Over the past few decades, organizations have mastered the science of data warehousing. They have increasingly applied descriptive analytics to large quantities of stored data, gaining insight into their core business operations. Conventional Business Intelligence (BI), which focuses on querying, reporting, and Online Analytical Processing, might have been a differentiating factor in the past, either making or breaking a company, but it’s no longer sufficient.

Today, not only do organizations need to understand past events using descriptive analytics, they need predictive analytics, which often uses machine learning (ML) to extract data patterns and make probabilistic claims about the future. The ultimate goal is to develop prescriptive analytics that combine lessons from the past with predictions about the future to automatically guide real-time actions.

Traditional data warehouse practices capture raw data from various sources, which are often Online Transactional Processing (OLTP) systems. Then, a subset of data is extracted in batches, transformed based on a defined schema, and loaded into the data warehouse. Because traditional data warehouses capture a subset of data in batches and store data based on rigid schemas, they are unsuitable for handling real-time analysis or responding to spontaneous queries. Google designed BigQuery in part in response to these inherent limitations.

Innovative ideas are often slowed by the size and complexity of the IT organization that implements and maintains these traditional data warehouses. It can take years and substantial investment to build a scalable, highly available, and secure data warehouse architecture. BigQuery offers sophisticated software as a service (SaaS) technology that can be used for serverless data warehouse operations. This lets you focus on advancing your core business while delegating infrastructure maintenance and platform development to Google Cloud.

BigQuery offers access to structured data storage, processing, and analytics that’s scalable, flexible, and cost effective. These characteristics are essential when your data volumes are growing exponentially—to make storage and processing resources available as needed, as well as to get value from that data. Furthermore, for organizations that are just starting with big data analytics and machine learning, and that want to avoid the potential complexities of on-premises big data systems, BigQuery offers a pay-as-you-go way to experiment with managed services.

With BigQuery, you can find answers to previously intractable problems, apply machine learning to discover emerging data patterns, and test new hypotheses. As a result, you have timely insight into how your business is performing, which enables you to modify processes for better results. In addition, the end user’s experience is often enriched with relevant insights gleaned from big data analysis, as we explain later in this series.

The migration framework

Undertaking a migration can be a complex and lengthy endeavor. Therefore, we recommend adhering to a framework to organize and structure the migration work in phases:

1. Prepare and discover: Prepare for your migration with workload and use case discovery.
2. Assess and plan: Assess and prioritize use cases, define measures of success, and plan your migration.
3. Execute: Iterate the following steps for each use case:
   1. Migrate (offload): Migrate only your data, schema, and downstream business applications.
   2. Migrate (full): Alternatively, migrate the use case fully end-to-end. The same as Migrate (offload), with the addition of the upstream data pipelines.
   3. Verify and validate: Test and validate the migration to assess return on investment.

The following diagram illustrates the recommended framework and shows how the different phases are connected:

For a deeper understanding, read Migrating data warehouses to BigQuery: Introduction and overview

Cloud Bigtable is a popular and widely used key-value database available on Google Cloud. The service provides scale elasticity, cost efficiency, excellent performance characteristics, and 99.999% availability SLA. This has led to massive adoption with thousands of customers trusting Bigtable to run a variety of their mission-critical workloads.

Bigtable has been in continuous production usage at Google for more than 15 years now. It processes more than 5 billion requests per second at peak and has more than 10 exabytes of data under management. It’s one of the largest semi-structured data storage services at Google.

One of the key use cases for Bigtable at Google is ad personalization. This post describes the central role that Bigtable plays within ad personalization.

Ad personalization

Ad personalization aims to improve user experience by presenting topical and relevant ad content. For example, I often watch bread-making videos on YouTube. If ads personalization is enabled in my ad settings, my viewing history could indicate to YouTube that I’m interested in baking as a topic and would potentially be interested in ad content related to baking products.

Ad personalization requires large-scale data processing in near real-time for timely personalization with strict controls for user data handling and retention. System availability needs to be high, and serving latencies need to be low due to the narrow window within which decisions need to be made on what ad content to retrieve and serve. Sub-optimal serving decisions (e.g. falling back to generic ad content) could potentially impact user experience. Ad economics requires infrastructure costs to be kept as low as possible.

Google’s ad personalization platform provides frameworks to develop and deploy machine learning models for relevance and ranking of ad content. The platform supports both real-time and batch personalization. The platform is built using Bigtable, allowing Google products to access data sources for ads personalization in a secure manner that is both privacy and policy compliant, all while honoring users’ decisions about what data they want to provide to Google

The output from personalization pipelines, such as advertising profiles are stored back in Bigtable for further consumption. The ad serving stack retrieves these advertising profiles to drive the next set of ad serving decisions.

