within specific geographic boundaries. To achieve this goal, the retailer turns to the GIS (Geographic Information System) functions built into BigQuery. Here are the steps what the retailer takes to analyze this dataset:

Step 1 : An initial orders dataset on AWS S3 contains 5.54 million rows, and with separate locations (300 rows) and zipcode (33144 rows) metadata files on AWS S3.

Orders Parquet files:

Location and Zipcode files:

Step  2 : The retailer uses BigQuery Omni to establish a connection between the data stored in AWS and BigQuery, enabling them to access the S3 datasets externally.

External Connection for AWS

External Table for orders

External Table for locations

External Table for zipcode

Step 3 : They combine the orders and locations datasets using BigQuery Omni, and remotely aggregate the data on AWS. Joining this dataset with geospatial datasets helps them derive geospatial coordinates.

The final aggregated dataset is reduced to 23 rows. Subsequently, they bring back the result dataset, which contains just 23 rows. This helps them reduce the extraction of millions of rows to just 23 rows for their geospatial analytics.

Select sales.store_city store_city,
sales.number_of_sales_last_10_mins number_of_sales_last_10_mins,
sales.store_zip store_zip,
 ST_GeogPoint(zip_lat_lng.longitude ,
   zip_lat_lng.latitude  ) geo
FROM (
 SELECT
   FORMAT_DATETIME("%X",
     CURRENT_DATETIME("America/Los_Angeles")) current_time,
   MAX(DATETIME(time_of_sale,
       "America/Los_Angeles")) time_of_last_sale,
   COUNT(1) number_of_sales_last_10_mins,
   locations.city store_city,
   locations.zip store_zip
 FROM
   `bqomni-blog.aws_locations.orders_small`  sales
 JOIN
   `bqomni-blog.aws_locations.locations` locations
 ON
   sales.store_id = locations.id
 GROUP BY
   locations.city,
   locations.zip ) sales
JOIN
   `bqomni-blog.aws_locations.zipcode` zip_lat_lng
ON
 cast(sales.store_zip as INT) = cast(zip_lat_lng.zipcode as INT)
 WHERE ST_WITHIN( ST_GeogPoint(zip_lat_lng.longitude , zip_lat_lng.latitude  ) ,ST_GeogFromText(zip_lat_lng.zipcode_geom ) )
 AND zip_lat_lng.state_name  = "New York"
 ORDER BY number_of_sales_last_10_mins

Aggregated Sales data by region

To build richer views of their sales volume data, the retailer uses BigQuery GeoViz integration, a powerful tool that allows for visualizing geographic data on maps.BigQuery Geo Viz is a web tool for visualization of geospatial data in BigQuery using Google Maps APIs. You can run a SQL query and display the results on an interactive map

BigQuery Geo view for sales data analysis

With the geo-tagged data in place, the retailer can now see regional sales volume, sales density, distribution by department by time, and distribution within department, all powered through BigQuery.

Satellite view

Benefits of using BigQuery Omni 

Beyond geospatial analysis, BigQuery Omni offers a number of benefits, including:

Reduced costs: BigQuery Omni’s ability to eliminate data transfers between clouds can help organizations reduce costs and simplify data management, making it a valuable tool for multi-cloud analytics. Also, the ability to access and analyze data across multiple clouds can reduce the need for data replication and synchronization, which can further simplify the ETL process and improve data consistency.

Unified governance: BigQuery Omni uses the same security controls as BigQuery, which include features such as encryption, access controls, and audit logs, to help protect data from unauthorized access.

Single pane for analytics: BigQuery Omni provides a single interface for querying data across all three clouds, which can simplify the process of analyzing data and reduce the need for organizations to use multiple analytics tools.

Flexibility: Analyze data stored in any of the supported cloud storage services, giving organizations the flexibility to work with the data they have regardless of where it’s located.

BigQuery Omni is a valuable tool for geospatial analysis because it allows you to analyze data from multiple sources without having to move the data. This can save you time and money, and it can also help you to get more accurate insights from your data.If you are looking for a way to improve the accuracy, efficiency, and decision-making of your business, using BigQuery Omni to analyze geospatial data can be a powerful tool.

References

• Omni-introduction 
• Omni-benefits 
• Omni-aws-create-connection
• Omni-azure-create-connection 
• BigQuery Omni Retail Demo on Looker
• Omni-pricing 
• Working with Geospatial Data
• Geospatial Analytics – Intro

Learn more about how BigQuery Omni can help your organization.

