Statsig is a modern feature management and experimentation platform used by hundreds of organizations. Statsig’s end-to-end product analytics platform simplifies and accelerates experimentation with integrated feature gates (flags), custom metrics, real-time analytics tools, and more, enabling data-driven decision-making and confident feature releases. Companies send their real-time event-stream data to Statsig’s data platform, which, on average, adds up to over 30B events a day and has been growing at 30-40% month over month. 

With these fast-growing event volumes, our Spark-based data processing regularly ran into performance issues, pipeline bottlenecks, and storage limits. In turn, this rapid data growth negatively impacted the team’s ability to deliver product insights on time. The sustained Spark tuning efforts that we were dealing with made it challenging to keep up with our feature backlog. Instead of spending time building new features for our customers, our engineering team was controlling significant increases in Spark’s runtime and cloud costs. 

Adopting BigQuery pulled us out of Spark’s recurring spiral of re-architecture and into a Data Cloud, enabling us to focus on our customers and develop new features to help them run scalable experimentation programs.

The growing data dilemma

At Statsig, our processing volumes were growing rapidly. The assumptions and optimizations we madea month ago would become irrelevant the next month. While our team was knowledgeable in Spark performance tuning, the benefits of each change were short lived and became obsolete almost as quickly as it was implemented. As the months passed, our data teams were dedicating moretime to optimizing and tuning Spark clusters instead of building new data products and features. We knew we needed to change our data cloud strategy.

We started to source advice from companies and startups who had faced similar data scaling challenges – the resounding recommendation was “Go with BigQuery.” Initially, our team was reluctant. For us, a BigQuery migration would require an entirely new data warehouse and a cross-cloud migration to GCP.

However, during a company-wide hackathon, a pair of engineers decided to test BigQuery with our most resource-intensive job. Shockingly, this unoptimized job finished much faster and at a lower cost than our current finely-tuned setup. This outcome made it impossible for us to ignore a BigQuery migration any longer. We set out to learn more. 

Investigating BigQuery

When we started our BigQuery journey, the first and obvious thing we had to understand was how to actually run jobs. With Spark, we were accustomed to daunting configurations, mapping concepts like executors back to virtual machines, and creating orchestration pipelines for all the tracking and variations required. 

When we approached running jobs on BigQuery, we were surprised to learn that we only needed to configure the number of slots allocated to the project. Suddenly, with BigQuery, there were no longer hundreds of settings affecting query performance. Even before running our first migration test, BigQuery had already eliminated an expensive and tedious task list of optimizations our team would typically need to complete.

Digging into BigQuery, we learned about additional serverless optimizations that BigQuery offered out of the box that addressed many of the issues we had with Spark. For example, we often had a single task get stuck with Spark because virtual machines would be lost or the right VM shape needed to be attainable. With BigQuery’s autoscaling, SQL jobs are much more granularly defined and can move resources as needed between multiple jobs. As another example, we sometimes encountered a storage issue with Spark due to the shuffled data overwhelming a machine’s disk. On BigQuery, there is a separate in-memory shuffle service that eliminates the need for our team to worry about predicting and sizing shuffle disk sizes. 

At this point, it was clear that the migration away from the DevOps of Spark and into the serverless BigQuery architecture would be worth the effort. 

Spark to BigQuery migration 

When migrating our pipelines over, we ran into situations where we had to rewrite large blocks of code, making it easy to introduce new bugs. We needed a way to simultaneously stage this migration without committing to huge rewrites . Dataproc is a very useful tool for this purpose. Dataproc provides us with a simple yet flexible API to spin up Spark clusters and gives us access to the full swath of configurations and optimizations that we’re accustomed to from our previous Spark deployments.

Additionally, BigQuery offers a direct Spark integration through stored procedures with Apache Spark, which provides a fully managed and serverless Spark experience native to BigQuery and allows you to call Spark code directly from BigQuery SQL. It can be configured as part of the BigQuery autoscaler and called from any orchestration tool that can execute SQL, such as dbt. 

This ability to mix and match BigQuery SQL and multiple options for Spark gave us the flexibility to move to BigQuery immediately but roll out the entire migration on our timeline.