Some of the storage requirements of the personalization platform include:

• Very high throughput access for batch and near real-time personalization
• Low latency (<20 ms at p99) lookup for reads on the critical path for ad serving
• Fast (i.e. in the order of seconds) incremental update of advertising models in order to reduce personalization delay

Bigtable

Bigtable’s versatility in supporting both low-cost, high-throughput access to data for offline personalization as well as consistent low-latency access for online data serving makes it an excellent fit for the ads workloads.

Personalization at Google-scale requires a very large storage footprint. Bigtable’s scalability, performance consistency and low cost required to meet a given performance curve are key differentiators for these workloads.

Data model

The personalization platform stores objects in Bigtable as serialized protobufs keyed by Object ids. Typical data sizes are less than 1 MB and serving latency is less than 20 ms at p99.

Data is organized as corpora, which correspond to distinct categories of data. A corpus maps to a replicated Bigtable.

Within a corpus, data is organized as DataTypes, logical groupings of data. Features, embeddings, and different flavors of advertising profiles are stored as DataTypes, which map to Bigtable column families. DataTypes are defined in schemas which describe the proto structure of the data and additional metadata indicating ownership and provenance. SubTypes map to Bigtable columns and are free-form.

Each row of data is uniquely identified by a RowID, which is based on the Object ID. The personalization API identifies individual values by RowID (row key), DataType (column family), SubType (column part), and Timestamp.

Consistency

The default consistency mode for operations is eventual. In this mode, data from the Bigtable replica nearest to the user is retrieved, providing the lowest median and tail latency.

Reads and writes to a single Bigtable replica are consistent. If there are multiple replicas of Bigtable in a region, traffic spillover across regions is more likely. To improve the likelihood of read-after-write consistency, the personalization platform uses a notion of row affinity. If there are multiple replicas in a region, one replica is preferentially selected for any given row, based on a hash of the Row ID.

For lookups with stricter consistency requirements, the platform first attempts to read from the nearest replica and requests that Bigtable return the current low watermark (LWM) for each replica. If the nearest replica happens to be the replica where the writes originated, or if the LWMs indicate that replication has caught up to the necessary timestamp, then the service returns a consistent response. If replication has not caught up, then the service issues a second lookup—this one targeted at the Bigtable replica where writes originated. That replica could be distant and the request could be slow. While waiting for a response, the platform may issue failover lookups to other replicas in case replication has caught up at those replicas.

Bigtable replication

The Ads personalization workloads use a Bigtable replication topology with more than 20 replicas, spread across four continents.

Replication helps address the high availability needs for ad serving. Bigtable’s zonal monthly uptime percentage is in excess of 99.9%, and replication coupled with a multi-cluster routing policy allows for availability in excess of 99.999%.

A globe-spanning topology allows for data placement that is close to users, minimizing serving latencies. However, it also comes with challenges such as variability in network link costs and throughputs. Bigtable uses Minimum Spanning Tree-based routing algorithms and bandwidth-conserving proxy replicas to help reduce network costs.

For ads personalization, reducing Bigtable replication delay is key to lowering the personalization delay (the time between a user’ action and when that action has been incorporated into advertising models to show more relevant ads to the user). Faster replication is preferred but we also need to balance serving traffic against replication traffic and make sure low-latency user-data serving is not disrupted due to incoming or outgoing replication traffic flows. Under the hood, Bigtable implements complex flow control and priority boost mechanisms to manage global traffic flows and to balance serving and replication traffic priorities.

Workload Isolation

Ad personalization batch workloads are isolated from serving workloads by pinning a given set of workloads onto certain replicas; some Bigtable replicas exclusively drive personalization pipelines while others drive user-data serving. This model allows for a continuous and near real-time feedback loop between serving systems and offline personalization pipelines, while protecting the two workloads from contending with each other.

For Cloud Bigtable users, AppProfiles and cluster-routing policies provide a way to confine and pin workloads to specific replicas to achieve coarse-grained isolation.

Data residency

By default, data is replicated to every replica—often spread out globally—which is wasteful for data that is only accessed regionally. Regionalization saves on storage and replication costs by confining data to the region where it is most likely to be accessed. Compliance with regulations mandating that data pertaining to certain subjects are physically stored within a given geographical area is also vital.

