Hundreds of millions of residents of South East Asia have only limited access to banking and finance services. However, help is at hand. One of South East Asia’s largest fintech businesses, Ascend Money is using digital technologies to realize its mission of enabling as many people as possible to access innovative financial services and live better lives.

According to Ascend Money, 60 percent of the region’s 620 million residents do not have access to a full suite of financial services. Even a relatively small proportion of this market represents a big opportunity for the business.

Ascend Money’s offerings include the TrueMoney regional payment platform for underserved and digital consumers. The TrueMoney platform supports more than 40 million consumers across six countries: Cambodia, Indonesia, Myanmar, the Philippines, Thailand, and Vietnam.

“We found, based on value, ease of use, effectiveness, and the skills and adaptability of our own team members, BigQuery and other Google Cloud Platform services were the best fit for our business.”

– Abraham Jarrett, Head of Engineering, Ascend Money

TrueMoney also provides an e-wallet app that provides easy ways to top up mobile phones, undertake online shopping, and pay for products and services; a network of 65,000 branded shops; and international remittances, initially between Thailand and Myanmar. In addition, Ascend Money offers financial products to small to medium businesses.

Ascend Money is part of the South East Asian online business Ascend Group, which also operates e-commerce, e-procurement, data centers, cloud services, fulfilment, and digital marketing services.

Cost is a key criteria

Ascend Money initially started operations using physical infrastructure and on-premises workforce productivity applications. However, as the business grew its customer base and expanded into new markets, its technology leaders began to explore options to improve value for money; reduce the maintenance load on in-house team members; improve its data management and analysis; and collaborate more effectively.

“Operating our own on-premises infrastructure entails a higher cost of ownership and maintenance, as well as requiring us to scale up our hardware as needed,” says Abraham Jarrett, Head of Engineering, Ascend Money. “We conducted an evaluation and found that moving to the cloud would deliver a range of benefits.”

G Suite and Google Cloud Platform the best fit

The business evaluated solutions available in the market and opted to move to G Suite and Google Cloud Platform.

“Moving to Google Cloud Platform was an optimization exercise, with lowering our costs our primary goal, and achieving better performance an added benefit,” says Jarrett. Google Cloud Platform monitoring, diagnostic, and analytics tools have enabled Ascend Money to reduce its infrastructure spending, becoming more efficient and cost-effective in the process.

“Managing software is a big cost for us. Through G Suite, we are significantly reducing our software deployment, licensing, and repair costs.”

– Abraham Jarrett, Head of Engineering, Ascend Money

The Ascend Money team saw Google Kubernetes Engine as enabling a seamless way of transitioning from an on-premises data center to a cloud service. “We considered industry trends such as what people were adopting and who we could hire when making our decision,” says Jarrett. “Engineering teams are focusing on using Kubernetes open source container management at scale and as a way of doing business, which was attractive to us.

“Google Kubernetes Engine presented the easiest way to manage and orchestrate our containers, and drive us away from our existing infrastructure.”

BigQuery best for cost and ease of use

Ascend Money also reviewed data infrastructure solutions, with its business intelligence and data platform teams considering a range of options. The teams quickly ruled out an on-premises solution due to the capital investment required and opted for a cloud service. Following a rigorous evaluation of Google Cloud Platform against the services offered by another cloud provider, Ascend Money opted to run on Google Cloud.

“We found, based on value, ease of use, effectiveness, and the skills and adaptability of our own team members, BigQuery and other Google Cloud Platform services were the best fit for our business,” says Jarrett. The business is now running an architecture comprising a BigQuery analytics data warehouse; Cloud Storage to store raw and archive data; Cloud Dataflow to process stream and batch data, and Cloud Pub/Sub for event ingestion and delivery.

“We’re expanding our utilization of BigQuery and other services on a daily basis,” says Jarrett.

