Cloud-native features are helping public sector teams innovate faster than ever. Ideas discussed in a morning meeting can be a working proof of concept later that day. Managed services can remove administrative burden and reduce the steps needed to design and provision cloud infrastructure. Security can be built-in from the beginning with Identity and Access Management (IAM), Virtual Private Cloud Service Controls (VPC-SC), and Data Loss Prevention (DLP). Short-lived services and infrastructure-as-code allow rapid and cost-effective prototyping. These technologies can be used to architect a solution that follows the principle of least privilege and helps you secure your data.

So the question becomes: given the complex problems agencies face, where do you start? Google Public Sector now offers “Getting Started” and “Scaling” service offerings for CCAI, DocAI, and BigQuery to help you jumpstart your AI journey, based on where you are.

Complex problems, simpler AI-based solutions

Solving more challenging problems with cloud-native technology doesn’t have to be overwhelming. You can approach them the same way you might solve a puzzle: start with one piece that follows another until the larger picture takes shape. Though you can simplify the steps, solving these problems still requires powerful tools. Google Cloud’s AI/ML capabilities may be the answer for your team.

Google Public Sector is making it easier to get started with advanced technologies, beginning with artificial intelligence and machine learning (AI/ML) workloads for government organizations, and it’s something you can do now, one piece at a time.

Automating your FAQs with CCAI

Does your agency require a team to answer commonly asked questions? What if you could train an agent to answer questions immediately and operate 24/7? Contact Center AI (CCAI) can do this and more. Already using CCAI and need the agent to level up to address complex interactive dialogs? Getting Started with CCAI and Scaling with CCAI are new Google service offerings specifically designed to help public sector organizations tackle situations like these.

Automate data entry with DocAI

How many hours does your team spend manually reviewing or entering data from standardized forms? What if you could automatically pull data right from the page? Google Document AI (DocAI) specializes in exactly this—even if the form has handwritten text. Getting Started with DocAI and Scaling with DocAI are new service offerings that help you remove this burden from your team. DocAI automates data entry and makes that data available to other teams while prioritizing both security and ease of use.

Making data and insights accessible with BigQuery

Then there’s all your existing data. You may have years of it stored in many places, and you may not have a way to make use of it when you need it. BigQuery is Google’s enterprise data warehouse. It was designed for data analytics—looking back at historical data to make conclusions about it. But BigQuery’s analytics don’t stop there. It can also look forward in time to make predictions, often using the same datasets. Getting Started with BigQuery and Scaling with BigQuery are new service offerings that help you take your first steps toward AI/ML capabilities by starting with a single table or pipeline that can help make sense of all your data.

The best help is the kind that meets you where you are and gets you where you want to be. Google Public Sector’s new service offerings do just that: help you work through complex problems by meeting you wherever your starting line is, whether you’re ready to start or ready to scale. Let us know if you would like us to contact you about the services mentioned in this article. Let’s solve your highest impact problems together, one puzzle piece at a time.

At Universe, we serve customers day and night and are always working to make sure they have a great experience, whether online or at one of our live events.

Our technology has to make that possible, and our legacy systems weren’t cutting it anymore. What we needed was a consistent, reliable infrastructure that would help our internal teams provide a fast and innovative ticket-buying experience to customers.

With our data well-managed, we could free up time for our developers to bring new web features to customers, like tailored add-ons at checkout.

Our team of about 20 software engineers needed more flexibility and agility in our infrastructure; we were using various data processing tools, and it wasn’t easy to share data across teams so that everyone saw the same information.

We also needed to incorporate streaming data into the data warehouse to ensure the consistency and integrity of data that’s read in a particular window of time from multiple sources. Our developer teams needed to be able to ship new features faster, and the data back ends were getting in the way.   

In addition, when GDPR regulations went into effect, we needed to make sure all our data was anonymized, and we couldn’t do that with our legacy tools.

Finding the right data tools for the job
To make sure our customers were getting a top-notch online experience, we had to make the right technology choices.

Our first step was to centralize multiple data sources and create a single data warehouse that could serve as the foundation for all of our reporting requirements, both internal and external.

The new technology infrastructure we built had to let us move and analyze data easily, so our teams could focus on using that data and insights to better serve our customers.

