When you think about climate change, you might not consider a daily commute to work or a drive around town as big contributing factors. And yet, transport is the fastest growing source of CO2 emissions from fossil fuel, which in turn is the largest contributor to climate change. This is the key insight behind the mission of Spanish carbon-neutral multinational Acciona. The group, which provides sustainable solutions for infrastructure and renewable energy projects across 65 countries, launched an electric scooter sharing service called Acciona Mobility in 2018.

Aiming to contribute to the decarbonization of the transport sector while helping relieve traffic congestion within cities, Acciona Mobility enables users to rent electric scooters powered 100% by energy from renewable sources. Users can find, reserve, and rent the scooters at the tap of their mobile screens via the Acciona Mobility application and pay on the basis of minutes spent riding. The app, which is available free to download online, is also the gateway through which new users can register for the service, which welcomes anyone with a valid driver’s license for motorbikes. After having their right to drive, identity, and card details validated, citizens can start enjoying the service, available 24 hours a day, every day of the year.

“We needed to deploy a reliable infrastructure for a new service before understanding exactly what its scale and demand would be. Our strategy was to adopt a serverless architecture that can grow with us to sustain our long-term vision. This kind of thinking led us directly to Google Cloud.”

—Jose Luis Rosell, CIO Services Division, Acciona

“Acciona Mobility is exciting because it stems from our vision to make people’s lives easier in a sustainable way,” says Jose Luis Rosell, CIO of Acciona’s Services Division. “40% of the pollution near cities comes from private transportation. We want to help solve that problem with zero-carbon, electric, multimodal transportation that’s also convenient for people to use.”

However, Jose Luis says that building the appropriate IT infrastructure to bring the new service to life required external support. “We needed to deploy a reliable infrastructure for a new service before understanding exactly what its scale and demand would be. Our strategy was to adopt a serverless architecture that can grow with us to sustain our long-term vision,” he explains. “This kind of thinking led us directly to Google Cloud.”

Developing the transportation platform of the future

Because Acciona was already a Google Cloud customer, Jose Luis and his team reached the decision to develop the new service on Google Cloud with ease, as he explains: “We had a very positive experience working closely with the Google Cloud team during Acciona’s first project on Google Cloud, and that collaborative mentality is very important for us. We feel Google Cloud is a trustworthy partner that is willing to take risks with us and is flexible enough to lead us through business uncertainty with the right technology,” he says. “So we were confident that its technology and people could help us bring the Acciona Mobility service from theory to reality.”

This time, Acciona partnered with cloud consultancy Altostratus as well, to consult on a selection of Google Cloud products that would help develop the application quickly by making use of managed services. Acciona’s strategy was to launch the new services first in Madrid, where the company is headquartered, and then spread the vision across more cities later, if the idea worked out well in practice. Due to the scale of the project and its data needs, BigQuery was chosen as the primary data warehouse to store all the information related to the service, such as the location of scooters and their availability status. With all its data readily available on BigQuery, Acciona is able to run specific queries that help it to gain insights such as which urban areas have the highest volume of scooters being rented. Knowing this, Acciona can reorganize availability to make sure there are always enough scooters in that specific location.

Integrating it with Pub/Sub and Dataflow, Acciona ensures that the data generated by scooters is ingested and processed in real time so that users searching for scooters nearby, using the Acciona Mobility application on their phones, can always have up-to-date information at hand. Using Google Maps Platform APIs such as the Directions API, Acciona’s mobile app translates the geographical coordinates of scooters into an easy-to-read address displayed beside a visual map. Using Cloud SQL, Acciona automates the storage capacity management of the database as the number of scooters and active users grow.

Meanwhile, Google Cloud Armor protects the service against cyber breaches and distributed denial of service attacks to keep it running uninterrupted. In addition, Google Cloud itself helps to reinforce the cyber security measures with secure-by-default managed services, such as data encryption. “The managed services mean more resilience and more uptime, because we don’t need to worry about maintenance or external threats,“ says Jose Luis of the solutions protecting all information generated by the new service, which as CIO is a topic he holds dearly. “I face cybersecurity issues more confidently as a Google Cloud partner,” he adds.