Ascend Money is also using a range of Google Cloud Platform services for the infrastructure outside its data platform, including Stackdriver logging and monitoring; Cloud KMS to manage cryptographic keys for its cloud services, Cloud Build to undertake continuous integration, delivery, and deployment; Cloud DNS to provide domain name system services; and Cloud Functions to enable its developers to run and scale code in the cloud.

With Google Cloud Platform, Ascend Money is now processing about 12GB of batch data per day and streaming data from about 200 data marts per day in Thailand alone.

Cost effective scalability and faster to market

Google Kubernetes Engine has enabled the business to scale and deliver to market faster. “We can build on the platform, take the application and container and deploy them to scale out,” says Jarrett. “The Google Kubernetes Engine orchestration mechanism that provides containerized, elastic scalability is extremely important in allowing us to manage costs and expend our effort efficiently. Through Kubernetes, we can write once and deploy everywhere.”

With Google Cloud Platform, the business has saved 3,000,000 THB (about US$90,000) in licensing costs and, through automation tools, reduced the time to complete infrastructure activities by 50 percent.

“We don’t have as many meetings since we deployed G Suite and we can work effectively in a distributed fashion.”

– Abraham Jarrett, Head of Engineering, Ascend Money

Ascend Money’s entire workforce is now using G Suite to collaborate and operate productively. The business is achieving a range of benefits including being able to get new team members up and running more quickly and reduced cost. “Managing software is a big cost for us,” says Jarrett. “Through G Suite, we are significantly reducing our software deployment, licensing, and repair costs.” Meanwhile, the shorter time to onboard team members to G Suite is paying off with improved productivity and an accelerated ability to collaborate.

Ascend Money team members primarily use Sheets, Slides, and Docs to create internal materials, including product requirement documents in Docs and workforce and project planning spreadsheets in Sheets.

The organization also uses Hangouts Meet to collaborate when working from different locations. “We don’t have as many meetings since we deployed G Suite and we can work effectively in a distributed fashion,” says Jarrett. “People can work from home and we have five regions plus Thailand where they can collaborate live on a document, such as a slide deck for board meetings or meetings with our chief executive officer, head of engineering, or other senior managers.”

20 hours per week saved

Jarrett describes the time savings of using web browser-based productivity software and G Suite as saving them an average of 20 hours per week. This has improved productivity and the quality of life for Ascend Money’s hard-working team, increasing overall staff satisfaction.

“The ability to work remotely is a big win for us,” Jarrett says. “Our team members can keep one computer at work and one at home and access the same information, they can work in a plane while it sits on the tarmac, or update a Google Doc or a Google Sheet in real time on their phone. They can work anytime, from any location, as long as they have an internet connection. They have less dead time, meaning more uptime.”

With Google Cloud Platform and G Suite, Ascend Money is ideally positioned to support growing demand for its fintech services in South East Asia, particularly among people with limited access to banking. “We have the agility and dynamism to support rising demand and look forward to continuing to work with Google to realize our business ambitions,” says Jarrett.

Today, a typical enterprise uses over 100 SaaS applications while large organizations commonly use over 400 apps. These applications contain valuable data on customers, suppliers, employees, products and more, offering the potential for valuable insights and powerful workflows. However, in the past, this data has often remained siloed and underutilized, even within an application.

Customer demand is driving an unprecedented wave of innovation across the SaaS industry, with providers building intelligence into their applications through rich analytical and AI/ML capabilities, and enabling their customers’ data ecosystems through real time data sharing. The data cloud platform powering these applications is a critical factor in companies’ ability to innovate and optimize efficiency, while keeping its customer’s data secure.

Today, over 700 tech companies including Zoominfo, Equifax, Exabeam, Bloomreach, and Quantum Metric power their products and businesses using Google’s data cloud. This week at the Data Cloud Summit, we announced the Built with BigQuery initiative which helps ISVs get started building applications using data and machine learning products. By providing dedicated access to technology, expertise and go to market programs, this initiative allows tech companies to accelerate, optimize and amplify their success.