The location of data can be either implicitly determined by the access location of requests or through location metadata and other product signals. Once the location for a user is determined, it is stored in a location metadata table which points to the Bigtable replicas that read requests should be routed to. Migration of data based on row-placement policies happens in the background, without downtime or serving performance regressions.

Conclusion

In this blog post, we looked at how Bigtable is used within Google to support an important use case—modeling user intent for ad personalization.

Over the past decade, Bigtable has scaled as Google’s personalization needs have scaled by orders of magnitude. For large-scale personalization workloads, Bigtable offers low cost storage with excellent performance characteristics. It seamlessly handles global traffic flows with simple user configurations. Its ease at handling both low-latency serving and high-throughput batch computations make it an excellent option for lambda-style data processing pipelines.

We continue to drive high levels of investment to further lower costs, improve performance, and bring new features to make Bigtable an even better choice for personalization workloads.

Learn more

To get started with Bigtable, try it out with a Qwiklab and learn more about the product here.

Acknowledgements
We’d like to thank Ashish Awasthi, Ashish Chopra, Jay Wylie, Phaneendhar Vemuru, Bora Beran, Elijah Lawal, Sean Rhee and other Googlers for their valuable feedback and suggestions.

Enterprise database workloads are the backbone of many of your applications and ecosystems. Also, guaranteed availability is critical when choosing a cloud provider.

Many enterprises built their mission-critical applications on Microsoft SQL Server 2008, and it’s common still to run into older versions of SQL Server as you’re working toward modernizing your on-prem environments.

According to Business insider, 60% of Microsoft users still use SQL Server 2008, which reached its end of life in July 2019. This provides the opportunity for many of you to find a place to host your SQL Server 2008 instances on newer technology with less operational burden. 

We’re announcing that Cloud SQL for SQL Server is generally available globally. This means that Cloud SQL now helps you keep your SQL Server workloads running by providing a 99.95% uptime service-level agreement (SLA), which is consistent with the other Cloud SQL database engines.

Cloud SQL for SQL Server is fully managed and compatible with SQL Server 2017. Now you can migrate your critical production SQL Server workloads to Google Cloud and rely on the service’s stability and reliability. 

We hear from enterprise companies how important the ability to migrate to Cloud SQL for SQL Server is to their larger goals of infrastructure modernization and a multi-cloud strategy. On-premises applications like HR, finance, and payroll often depend on these legacy databases to keep running.

Customers often cite the challenge of wanting to maintain compatibility with these existing systems and datasets, while also streamlining deployments and scale-out at a fraction of the overhead. Migrating these instances to Cloud SQL for SQL Server can save costs and maintenance time and improve efficiency and speed. 

Getting started migrating SQL Server 2008

The migration for Microsoft SQL Server 2008 to Cloud SQL for SQL Server can be achieved in a simple five steps. For details, check out the full migration guide: SQL Server 2008 R2 server to Cloud SQL for SQL Server. 

1. Create a Cloud SQL for SQL Server instance

gcloud beta sql instances create target  \
    --database-version=SQLSERVER_2017_ENTERPRISE \
    --cpu=2 \
    --memory=5GB \
    --root-password=sqlserver12@ \
    --zone=us-central1-f

2. Create a Cloud Storage bucket

  gsutil mb -b off -l US "gs://bucket-name"

3. Back up your Microsoft SQL Server 2008 database

osql -E -Q “BACKUP DATABASE db-name TO DISK=’c:\backup\db-name.bak'”

4. Import the database into Cloud SQL for SQL Server

gcloud beta sql import bak target \
    gs://bucket-namedb-name.bak \
    --database db-name

5. Validate the imported data

/opt/mssql-tools/bin/sqlcmd -U sqlserver -S 127.0.0.1 -Q “query-string”

If you’re working with newer versions of SQL Server, check out the SQL Server 2017 to Cloud SQL for SQL Server migration guide.

Since the launch of Cloud SQL for SQL Server, we’ve heard your feedback and have continued to improve the performance and durability of the service. We expect to continue our rapid pace of innovation and feature releases to meet our customers’ needs and address feedback. Cloud SQL for SQL Server has proven itself as a key component when migrating existing enterprise applications and infrastructure.

We’re continuing to rapidly improve Cloud SQL for SQL Server to meet all of your cloud database needs. Stay tuned for features in development that can help with Active Directory integration, online migrations, and more options for replicas and machine types. 

