Every hour of every day, Google moves a lot of data around the world. But how? With the Effingo (Latin for ‘to duplicate’ or ‘copy’) data copying service, a Google-only service that moves data internally and on Google Cloud customers’ behalf.

As a Google Cloud customer perhaps you move data with Storage Transfer Service, a managed transfer option that provides out-of-the-box security, reliability, and performance, that eliminates the need to optimize and maintain scripts, and handle retries. Storage Transfer Service is useful for consolidating data from separate projects, to move data into a backup location, or to change the location of your data.

Likewise, Google’s internal services need very similar functionality to manage their data. This includes data that is in charge of the ecosystem of Google Cloud services, such as BigQuery and Cloud Spanner.

Effingo solves a challenging problem of data movement at a global scale. It supports a wide range of workloads with different traffic characteristics and different requirements for data replication, durability, and latency. In this article we will explore the main motivations behind data movement and solutions to core infrastructure problems that we face when copying exabytes of data on a daily basis.

Why move data in the first place?

But first, let’s think about why you even want to move or geographically distribute your data.

For one thing, you want to replicate your data for durability and reliability. You definitely don’t want to keep a single copy of your data. In the end, we’re talking about a file that is stored on a hard drive. Any hard drive can fail at any point. For this reason, keeping copies of your data in different locations helps ensure that at any point of time there is a copy of your file that you can read from. Moreover, these locations should be geographically distant from each other to avoid even temporary data loss due to natural disasters, network cuts, and other incidents that can impact a local area.

Second, you want your data to be close to your users to reduce latency. You want to serve data with minimal delay to give a great user experience. In many cases, delays caused by data transfers from remote locations can spoil the usability of your application or create the perception that your service doesn’t work. According to Google research, 53% of mobile users abandon sites that take over 3 seconds to load. There are other consequences of lack of data proximity: for example, transferring data from distant locations wastes network resources, which can have a negative impact on the environment. Google has been carbon-neutral for our operations since 2007, and we have matched 100% of the electricity consumption of our operations with renewable energy purchases annually since 2017, but we aim higher: Our goal is to run all our data centers and offices on carbon-free energy, 24/7, by 2030.

The third reason to geographically distribute your data is to balance storage capacity between clusters. This reduces the cost of storing data because it enables us to increase the overall capacity of our data centers and use computation power in less loaded clusters. We can then limit the amount of wasted resources and use otherwise idle hardware. This is especially relevant for batch processing and storing cold data where the exact storage location is not that important.

What are the challenges?

Operating at such a large scale as Google makes data movement a challenging problem for several reasons. Here are a few examples.

Data must be secure and consistent at its destination. Achieving this is complex. For example, to copy a file from one location to another, you need the following: throughput to read and write to disks, network capacity to transfer bytes between data centers, and compute resources to instrument the whole operation. 

Considering the volume of data that Google operates on, high and predictable throughput is one of the key features of a copy service at scale. It is typical to transfer terabytes of data per second, and so it’s essential to have a scalable service that can handle traffic with sometimes rapidly changing patterns. It poses a further question: how to run such a service and still be resource efficient?

Finally, it’s crucial to ensure high system availability. Google infrastructure is dynamic and copying data between any two clusters may behave differently at different points of time. Effingo needs to be resilient to cluster turndowns, network bottlenecks, rapid changes in resource demands and capacity, and so on.

How do we solve (some) of these problems?

One of the core use cases for data movement is replication. If you want to ensure that your data is reliable, you want to keep more than one copy, ideally in different locations, to be less prone to data loss.

Let’s consider the following example. You have a file in cluster A, and you want to make copies in destinations B – G.

In the simplistic approach, you would start copying to all destinations in parallel.

Unfortunately, it is highly inefficient to replicate your file this way. This solution doesn’t scale and requires significant investment in infrastructure. 

If you consider the cost of network infrastructure, moving data across the ocean is an expensive operation. Effingo creates data transfer plans that reduce the volume of data to transfer across the ocean. If possible, it reuses already replicated data as a new source.

To create data transfer plans Effingo needs to know alternative data sources and be aware of the network topology. 

If it goes to the alternative sources, Effingo stores a recent history of transferred files together with their metadata and time-bound permissions to access the files. Whenever a new copy is issued, Effingo checks whether there were copies of the original file that could serve as the same source but in the alternative location. Worth noting is that Effingo is very strict in verifying that both files — the requested source file and the alternative transfer file — are indeed the same to follow high security standards. Effingo not only checks whether the same user issued a copy from the same source but also if file properties including checksum, ciphertext checksum, mtime and several others match.

