Cloud Spanner is the first scalable, enterprise-grade, globally-distributed, and strongly consistent database service built for the cloud specifically to combine the benefits of relational database structure with non-relational horizontal scale.

This combination delivers high-performance transactions and strong consistency across rows, regions, and continents with an industry-leading 99.999% availability SLA, no planned downtime, and enterprise-grade security.

Cloud Spanner revolutionizes database administration and management and makes application development more efficient.

Find out How Streak built a graph database on Cloud Spanner to wrangle billions of emails

What Are Cloud Spanner’s Strengths?

What is Cloud Spanner Useful For?

What Does it Cost?

Pricing for Cloud Spanner is simple and predictable. You are only charged for the number of nodes in your instance, the amount of storage that your tables and secondary indexes use (not pre-provisioned), and the amount of network bandwidth used. Note that there is no additional charge for replication. For more detailed pricing information, please view the pricing guide.

Every company these days is becoming a data company whether they know it or not. This results in preparing the company to create an ecosystem for data processing. Traditionally, organisations’ data ecosystems consisted of point solutions that used to provide data services. For example, one of the most common questions we get from customers is, “Do I need a data lake, or should I consider a data warehouse? Do you recommend I consider both?” Traditionally, these two architectures have been viewed as separate systems, applicable to specific data types and user skill sets. Increasingly,  we are seeing a blurring of lines between data warehouses and data lakes, which provide customers with an opportunity to create a more comprehensive platform that gives them the best of both worlds.

What if we don’t need to compromise? And we create an end-to-end solution compromising the entire data management and processing stages, from data collection to serving. Thus, Data platforms are typically used to store vast amounts of data in varying formats and doing so without compromising on latency. At the same time, providing a platform for all users throughout the data lifecycle. 

Emerging Trends

There are data solutions and architectures we’re seeing and anticipating. Emerging concepts include data lakehouses, data meshes, and data vaults. Some are not new and have been around in different shapes and formats, however, all of them work naturally within a Google Cloud environment. Lets look into both ends of the spectrum of enabling data and enabling teams. 

Data mesh facilitates a decentralized approach to data ownership, allowing individual lines of business to publish and subscribe to data in a standardized manner, instead of forcing data access and stewardship through a single, centralized team. On the other hand, a data lake house brings raw and processed data closer together, allowing for a more streamlined and centralized repository of data needed throughout the organization. Processing can be done in transit via ELT, reducing the need to copy datasets across systems. This allows for easier data exploration and easier governance. Data vault is designed to separate data driven and model driven parts, so the way data is integrated in the raw vault enables parallel loading so that large implementations can scale out easily.

In Google Cloud, there is no need to keep them separate. In fact, with interoperability among our portfolio of data analytics products, you can easily provide access to data residing in different places, effectively bringing your data lake and data warehouse together on a single platform.

Let’s look at some of the technological innovations that make this reality. BigQuery’s storage API allows treating a data warehouse like a data lake, letting you access the data residing in BigQuery. For example, you can use Spark to access data residing in the data warehouse without it affecting performance of any other jobs accessing it. This is all made possible by the underlying architecture, which separates compute and storage. 

We continue to offer specialized products and solutions around data lake and data warehouse functionality but over time we expect to see a significant enough convergence of the two systems that the terminology will change. At Google Cloud, we consider this combination an “analytics data platform”.

Tactical or Strategical

Key differentiators of Google Cloud’s data analytics platform are being open, intelligent, flexible, and tightly integrated. There are many technologies in the market which provide tactical solutions that may feel comfortable and familiar. However, this can be a rather short-term approach that simply lifts and shifts a siloed solution into the cloud. In contrast, an analytics data platform built on Google Cloud provides modern data warehousing and data lake capabilities with close integration to our AI Platform. It also provides built-in streaming, ML, and geospatial capabilities and an in-memory solution for BI use cases. Depending on your organizational data needs, Google Cloud has the set of products, tools, and services to create the right data platform for you. 

To become a truly data-driven organization, the first step is to design and implement an analytics data platform that meets your technical and business needs. Whether you want to empower teams to own, publish, and share their data across the organization, or you want to create a streamlined store of raw and processed data for easier discovery, there is a solution that best meets the needs of your company.

To learn more about the elements of a unified analytics data platform built on Google Cloud, and the differences in platform architectures and organizational structures, read our Unified Analytics Platform paper.

Since 2007, PrestaShop has helped companies unlock the power of e-commerce through its open-source platform. Over 300,000 merchants worldwide use the PrestaShop platform to grow their business and serve online shoppers.

“Our open-source strategy to ecommerce enablement sets us apart,” says Rémi Paulin, Ph.D., Data Architect at PrestaShop. “Customization is becoming more crucial to retailers, and our open-source platform allows companies to continually evolve their sites and services to stand out from competitors.”

As PrestaShop grew, it wished to derive more value from its data, but the company ran into issues caused by a legacy, siloed architecture that negatively impacted data consistency and accessibility.