With BigQuery, we’re back to building features 

With BigQuery, we could to tap into performance improvements, direct cost savings, and experienced a reduction in our data pipeline error rates. However, BigQuery really changed our business by unlocking new real-time features that we didn’t have before. A couple of examples are:

1. Fresh, fast data results

On our Spark cluster, we needed to pre-compute tens of thousands of possible results each day if a customer wanted to look at a specific detail. While only a small percentage of results would get viewed each day, we couldn’t predict which results would be needed, so we had to pre-compute it all. With BigQuery, the queries run much faster, so we now compute specific results when customers need them. We benefit from avoiding expensive jobs. To our customers, this translates into fresher data.

2. Real-time decision features

Since our migration to BigQuery began, we have rolled out several new features powered by BigQuery’s ability to compute things in near real-time, enhancing our customers’ ability to make real-time decisions.

1) A metrics explorer that lets our customers query their metric data in real-time.

2) A deep dive experience that lets our customers instantly dig into a specific user’s details instead of waiting on a 15-minute Spark job to process. 

3) A warehouse-native solution that lets our customers use their own BigQuery project to run analysis. 

Migrating from Spark to BigQuery has simplified many of our workflows and saved us significant money. But equally importantly, it has made it easier to work with massive data, reduced the strain on our perpetually stretched-thin data team, and allowed us to build awesome products faster for our customers.

Getting started with BigQuery

There are a few ways to get started with BigQuery. New customers get $300 in free credits to spend on BigQuery. All customers get 10GB storage and up to 1TB queries free per month, not charged against their credits. You can get these credits by signing up for the BigQuery free trial. Not ready yet? You can use the BigQuery sandbox without a credit card to see how it works. 

The Built with BigQuery advantage for ISVs 

Google is helping tech companies like Statsig build innovative applications on Google’s data cloud with simplified access to technology, helpful and dedicated engineering support, and joint go-to-market programs through the Built with BigQuery initiative. 

More on Statsig

Companies want to understand the impact of their new features on the business, test different options, and identify optimal configurations that balance customer success with business impact. But running these experiments is hard. Simple mistakes can cause you to make wrong decisions, and lack of standardization can make results hard to compare, and reduce user trust. When implemented poorly, an experimentation program can become a bottleneck, causing feature releases to slow down or, in some cases, causing teams to skip experimentation altogether. Statsig provides an end-to-end product experimentation and analytics platform that makes it simple for companies to leverage best-in-class experimentation tooling.

If you’re looking to improve your company’s experimentation process, visit statsig.com and explore what our platform can offer. We have a generous free tier and a community of experts on Slack to help ensure your efforts are successful. Boost your business’s growth with Statsig and unlock the full potential of your data.

About three years ago, JCB, one of the biggest Japanese payment companies, launched a project to develop new high-value services with agility. We set up a policy of starting small from scratch without using the existing system, which we call the concept of “Dejima”, where we focused on improving various aspects such as team structure, risk management, and application and platform development process.

Until now, large Japanese enterprises have built decision-making systems focused on eliminating unnecessary business processes and efficiently increasing quarterly profits. As a result, we are seeing more organizational structures that make it difficult to take on new challenges or experiments with trial and error. We wanted to breathe a new life into this situation, and that is how the concept of Dejima came up. In the Edo period, Japan closed its national border to other countries under its national isolation policy. At the time, Dejima was the only area where special rules were applied and allowed people from different cultures to come and go, and trade. This special rule generated the culture of inclusion and led to Dejima’s prosperity. Like Dejima, we believe that creating an organization that is independent from other business practices can be effective in enabling digital transformation for the organization. 

We have been able to make this transformation with the direct help of the Google Cloud and its products such as Google Kubernetes Engine (GKE), Cloud Spanner and Anthos Service Mesh, applying domain-driven design and microservice architecture. We named this the “JCB Digital Enablement Platform (JDEP),” which now hosts multiple business critical production services.

A key benefit of GKE is that the team can easily add resources and release them when they are finished, allowing them to be flexible to accommodate busy periods and off-seasons. Meanwhile, Anthos Service Mesh helps us manage complex environments easily. With containerization and managed services, we are prepared for the future for when more services go into production, as it would be easy to maintain and provide version upgrade support. At the same time, Cloud Spanner ensures that we maintain a 99.99% availability at all times.

