If you are like many of your peers you are looking to Google Cloud to reduce overheads, drive innovation, and agility with a wide variety of databases for your Oracle workloads. From Bare Metal Solution to cloud-native databases, Google Cloud offers the infrastructure to meet your needs.

The Benefits

Optimizing your Oracle workloads with Google Cloud solutions has a number of benefits.

Dramatically reduce TCO: Reduce up-front hardware, software, and maintenance costs. Migrating Oracle workloads to Google Cloud could result in up to 78% in TCO savings.

High scale and high availability: Mission-critical applications need dependable databases that scale with your business. Google Cloud offers SLA-backed databases to protect your apps.

Simplify operation: Reduce data center and database management by leveraging managed infrastructure and fully managed database services.

Migration Strategies

Google Cloud provides multiple strategies for your specific migration journey to help you with licensing, maintenance cost, and manageability.

Rehost: To migrate Oracle workloads with specific configurations, Google Cloud offers Bare Metal Solution, where you can simply lift and shift your workloads. Bare Metal Solution provides hardware, hardware support, and integrated billing and support. This infrastructure is connected with a highly resilient interconnect and connects to all native Google Cloud services with less than 2ms latency.

Replatform: Keep your core application code and migrate to compatible platforms to help reduce licensing fees and maintenance costs. You can migrate to an open source database such as Cloud SQL for PostgreSQL that has a certain amount of compatibility with PL/SQL and stored procedure. You can also offload data analytics-centric workloads to SQL-compatible data warehousing solutions like BigQuery.

Rewrite: Rewrite your application to take full advantage of cloud-native databases. If your application requires a relational database with global scalability, you can migrate to Cloud Spanner, which provides scalability with industry-leading high availability of 99.999% SLA.

If you’re managing a crucial application that has to be fully fault-tolerant, you need your system to be able to handle every fault, no matter the type and scope of failure, with minimal downtime and data loss. Protecting against these faults means juggling numerous variables that can impact performance as well as recovery time and cost.

Today’s managed database services take over the operational complexity that used to exist for database administrators. Growing your organization’s tolerance required adding machines, compute, and storage, plus the operational costs of IT management: performing backups, writing scripts, creating dashboards, and carrying out testing to make sure your platform is ready when problems arise–all in a secure way.

At Google, our Cloud SQL managed database service offers three fault tolerance mechanisms —backup, high availability, and replication—and there are three major factors to consider for each of them:

• RTO (recovery time objective): When a failure happens, how much time can be lost before significant harm occurs?
• RPO (recovery point objective): When a failure happens, how much data can be lost before significant harm occurs?
• Cost: How cost-effective is this solution?  

We’ve heard from customers like Major League Baseball, HSBC, and Equifax that they have strict data-protection needs and require highly fault-tolerant multi-region applications—and they’ve all chosen Cloud SQL to meet those needs. 

Let’s take a closer look at how the decision-making process plays out for each recovery solution.  

High availability (HA)

If your application is business critical, you require minimum RTO and zero RPO— a high availability configuration ensures that you and your customers are protected. If the primary instance fails, there’s another standby instance ready to take over with no data loss. There’s an additional cost here, but doing this manually brings a great operational cost, since you have to detect and verify the fault, do the failover, and make sure it’s correct—you can’t have two primary instances or you risk data corruption—then finally connect the application to the new database.

Cloud SQL removes all that complexity. Choose high availability for a given instance and we’ll replicate the data across multiple zones, synchronously, to each zone’s persistent disk. If an HA instance has a failure, you don’t have to think about when to fail over because Cloud SQL detects the failure and automatically initiates failover, for a full recovery and no data loss within minutes. Cloud SQL also moves the IP address during failover so your application can easily reconnect. MLB, for example, uses Cloud SQL high availability to serve prediction data to live games with minimal downtime. Dev/test instances don’t need those same guarantees, but can use local backups to recover from any potential failure.

Cross-region replica

If a whole Google Cloud region goes down you still need your business to continue to run. That’s where cross-region replication comes in, a hot standby replica in another Google Cloud region provides RTO of minutes and RPO typically less than a minute . If you create a read replica in a region separate from your primary instance and you get hit with a regional outage, your application and database can start serving customers from another region within minutes. But this solution can be complex and enabling it yourself can be difficult and time-consuming. Securing cross-geography traffic demands end-to-end encryption and can bring connectivity issues too. 