Millions of people annually view esports, as top players and teams compete in thrilling, fast-paced tests of reflexes, strategy, and teamwork. Fans are a diverse group, sharing a passion for action. A new, revolutionary project we’re working on with Call of Duty League just made that action a lot better.

ActivStat brings fans, players, and commentators the power of competitive statistics in real-time—stats that matter not only to the game at hand, but also for a full roster of competitors globally. Using ActivStat, live broadcasts will soon be enhanced with more depth and color-of-play while they’re happening, building excitement and adding to the overall experience.

Technically, ActivStat is an entirely new capability for esports. It’s a constantly updating catalog of statistics that is sourced, analyzed, updated, and delivered in an easy-to-consume way across global-scale computing systems, with a latency of milliseconds or seconds, rather than minutes or hours. By comparison, many of these stats today are available to fans after a day or more of processing.

Call of Duty League plans to begin rolling out ActivStat during the 2021 season. The initial rollout will include critical information like player and team standings and winning ratios across multiple aspects of virtual combat—including ultimately what these numbers mean for rankings. ActivStats are delivered both in raw statistics and via visualizations and graphics, providing commentators with fast access to the types of insights fans crave. 

For engineers at both companies, building the service has been an epic success all its own. Due to a sponsorship with Call of Duty signed last February, our dedicated game engineering team quickly innovated a new solution incorporating high-speed networking, data pipelines from multiple cloud sources, and data warehousing to create a user-friendly dashboard. Real data was flowing into commentator dashboards by April.

Esports are more complex to cover in many ways than regular sports. Instead of a well-defined physical playing field (often a simple rectangular space), multiplayer games involve complex and sprawling virtual environments that can be the size of a large campus or airport with multiple levels and hidden locations.   

Gameplay between competitors happens across many of these locations simultaneously. In addition, competitors also each choose their own configurations of equipment, known as “loadouts,” which can dramatically affect gameplay and strategy. All of this additional complexity in online gaming involves data that needs to be captured, analyzed, and communicated to the fans in insightful ways.

Two Google Cloud technology capabilities play central roles in the operations of ActivStat. BigQuery—a planet-scale data warehouse that can store and query petabytes of information in real time—is the foundation of the ActivStat platform for gathering and summarizing millisecond-level statistics. Looker, an intuitive analytic dashboard, surfaces those insights to commentators in an easy-to-use, real-time dashboard that enables the commentators to speak to compelling insights and statistics in sync with the gameplay as it is happening in the live broadcast.

While the initial release of ActivStat for this season of Call of Duty League provides compelling and powerful capabilities, it’s only the beginning of this Call of Duty League-Google Cloud co-innovation partnership. Our future vision is mapping gameplay hotspots on the field, and predicting where to place cameras with machine learning as the match evolves–enabling broadcast producers to build excitement for fans by always being in the middle of the best action. Call of Duty League and Google will also look to drive statistics and metrics directly into the broadcast feed.

The implications of the real-time statistical capabilities of ActivStat go beyond gaming and esports. Historically, gaming has been at the leading edge of what computer processing, computer graphics, wide-area networking, data analysis, and insight can do. The real-time data ingestion and output used in ActivStat could one day be useful powering live broadcasts in other types of sporting events, as well as blending live video feeds and data to support use cases in media & entertainment, healthcare, finance, manufacturing, and other verticals. We’re excited about the potential for this bold new solution and partnership between Call of Duty League and Google in esports and beyond.

To learn more about Call of Duty League-Google Cloud partnership, visit our press release.

Editor’s note: The post is part of a series highlighting our awesome partners, and their solutions, that are Built with BigQuery

In the field of producing engaging video content such as ads, many marketers ignore the power of data to improve their creative efforts to meet the consumers’ need for personalized messages. The demand for creative tech to efficiently personalize is real as marketers need personalized video Ads to reach their audience with the right message at the right time. Data, Insights and Technology are the main ingredients to deliver this value while ensuring security and privacy requirements are met. The Connected-Stories team partnered with Google Cloud to build a platform for Ad personalization. Google Data Cloud and BigQuery are at the forefront to assimilate data, leverage ML models, create personalized ads, and capitalize on real-time intelligence as the core features of the Connected-Stories NEXT platform.

Connected-Stories NEXT is an end-to-end creative management platform to develop, serve, and optimize interactive video and display ads that scale across any channel. The platform ingests first-party data to create custom ML models, measure numerous third-party data points to help brands develop unique customer journeys and create videos that their data signals can drive. An intelligent feedback loop passes real-time data back, enabling brands to make data-driven and actionable video ads that take the brand’s campaigns to the next level.