“Enabling customers to gain superior insights and intelligence from data is core to the ZoomInfo strategy,” ZoomInfo CEO Henry Schuck says. “We are excited about the innovation Google Cloud is bringing to market and how it is creating a differentiated ecosystem that allows customers to gain insights from their data securely, at scale, and without having to move data around. Working with the Built with BigQuery initiative team enables us to rapidly gain deep insight into the opportunities available and accelerate our speed to market.”

Building intelligent SaaS applications with a unified data platform

Google’s data cloud provides a complete platform for building data-driven applications, from simplified data ingestion, processing and storage to powerful analytics, AI/ML and data sharing capabilities, all seamlessly integrated with Google Cloud’s open, secure, sustainable platform. With a huge partner ecosystem and support for multicloud, open source tools and APIs, we provide technology companies the portability and extensibility they need to avoid data lock-in.

Through the Built with BigQuery initiative, we are helping tech companies to build the next-gen SaaS applications on Google’s data cloud with simplified access to technology, dedicated engineering support and joint go to market programs.

Exabeam, a next generation cybersecurity solution, is a great example. The company leverages Google’s data cloud to provide their customers with a “limitless-scale” cybersecurity solution.

“Built with Google’s data cloud, Exabeam’s limitless-scale cybersecurity platform helps enterprises respond to security threats faster and more accurately” said Sanjay Chaudhary, VP of Products at Exabeam. “We are able to ingest data from over 500 security vendors, convert unstructured data into security events, and create a common platform to store them in a cost effective way. The scale and power of Google’s data cloud enables our customers to search multi-year data and detect threats in seconds.”

Foster innovation and unlock new business models through data sharing

Data becomes even more valuable when shared. According to research, the global data monetization market size is growing at a CAGR of 47.9% and is projected to reach $11.7 Billion by 2026. It’s a compelling proposition for SaaS companies as it enables them to deliver increased value for their customers while expanding their own partner ecosystem, increasing stickiness and unlocking new revenue streams.

Google Cloud’s strategy for data sharing encompasses three key areas: secure data sharing in BigQuery, the ability to create private and public exchanges alongside commercial and public datasets in Analytics Hub, and a robust ecosystem of premier partner and Google data.

Through the real-time data sharing capabilities of BigQuery, SaaS companies are enabling their customers to combine their data with the customer’s own proprietary data, and other third-party data sources, to derive 360-degree insights. Over 4,500 organizations share more than 250 Petabytes of data weekly in BigQuery, not accounting for intra-organizational data sharing*.

For example, manufacturers can get real-time visibility into their entire supply chain by combining datasets from ISVs, such as supply chain innovator, Blume Global, and data publishers.

“Our partnership with Google Cloud is helping us achieve our mission to build the next-generation supply chain operating system. Blume Maps, our digital twin of the supply chain world built with Google’s data cloud, allows our customers to generate accurate lead times, real-time shipment location and ETAs” said Blume Global CEO Pervinder Johar. “We apply the power of Google Cloud’s data and analytics capabilities to our growing database of over 1.5 million global data points to feed our lead time and dynamic ETA engine. We are also able to create data twins of unique logistics data and share with users around the globe.”

Google Cloud’s Analytics Hub is a fully-managed service built on BigQuery that allows organizations to efficiently and securely exchange valuable data and analytics assets across any organizational boundary. With unique datasets that are always-synchronized and bi-directional sharing, you can create a rich and trusted data ecosystem between business units or partnerships–one in which everyone gains value immediately.

“As external data becomes more critical to organizations across industries, the need for a unified experience between data integration and analytics has never been more important. We are proud to be working with Google Cloud to power the launch of Analytics Hub, feeding hundreds of pre-engineered data pipelines from hundreds of external datasets,” said Dan Lynn, SVP Product at Crux. “The sharing capabilities that Analytics Hub delivers will significantly enhance the data mobility requirements of practitioners, and the Crux data integration platform stands ready to quickly integrate any external data source and deliver on behalf of Google Cloud and its clients.”