This is where the fully managed Cloud SQL solution shines. We offer MySQL, PostgreSQL and SQL Server database engines as a cross-region replication solution that’s easily configured and bolstered by Google’s interconnected global network. Just say, “I’m in U.S. East, I want to create a replica in U.S. West,” and it’s done, reliably and securely.

Backup

When you suffer data loss because of an operations error (for example, a bug in a script dropped your tables) or human error (for example, someone dropped the wrong table by accident), backups help you restore lost data to your Cloud SQL instance. Our low cost backup mechanism features point-in-time, granular recovery, meaning that if you accidentally delete data or something else goes wrong, you can ask for recovery of, for example, the state of that database down to the millisecond, such as Monday at 12:53pm. Your valuable data is replicated multiple times in multiple geographic locations automatically. This enables the automatic handling of failover in cases of major failure. You can always rest assured that your database is available and data is secure, even in the times of major failure crises. 

Cloud SQL provides automated and on-demand backups. With automated backups, Google manages the backups so that you can easily restore them when required. Also, the scheduled backing is automatically taken by default. With on-demand backup, you can create a backup at any time. This could be useful if you are about to perform a risky operation on your database, as Cloud SQL lets you select a custom location for your backup data. When the backup is stored in multiple regions, and there’s an outage in the region that contains the source instance, you can restore a backup to a new or existing instance in a different region. This is also useful if your organization needs to comply with data residency regulations that require you to keep your backups within a specific geographic boundary.

Putting it all together

For critical workloads, MLB configures their Cloud SQL instances with backups, high availability, and cross-region replication. Doing so ensures they can recover from many failure types.

• To recover from human error (“Oops, I didn’t mean to delete that”), MLB uses backups and point-in-time recovery to recovery to a millisecond or specific database transaction
• To automatically recover from primary instance failures and zonal outages, MLB uses Cloud SQL’s high availability configuration
• To protect against regional outages, MLB uses cross-region replication

Creating a robust configuration, like MLB did, takes just a few minutes. Get started in our Console or review documentation.

There is a data explosion challenge that most IT admins and companies are tackling regularly. The cost-effectiveness, durability and scalability make object storage the first choice for unstructured data. Cloud Storage is an object storage service capable of storing data at exabyte scale and easily managing billions, if not trillions, of objects. However, cost optimization for data stored in object storage is a hard problem.

Choosing the right storage class for your data requires you to know about the access patterns for this data. However, if the access patterns are unknown or unpredictable, it’s often difficult for customers to choose the right storage class for their data. Data placement can have a significant impact on cost as the storage price for the archive class is 6% the storage price for the standard storage class. Having frequently accessed data in colder storage classes can lead to unexpected operations charges. Having data that is never accessed in standard storage can lead to large at-rest storage bills.
As IT administrators and organizations enable different applications and workloads to start leveraging object storage at massive scale, optimizing for cost is only getting exponentially harder to solve. As they migrate workloads to cloud they would like to have predictability and control in their overall storage spend. Wouldn’t it be great if there was a way to automatically place your data in the best storage class?

Introducing Autoclass – a new managed data placement service for Cloud Storage.

Simplifying Lifecycle Management while Reducing TCO

Autoclass is an easy to use bucket-level setting that simplifies and automates lifecycle management of all your Cloud Storage data based on last access time.

This is very useful for workloads with unpredictable and unknown access patterns. It automatically moves data that is not accessed to colder storage classes to reduce at-rest storage cost. When cold data is accessed, it is automatically promoted to Standard storage class to optimize the operations costs for future accesses.

Autoclass automatically delivers cost savings and removes inefficiencies by moving data to the storage class with the most favorable pricing for customer workloads.
It helps organizations achieve price predictability by removing surcharges associated with colder storage classes – there are no early deletion fees, retrieval fees or operations charges for class transitions when Autoclass is enabled on a bucket.

What are customers saying about Autoclass?

“Redivis is a data platform for academic research. We connect data managers with researchers to discover, understand, and analyze Petabytes of data. With Cloud Storage Autoclass, this shifts the paradigm in the research community,” said Ian Matthews, co-founder and CEO of Redivis Inc. “We can now keep more data for longer periods of time while reducing our costs. In fact, I’d estimate 90% of our data will benefit from Autoclass.”