The core use case of the NEXT platform revolves around collecting user’s interaction data and optimizing for precision and speed to create an actionable Ad experience that is personalized for each user. The platform processes complex data points to create interactive data visualizations that allow for accurate analysis. The platform uses Vertex AI to access managed tools, workflows, and infrastructure to build, deploy, and scale ML models that have improved the accuracy to identify segments for further analysis.

The platform ingests 200M data events with peaks and valleys of activity. These events are processed to generate dashboards that enable users to visualize metrics based on filters in real-time. These dashboards have high performance requirements in terms of a responsive user interface under constantly changing data dimensions.

Google Cloud’s serverless stack coupled with limitless data cloud infrastructure has been the core to the NEXT platform’s data-driven innovation. The growing volume of data ingested, streamed and processed were scaled uniformly across the compute, storage and analytical layers of solution. A lean development team at Connected-Stories were able to focus all-in on the solution, while the serverless stack scaled, lowered attack service in terms of security and optimized the cost footprint through pay-as-you-go features.

BigQuery has been the backbone to support the vast amounts of data spreading over multiple geos resulting in workloads running at petabyte scale. BigQuery’s fully managed serverless architecture, real-time streaming, built-in machine learning and rich business intelligence capabilities distinguishes itself from a cloud data warehouse. It is the foundation needed to approach data and serve users in an unlimited number of ways. For an application with zero tolerance for failure, given its fully managed nature, BigQuery handles replication, recovery, data distributed optimization and management.

The platform’s requirements include the need for low maintenance, constantly ingesting and refreshing data and smart-tuning of aggregated data. These capabilities can be implemented by BigQuery’s materialized views feature. Materialized views are useful for precomputed views that regularly cache query results for better performance. These views possess the innate feature to read only the delta change from base tables and calculate the up-to-date aggregations. Materialized views impart faster outputs and consume fewer resources while reducing the cost footprint.

Some key considerations in using Google cloud and focusing on the Serverless stack include: quick onboarding to development, prototyping in short sprints and ease of preparing data in a rapidly changing environment. Typical considerations around low code / no code include data transformation, aggregation and reduced deployment time. These considerations are fulfilled through using serverless capabilities within Google Cloud such as PubSub, Cloud Storage, Cloud Run, Cloud Composer, Dataflow and BigQuery as described in the Architecture diagram below. The use of each of these components and services are described below.

1. Input/Ingest: At a high-level, microservices hosted in Cloud Run collect and aggregate incoming Ads events.
2. Enrichment: The output of this stage is a Pub-Sub message enriched with more attributes based on a pre-configured campaign.
3. Store: a Cloud Dataflow streaming job to create text files in Cloud Storage buckets.
4. Trigger: Cloud Composer triggers the spark jobs based on text files to process and group them to produce desired output as one record per impression, a logical group of events.
5. Deploy: Cloud Build is then used to automate all deployments.

Thus far, all Google cloud managed services work together to ingest, store and trigger the orchestration, all of which are scalable based on configurations including autoscaling capabilities.

1. Visualization: A visualization tool reads data from BigQuery to compute pre-aggregations required for each dashboard.
2. Data Model Evolution considerations: Though the solution served the purpose of creating pre-aggregations, as the data model evolved by adding a column or creating a new table, it led to recreating pre-aggregations and querying the data again. Alternatively, creating aggregate tables as an extra output of current ETLs seemed like a viable option. However, this would increase the cost and complexity of jobs. A similar situation to reprocess or update aggregated tables would occur as data is updated.

Precomputed views of data that is periodically cached are critical to reach the audience with the right message at the right time.

1. Performance: In order to increase the performance of the platform, we need to have regularly precomputed views of the data, cached .
2. Materialized Views: Consumers of these views needed faster response times, to consume fewer resources and output only the changes in comparison to a base table. BigQuery Materialized views were used to solve this very requirement. Materialized views have been highly leveraged to optimize the design resulting in lesser maintenance and access to fresh data with high performance with a relatively low technical investment in creating and maintaining SQL code.
3. Dashboards: Application dashboards pointing to the Materialized views are highly performant and provide a view into fresh data.
4. Custom Reports with Vertex AI Notebooks: Vertex AI notebooks directly read data from BigQuery to produce custom reports for a subset of customers. Vertex AI has been hugely beneficial to data analysts, where an environment with pre-installed libraries simplifies the readiness to use. Vertex AI Workbench notebooks are used to share these reports within the team allowing them to work always on the cloud without having the need to download data at any time. Besides, it increases the velocity to develop and test ML models faster.