Innovate, optimize and amplify with Google Cloud

The Built with BigQuery initiative provides access to technology, expertise and go-to-market:.

• Access to Technology: Get started fast with a Google-funded, pre-configured sandbox. Access Cloud Credits to fund your, and your customer’s, POCs and apply for innovative pricing models designed for ISVs.
• Access to Expertise: Accelerate and optimize product design and architecture with access to designated experts in building data-driven SaaS applications from the ISV Center of Excellence, providing insight into key use cases, architectural patterns and best practices. Access experts from across Google to enable co-innovation with products such as Earth Engine and Google Marketing Platform.
• Access to Go-to-market: Amplify your success with joint marketing programs to drive awareness, generate demand and increase adoption.

Get Started

Start building your applications on Google Cloud with $300 in free credits or apply for the Built with BigQuery initiative.

*As of November, 2021, within a typical 7 day period in BigQuery, and not accounting for intra-organizational data sharing.

Sabre is an innovative software and technology company that leverages highly scalable databases and artificial intelligence (AI) to power travel industry partners around the globe. Our company processes more than 12 billion shopping requests and serves over 1 billion travelers every year.

The companies that partner with us include airlines, travel websites, agencies, and hotels. We are distinguished by a long history of integrating cutting edge technology into our operations. Our earliest innovations include building one of the first transaction processing systems in existence and a travel distribution network that predates the Internet! Through our next generation of AI-powered solutions, we are focused on optimizing the retailing, distribution, and fulfillment experience of the travel industry.

In my role as chief architect for Sabre Labs, I spearhead the long-term technology choices we are making to enhance the development, deployment, and operation of our software. We’ve embarked on a multi-year strategy to transform our technology and solutions. Rearchitecting our infrastructure to become fully cloud native is a central tenet of this transformation. In these early iterations of our plan, we have seen the significant impact of machine learning and its potential in bringing truly personalized travel experiences to customers. As part of this effort, we’ve established a 10-year partnership with Google to help accelerate our transformation and bring innovation to the travel industry.

Database choices require tradeoffs

The complexity and scale of the travel industry places high demands on the cloud services we utilize. We tend to place more emphasis on how a particular cloud service will impact an application’s reliability, performance, or development time, rather than choosing a service purely for its functionality. When it comes to databases, this can often mean making a tradeoff between latency and consistency.

The tradeoff exists for any database that serves multiple copies of its data in different availability zones or geographic regions for reliability. A database designed to ensure that everyone sees a consistent view of the latest data might update those copies synchronously using a consensus algorithm, which affects how quickly the data can be served. On the other hand, a database that’s optimized for faster data serving might update each copy asynchronously and not guarantee consistent reads across records.

Cloud Spanner and Bigtable–two of Google Cloud’s managed databases–are both highly effective services, and each one could support many of our travel applications. But as you will see, the latency vs. consistency tradeoff made it clear which one was best suited for two of our most critical cases.

Google Cloud Spanner facilitates strong, global consistency for Sabre

An airline’s reservation database stores a passenger’s booking information, seat selection, tickets, special requests, and other critical information about their trip. As a result, this data sits at the consistency end of that consistency/latency spectrum. Sabre typically processes thousands of reservation updates per second on behalf of our carrier customers. An airline’s reservation database must be served from many availability zones (and data is replicated across these availability zones) so that it remains available in the event of an outage. It also requires ACID properties for transactional updates across records since airlines often make changes to multiple passengers and multiple flights at the same time.