“Today, we have data coming in every 15 seconds from 10,000 electric scooters around Europe. BigQuery and Cloud SQL-managed services help us handle that data very carefully and with precision so that our users don’t experience any lags when searching for scooters or returning them.”

—Jose Luis Rosell, CIO Services Division, Acciona

Expanding an 100% sustainable transport solution throughout Spain

Within two months of the decision to deploy Acciona Mobility on Google Cloud, the service was ready for launch, filling the streets of Madrid with 500 electric scooters that run 100% on renewable energy. By 2020, the number of scooters has grown to 10,000 and the service has scaled to multiple cities in and outside of Spain, including Lisbon, Milan, and Rome. To date, more than 3.5 million intracity rides have used the Acciona Mobility app, reducing CO2 emissions by more than 1,000 tons.

“Today, we have data coming in every 15 seconds from 10,000 electric scooters around Europe. BigQuery and Cloud SQL-managed services help us handle that data very carefully and with precision so that our users don’t experience any lags when searching for scooters or returning them,” says Jose Luis of the fast-growing scale of the project.

“Google Cloud enables us to keep our services always available and to scale quickly to respond to its growing demand. Our ratings have been very positive as a result. More importantly, our vision of transportation as a cleaner, more sustainable commodity is really spreading and resonating with the public.”

—Jose Luis Rosell, CIO Services Division, Acciona

Set to continue expanding, Acciona is using Google Kubernetes Engine to make sure the Acciona Mobility application has the computing capacity it needs to continue scaling to reach new markets and serve more users. On that note, Google operations tools (formerly Stackdriver) are also being deployed to help monitor, troubleshoot, and improve the performance of the application as it scales.

“Google Cloud enables us to keep our services always available and to scale quickly to respond to its growing demand. Our ratings have been very positive as a result. More importantly, our vision of transportation as a cleaner, more sustainable commodity is really spreading and resonating with the public,” he concludes.

For 117 years, the U.S. Department of Agriculture’s Forest Service has been a steward of America’s forests, grasslands, and waterways. It directly manages 193 million acres and supports sustainable management on a total of 500 million acres of private, state, and tribal lands. Its impact reaches far beyond even that, offering its research and learning freely to the world.

At Google, we’re big admirers of the Forest Service’s mission. So we were thrilled to learn in 2011 that its scientists were using Google Earth Engine, our planetary-scale platform for Earth Science data and analysis, to aid its research, understanding, and effectiveness. In the years since, Google has worked with the Forest Service to meet its unique requirements for visual information about the planet. Using both historical and current data, the Forest Service built new products, workflows, and tools that help more effectively and sustainably manage our natural resources. The Forest Service also uses Earth Engine and Google Cloud to study the effects of climate change, forest fires, insects and disease, helping them create new insights and strategies.

Besides gaining newfound depths of insight, the Forest Service has also sped up its research dramatically, enabling everyone to do more. Using Google Cloud and Earth Engine, the Forest Service reduced the time it took to analyze 10 years worth of land-cover changes from three months to just one hour, using just 100 lines of code. The agency built new models for coping with change, then mapped these changes over time, in its Landscape Change Monitoring System (LCMS) project.

Emergency responders can now work better on new threats that arise after wildfires, hurricanes, and other natural disasters. Forest health specialists can detect and monitor the impacts of invasive insects, diseases, and drought. More Forest Service personnel can use new tools and products within Earth Engine, thanks to numerous training and outreach sessions within the Forest Service.

Researchers elsewhere also benefited when the Forest Service created new toolkits, and posted them to GitHub for public use. For example, there’s geeViz, a repository of Google Earth Engine Python code modules useful for general data processing, analysis, and visualization.

