Unstructured data such as images, speech and textual data can be notoriously difficult to manage, and even harder to analyze. The analysis of unstructured data includes use cases such as extracting text from images using OCR, sentiment analysis on customer reviews and simplifying translation for analytics. All of this data needs to be stored, managed and made available for machine learning.

The new BigQuery ML inference engine empowers practitioners to run inferences on unstructured data using pre-trained AI models. The results of these inferences can be analyzed to extract insights and improve decision making. This can all be done in BigQuery, using just a few lines of SQL.

In this blog, we’ll explore how the new BigQuery ML inference engine can be used to run inferences against unstructured data in BigQuery. We’ll demonstrate how to detect and translate text from movie poster images, and run sentiment analysis against movie reviews.

BigQuery ML’s new inference engine

Google Cloud is home to a suite of pre-trained AI models and APIs. The BigQuery ML inference engine can call these APIs and manage the responses on your behalf. All you have to do is define the model you want to use and run inferences against your data. All of this is done in BigQuery using SQL. The inference results are returned in JSON format and stored in BigQuery for analysis.

Why run your inferences in BigQuery?

Traditionally, working with AI models to run inferences required expertise in programming languages like Python. The ability to run inferences in BigQuery using just SQL can make generating insights from your data using AI simple and accessible. BigQuery is also serverless, so you can focus on analyzing your data without worrying about scalability and infrastructure.

The inference results are stored in BigQuery, which allows you to analyze your unstructured data immediately, without the need to move or copy your data. A key advantage here is that this analysis can also be joined with structured data stored in BigQuery, giving you the opportunity to deepen your insights. This can simplify data management and minimize the amount of data movement and duplication required.

Which models are supported?

For now, the BigQuery ML inference engine can be used with these pre-trained Vertex AI models:

• Vision AI API: This model can be used to extract features from images managed by BigQuery Object Tables and stored on Cloud Storage. For example, Vision AI can detect and classify objects, or read handwritten text.
• Translation AI API: This model can be used to translate text in BigQuery tables into over one hundred languages.
• Natural Language Processing API: This model can be used to derive meaning from textual data stored in BigQuery tables. For example, features like sentiment analysis can be used to determine whether the emotional tone of text is positive or negative.

So, how does this work in practice? Let’s look at an example using images of movie posters

1. We will define our pre-trained models for Vision AI, Translation AI and NLP AI in BigQuery ML.
2. We’ll then use Vision AI to detect the text from some classic movie posters images. 
3. Next, we’ll use Translation AI to detect any foreign posters and translate them to a language of our choosing – English in this case. 
4. Finally, we’ll combine our unstructured data with structured data in BigQuery.
   We’ll use the extracted movie titles from our movie posters to look up the viewer reviews from the BigQuery IMDB public dataset. We can then run sentiment analysis against these reviews using NLP AI.

Note: The BigQuery ML inference engine is currently in Preview. You will need to complete this enrollment form to have your project allowlisted for use with the BQML Inference Engine.

We’ll give examples of the BigQuery SQL needed to define your models and run your inferences. You’ll want to check out our notebook for a detailed guide on how to get this up and running in your Google Cloud project.

1. Define your AI Models in BigQuery

You will need to enable the APIs listed below, and also create a Cloud resource connection to enable BigQuery to interact with these services.

You can then run the CREATE MODEL query for each AI service to create your pretrained models, replacing the model_name as required.

CREATE OR REPLACE MODEL 
`{PROJECT_ID}.{DATASET_ID}.{VISION_MODEL_NAME}`
REMOTE WITH 
CONNECTION `{PROJECT_ID}.{REGION}.{CONN_NAME}`
OPTIONS ( remote_service_type = '<model_name>' );

2. Use the Vision AI API to detect text in images stored in Cloud Storage

You will need to create an object table for your images in Cloud Storage. This read-only object table provides metadata for images stored in Cloud Storage:

CREATE OR REPLACE EXTERNAL TABLE
`{PROJECT_ID}.{DATASET_ID}.{OBJECT_TABLE_NAME}`
WITH
CONNECTION `{REGION}.{CONN_NAME}`
OPTIONS (object_metadata = 'SIMPLE', uris = ['{BUCKET_LOCATION}/*']);

To detect the text from our posters, you can then use ML.ANNOTATE_IMAGE and specify the text_detection feature.