Once Effingo knows the source files and their alternative sources, it creates a transfer plan over the Google network. Effingo has a model of the network in the form of a graph where each location is a node and each edge is a weighted link between each node. For each copy Effingo creates a Minimum Spanning Tree over such a graph, which serves as an input to the transfer engine. Thanks to this approach we can select a plan that is optimal for each copy.  

In the next example, we show a more efficient approach. Effingo first makes an expensive copy to a remote location and then uses it as a new source. For instance, Effingo first copies a file from the US (source A) to Europe (destination C) and then uses the Europe-based file as a copy source for copies on the same continent. Note that Effingo never stores files in temporary locations for optimization purposes – the service only uses locations explicitly requested by our users.

In addition to resource efficiency, the main challenge is to ensure high throughput of data transfer. We solve this problem by applying several approaches that address different problems that may arise.

As described above, our customers’ workloads exhibit a range of different traffic patterns, which results in dynamic changes in capacity on each network link. For this reason, Effingo not only needs to respond to altering resource availability, but also to ensure user fairness in such conditions. In other words, Effingo wants all copy operations to progress while using resources in the best possible way. To achieve this, it uses sophisticated parallelism controls. These controls scale the service up to meet increased demand and limit service capacity if there is service overload, performance degradation, or resource waste.

Further, Effingo must be very resilient and react quickly to errors. On the file level, the service extensively uses retries for transient failures and aborts transfers quickly if it detects non-retryable errors. Effingo uses metrics, logs, and other observability signals to adjust data transfers when needed. For example, it can detect that copies from a specific source are slow and there is another file replica available. Effingo then reconfigures the copy operation to use the other replica to complete the data move.

Data movement at global scale is hard

Hopefully at this point you have a better understanding of why moving data is a challenging problem for infrastructure that operates at Google’s global scale. Effingo supports a range of services that run on Google infrastructure, including Google Cloud services and many internal Google services. While moving data at large scale requires a lot of attention to resource usage and resilience to support high throughput, there is good news: We keep working on this problem and make our infrastructure better every day, so you can run your business on Google Cloud and all data movement is transparent to you.

If you’re managing a crucial application that has to be fully fault-tolerant, you need your system to be able to handle every fault, no matter the type and scope of failure, with minimal downtime and data loss. Protecting against these faults means juggling numerous variables that can impact performance as well as recovery time and cost.

Today’s managed database services take over the operational complexity that used to exist for database administrators. Growing your organization’s tolerance required adding machines, compute, and storage, plus the operational costs of IT management: performing backups, writing scripts, creating dashboards, and carrying out testing to make sure your platform is ready when problems arise–all in a secure way.

At Google, our Cloud SQL managed database service offers three fault tolerance mechanisms —backup, high availability, and replication—and there are three major factors to consider for each of them:

• RTO (recovery time objective): When a failure happens, how much time can be lost before significant harm occurs?
• RPO (recovery point objective): When a failure happens, how much data can be lost before significant harm occurs?
• Cost: How cost-effective is this solution?  

We’ve heard from customers like Major League Baseball, HSBC, and Equifax that they have strict data-protection needs and require highly fault-tolerant multi-region applications—and they’ve all chosen Cloud SQL to meet those needs. 

Let’s take a closer look at how the decision-making process plays out for each recovery solution.  

High availability (HA)

If your application is business critical, you require minimum RTO and zero RPO— a high availability configuration ensures that you and your customers are protected. If the primary instance fails, there’s another standby instance ready to take over with no data loss. There’s an additional cost here, but doing this manually brings a great operational cost, since you have to detect and verify the fault, do the failover, and make sure it’s correct—you can’t have two primary instances or you risk data corruption—then finally connect the application to the new database.

Cloud SQL removes all that complexity. Choose high availability for a given instance and we’ll replicate the data across multiple zones, synchronously, to each zone’s persistent disk. If an HA instance has a failure, you don’t have to think about when to fail over because Cloud SQL detects the failure and automatically initiates failover, for a full recovery and no data loss within minutes. Cloud SQL also moves the IP address during failover so your application can easily reconnect. MLB, for example, uses Cloud SQL high availability to serve prediction data to live games with minimal downtime. Dev/test instances don’t need those same guarantees, but can use local backups to recover from any potential failure.

Cross-region replica

If a whole Google Cloud region goes down you still need your business to continue to run. That’s where cross-region replication comes in, a hot standby replica in another Google Cloud region provides RTO of minutes and RPO typically less than a minute . If you create a read replica in a region separate from your primary instance and you get hit with a regional outage, your application and database can start serving customers from another region within minutes. But this solution can be complex and enabling it yourself can be difficult and time-consuming. Securing cross-geography traffic demands end-to-end encryption and can bring connectivity issues too. 