This is only the start. Recently, the Forest Service started using Google Cloud’s processing and analysis tools for projects like California’s Wildfire and Forest Resilience Action Plan. Forest Service researchers also use Google Cloud to better understand ecological conditions across landscapes in projects like Fuelcast, which provides actionable intelligence for rangeland managers, fire specialists, and growers, and the Scenario Investment Planning Platform for modeling local and national land management scenarios.

The Forest Service is a pioneer in building technology to help us better understand and care for our planet. With more frequent imaging, rich satellite data sets, and sophisticated database and computation systems, we can view and model the Earth as a large-scale dynamic system.

We are honored and excited to respond to the unique set of requirements of the scientists, engineers, rangers, and firefighters of the USFS, and look forward to years of learning about — and better caring for — our most precious resources.

*Image 1: The USDA Forest Service (USFS) Geospatial Technology and Applications Center (GTAC) uses science-based remote sensing methods to characterize vegetation and soil condition after wildland fire events. The results are used to facilitate emergency assessments to support hazard mitigation, to inform post-fire restoration planning, and to support the monitoring of national fire policy effectiveness. GTAC currently conducts these mapping efforts using long-established geospatial workflows. However, GTAC has adapted its post-fire mapping and assessment workflows to work within Google Earth Engine (GEE) to accommodate the needs of other users in the USFS. The spatially and temporally comprehensive coverage of moderate resolution multispectral data sources (e.g., Landsat, Sentinel 2) and analytical power provided by GEE allows users to create geospatial burn severity products quickly and easily. Box 1 shows a pre-fire Sentinel-2 false color composite image. Box 2 shows a post-fire Sentinel-2 false color composite image with the fire scar apparent in reddish brown. Box 3 shows a differenced Normalized Burn Ratio (dNBR) image showing the change between the pre- and post-fire images in Boxes 1 and 2. Box 4 shows a thresholded dNBR image of the burned area with four classes of burn severity (unburned to high severity), which is the final output delivered to forest managers.

*Image 2: Leveraging Google Earth Engine (GEE), the USDA Forest Service (USFS) Geospatial Technology and Applications Center (GTAC) and USFS Region 8, developed the Tree Structure Damage Impact Predictive (TreeS-DIP) modeling approach to predict wind damage to trees resulting from large hurricane events and produce spatial products across the landscape. TreeS-DIP results become available within 48 hours following landfall of a large storm event to allow allocation of ground resources to the field for strategic planning and management. Boxes 1 and 3 above show TreeS-DIP modeled outputs with varying data inputs and parameters. Box 2 shows changes in greenness (Normalized Burn Ratio; NBR) that was measured with GEE during the recovery from Hurricane Ida and is shown as a visual comparison to the rapidly available products from TreeS-DIP.

*Image 3: Severe drought conditions across the American West prompted concern about the health and status of pinyon-juniper woodlands, a vast and unique ecosystem. In a cooperative project between the USDA Forest Service (USFS) Geospatial Technology and Applications Center (GTAC) and Forest Health Protection (FHP), Google Earth Engine (GEE) was used to map pinyon pine and juniper mortality across 10 Western US States. The outputs are now being used to plan for future work including on-the-ground efforts, high-resolution imagery acquisitions, aerial surveys, in-depth mortality modeling, and planning for 2022 field season work.

Box 1 contains remote sensing change detection outputs (in white) generated with GEE, showing pinyon-juniper decline across the Southwestern US. Box 2 shows NAIP imagery from 2017 with, with box 3 showing NAIP imagery from 2021. NAIP imagery from these years shows trees changing from healthy and green in 2017 to brown and dying in 2021. In addition, box 2 and box 3 show change detection outputs from Box 1 for a location outside of Flagstaff, AZ converted to polygons (in white). The polygon in box 2 is displayed as a dashed line to serve as a reference, while the solid line in box 3 shows the measured change in 2021. Converting rasters to polygons allows the data to be easily used on tablet computers, as well as the ability to add information and photographs from field visits.