May is Asian American Pacific Islander 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 founder Georgene Huang and how her startup Fairygodboss uses easy to use Google Cloud tools to grow and innovate. 

Coming from a male-dominated industry like Wall Street, I never thought much about my gender. I was so focused on work that I didn’t feel like I could focus on my identity as a woman of color. That all changed when I found myself thrust into the job market while two months pregnant.

Traditionally, job-seeking sites are built by – and geared towards – men and their professional needs. There was no recruiting site that broke down what candidates who identify as women might expect from a company, let alone women of color like myself. I founded Fairygodboss to create a space where women can crowdsource information about opportunities and employers in order to make informed career decisions. 

Fairygodboss 

Fair•y•god•boss (noun): A person who elevates women at work.

Fairygodboss is the largest career community for women. In researching potential employers, I had many questions surrounding company culture, maternity leave, and women in leadership positions. While sites like Glassdoor provide high-level details for job seekers, I wanted more concrete examples and data than general salary information and anecdotes about work-life balance. 

Fairygodboss began as a job review site geared towards women, but has expanded significantly since our initial launch. In addition to providing free resources like career connections, job listings, virtual recruiting events, community advice, and real research on how companies treat women, we also proactively help companies improve gender diversity. Fairygodboss partners with hundreds of top employers to create a 360-degree recruiting and employer branding program designed to drive female applicants to job postings by showcasing a company’s track record of women in leadership within a company, and their diversity investments. 

Google Cloud + Fairygodboss: Data defined 

At our core, Fairygodboss is a data-driven company, and we rely on Google Cloud to help us process, store, and analyze user-driven events occurring on the site. As a highly scalable, serverless, and cost-effective data warehousing solution, BigQuery collect millions of events each day, helping us identify where traffic is coming from (and where it goes next), and it produces actionable insights to provide better recommendations, information, and opportunities for women visiting our platform. 

After seamlessly connecting our BigQuery data to Data Studio, we have been able to visualize and make sense of all the data we receive on our platform. Unlike some other data visualization tools, Data Studio allows us to analyze data in real time. With tools like BigQuery, we are now able to identify trends and gain a deep understanding for how our users interact with our site. 

Google for Startups Accelerator: Women Founders 

In order to provide more accurate job recommendations for our users, the Fairygodboss team wanted to build a machine learning prototype to classify our users and jobs into categories to facilitate more effective matching. So we applied for the Google for Startups Accelerator: Women Founders, a three-month digital accelerator program for high-potential Seed to Series A tech startups based in the U.S. and Canada. Along with tailored mentorship and product support from Googlers and industry experts, the Google for Startups Accelerator provided $100K in Google Cloud credits to scale our businesses. The mentorship we received from the Google Cloud technical experts as part of the Accelerator – special shout out to Peter Novig! – empowered us to integrate Cloud AutoML into our systems and ultimately curate more accurate job suggestions for our users.  

Not only did the Google for Startups Accelerator program help to achieve our business goals for the year, it was also extremely beneficial to be connected with a cohort of other women founders of color. While we are all building different kinds of businesses across different industries, the guidance around fundraising, scaling teams, and coping with the struggles of being a founder rang true for all of us. 

Advancing the community

It can be daunting to launch your own business – and even more so as an AAPI woman. Throughout my career, I am certain I have experienced unconscious bias around my race. Specifically, there have likely been “model minority” stereotypes about my demeanor and math abilities, assumptions that I may be mild-mannered or agreeable. While frustrating, these unfair tropes actually inspired me to see myself beyond others’ perceptions. Why be either analytical or creative, meek or brash, inspiring or agreeable? It’s the ors of life that prevent us from seeing ourselves as truly multi-dimensional individuals. I aspired to be more instead of or, and want my company to be as well. 

As an inclusion-focused business, it is extremely important that Fairygodboss mindfully engage with all underrepresented groups. Diversity can be sliced in many different ways, and these intersections lead to great opportunities for change. We have a team of volunteer ‘culture wizards’ at work that provide educational resources and videos for those wanting to learn more about different cultures and identities from members of under-represented groups. We also practice what we preach at Fairygodboss by prioritizing diverse slates and diversity in our own hiring process. We aim for the talent we hire to be representative of the diverse communities we support. It is imperative to take an intentional approach to hiring diverse talent, as it falls on all employers to promote equity and inclusion—there are always ways in which we can all improve. Fairygodboss looks forward to evolving with Google Cloud in 2021 and beyond as we work together to foster inclusive teams around the world. 

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.