We needed a system that can handle bursts of concurrent updates, as would occur during a snowstorm when hundreds of thousands of passengers might be automatically moved to alternate flights. Spanner is a great fit for the reservations case because of its unique consistency guarantees. It processes over 1 billion requests per second at peak and provides five 9s SLA (99.999 percent) to support our applications. Spanner also helps us maintain compliance, business continuity, redundancy, and reliability using the same secure-by-design infrastructure, built-in data protection and replication, and multi-layered security that are essential to our Google Cloud workloads.

Spanner’s client libraries also provide built-in mechanisms to handle retrying in the event of write conflicts with another transaction and allow developers to choose stale reads in read-only transactions for improved performance. Of course, consistency across multiple zones or regions doesn’t come for free. It means higher write latencies than if we wrote to a comparable database running in a single availability zone, but for an application that manages flight reservations, it’s a tradeoff that makes sense.

Bigtable provides predictable, low latency at scale

Our flight shopping systems sit at the other end of the latency/consistency spectrum. Sabre’s shopping engine generates millions of itineraries per second on behalf of travelers using mobile apps, third-party travel websites, and airline call centers. Each itinerary requires significant compute resources to calculate: We need to find which combinations of flights make sense and evaluate complex rules about their availability and pricing. Users are typically less patient while searching for a flight than they are when booking it, so we needed a low latency solution.  But we can cache many of these shopping results to reduce our compute usage. For instance we can decide how long to cache results based on factors like how far the flight results are from the departure. 

Bigtable makes an excellent choice for this shopping cache. It’s a NoSQL database service built to handle high-throughput, low-latency applications, with more than 10 exabytes of data under management. Bigtable’s unique latency properties—such as predictability and single-digit millisecond response time even for multi-petabyte tables—enable us to serve large volumes of shopping results cost effectively, while providing low response times to travelers.

Google Cloud supports a focus on innovation

Managed databases like Bigtable and Spanner are a significant part of Sabre’s cloud strategy. The combination of unique tools like Key Visualizer for Bigtable and Spanner as well as integration with other Google Cloud services like Cloud IAM, Cloud Monitoring, and now Datastream, make the experience of operating managed databases with Google Cloud much easier than it is with several of their self-hosted counterparts. As a result of their granular pricing models and ability to be deployed and automated by SREs, the managed databases we use also end up with a lower total cost of ownership.

We’re particularly excited about a few recent database-related announcements from Google Cloud. Bigtable’s SLA update gives us more concrete expectations in terms of multi-cluster, multi-region uptime. Spanner’s change to provisioning in Processing Units increases its cost efficiency when deploying in non-production environments where we may need many isolated instances, but won’t come close to the limits of a single node. In those cases, Spanner instances may now be configured in one-tenth of a node increments.

Our cloud transformation depends on having a choice of databases for different use cases, trade offs, and migration schedules. In addition to managed databases, we expect to use self-hosted databases, database solutions available in Cloud Marketplace, and transitional services like Cloud SQL for a few more years. In an industry as demanding as travel, accelerating our most critical applications using technology unique to Google Cloud means less time spent optimizing latency and consistency, and more time spent innovating.

Learn more about how your organization can use Bigtable and Spanner.

Introduction

We recently announced BigQuery support for sparse features which help users to store and process the sparse features efficiently while working with them. That functionality enables users to represent sparse tensors and train machine learning models directly in the BigQuery environment. Being able to represent sparse tensors is a useful feature because sparse tensors are used extensively in encoding schemes like TF-IDF as part of data pre-processing in NLP applications and for pre-processing images with a lot of dark pixels in computer vision applications.

There are numerous applications of sparse features such as text generation and sentiment analysis. In this blog, we’ll demonstrate how to perform sentiment analysis with the space features in BigQuery ML by training and inferencing machine learning models using a public dataset. This blog also highlights how easy it is to work with unstructured text data on BigQuery, an environment traditionally used for structured data.

Using sample IMDb dataset

Let’s say you want to conduct a sentiment analysis on movie reviews from the IMDb website. For the benefit of readers who want to follow along, we will be using the IMDb reviews dataset from BigQuery public datasets. Let’s look at the top 2 rows of the dataset.