Ian adds “Not only would it cost valuable engineering resources to build cost-optimization ourselves, but it would open us up to potentially costly mistakes in which we incur retrieval charges for prematurely archived data. Autoclass helps us reduce storage costs and achieve price predictability in a simple and automated way.”

Using Autoclass

With this release, Autoclass can be enabled when you create a new Cloud Storage bucket by using CLI, API, or Cloud Console. Once enabled on a bucket, Autoclass will manage the lifecycle of all objects that are uploaded to the bucket until disabled.

If you choose to disable Autoclass, all the objects in the bucket will remain in their current storage class. This disablement doesn’t require any data movement or any changes to storage classes of objects and hence, incurs no charge.

The support for enabling Autoclass on existing Cloud Storage buckets will be available in an upcoming release in 2023.

All transitions carried out by Autoclass can be observed using Cloud Monitoring dashboards. This provides easy observability and allows you to validate what Autoclass is doing for your data in an automated way. These dashboards can leverage 2 new Cloud metrics published for Autoclass:

• Number of objects transitioned by Autoclass (autoclass/transition_operation_count)
• Number of bytes transitioned between classes by Autoclass (autoclass/transitioned_bytes_count)

Getting Started

All Cloud Storage customers can enable Autoclass on their new buckets and realize the cost savings for themselves. For more information and detailed instructions on how to enable Autoclass, refer to the documentation here.

Explainable AI (XAI) helps you understand and interpret how your machine learning models make decisions. We’re excited to announce that BigQuery Explainable AI is now generally available (GA). BigQuery is the data warehouse that supports explainable AI in a most comprehensive way w.r.t both XAI methodology and model types. It does this at BigQuery scale, enabling millions of explanations within seconds with a single SQL query.

Why is Explainable AI so important? To demystify the inner workings of machine learning models, Explainable AI is quickly becoming an essential and growing need for businesses as they continue to invest in AI and ML. With 76% of enterprises now prioritizing artificial intelligence (AI) and machine learning (ML) over other initiatives in 2021 IT budgets, the majority of CEOs (82%) believe that AI-based decisions must be explainable to be trusted according to a PwC survey.

While the focus of this blogpost is on BigQuery Explainable AI, Google Cloud provides a variety of tools and frameworks to help you interpret models outside of BigQuery, such as with Vertex Explainable AI, which includes AutoML Tables, AutoML Vision, and custom-trained models.

So how does Explainable AI in BigQuery work exactly? And how might you use it in practice? 

Two types of Explainable AI: global and local explainability

When it comes to Explainable AI, the first thing to note is that there are two main types of explainability as they relate to the features used to train the ML model: global explainability and local explainability.

Imagine that you have a ML model that predicts housing price (as a dollar amount), based on three features: (1) number of bedrooms, (2) distance to the nearest city center, and (3) construction date.

Global explainability (a.k.a. global feature importance) describes the features’ overall influence on the model and helps you understand if a feature had a greater influence than other features over the model’s predictions. For example, global explainability can reveal that the number of bedrooms and distance to city center typically has a much stronger influence than the construction date on predicting housing prices. Global explainability is especially useful if you have hundreds or thousands of features and you want to determine which features are the most important contributors to your model. You may also consider using global explainability as a way to identify and prune less important features to improve the generalizability of their models.

Local explainability (a.k.a. feature attributions) describes the breakdown of how each feature contributes towards a specific prediction. For example, if the model predicts that house ID#1001 has a predicted price of $230,000, local explainability would describe a baseline amount (e.g. $50,000) and how each of the features contributes on top of the baseline towards the predicted price. For example, the model may say that on top of the baseline of $50,000, having 3 bedrooms contributed an additional $50,000, close proximity to the city center added $100,000, and construction date of 2010 added $30,000, for a total predicted price of $230,000. In essence, understanding the exact contribution of each feature used by the model to make each prediction is the main purpose of local explainability.

What ML models does BigQuery Explainable AI apply to?