We wanted to find out which of the most popular database services on two of the biggest cloud provider (AWS and Google), developers preferred to use.

More specifically, we wanted views from enterprises in the region (it’s hard to impossible to find data that’s specific to India.)

To simplify our quest, we looked at the most popular database services on AWS and then looked at the equivalent Google Cloud offering.

That’s how we came to pit AWS RDS and Google Cloud SQL.

For the uninitiated: Cloud SQL is a fully managed database service that makes it easy to set up and manage your relational PostgreSQL, MySQL, and SQL Server databases in the cloud.

It is ideal for: WordPress, backends, game states, CRM tools, MySQL, PostgreSQL, and Microsoft SQL Servers.

Based on real feedback from companies in the APAC region, there are 11 reasons why Google Cloud SQL is better.

In addition, according to Stackshare and ITcentralstation, the biggest reason developers prefer Google Cloud SQL? Because it’s fully managed, easy to set up, easy to manage and it’s really scalable.

As one user says:

Its most valuable feature is that it’s scalable. I can start off with a base of a lot of data and move as much as I want and it’s the same as if asked to do a lot of infrastructure changes… it’s easy to use, simple, and user-friendly. The setup was straightforward. Just a couple of clicks, and we were done. My suggestion to anyone thinking about this solution is to jump into it head-first!

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. 

Access to the cloud has advanced the distribution and consumption of financial information on a global scale. In parallel, the global financial data landscape has been transformed by an influx of alternative data sources, including social media, meteorological data, satellite imagery, and other data. Exchanges and market data providers now find they need to include these new datasets to enrich their products and compete, which has meant they now must consider cloud-based models to keep up with the demands of their customers who expect easy, quick, flexible and cost-efficient ways to consume market data.

To address these needs, today we’re announcing the general availability of Datashare for financial services, a new Google Cloud solution that brings together the entire capital markets ecosystem—data publishers, and data consumers—to exchange market data securely and easily.

Datashare helps organize third-party financial information, making it accessible and useful to market data publishers and data consumers. We open-sourced the entire Datashare solution so market data publishers can now onboard their licensed datasets to Google Cloud securely, quickly and easily, while data consumers can consume that data as a service in tools of their preference, such as BigQuery.

Three ways to distribute and consume your data

Batch data delivery

Datashare provides a batch data delivery mechanism for data publishers to deliver their reference data, historical tick data, alternative market data sources and more via BigQuery, reducing the administrative burden on data consumers to extract insights from data. 

Real-time data streaming delivery

By using this event-based data delivery channel for rapidly changing instrument prices, tick data, orders, news and others via Pub/Sub, data consumers can reliably process individual messages or rewind to a point in time to replay a prior market scenario and test model changes.

Monetizing licensed datasets

Market data publishers can onboard their licensed datasets to Google Cloud and make them available via a one-stop-shop on Google Cloud Marketplace, enabling a new sales channel to expand market reach.

Reference architecture

Check out the diagram below to see how you can share your batch and real-time data directly to your Google Cloud customers with BigQuery and Pub/Sub.

As you can see in the above reference architecture, both publishers and consumers can derive several benefits from the solution:

Benefits for data publishers

• You no longer have to maintain your own delivery and licensing infrastructure.
• You can easily package and deliver granular data products and experiments with SQL.
• You can have a solution that scales with your business as data volumes and number of customers grow.

Benefits for data consumers

• Your data is ready for analysis and machine learning (ML)— you no longer have to maintain extract, transform, and load (ETL) pipelines to load files and transform data.
• You can avoid the expense and burden of maintaining multiple copies of large data files.
• You can be more targeted with consumption of data using BigQuery queries, improving performance, and compliance, and reducing cost.

Accessing the datasets

Google Cloud has been working with multiple industry firms on innovating in the market data space. By using Datashare for publishing, data publishers can make their entire datasets available on Google Cloud. Early adopters of Datashare include firms such as OneTick and Accern. OneTick’s datasets include reference and historical futures data (that can be accessed in our console with your login). Accern’s datasets include alternative data such as market sentiment and credit analysis data (that can be accessed in our console with your login).