Thirteen years’ experience in e-commerce has given Teddy Chan, Chief Executive Officer and Chief Technology Officer, AfterShip, a deep understanding of the challenges of shipping and tracking packages to customers worldwide.

“The key problem many merchants face is customers asking ‘where is my order?’ and ‘when I will get the package?’” Chan says. “When I considered this issue, I came up with the idea of AfterShip.” Chan helped found AfterShip in 2011 to enable merchants to keep track of packages sent to customers via a web portal or an API. AfterShip also allows merchants to notify customers of anticipated delivery times.

“Using AfterShip, merchants can provide the same experience to buyers regardless of which couriers they use,” Chan says. “Merchants can also improve their customer engagement by including up-selling or marketing content with their delivery notifications.”

Hong Kong-headquartered AfterShip continues to grow quickly and now has a 40-person team. Thirty members of this team are based on the China special administrative region and 10 are based in India.

“Google Cloud Platform has a network of global datacentres with deep connectivity that enables us to put our infrastructure close to our customers. In addition, the ability to horizontally scale our global database using tools such as Google Cloud Spanner eliminates any limits on our geographic expansion.”

—Teddy Chan, Chief Executive Officer and Chief Technology Officer, AfterShip

By October 2017, the business was tracking about 30 million packages per month and had expanded its services to include label and rate calculation and self-service return. Revenue, package transaction numbers and team size have doubled every year for the past three years, while more than 300,000 merchants and 426 couriers are signed up to the service. Key customers include Wish, Etsy and Groupon.

Close to half AfterShip’s customers are based in the United States, about one third in Europe and the remainder are located in Asia. AfterShip had initially delivered its applications and services from an incumbent public cloud service. However, the company wanted to continue its growth trajectory while automating key infrastructure processes, implementing a continuous deployment model and controlling costs.

The business needed to achieve these objectives while maintaining a global presence and high-quality service. AfterShip started reviewing its options and decided to migrate to Google Cloud Platform (GCP). “Google Cloud Platform has a network of global datacentres with deep connectivity that enables us to place our infrastructure close to our customers,” Chan says.

“In addition, the ability to horizontally scale our global database using tools such as Google Cloud Spanner eliminates any limits to our geographic expansion. Furthermore, the managed services provided through GCP would allow us to focus on building better features for online merchants.” The reliability provided by GCP would also enable AfterShip to meet the stringent service level requirements of large digital marketplaces in the United States, Asia and elsewhere.

“Google Cloud Platform could manage the high volumes and enable us to deliver the service levels that would realise our ambition of becoming the number one tracking API platform in the world,” Chan says. “For example, with Google Cloud Platform, we can provide a 99.95% monthly uptime service level to our customers.” Finally, GCP provided managed solutions, including Google Kubernetes Engine powered by open source container orchestrator Kubernetes, that would enable AfterShip to automate processes such as scaling and enable its team to focus on developing applications.

AfterShip has moved its websites into GCP infrastructure in three datacentres around the world and anticipates completing the migration in Q4 2017. “Google provided a lot of assistance, particularly early in the project when we needed it,” Chan says. “They briefed us on several services we hadn’t known about that could replace the equivalents in the public cloud we were using previously.” The business then completed the migration using its own skilled team members. As well as Google Kubernetes Engine and Google Cloud Spanner, AfterShip is using Google BigQuery to store and analyse transaction information.

“Google Cloud Platform could manage the high volumes and enable us to deliver the service levels that would realise our ambition of becoming the number one tracking API platform in the world.”

—Teddy Chan, Chief Executive Officer and Chief Technology Officer, AfterShip

Deployment times down from one hour to two minutes

With deployment times falling from up to one hour in its previous cloud environment to about two minutes in GCP, AfterShip has been able to adopt a continuous deployment model. “This has improved our service levels,” Chan says. “If there are any issues we can fix them quickly, while we can iterate faster to create new features in response to customer requests or changes in the market. “This enables us to continue to lead our competitors.”

Targeting a 30 percent reduction in costs

AfterShip is now targeting a 30% reduction in costs by optimising its use of Docker containerisation technology on GCP.

“By using Docker with Kubernetes, we have been able to fine-tune our use of compute resources and better control our costs,” Chan says. “We’re extremely pleased with Google Cloud Platform as it really is built for engineers,” he adds. “In addition, its documentation is extremely clear, allowing us to troubleshoot or carry out activities on the platform ourselves. “We look forward to continuing to grow and extend our package tracking and associated services with Google Cloud Platform.”