Our initial motivation for introducing SRE practices was to break proverbial walls between business, development and operations, which was a success. Now we are focused on ensuring its reliability and maintaining customer satisfaction with our SRE practices.

To ensure the success of SRE practices that we implemented, there were a few categories we needed to address, from defining the organizational culture and practices to ensuring the policies attached to the new models created were practical enough to be implemented on the ground level. This is so that the Dejima concept remains sustainable for the long run.  

Instilling a culture of measurement 

Here, “appropriate” reliability is the key. According to the conventional way of thinking at JCB, “service failure must not occur” and “SLA should be maintained as high as possible.” We started by discussing what was the “appropriate reliability” that our customers really needed, but it was not as easy as we thought because the level of reliability for user satisfaction differed from application to application. 

Eventually, the business, development and operations teams formulated specific SLIs and SLOs together, something we would never have been able to do if we discussed separately. This is because the business is required to compromise on lower service levels, since our reliability standard used to be too high. The collaboration of development and operations teams is necessary to understand how our system works upon our users’ interactions.

After Google Cloud helped us run a series of workshops where all teams participated, we saw change within the organization. The business team started evangelizing SRE to other members in the business department, and the development and operations teams started collaborating autonomously. We felt like we were working at Google speed, accomplishing so much in a short amount of time. 

Understanding SRE as an entire company is necessary to progress. We are now working on creating internal training materials to spread the SRE concept throughout the company.

Eliminating ambiguity

With the cooperation of Google Cloud, we have created a Team Charter that defines the team’s mission, values and engagement models. We also created policy documents that include Incident Response Policy, Postmortem Policy, On-call Policy, Toil Policy and Error Budget Policy, to eliminate ambiguity in day-to-day operations.

For example, when an incident occurs, we can identify exactly the level of importance, the roles that are assigned to each person, and in what order they need to follow. When to do a postmortem, who owns it? What to do if the error budget is exhausted? How do other teams reach out to SRE when they have problems? The written policy documents will dramatically improve efficiency and motivate teams to adopt a culture of learning from failures.

The format for such policies are written in Google’s SRE book, but when we adopt it, it needs to take into account the circumstances specific to our company. Simply copying an existing policy won’t work, which is why it’s important to formulate a policy that fits the situation each team is in.

Reformalizing teams

Based on these policies, JCB’s SRE team has two sub-teams. One is called Sheriff which works as the platform SRE, and provides infrastructure services for the application team. The other is called the Diplomat which works as the embedded SRE, and participates in the application team to lead productionisation. There is also a team called Architecture that is separate from the SRE teams whose role is to consult SRE on system design and review architecture.

The SRE team was a single role when it was first launched, but now has two sub-teams. This is because as the number of application teams increases, the number of support tasks for the teams also increases, which can result in a shortage of resources to work on overall improvement. Securing people who are not interrupted from day-to-day support tasks and focusing on the main task improves efficiency.

Whereas both sub-teams share the on-call duty, some engineers are not allowed to do it by contract as they are not allowed to get paged. For those who cannot participate in on-call duties, we created what’s called a Toil Shift, which allows them to focus on resolving tickets in our backlogs instead.

This works well so far, but we will keep evolving as our business grows.

Cloud Firestore is a NoSQL document database that simplifies storing, syncing, and querying data for your mobile and web apps at global scale.

Cloud Firestore is a fast, fully managed, serverless, cloud-native NoSQL document database that simplifies storing, syncing, and querying data for your mobile, web, and IoT apps at global scale.

Its client libraries provide live synchronization and offline support, while its security features and integrations with Firebase and Google Cloud Platform (GCP) accelerate building truly serverless apps.

Here’s other stuff it’s good at:

Sync data across devices, on or offline

With Cloud Firestore, your applications can be updated in near real time when data on the back end changes. This is not only great for building collaborative multi-user mobile applications, but also means you can keep your data in sync with individual users who might want to use your app from multiple devices.

With Firebase Realtime Database, we felt we had built the best force-plate testing software on the market. Thanks to Cloud Firestore, in only two weeks, we built a system that’s significantly better and includes features we never thought possible to ship on Day 1.

Chris Wales, CTO, Hawkin Dynamics

Cloud Firestore has full offline support, so you can access and make changes to your data, and those changes will be synced to the cloud when the client comes back online. Built-in offline support leverages local cache to serve and store data, so your app remains responsive regardless of network latency or internet connectivity.