SELECT
       ml_annotate_image_result.full_text_annotation.text AS text_content,
       *
FROM
       ML.ANNOTATE_IMAGE(
         MODEL `{PROJECT_ID}.{DATASET_ID}.{VISION_MODEL_NAME}`,
         TABLE `{DATASET_ID}.{OBJECT_TABLE_NAME}`,
         STRUCT(['TEXT_DETECTION'] AS vision_features));

A JSON response will be returned to BigQuery that includes the text content and language code of the text. You can parse the JSON to a scalar result using the dot annotation highlighted above.

3. Use the Translation AI API to translate foreign movie titles 

ML.TRANSLATE can now be used to translate the foreign titles we’ve extracted from our images into English. You just need to specify the target language and the table of the movie posters for translation:

SELECT
text_content,
STRING(ml_translate_result.translations[0].detected_language_code)
as original_language,
STRING(ml_translate_result.translations[0].translated_text)
as translated_title
FROM
  ML.TRANSLATE(
MODEL `{PROJECT_ID}.{DATASET_ID}.{TRANSLATE_MODEL_NAME}`,
TABLE `{DATASET_ID}.image_results`,
STRUCT('TRANSLATE_TEXT' as translate_mode, "en" as target_language_code));

Note: The table column with the text you want to translate must be named text_content:

The table of results will include json that can be parsed to extract both the original language and the translated text. In this case, the model has detected that title text is in French and has translated it to English:

4. Finally, use natural language processing (NLP) to run sentiment analysis against movie reviews

You can easily join inference results from your unstructured data with other BigQuery datasets to bolster your analysis. For example, we can now join the movie titles we extracted from our posters with thousands of movie reviews stored in BigQuery’s IMDB public dataset `bigquery-public-data.imdb.reviews`.

You can use ML.UNDERSTAND_TEXT with the analyze_sentiment feature to run sentiment analysis against some of these reviews to determine whether they are positive or negative:

SELECT
   primary_title, start_year, text_content AS review,
   FLOAT64(ml_understand_text_result.document_sentiment.score) AS score,
   FLOAT64(ml_understand_text_result.document_sentiment.magnitude) AS magnitude,
FROM
   ML.UNDERSTAND_TEXT(
     MODEL `{PROJECT_ID}.{DATASET_ID}.{NLP_MODEL_NAME}`,
           (
           SELECT
             primary_title, start_year, review AS text_content
           FROM
             `bigquery-public-data.imdb.title_basics` titles
           JOIN
             `bigquery-public-data.imdb.reviews` reviews
           ON
             reviews.movie_id = titles.tconst
           WHERE
             UPPER(titles.primary_title) = 'THE LOST WORLD' AND
             start_year = 1925
           ),
      STRUCT("analyze_sentiment" AS nlu_option)) ;

Note: The table column with the text you want to analyze must be named text_content:

The JSON response will include a score and magnitude. The score indicates the overall emotion of the text while the magnitude indicates how much emotional content is present:

So, how did the Lost World compare with other movies that year?