To make it more helpful, we partnered with Accern to create a hypothetical scenario to describe the data acquisition and analytics process step-by-step.

Accern use case 

As a sustainability analyst, you require an economic, social and governance (ESG) dataset to determine which sector is the most widely covered ESG sector by analysts, and to also identify the sector with the lowest ESG sentiment score. Now, you can discover and acquire an ESG dataset in Google Cloud.

Step 1. Navigate to the Financial Services solutions page in the Google Cloud console:

Step 2. Click a dataset, for example Accern AI-Generated ESG Insights, then review the overview details, plans and pricing, documentation and support information. To view the available pricing tiers, click ‘View All Plans’. Once you’ve decided on a tier that you would like to subscribe to, click ‘Select’, choose a billing account and review and accept the terms of service to complete the subscription. Once the steps are complete, click ‘Subscribe’ at the bottom. An overlay window will appear, click ‘Register with Accern’ to activate and complete the subscription.

Step 3. Once activation is complete, you’ll be directed to the Datashare ‘My Products’ screen. Voila! You are now subscribed to Accern’s ESG Scores dataset and can access it in your Google Cloud instance using BigQuery. To access the data, click the hour glass icon on the corresponding ‘My Products’ record that you just purchased. An overlay will present you with the details on the dataset and/or table. Click the ‘Navigate to Table’ button to navigate through to the BigQuery console.

Step 4. Now that you have access and are in the BigQuery console, it’s time to generate data insights.

^{For this example, we’ve eliminated the company identifying information that is included as part of the subscription and aggregated company ESG in a view where each row represents a day, an industry sector, a specific identified ‘ESG Issues’ (event_group and event) and the respective ‘ESG Sentiment’ per issue.}

For example, row 1 indicates that within the ‘Healthcare’ sector, there was a ‘Social – Civil Society’ issue identified and it had a negative ESG sentiment score of -15.35.

Step 5. Generate a report by exporting it to Data Studio to build visualizations and conduct additional analysis on the ESG data.

Select ‘Export’ and ‘Explore with Data Studio’.

Step 6. Build a simple/basic report.

Now that the ESG data appears in Data Studio, you can start by building a simple chart to help you understand which industry sectors have the highest volume of discussions around ESG and the overall ESG Sentiment per industry sector.

To build the chart:

• Select the chart type ‘Table’.

• Include Entity_Sector as your dimension to aggregate results by ‘Industry Sector.’
• Include Signal_ID as a measure to count the number of ESG passages identified per ‘Industry Sector.’
• Include AVG(Event_Sentiment) as a measure to display the overall ESG Sentiment per ‘Industry Sector’ across ESG Issues.

You can see sectors that are  most discussed when it comes to ESG related topics and their corresponding ‘ESG Sentiment’ scores.

Step 7. Build your final report in Data Studio.

As a next step you can further drill into the data to understand ESG data specific to each ‘Industry Sector’ and identify positive and negative ESG practices. 

Accern has built a more complex sample dashboard and made it available publicly here. You can interact with this report and play around with the data. The dashboard can help to identify material ESG insights for each sector to inform your investment and risk processes. If you have additional questions, you can reach out to Accern directly.

Discovering, accessing and analyzing licensed datasets is quick and easy. Stay tuned for more updates on new licensed datasets.

Publishing your data via Datashare

If you are a publisher of market data, alternative, or exotic data, you can use Datashare to get it published on Google Cloud Marketplace.

Start by joining the Partner Advantage program by registering for the Partner Advantage Portal and applying for the Partner Advantage Build Model engagement. Visit our getting started guide for information to get started on publishing licensed datasets in the Marketplace. Stay tuned for a future blog post about using Datashare to publish datasets in the Marketplace.

More solutions for capital markets

Check out other Google Cloud solutions for capital markets.