BigQuery Explainable AI applies to a variety of models, including supervised learning models for IID data and time series models. The documentation for BigQuery Explainable AI provides an overview of the different ways of applying explainability per model. Note that each explainability method has its own way of calculation (e.g. Shapley values), which are covered more in-depth in the documentation.

Examples with BigQuery Explainable AI

In this next section, we will show three examples of how to use BigQuery Explainable AI in different ML applications: 

• regression 
• classification
• time-series forecasting

Regression models with BigQuery Explainable AI

Let’s use a boosted tree regression model to predict how much a taxi cab driver will receive in tips for a taxi ride, based on features such as number of passengers, payment type, total payment and trip distance. Then let’s use BigQuery Explainable AI to help us understand how the model made the predictions in terms of global explainability (which features were most important?) and local explainability (how did the model arrive at each prediction?).

The taxi trips dataset comes from the BigQuery public datasets and is publicly available in the table: bigquery-public-data.new_york_taxi_trips.tlc_yellow_trips_2018. 

First, you can train a boosted tree regression model.

  CREATE OR REPLACE MODEL bqml_tutorial.taxi_tip_regression_model
OPTIONS (model_type='boosted_tree_regressor',
         input_label_cols=['tip_amount'],
         max_iterations = 50,
         tree_method = 'HIST',
         subsample = 0.85,
         enable_global_explain = TRUE
) AS
SELECT
  vendor_id,
  passenger_count,
  trip_distance,
  rate_code,
  payment_type,
  total_amount,
  tip_amount
FROM
  `bigquery-public-data.new_york_taxi_trips.tlc_yellow_trips_2018`
WHERE tip_amount >= 0
LIMIT 1000000

Now let’s do a prediction using ML.PREDICT, which is the standard way in BigQuery ML to make predictions without explainability.

  SELECT *
FROM
ML.PREDICT(MODEL bqml_tutorial.taxi_tip_regression_model,
 (
 SELECT
   "0" AS vendor_id,
   1 AS passenger_count,
   CAST(5.85 AS NUMERIC) AS trip_distance,
   "0" AS rate_code,
   "0" AS payment_type,
   CAST(55.56 AS NUMERIC) AS total_amount))

But you might wonder—how did the model generate this prediction of ~11.077?

BigQuery Explainable AI can help us answer this question. Instead of using ML.PREDICT, you use ML.EXPLAIN_PREDICT with an additional optional parameter top_k_features. ML.EXPLAIN_PREDICT extends the capabilities of ML.PREDICT by outputting several additional columns that explain how each feature contributes to the predicted value. In fact, since ML.EXPLAIN_PREDICT includes all the output from ML.PREDICT anyway, you may want to consider using ML.EXPLAIN_PREDICT every time instead.

  SELECT *
FROM
ML.EXPLAIN_PREDICT(MODEL bqml_tutorial.taxi_tip_regression_model,
 (
 SELECT
   "0" AS vendor_id,
   1 AS passenger_count,
   CAST(5.85 AS NUMERIC) AS trip_distance,
   "0" AS rate_code,
   "0" AS payment_type,
   CAST(55.56 AS NUMERIC) AS total_amount),
 STRUCT(6 AS top_k_features))

The way to interpret these columns is:

Σfeature_attributions + baseline_prediction_value = prediction_value

Let’s break this down. The prediction_value is ~11.077, which is simply the predicted_tip_amount. The baseline_prediction_value is ~6.184, which is the tip amount for an average instance. top_feature_attributions indicates how much each of the features contributes towards the prediction value. For example, total_amount contributes ~2.540 to the predicted_tip_amount. 

ML.EXPLAIN_PREDICT provides local feature explainability for regression models. For global feature importance, see the documentation for ML.GLOBAL_EXPLAIN.

Classification models with BigQuery Explainable AI

Let’s use a logistic regression model to show you an example of BigQuery Explainable AI with classification models. We can use the same public dataset as before: bigquery-public-data.new_york_taxi_trips.tlc_yellow_trips_2018.

Train a logistic regression model to predict the bracket of the percentage of the tip amount out of the taxi bill.