To wrap up, we’ll compare the average review score of the 1925 Lost World movie to other movies released that year to see which was more popular. This can be done using familiar SQL analysis:

SELECT
 primary_title, start_year,
 AVG(FLOAT64(ml_understand_text_result.document_sentiment.score))AS av_score,
 AVG(FLOAT64(ml_understand_text_result.document_sentiment.magnitude)) AS av_magnitude
FROM
 ML.UNDERSTAND_TEXT(
   MODEL `{PROJECT_ID}.{DATASET_ID}.{NLP_MODEL_NAME}`,
         (
         SELECT
           primary_title, start_year, movie_id, review AS text_content
         FROM
           `bigquery-public-data.imdb.title_basics` titles
         JOIN
           `bigquery-public-data.imdb.reviews` reviews
         ON
           reviews.movie_id = titles.tconst
         WHERE
           start_year = 1925
         ),
   STRUCT("analyze_sentiment" AS nlu_option))
GROUP BY
   primary_title, start_year
ORDER BY
   av_score DESC;

It looks like The Lost World narrowly missed out on the top spot to Sally of the Sawdust!

Want to learn more?

Check out our notebook for a step by step guide on using the BQML inference engine for unstructured data in Google Cloud. You can also check out our Cloud AI service table-valued functions overview page for more details. Curious about pricing? The BQML Pricing page gives a breakdown of how costs are applied across these services.

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.

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.

This is a guest post authored by Firebase customer, Anton Ivanov, Founder & CEO of DealCheck

Real estate investing is a fantastic way to build a stream of passive income and grow your wealth. Numerous studies have pointed out that real estate investing has created more millionaires throughout history than any other form of investing (like this one and this one). So why don’t more people do it?

I asked myself this very question a few years ago after talking to a group of friends about the success I’ve had with real estate, and listening to their reasons why they think it’s out of their reach.

A common theme among them was that they viewed it as something too difficult to learn and master. There were too many steps, the learning curve was steep and there was a lot of room for mistakes for somebody just starting out, especially when analyzing the financial performance of potential investment properties.

Traditionally, most investors used spreadsheets to do the math – which works only if you know what and how you’re calculating something. But if you don’t know that, it’s very easy to make mistakes and overlook things. And no one wants to make mathematical errors before a huge purchase like an investment property.

Where do I even begin?!

And that’s when I had the idea to build DealCheck – a cloud-based, easy-to-use property analysis tool for real estate investors and agents. I wanted to create a platform that would help new investors learn the ropes and avoid costly mistakes, but at the same time provide the flexibility to perform more advanced analysis with a click of a button.

Making real estate investing easier and more accessible.

The Challenges of Solo Development

I was working as a front-end engineer at the time, so I knew I could build the UI myself, but what about the back-end, data storage, authentication, and a bunch of other things you need for a full-functioning cloud app?

I didn’t know anybody I could bring on as a co-founder, so I set out to research what technologies and platforms I could leverage to help me with the back-end and server infrastructure.

Firebase kept popping up again and again and I began to look at it in more detail. It was then recently acquired by Google and its collection of BaaS (backend-as-a-service) modules seemed to offer the exact solution I needed to build DealCheck.

I was especially impressed with the documentation for each feature and how well all of the different technologies could be tied together to create one unified platform.

It wasn’t long before I signed up and started building the first MVP of the app.

Using Firebase to Quickly Build a Scalable Backend

As the only developer on the project, I had limited time and resources to spend on building the back-end, so I set out to use every Firebase feature that was available at the time to my advantage.

My goal was actually to write as little server-side code as possible and instead focus on leveraging the different Firebase modules to solve three specific challenges:

Challenge #1 – Authentication and User Management

The first one was authentication and user management. DealCheck’s users needed the ability to create their accounts so they can view and analyze properties on any device (more on that later). I wanted to have the ability to sign in with email, Facebook or a Google account.

Firebase Authentication was designed specifically for this purpose and I used it to handle pretty much the entire authentication flow. Out-of-the-box, it has support for all the major social networks, cross-network credential linking and the basic account management operations like email changes, password resets and account deletions.

There was no server-side code required at all – I just needed to build the UI on the front-end.

Email, Facebook and Google sign in powered by Firebase.

And as an added benefit, Firebase Authentication ties directly into the Realtime Database product to create a declarative permissions and access control framework that’s easy to implement and maintain. This helped me make sure user data was protected from unauthorized access, but also facilitate data sharing among users.