This is where the fully managed Cloud SQL solution shines. We offer MySQL, PostgreSQL and SQL Server database engines as a cross-region replication solution that’s easily configured and bolstered by Google’s interconnected global network. Just say, “I’m in U.S. East, I want to create a replica in U.S. West,” and it’s done, reliably and securely.

Backup

When you suffer data loss because of an operations error (for example, a bug in a script dropped your tables) or human error (for example, someone dropped the wrong table by accident), backups help you restore lost data to your Cloud SQL instance. Our low cost backup mechanism features point-in-time, granular recovery, meaning that if you accidentally delete data or something else goes wrong, you can ask for recovery of, for example, the state of that database down to the millisecond, such as Monday at 12:53pm. Your valuable data is replicated multiple times in multiple geographic locations automatically. This enables the automatic handling of failover in cases of major failure. You can always rest assured that your database is available and data is secure, even in the times of major failure crises. 

Cloud SQL provides automated and on-demand backups. With automated backups, Google manages the backups so that you can easily restore them when required. Also, the scheduled backing is automatically taken by default. With on-demand backup, you can create a backup at any time. This could be useful if you are about to perform a risky operation on your database, as Cloud SQL lets you select a custom location for your backup data. When the backup is stored in multiple regions, and there’s an outage in the region that contains the source instance, you can restore a backup to a new or existing instance in a different region. This is also useful if your organization needs to comply with data residency regulations that require you to keep your backups within a specific geographic boundary.

Putting it all together

For critical workloads, MLB configures their Cloud SQL instances with backups, high availability, and cross-region replication. Doing so ensures they can recover from many failure types.

• To recover from human error (“Oops, I didn’t mean to delete that”), MLB uses backups and point-in-time recovery to recovery to a millisecond or specific database transaction
• To automatically recover from primary instance failures and zonal outages, MLB uses Cloud SQL’s high availability configuration
• To protect against regional outages, MLB uses cross-region replication

Creating a robust configuration, like MLB did, takes just a few minutes. Get started in our Console or review documentation.

As data environments become more complex, companies are turning to streaming analytics solutions that analyze data as it’s ingested and deliver immediate, high-value insights into what is happening now. These insights enable decision makers to act in real time to take advantage of opportunities or respond to issues as they occur.

While understanding what is happening now has great business value, forward-thinking companies are taking things a step further, using real-time analytics integrated with artificial intelligence (AI) and business intelligence (BI) to answer the question, “what might happen in the future?” Arkansas-based Tyson Foods has embraced AI/BI analytics to enable predictive insights that unlock new opportunities and drive future growth.

Creating a digital twin for connected intelligence company wide

Before using AI/BI, Tyson’s analytics capabilities consisted of traditional BI solutions focused on KPIs and simplifying data so that humans could understand it. Tyson wanted to leverage its data to uncover ways to improve current processes and grow its business. But with BI alone, Tyson struggled to use data to run the simulations and scenarios essential to make educated decisions. To keep growing, it had to embrace the complexity of its data, building ways to analyze it and use it to inform decision making. 

Tyson’s on-premises analytics solutions limited its ability to be aggressive and make intelligent, timely, prescriptive decisions. The solution was to create a digital twin to scale optimizations within business processes, moving from local optimizations to system-wide connected optimizations. Doing so meant shifting entirely to cloud computing, with an initial focus on building the ingestion component of the digital twin platform.

Investing in a digital twin enabled Tyson to accelerate new capabilities like supply chain simulation “what-if” scenarios, prescriptive price elasticity recommendations, and improvement of customer intimacy. 

Solving the ingestion problem for faster time to insights

Before its migration to Google Cloud, analytics projects that Tyson suffered from uncertainty over how to obtain the data. This problem was prolific and caused project times to be extended for weeks or even months due to the need to write and support one-off data ingestion processes at the front end. This problem also prevented the IT team from delivering analytics solutions fast enough for the business to take full advantage of them. 

To solve this analytics problem, the team created Data Ingestion Compute Engine (DICE). DICE is a Google Cloud-hosted, open-source, cloud-native ingestion platform developed to provide configuration-based, no-ops, code-free ingestion from disparate enterprise data systems, both internal and external. It is centered on three high-level goals:

1. Accelerate the speed of delivery of IT analytics solutions
2. Enable growth of IT capabilities to produce meaningful insight
3. Reduce long-term total cost of ownership for ingestion solutions

Creating DICE ingestion platform with Google Cloud services

Teams use DICE to set up secure data ingestion jobs in minutes without having to manage complex connections or write, deploy, and support their own code. DICE enables unbound scale, highly parallel processing, DevSecOps, open source, and the implementation of Lambda Data Architecture.