Simple and effortless

Cloud Firestore’s robust client libraries make it easy for you to update and receive new data while worrying less about establishing network connections or unforeseen race conditions. It can scale effortlessly as your app grows. Cloud Firestore allows you to run sophisticated queries against your data. This gives you more flexibility in the way you structure your data and can often mean that you have to do less filtering on the client, which keeps your network calls and data usage more efficient.

Enterprise-grade, scalable NoSQL

Cloud Firestore is a fast and fully managed NoSQL cloud database. It is built to scale and takes advantage of GCP’s powerful infrastructure, with automatic horizontal scaling in and out, in response to your application’s load. Security access controls for data are built in and enable you to handle data validation via a configuration language.

Education data can inform strategy, enhance both teaching and learning, and help us better understand customer needs. But that data is only as useful as an organization’s ability to access, analyze, and act upon it. Education Technology (EdTech) companies with a strong data warehouse management policy can help to make the most of educational data at all levels.

Creating a data team

When an EdTech doesn’t have a dedicated data team, databases will likely be poorly maintained and data can be hard to find, it can lack critical security, or fail to meet a customer’s specific requirements. A dedicated data team can prevent this and better support an organization’s data needs.  There’s a correlation between the maturity of a data team and the success of an organization’s data utilization. When considering the three primary components of team, outlook, and results, we can better understand an organization’s maturity. By using this matrix from Looker’s Analytical Maturity Playbook, you can assess the maturity of your organization’s data team. Most would agree, the goal is for your operation to move from data chaos to data-driven. But, how can you do that? Think about your data this way: the intelligence you gain from your data is not only an asset…it’s a product. When your data team views your business intelligence as a product—one that’s just as important as the services you offer—they can set the stage for creating a successful data-driven company. Mature data organizations:

• Report functionality regularly
• Constantly assess potential improvements to existing systems
• Manage content rollout to internal employees

Redivis: Making data accessible, connected & secure

Redivis is a platform that connects academic researchers with data and the tools to understand education intelligence. Their customers, who are researchers, expressed how difficult it is to find new datasets for their studies. Once they found a dataset, they often had to access and work with it before they could tell if the information would be useful for their studies. Redivis Data administrators were also concerned about data security. Redivis built their platform on top of Google Cloud’s security infrastructure, making it a highly scalable data processing environment. 

Redivis developed a transparent, tiered access system for datasets that allows researchers to request separate access to a dataset’s documentation to determine whether they can use the dataset. Detailed audit logs (supported by Google Cloud Logging) and robust application-level security controls tighten data access. To offer more insights for their researchers, they securely connected their private datasets with public datasets hosted in BigQuery allowing researchers to run queries on billions of records in seconds, not hours.

With a data team in place, it’s time to visualize where to store and organize your data. Data is an asset. You want to invest in collecting, operationalizing, and storing it properly. “Inadequate data aggregation, harmonization, and processing impedes teams from making the right data-driven decisions and pivoting when needed,” according to Jesus Trujillo Gomez, a strategic business executive at Google Cloud. You can determine how mature your warehouse is by using this matrix to help you identify where your organization falls on the spectrum (least mature on the left, most mature on the right). A company’s data warehouse can be assessed in four areas: Technology, Pipeline, Quality, and Performance. Here are some suggested steps to bring your EdTech company one step closer to a data powerhouse. 

Classcraft: Using a data-driven approach to make learning fun

Launched in 2014, learning company Classcraft is reimagining the classroom, creating better learning environments with gamification. To support their mission, their team has created a complete data platform that uses data analytics to find new ways to engage students in the classroom. Classcraft became a Google for Education Build Partner to keep up with rising demand. They expanded their footprint to reach 6 million students and educators in 165 countries. 

They also migrated to Google Cloud, building on several integrations with Google Workspace for Education, Google Classroom, and Chrome for Education, allowing them to support customers ranging from individual teachers to entire school districts.  Classcraft migrated its database and analytics infrastructure to BigQuery and Cloud Storage. “We capture about 10 million new data points every month related to school culture and student performance,” says Chief Technology Officer and VP of Product Stéphane Guillemette. “With BigQuery and other Google Cloud tools, we can quickly deliver valuable insights to educators.”