Although the reviews table has 7 columns, we only use reviews and label columns to perform sentiment analysis for this case. Also, we are only considering negative and positive values in the label columns. The following query can be used to select only the required information from the dataset.

SELECT
 review,
 label,
FROM 
 `bigquery-public-data.imdb.reviews`
WHERE
 label IN ('Negative', 'Positive')

The top 2 rows of the result is as follows:

Methodology

Based on the dataset that we have, the following steps will be carried out:

1. Build a vocabulary list using the review column
2. Convert the review column into sparse tensors
3. Train a classification model using the sparse tensors to predict the label (“positive” or “negative”)
4. Make predictions on new test data to classify reviews as positive or negative.

Feature engineering

In this section, we will convert the text from the reviews column to numerical features so that we can feed them into a machine learning model. One of the ways is the bag-of-words approach where we build a vocabulary using the  words from the reviews and select the most common words to build numerical features for model training. But first, we must extract the words from each review. The following code creates a dataset and a table with row numbers and extracted words from reviews.

-- Create a dataset named `sparse_features_demo` if doesn’t exist
CREATE SCHEMA IF NOT EXISTS sparse_features_demo;




-- Select unique reviews with only negative and positive labels
CREATE OR REPLACE TABLE sparse_features_demo.processed_reviews AS (
 SELECT
   ROW_NUMBER() OVER () AS review_number,
   review,
   REGEXP_EXTRACT_ALL(LOWER(review), '[a-z]{2,}') AS words,
   label,
   split
 FROM (
   SELECT
     DISTINCT review,
     label,
     split
   FROM
     `bigquery-public-data.imdb.reviews`
   WHERE
     label IN ('Negative', 'Positive')
 )
);

The output table from the query above should look like this:

The next step is to build a vocabulary using the extracted words. The following code creates a vocabulary including word frequency and word index from reviews. For this case, we are going to select only the top 20,000 words to reduce the computation time.

-- Create a vocabulary using train dataset and select only top 20,000 words based on frequency
CREATE OR REPLACE TABLE sparse_features_demo.vocabulary AS (
 SELECT
   word,
   word_frequency,
   word_index
 FROM (
   SELECT
     word,
     word_frequency,
     ROW_NUMBER() OVER (ORDER BY word_frequency DESC) - 1 AS word_index
   FROM (
     SELECT
       word,
       COUNT(word) AS word_frequency
     FROM
       sparse_features_demo.processed_reviews,
       UNNEST(words) AS word
     WHERE
       split = "train"
     GROUP BY
       word
   )
 )
 WHERE
   word_index < 20000 # Select top 20,000 words based on word count
);

The following shows the top 10 words based on frequency and their respective index from the resulting table of the query above.

Creating a sparse feature

Now we will use the newly added feature to create a sparse feature in BigQuery. For this case, we aggregate word_index and word_frequency in each review, which generates a column as ARRAY[STRUCT] type. Now, each review is represented as ARRAY[(word_index, word_frequency)].

-- Generate a sparse feature by aggregating word_index and word_frequency in each review.
CREATE OR REPLACE TABLE sparse_features_demo.sparse_feature AS (
 SELECT
   review_number,
   review,
   ARRAY_AGG(STRUCT(word_index, word_frequency)) AS feature,
   label,
   split
 FROM (
   SELECT
     DISTINCT review_number,
     review,
     word,
     label,
     split
   FROM
     sparse_features_demo.processed_reviews,
     UNNEST(words) AS word
   WHERE
     word IN (SELECT word FROM sparse_features_demo.vocabulary)
 ) AS word_list
 LEFT JOIN
   sparse_features_demo.vocabulary AS topk_words
   ON
     word_list.word = topk_words.word
 GROUP BY
   review_number,
   review,
   label,
   split
);

Once the query is executed, a sparse feature named `feature` will be created. That `feature` column is an `ARRAY of STRUCT` column which is made of `word_index` and `word_frequency` columns. The picture below displays the resulting table at a glance.