Let’s look at how PrestaShop works with Google Cloud and partners Fivetran and Hightouch to gain more control over data, enable a beyond-BI data strategy, and increase employee engagement from less than 10% to more than 40%.

Improving trust in data

Core systems at PrestaShop, including SQL and NoSQL databases, and SaaS Applications, were siloed; each presenting its own data, often captured from different sources such as support tickets, marketing engagement, purchase activity, and product usage. This setup made data overall inconsistent as no single system would contain a source of truth, resulting in many inefficiencies, poor collaboration across teams, and a reluctance to use data to support key decisions.

“Not long ago, less than 10% of the company regularly relied on data, so we were missing opportunities to make more data-driven decisions,” says Paulin. “Data was underutilized, and people were rapidly losing trust in data.”

PrestaShop set out to design a new architecture to address past challenges, such as lack of data consistency, and improve data accessibility.

“Google Cloud, along with Hightouch and Fivetran, allowed us to build a modern stack to solve these challenges and support our beyond-BI data strategy.”

Building a modern data stack

The first step was to build a robust data ingestion pipeline. After considering several vendors, PrestaShop chose to work with Fivetran to extract data from SaaS applications, including Zendesk, HubSpot, and GitHub, to load into BigQuery. They also use Datastream to stream Change Data Capture (CDC) data from transactional databases into BigQuery in real-time.

“Fivetran and Datastream are no-ops, efficient and highly reliable, and relieve our Data Engineers of management tasks. This brings us a high degree of confidence to build the rest of the stack atop these services,” says Paulin.

PrestaShop relies on several Google Cloud solutions, including Dataflow, and a managed Spark service by Ascend.io, for data transformation. It also uses Looker for its semantic modeling capacities and as a self-serve data platform.

As the company continued on its journey to transform how it manages and benefits from data, it engaged Hightouch to enable data accessibility through activation. Sitting on top of Looker, Hightouch unlocks all data models for operational intelligence. For example, in just a few days, the team built a customer knowledge model combining data from multiple sources and used Hightouch to sync data from the semantic layer to Zendesk via Reverse ETL. This allowed the care team to make more data-informed decisions, speeding up the time to resolve support tickets submitted through Zendesk by 33%.

“Hightouch feels like a natural extension of Looker and reinforces the position of the semantic data model as the single source of truth,” says Paulin. “It powers a variety of Data Activation use cases, supporting our beyond-BI strategy by providing teams with access to data when and where they need it to improve everyday operations. This has a big impact on the company, bolstering employee trust in available data.”

Becoming data-driven

In less than six months, PrestaShop managed to get the entire data stack up and running, build over 30 data models and engage over 120 employees with a small team of only two Data Engineers.

“Data is now accessible to every stakeholder within the company, regardless of their technical abilities,” says Paulin.

PrestaShop has already seen much progress in its shift to a more data-driven company and is excited to roll out more self-service intelligence capabilities in the future.

“Google Cloud drives home a culture of simplicity around our data stack, which is essential for us, especially given the small size of our engineering team,” says Paulin. “Fivetran and Hightouch share this culture of simplicity. Together, they offer strong foundations to support our data needs.”

Dashboards, which the company had always had an appetite for, are seamlessly created today. Before moving to Looker, a full-fledged dashboard would take an average of six weeks to develop. Now, it takes less than two days – and a simple dashboard can be created autonomously by business users in as little as 15 minutes.

Furthermore, data usage goes beyond dashboards. Thanks to Looker’s self-service exploration capabilities, many stakeholders can now glean insights surrounding product issues and business opportunities. Thanks to Hightouch, teams can activate their data to make better and smarter operational decisions.

“This is a big leap forward and one of many to come as we continue to add new models, activate our data, and onboard more users,” says Paulin. “Given our global reach and unique approach to e-commerce enablement, we know this is just the start of the great things we can accomplish with Google Cloud, Fivetran, and Hightouch.”

Check out Fivetran on Google Cloud Marketplace, or sign up for a free Hightouch workspace to learn more about what partners can do for your business.

Running time-based, scheduled workflows to implement business processes is regular practice at many financial services companies. This is true for Deutsche Bank, where the execution of workflows is fundamental for many applications across its various business divisions, including the Private Bank, Investment and Corporate Bank as well as internal functions like Risk, Finance and Treasury. These workflows often execute scripts on relational databases, run application code in various languages (for example Java), and move data between different storage systems. The bank also uses big data technologies to gain insights from large amounts of data, where Extract, Transform and Load (ETL) workflows running on Hive, Impala and Spark play a key role.

Historically, Deutsche Bank used both third-party workflow orchestration products and open-source tools to orchestrate these workflows. But using multiple tools increases complexity and introduces operational overhead for managing underlying infrastructure and workflow tools themselves.

Cloud Composer, on the other hand, is a fully managed offering that allows customers to orchestrate all these workflows with a single product. Deutsche Bank recently began introducing Cloud Composer into its application landscape, and continues to use it in more and more parts of the business.