A DICE job is the logical unit of work in the DICE platform, consisting of immutable and mutable configurations persisted as JSON documents stored in Firestore. The job exists as an instruction set for the DICE data engine, which is Apache Beam running Dataflow to instruct which data to pull, how to pull it, how often to pull it, how to process it, when it changes, and where to direct it.

Two of DICE’s primary layers include the metadata engine and the data engine. The metadata engine is responsible for the creation and management of DICE job configuration and orchestration. It is made up of many microservices that interact with multiple Google Cloud services, including the job configuration creation API, job build configuration helper API, and job execution scheduler API.

The data engine is responsible for the physical ingestion of data, the change detection processing of that data, and the delivery of that data to specified targets. The data engine is Java code that uses the Apache Beam unified programming model and runs in Dataflow. It is comprised of streaming, jobs, and Dataflow flex template batch jobs. Logically, the data engine is segmented across three layers: the inbound processing layer, the DICE file system layer, and the target processing layer, which takes the data from the DICE file system and moves it to targets.

Rolling DICE for thousands of ingestion jobs each day

DICE was first deployed to a production environment in November 2019, and just two years later, it has more than 3,000 data ingestion jobs from more than a hundred disparate data systems, both internal and external to Tyson Foods. Most of these jobs run multiple times a day. On a daily basis the DICE environment sees more than 25,000 Dataflow jobs running and an average of 3.25 terabytes of new data being ingested.

DICE supports ingestion from many different types of technologies, including BigQuery, SQL Server, SAP HANA, Postgres, Oracle, MySQL, Db2, various types of file systems, and FTP servers. Additionally, DICE supports target platform technologies for ingestion jobs that include multiple JDBC targets, multiple file system targets, and BigQuery and queue-based store and forward technologies. 

The platform continues to see linear growth of DICE jobs, all while keeping platform costs relatively flat. With increasing demand for the platform, Tyson’s IT team is constantly enhancing DICE to support new sources and targets.

This intelligent platform keeps adding new value and makes it simple for Tyson to take advantage of its data. This innovation is a necessity in this fast-changing world of digital business in which companies must transform a high volume of complex data into actionable insight.

Customers tell us that sharing and exchanging data with other organizations is a critical element of their analytics strategy, but it’s hamstrung by unreliable data and processes, and only getting harder with security threats and privacy regulations on the rise. 

Furthermore, traditional data sharing techniques use batch data pipelines that are expensive to run, create late arriving data, and can break with any changes to the source data. They also create multiple copies of data, which brings unnecessary costs and can bypass data governance processes. These techniques do not offer features for data monetization, such as managing subscriptions and entitlements. Altogether, these challenges mean that organizations are unable to realize the full potential of transforming their business with shared data.

To address these limitations, we are introducing Analytics Hub, a new fully managed service, available in Q3, in preview, that helps you unlock the value of data sharing, leading to new insights and increased business value. With Analytics Hub you get:

• A rich data ecosystem by publishing and subscribing to analytics-ready datasets. 
• Control and monitoring over how your data is being used, because data is shared in one place.
• A self-service way to access valuable and trusted data assets, including data provided by Google. For example, a unique dataset from Google Search Trends will be available, that you can query and combine with your own data.
• An easy way to monetize your data assets without the overhead of building and managing the infrastructure. 

Built on a decade of cross-organizational sharing

While Analytics Hub is a new service, it builds on BigQuery, Google’s petabyte-scale, serverless cloud data warehouse. BigQuery’s unique architecture provides separation between compute and storage, enabling data publishers to share data with as many subscribers as you want without having to make multiple copies of your data. With BigQuery, there are no servers to deploy or manage, which means that data consumers get immediate value from shared data. Data can be provided and consumed in real-time using the streaming capabilities of BigQuery and you can leverage the built in machine learning, geospatial, and natural language capabilities of BigQuery or take advantage of the native business intelligence support with tools like Looker, Google Sheets, and Data Studio.

BigQuery has had cross-organizational, in-place data sharing capabilities since it was introduced in 2010. We took a look at usage metrics in BigQuery and found that over a 7 day period in April, we had over 3,000 different organizations sharing over 200 petabytes of data. These numbers don’t include data sharing between departments within the same organization.

As you can see, data sharing in BigQuery is already popular. But we want to make it easier and even more scalable.