Training a BigQuery ML model 

We just created a dataset with a sparse feature in BigQuery. Let’s see how we can use that dataset to train with a machine learning model with BigQuery ML. In the following query, we will train a logistic regression model using the review_number, review, and feature to predict the label:

-- Train a logistic regression classifier using the data with sparse feature
CREATE OR REPLACE MODEL sparse_features_demo.logistic_reg_classifier
 TRANSFORM (
   * EXCEPT (
       review_number,
       review
     )
 )
 OPTIONS(
   MODEL_TYPE='LOGISTIC_REG',
   INPUT_LABEL_COLS = ['label']
 ) AS
 SELECT
   review_number,
   review,
   feature,
   label
 FROM
    sparse_features_demo.sparse_feature
 WHERE
   split = "train"
;

Now that we have trained a BigQuery ML Model using a sparse feature, we evaluate the model and tune it as needed.

-- Evaluate the trained logistic regression classifier
SELECT * FROM ML.EVALUATE(MODEL sparse_features_demo.logistic_reg_classifier);

The score looks like a decent starting point, so let’s go ahead and test the model with the test dataset.

-- Evaluate the trained logistic regression classifier using test data
SELECT * FROM ML.EVALUATE(MODEL sparse_features_demo.logistic_reg_classifier,
 (
   SELECT
     review_number,
     review,
     feature,
     label
   FROM
     sparse_features_demo.sparse_feature
   WHERE
     split = "test"
 )
);

The model performance for the test dataset looks satisfactory and it can now be used for inference. One thing to note here is that since the model is trained on the numerical features, the model will only accept numeral features as input. Hence, the new reviews have to go through the same transformation steps before they can be used for inference. The next step shows how the transformation can be applied to a user-defined dataset.

Sentiment predictions from the BigQuery ML model

All we have left to do now is to create a user-defined dataset, apply the same transformations to the reviews, and use the user-defined sparse features to perform model inference. It can be achieved using a WITH statement as shown below.

WITH
 -- Create a user defined reviews
 user_defined_reviews AS (
   SELECT
     ROW_NUMBER() OVER () AS review_number,
     review,
     REGEXP_EXTRACT_ALL(LOWER(review), '[a-z]{2,}') AS words
   FROM (
     SELECT "What a boring movie" AS review UNION ALL
     SELECT "I don't like this movie" AS review UNION ALL
     SELECT "The best movie ever" AS review
   )
 ),


 -- Create a sparse feature from user defined reviews
 user_defined_sparse_feature AS (
   SELECT
     review_number,
     review,
     ARRAY_AGG(STRUCT(word_index, word_frequency)) AS feature
   FROM (
     SELECT
       DISTINCT review_number,
       review,
       word
     FROM
       user_defined_reviews,
       UNNEST(words) as word
     WHERE
       word IN (SELECT word FROM sparse_features_demo.vocabulary)
   ) AS word_list
   LEFT JOIN
     sparse_features_demo.vocabulary AS topk_words
     ON
       word_list.word = topk_words.word
   GROUP BY
     review_number,
     review
 )


-- Evaluate the trained model using user defined data
SELECT review, predicted_label FROM ML.PREDICT(MODEL sparse_features_demo.logistic_reg_classifier,
 (
   SELECT
     *
   FROM
     user_defined_sparse_feature
 )
);

Here is what you would get for executing the query above:

And that’s it! We just performed a sentiment analysis on the IMDb dataset from a BigQuery Public Dataset using only SQL statements and BigQuery ML. Now that we have demonstrated how sparse features can be used with BigQuery ML models, we can’t wait to see all the amazing projects that you would create by harnessing this functionality. 

If you’re just getting started with BigQuery, check out our interactive tutorial to begin exploring.