Previously, we had lots of siloed systems and applications running in AWS. We did a trial using Redshift, but we needed more flexibility than it offered in how we loaded historical data into our cloud data warehouse. Though we were using MongoDB Atlas for our transactional database, it was important to continue using SQL for querying data.  

The trial task that really sold us on BigQuery was when we wanted to alter a small table that had about 20 million rows, used for internal reporting.

We needed to add a value, but our PostgreSQL system wouldn’t allow it. Using Apache Beam, we set up a simple pipeline that moved data from the original source into BigQuery to see if we could add the column there.

BigQuery ingested the data and let us add the new value in seconds. That was a significant moment that led us to start looking at how we could build end-to-end solutions on Google Cloud. BigQuery gave us multiple options to load our historical data in batches and build powerful pipelines. 

We also explored Google Cloud’s migration tools and data pipeline options. Once we saw how Cloud Dataflow worked, with its Apache Beam back-end, we never looked back. Google Cloud provided us with the data tools we needed to build our data infrastructure. 

Cloud for data, and for users
Introducing new technologies isn’t always simple—companies sometimes avoid it altogether because it’s so hard. But our Google Cloud onboarding process has been easy. 

It took us less than two months to fully deploy our BigQuery data warehouse using the Cloud Dataflow-Apache Beam combination.

Moving to Google Cloud brought us a lot of technology advantages, but it’s also been hugely helpful for our internal users and customers. The data analytics capabilities that we’re now able to offer users has really impressed our internal teams, including developers and DevOps, even those who haven’t used this type of technology before.

Some internal clients are already entirely self-service. We’ve hosted frequent demos, and also hosted some “hack days,” where we share knowledge with our internal teams to show them what’s possible. 

We quickly found that BigQuery helped us solve scale and speed problems.

One of our main pain points had been adding upsell opportunities for customers during the checkout process. The legacy technology hadn’t allowed us to quickly reflect those changes in the data warehouse. With BigQuery, we’re able to do that, and devote fewer resources to making it happen. We’ve also eliminated the time we were spending tuning memory and availability, since BigQuery handles that. Database administration and tuning required specialized knowledge and experience and took up time. With BigQuery, we don’t have to worry about configuring that hardware and software. It just works.

We’ve also eliminated the time we were spending tuning memory and availability, since BigQuery handles that. Database administration and tuning required specialized knowledge and experience and took up time. With BigQuery, we don’t have to worry about configuring that hardware and software. It just works.

Two features in particular that we implemented using BigQuery have helped us improve the performance of our core transactional database. First, using Cloud Dataflow to convert raw MongoDB logs to structured rows under a BigQuery table, which we can then query using SQL to identify slow or underperforming queries. Second, we can now query multiple logging tables using wildcards, since we load Fastly logs to BigQuery. 

Along with MongoDB Atlas as our main transactional database, much of our infrastructure now runs as Google Cloud microservices using Google Kubernetes Engine (GKE), including the home page and our payment system. Kubernetes cron jobs power background scheduled jobs, and we also use Cloud Pub/Sub. Cloud Storage handles any data storage if any space constraints emerge. 

Our overall performance has increased by about 10x with BigQuery. Both our customers and internal clients, like our sales and finance teams, benefit from the new low-latency reporting. Reports that used to be weekly or monthly are now available in near-real time. It’s not only faster to read records, but faster to move the data, too.

We have Cloud Dataflow pipelines that write to multiple places, and the speed of moving and processing data is incredibly helpful. We stream in financial data using Cloud Dataflow in streaming mode, and plan to have different streaming pipelines as we grow. We have several batch pipelines that run every day. We can move terabytes of data without performance issues, and process more than 100,000 rows of data per second from the underlying database. It used to take us a month to move that volume of data into our data warehouse. With BigQuery, it takes two days.

We’re also enjoying how easy and productive these tools are. They make our life as software engineers easier, so we can focus on the problem at hand, not fighting with our tools. 

What’s next for Universe
Our team will continue to push even more into Google Cloud’s data platform. We have plans to explore Cloud Datastore next. We’re also moving our databases to PostgreSQL on GCP, using Cloud Dataflow and Beam. BigQuery’s machine learning tools may also come into play as Universe’s cloud journey evolves, so we can start doing predictive analytics based on our data. We’re looking forward to gaining even more speed and agility to meet our business goals and customer needs.    

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. 

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.”