Raising the bar on data sharing 

To make data sharing easier and more scalable in BigQuery, Analytics Hub introduces the  concepts of shared datasets and exchanges. As a data publisher, you create shared datasets that contain the views of data that you want to deliver to your subscribers. Next, you create exchanges, which are used to organize and secure shared datasets. By default, exchanges are completely private, which means that only the users and groups that you give access to can view or subscribe to the data. You can also create internal exchanges or leverage public exchanges provided by Google. Finally, you publish shared datasets into an exchange to make them available to subscribers. 

Data subscribers search through the datasets that are available across all exchanges for which they have access and subscribe to relevant datasets. This creates a linked dataset in their project that they can query and join with their own data. Subscribers pay for the queries that they run against the data while the publisher pays for the storage of the data. Data providers can add new data, new tables, or new columns to the shared dataset and these will be immediately available to subscribers. In addition, the publisher can track subscribers, disable subscriptions, and see aggregated usage information for the shared data. 

Analytics Hub makes it easy for you to publish, discover, and subscribe to valuable datasets that you can combine with your own data to derive unique insights. Here are some types of data that will be available through Analytics Hub:

• Public datasets: Easy access to the existing repository of over 200 public datasets, including data about weather and climate, cryptocurrency, healthcare and life sciences, and transportation. 
• Google datasets: Unique, freely-available datasets from Google. One example of this is the COVID-19 community mobility dataset. Another example is the forthcoming Google Trends dataset, which will provide the top 25 search terms and top 25 rising search terms over a 5 year window in 210 distinct locations in the US. Trends data can be used by everyone in the organization to gain insights into what customers care about.
• Commercial (paid for) datasets: We are working with leading commercial data providers to bring their data products to Analytics Hub. If you are interested in delivering your data via Analytics Hub, we’re also introducing Data Gravity, an initiative that provides storage benefits and new distribution paths for data published through Analytics Hub. 
• Internal datasets: We know that data sharing can be challenging in larger organizations. Analytics Hub can be used for internal data, for example, to share standardized customer demographics with your sales engineering and data science teams.

Customers and partners using Analytics Hub

“Google Search Trends data has always been an important tool for our WPP agency data teams. At WPP we believe that data variety is a superpower which is why we are excited to use the new Trends dataset availability within BigQuery, plus the launch of Analytics Hub. The best creativity in the world is informed by data insights, and influenced by what people search for, so the operational efficiencies we’ll gain via the Analytics Hub and the insights we can drive with Trends data are just phenomenal.”
—Di Mayze Global Head of Data and AI, WPP

“Equifax Ignite is our shared data analytics environment within our Equifax data fabric. We are excited to partner with Google to leverage Analytics Hub and BigQuery to deliver data to over 400 statisticians and data modelers as well as securely sharing data with our partner financial institutions.”
—Kumar Menon, SVP Data Fabric and Decision Science, Equifax

“The flow of data and insights between our teams at Deloitte and our clients is paramount for building truly transformational data cultures. With its purpose-built architecture for secure data exchanges and sharing analytics resources, Google Cloud’s Analytics Hub can help provide significant operational efficiencies for how Deloitte teams support our clients’ data-driven initiatives within their industry ecosystems. It will also help minimize the worries about scale, privacy and security, or the administrative burden associated with each.”
—Navin Warerkar, Managing Director, Deloitte Consulting LLP, and US Google Cloud Data & Analytics GTM Lead

“Crux Informatics is proud to partner with Google to support the launch of Analytics Hub, removing friction for those who need access to analytics-ready data. With thousands of datasets from over 140 sources, Crux Informatics will accelerate access to data on Analytics Hub and together provide a more efficient and cost effective solution to deliver datasets in Google Cloud’s ecosystem.”
—Will Freiberg, CEO, Crux Informatics

Next steps for Analytics Hub

This is just the beginning for Analytics Hub. As we get to preview and general availability, we will be adding additional capabilities, including workflows for publishing and subscribing, publishing analytics assets (Looker Blocks, Data Studio reports, Connected Google Sheets) along with the shared data, the ability for data publishers to specify query restrictions on the usage of their data, and making it easy for data publishers to create sandbox environments for subscribers to work with their data, even if they are not yet on Google Cloud. We will provide features in Analytics Hub for monetization of data, including managing subscriptions, data entitlements, and billing.

Please sign up for the preview, which is scheduled to be available in the third quarter of 2021. In the meantime, you can learn more about BigQuery and how to leverage its built-in data sharing capabilities. Please go to g.co/cloud/analytics-hub to register your interest in Analytics Hub.