Challenge #2 – Cloud Storage with Cross-Device Sync

Next up was data storage. I knew that I wanted DealCheck’s users to be able to use the app and analyze properties online, on iOS and Android. So I needed a real-time, cloud-based database solution that could sync data across any device.

Syncing data across web and mobile is not easy!

Firebase Realtime Database is a NoSQL, JSON-based database solution that was designed exactly for this purpose, and I was actually surprised how great it worked. I used the official AngularJS bindings for Firebase on the front-end to read and write to it directly from the client.

I had to do some extra work on mobile to implement an offline mode with syncing after reconnections, but all-together the code required to make everything work was minimal.

As I mentioned, Firebase Authentication tied directly to the database to facilitate access control, so I really didn’t need to do anything extra there. And I was able to set up automatic daily backups of all the data with a click of a button.

Challenge #3 – Third-Party Integrations

Up to now, I had written exactly 0 lines of server-side code and everything was handled by the client directly. As DealCheck’s development progressed, however, I knew that I would need a server to handle some operations that could not be done in the client.

I wasn’t very experienced with server maintenance and DevOps, but fortunately the Firebase Cloud Functions product was able to solve all of my needs. Cloud Functions are essentially single-purpose functions that can be triggered (or executed) based on a specific HTTP request or events coming from the Authentication, Realtime Database or other Firebase products.

Each function can be run once based on a specific event trigger to perform its prescribed task. You don’t have to worry about provisioning a server instance or managing load – everything is done automatically for you by Firebase.

What’s even cooler, is that Cloud Functions can access the Realtime Database and Cloud Storage buckets of the same project, performing operations on them server-side, as needed.

This is how DealCheck processes subscription payments through Stripe, validates Apple and Google Play mobile subscription receipts, integrates with third-party APIs and updates database records without user interaction.

Bringing in sales comparable data from third-party providers into DealCheck.

Cloud Functions became the “glue” that tied the entire back-end infrastructure together.

Growing from an MVP to 100,000 Users with Firebase

The first version of the DealCheck app was built and launched in less than 5 months with just me on the development team. I definitely don’t think that would have been possible without Firebase powering the back-end infrastructure. Maybe the project wouldn’t have ever launched at all.

While Firebase is awesome for quick MVP development, it’s definitely designed to power production applications at scale as well. As DealCheck grew from a small side-project to one of the most popular real estate apps with over 100k users, all of the Firebase products that we use scaled to support the increasing load.

Moreover, the fantastic interoperability of all Firebase modules allows us to develop and release new features much faster because of the reduced coding requirements and ease of configuration.

So next time you’re looking to build an ambitious project with a small team – take a look at how Firebase can help you reduce development time and provide a suite of powerful tools that scale as your business grows.

This is exactly how DealCheck grew from a simple idea to make property analysis easier and faster, to an app that is helping tens of thousands of people grow their wealth and passive income through real estate investing. It’s a truly awesome and fulfilling experience to see your work positively impact so many people and it wouldn’t have been possible without Firebase.

Companies today are inundated with vast amounts of data from various sources. This overwhelming amount of data is meant to benefit the company, but often leaves data teams feeling overwhelmed, which can create data bottlenecks and result in a slow time to value. In fact, only twenty seven percent of companies agree that data and analytics projects produce insights and recommendations that are highly actionable (Accenture). This means that nearly 3 in 4 companies are not unlocking value in their data, which poses a huge challenge for organizations trying to move the needle and drive real business results. We at Google Cloud, however, saw opportunity in this challenge, which is why we created Data Analytics Design Patterns: cross-product technical solutions designed to accelerate a customer’s path to value realization with their data. These industry solutions bring together product capabilities alongside design methodology, open source deployable code, data models, and reference architectures to accelerate your business outcomes.