The NEXT platform has yielded benefits such as customers having the ability to create unique consumer journeys powered by AI / ML personalization triggers, using first-party data and business intelligence tools to capitalize on real-time creative intelligence, which is a dashboard to measure campaign performance for cross-functional teams to analyze the impact of Ad content experience at a granular level. All of these while ensuring controlled access to data to enrich data without moving across clouds. The NEXT platform can keep up with increased demands for agility, scalability and reliability through the underlying usage of Google Cloud.

Partnering with Google, in the context of the Google Built with BigQuery program has surfaced the differentiated value in areas of creating interactive personalized Ads by using real-time data. In addition, by sharing this data across organizations as assets, ML models have fueled higher levels of innovation. Connected-Stories plan to deepen the penetration into the entire spectrum of services offered in the AI/ML area to enhance core functionality and provide newer capabilities to the platform.

Click here to learn more about Connected-Stories NEXT Platform capabilities.

The Built with BigQuery Advantage for ISVs

Through Built with BigQuery, launched in April ‘22 as part of Google Data Cloud Summit, Google is helping tech companies like Connected-Stories co-innovate in building applications that leverage Google’s data cloud with simplified access to technology, helpful and dedicated engineering support, and joint go-to-market programs. Participating companies can:

• Get started fast with a Google-funded, pre-configured sandbox.
• Accelerate product design and architecture through access to designated technical experts from the ISV Center of Excellence who can share insights from key use cases, architectural patterns, and best practices encountered in the field.
• Amplify success with joint marketing programs to drive awareness, generate demand, and increase adoption.

The Google Data Cloud spectrum of products and specifically BigQuery give ISVs the advantage of a powerful, highly scalable data warehouse that’s integrated with Google Cloud’s open, secure, sustainable platform. And with a huge and expanding partner ecosystem and support for multi-cloud, open source tools and APIs, Google provides technology companies the portability and extensibility they need to avoid data lock-in and exercise choice.

We thank the Google Cloud and Connected-Stories team members who co-authored the blog: Connected-Stories: Luna Catini, Marketing Director, Google: Sujit Khasnis, Cloud Partner Engineering

Database disaster recovery (DR) planning is an important component of a bigger DR plan, and for enterprises using Microsoft SQL Server on Compute Engine, it often involves critical data.

When you’re architecting a disaster recovery solution with Microsoft SQL Server running on Google Cloud Platform (GCP), you have some decisions to make to build an effective, comprehensive plan. 

Microsoft SQL Server includes a variety of disaster recovery strategies and features, such as Always On availability groups or Failover Cluster Instances.

And Google Cloud is designed from the start for resilience and availability. There are several types of data centers available within GCP where you can map SQL Server’s availability features based on your specific requirements: zones and regions. Zones are autonomous data centers co-located within a GCP region. These regions are available in different geographies such as North America or APAC. 

However, there is no single disaster recovery strategy to map Microsoft SQL Server DR features to Google Cloud’s data center topology that satisfies every possible combination of disaster recovery requirements.

As a database architect, you have to design a custom disaster recovery strategy based on your specific use cases and requirements.

Our new Disaster Recovery for Microsoft SQL Server solution provides information on Microsoft’s SQL Server disaster recovery strategies, and shows how you can map them to zones and regions in GCP based on your business’s particular criteria and requirements. One example is deploying an availability group within a region across three zones (shown in the diagram below). 

For successful DR planning, you should have a clear conceptual model and established terminology in place. In this solution, you’ll find a base set of concepts and terms in context of Google Cloud DR. This includes defining terms like primary database, secondary database, failover, switchover, and fallback.

You’ll also find details on recovery point objective, recovery time objective and single point of failure domain, since those are key drivers for developing a specific disaster recovery solution.

Building a DR solution with Microsoft SQL Server in GCP regions
To get started with implementing the availability features of Microsoft SQL Server in the context of Google Cloud, take a look at this diagram, which shows the implementation of an Always On availability group in a GCP region, using several zones:

In the new solution, you’ll see other availability features, like log shipping, along with how they map to GCP. In addition, features in Microsoft SQL Server that are not deemed availability features—like server replication and backup file shipping—can actually be used for disaster recovery, so those are included as well. 

Disaster recovery features of Microsoft SQL Server do not have to be used in isolation and can be combined for more complex and demanding use cases. For example, you can set up availability groups in two regions with log shipping as the transfer mechanism between the regions.

Disaster Recovery for Microsoft SQL Server also describes the disaster recovery process itself, how to test and verify a defined disaster recovery solution, and outlines a basic approach, step-by-step.