“Cloud Composer is our strategic workload automation (WLA) tool. It enables us to further drive an engineering culture and represents an intentional move away from the operations-heavy focus that is commonplace in traditional banks with traditional technology solutions. The result is engineering for all production scenarios up front, which reduces risk for our platforms that can suffer from reactionary manual interventions in their flows. Cloud Composer is built on open-source Apache Airflow, which brings with it the promise of portability for a hybrid multi-cloud future, a consistent engineering experience for both on-prem and cloud-based applications, and a reduced cost basis.

We have enjoyed a great relationship with the Google team that has resulted in the successful migration of many of our scheduled applications onto Google Cloud using Cloud Composer in production.” – Richard Manthorpe, Director Workload Automation, Deutsche Bank

Why use Cloud Composer in financial services

Financial services companies want to focus on implementing their business processes, not on managing infrastructure and orchestration tools. In addition to consolidating multiple workflow orchestration technologies into one and thus reducing complexity, there are a number of other reasons companies choose Cloud Composer as a strategic workflow orchestration product.

First of all, Cloud Composer is significantly more cost-effective than traditional workflow management and orchestration solutions. As a managed service, Google takes care of all environment configuration and maintenance activities. Cloud Composer version 2 introduces autoscaling, which allows for an optimized resource utilization and improved cost control, since customers only pay for the resources used by their workflows. And because Cloud Composer is based on open source Apache Airflow, there are no license fees; customers only pay for the environment that it runs on, adjusting the usage to current business needs.

Highly regulated industries like financial services must comply with domain-specific security and governance tools and policies. For example, Customer-Managed Encryption Keys ensure that data won’t be accessed without the organization’s consent, while Virtual Private Network Service Controls mitigate the risk of data exfiltration. Cloud Composer supports these and many other security and governance controls out-of-the box, making it easy for customers in regulated industries to use the service without having to implement these policies on their own.

The ability to orchestrate both native Google Cloud as well as on-prem workflows is another reason that Deutsche Bank chose Cloud Composer. Cloud Composer uses Airflow Operators (connectors for interacting with outside systems) to integrate with Google Cloud services like BigQuery, Dataproc, Dataflow, Cloud Functions and others, as well as hybrid and multi-cloud workflows. Airflow Operators also integrate with Oracle databases, on-prem VMs, sFTP file servers and many others, provided by Airflow’s strong open-source community.

And while Cloud Composer lets customers consolidate multiple workflow orchestration tools into one, there are some use cases where it’s just not the right fit. For example, if customers have just a single job that executes once a day on a fixed schedule, Cloud Scheduler, Google Cloud’s managed service for Cron jobs, might be a better fit. Cloud Composer in turn excels for more advanced workflow orchestration scenarios.

Finally, technologies based on open source technologies also provide a simple exit strategy from cloud — an important regulatory requirement for financial services companies. With Cloud Composer, customers can simply move their Airflow workflows from Cloud Composer to a self-managed Airflow cluster. Because Cloud Composer is fully compatible with Apache Airflow, the workflow definitions stay exactly the same if they are moved to a different Airflow cluster.

Cloud Composer applied

Having looked at why Deutsche Bank chose Cloud Composer, let’s dive into how the bank is actually using it today. Apache Airflow is well-suited for ETL and data engineering workflows thanks to the rich set of data Operators (connectors) it provides. So Deutsche Bank, where a large-scale data lake is already in place on-prem, leverages Cloud Composer for its modern Cloud Data Platform, whose main aim is to work as an exchange for well-governed data, and enable a “data mesh” pattern.

At Deutsche Bank, Cloud Composer orchestrates the ingestion of data to the Cloud Data Platform, which is primarily based on BigQuery. The ingestion happens in an event-driven manner, i.e., Cloud Composer does not simply run load jobs based on a time-schedule; instead it reacts to events when new data such as Cloud Storage objects arrives from upstream sources. It does so using so-called Airflow Sensors, which continuously watch for new data. Besides loading data into BigQuery, Composer also schedules ETL workflows, which transform data to derive insights for business reporting.

Due to the rich set of Airflow Operators, Cloud Composer can also orchestrate workflows that are part of standard, multi-tier business applications running non-data-engineering workflows. One of the use cases includes a swap reporting platform that provides information about various asset classes, including commodities, credits, equities, rates and Forex. In this application, Cloud Composer orchestrates various services implementing the business logic of the application and deployed on Cloud Run — again, using out-of-the-box Airflow Operators.

These use cases are already running in production and delivering value to Deutsche Bank. Here is how their Cloud Data Platform team sees the adoption of Cloud Composer:

“Using Cloud Composer allows our Data Platform team to focus on creating Data Engineering and ETL workflows instead of on managing the underlying infrastructure. Since Cloud Composer runs Apache Airflow, we can leverage out of the box connectors to systems like BigQuery, Dataflow, Dataproc and others, making it well-embedded into the entire Google Cloud ecosystem.”—Balaji Maragalla, Director Big Data Platforms, Deutsche Bank

Want to learn more about how to use Cloud Composer to orchestrate your own workloads? Check out this Quickstart guide or Cloud Composer documentation today.

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.”