With Data Analytics Design Patterns, you get access to more than 30 ready-to-deploy data analytics solutions. Design patterns leverage the best of Google and our rich partner ecosystem, including Technology Partners & System Integrators. In this blog, we will cover 3 examples on how a design pattern can be applied to unlock the value of data:

1. Improve mobile app experience with Unified App Analytics
2. Maximize digital shop’s revenue with Price Optimization 
3. Protect internal systems from security and malware threat with Anomaly Detection 

Unified App Analytics

If mobile apps are part of your go-to-market strategy, you have several data sources that can provide invaluable customer insights. In addition to tools such as CRM (e.g. Salesforce) and customer care (e.g. Zendesk), you likely use Google Analytics to log app events and Firebase Crashlytics to gather data about app errors. But can you easily combine back-end server data with app front-end data to unlock customer insights? 

The Unified App Analytics design pattern makes it easy to plug all the disparate data sources into a single warehouse (BigQuery) and start analyzing it with a Business Intelligence tool (Looker). Once you have a complete and real time view of your customer experience with your app, you can take action. For example, if you notice an increase in app errors, you can quickly combine your Crashlytics data with your CRM data to narrow down the crashes with the highest revenue impact and prioritize their resolution. Further, you can automate your issue resolution workflow by creating a rule for any future crash that impacts a subset of VIP customers.

With the Unified App Analytics design pattern, you’ll gain access to valuable insights about your user experience with your app so you can inform your future app strategy. For example, NPR, an American media company, increased user engagement by showing content that better mapped to listener interests and behaviors.

Price Optimization

In a competitive and hectic global marketplace, strategic pricing matters more than ever, but often projects are consumed by the tedium of standardizing, cleaning, and preparing data—from transactions, inventory, demand, among other sources.

Price Optimization solution allows retailers to build a data driven pricing model. The solution consists of three main components:

• Dataprep by Trifacta: integrates different data sources into a single Common Data Model (CDM). Dataprep is an intelligent data service for visually exploring, cleaning, and preparing structured and unstructured data for analysis, reporting, and machine learning.
• BigQuery: allows you to create and store pricing models in a consistent and scalable way as a serverless Cloud Data Warehouse service
• Looker dashboards: surface insights and enable business teams to take action with enterprise ready BI platform

With the Price Optimization design pattern from Google Cloud and our partner Trifacta, you’ll be able to rapidly unify multiple data sources and create a real-time and ML-powered analysis, leveraging predictive models to estimate future sales. For example, PDPAOLA, an online jewelry company, doubled sales with dynamic pricing adjustments enabled by a single data view.

Anomaly Detection

Organizations need to anticipate and act on risks and opportunities to stay competitive in a digitally transforming society. Anomaly detection helps organizations identify and respond to data points and data trends in high velocity, high volume data sets that deviate from historical standards and expected behaviors, allowing them to take action on changing user needs, mitigate malicious actors and behaviors, and prevent unnecessary costs and monetary losses.

The Anomaly Detection design pattern uses Google Pub/Sub, BigQuery, Dataflow, and Looker to:

• Stream events in real time 
• Process the events, extract useful data points, train the detection algorithm of choice
• Apply the detection algorithm in near-real time to the events to detect anomalies
• Update dashboards and/or send alerts

The challenge of finding the important insights and anomalies in vast amounts of data applies to organizations across all industries and lines of business, but is especially important to protecting the security of an organization. For example, TELUS, a national communications company, modernized their security analytics platform leveraging this pattern, allowing them to detect anomalies in near real time to detect and mitigate suspicious activity.

Get started

Turn your data into business outcomes with Google Cloud and our broad partner ecosystem by deploying Data Analytics Design Patterns at your organization. There are more than 30 Data Analytics Design Patterns ready for you to use. We have more than 200+ more ideas in the pipeline, so be sure to check in regularly as new patterns will be added soon. 

To dive deeper and find out more about how Data Analytics Design Patterns can help your organization accelerate use cases and create faster time to value, check out this video.