  CREATE OR REPLACE MODEL bqml_tutorial.taxi_tip_classification_model
OPTIONS
 (model_type='logistic_reg',
  input_label_cols=['tip_bucket'],
  enable_global_explain=true
) AS
SELECT
  vendor_id,
  passenger_count,
  trip_distance,
  rate_code,
  payment_type,
  total_amount,
  CASE
    WHEN tip_amount > total_amount*0.20 THEN '20% or more'
    WHEN tip_amount > total_amount*0.15 THEN '15% to 20%'
    WHEN tip_amount > total_amount*0.10 THEN '10% to 15%'
  ELSE '10% or less'
  END AS tip_bucket
FROM
  `bigquery-public-data.new_york_taxi_trips.tlc_yellow_trips_2018`
WHERE tip_amount >= 0
LIMIT 1000000

Next, you can run ML.EXPLAIN_PREDICT to get both the classification results and the additional information for local feature explainability. For global explainability, you can use ML.GLOBAL_EXPLAIN. Again, since ML.EXPLAIN_PREDICT includes all the output from ML.PREDICT anyway, you may want to consider using ML.EXPLAIN_PREDICT every time instead.

  SELECT *
FROM
ML.EXPLAIN_PREDICT(MODEL bqml_tutorial.taxi_tip_classification_model,
 (
 SELECT
   "0" AS vendor_id,
   1 AS passenger_count,
   CAST(5.85 AS NUMERIC) AS trip_distance,
   "0" AS rate_code,
   "0" AS payment_type,
   CAST(55.56 AS NUMERIC) AS total_amount),
 STRUCT(6 AS top_k_features))

Similar to the regression example earlier, the formula is used to derive the prediction_value:

Σfeature_attributions + baseline_prediction_value = prediction_value

As you can see in the screenshot above, the baseline_prediction_value is ~0.296. total_amount is the most important feature in making this specific prediction, contributing ~0.067 to the prediction_value, though followed by trip_distance. The feature passenger_count contributes negatively to prediction_value by -0.0015. The features vendor_id, rate_code, and payment_type did not seem to contribute much to the prediction_value.

You may wonder why the prediction_value of ~0.389 doesn’t equal the probability value of  ~0.359. The reason is that unlike for regression models, for classification models, prediction_value is not a probability score. Instead, prediction_value is the logit value (i.e., log-odds) for the predicted class, which you could separately convert to probabilities by applying the softmax transformation to the logit values. For example, a three-class classification has a log-odds output of [2.446, -2.021, -2.190]. After applying the softmax transformation, the probability of these class predictions is [0.9905, 0.0056, 0.0038].

Time-series forecasting models with BigQuery Explainable AI

Explainable AI for forecasting provides more interpretability into how the forecasting model came to its predictions. Let’s go through an example of forecasting the number of bike trips in NYC using the new_york.citibike_trips public data in BigQuery.

You can train a time-series model ARIMA_PLUS:

  CREATE OR REPLACE MODEL bqml_tutorial.nyc_citibike_arima_model
OPTIONS
  (model_type = 'ARIMA_PLUS',
   time_series_timestamp_col = 'date',
   time_series_data_col = 'num_trips',
   holiday_region = 'US'
  ) AS
SELECT
   EXTRACT(DATE from starttime) AS date,
   COUNT(*) AS num_trips
FROM
  `bigquery-public-data.new_york.citibike_trips`
GROUP BY date

Next, you can first try forecasting without explainability using ML.FORECAST:
SELECT
  *
FROM
  ML.FORECAST(MODEL bqml_tutorial.nyc_citibike_arima_model,
              STRUCT(365 AS horizon, 0.9 AS confidence_level))

This function outputs the forecasted values and the prediction interval. Plotting it in addition to the input time series gives the following figure.

But how does the forecasting model arrive at its predictions? Explainability is especially important if the model ever generates unexpected results.

With ML.EXPLAIN_FORECAST, BigQuery Explainable AI provides extra transparency into the seasonality, trend, holiday effects, level (step) changes, and spikes and dips outlier removal. In fact, since ML.EXPLAIN_FORECAST includes all the output from ML.FORECAST anyway, you may want to consider using ML.EXPLAIN_FORECAST every time instead.

  SELECT
  *
FROM
  ML.EXPLAIN_FORECAST(MODEL bqml_tutorial.nyc_citibike_arima_model,
                      STRUCT(365 AS horizon, 0.9 AS confidence_level))

Compared to the previous figure which only shows the forecasting results, this figure shows much richer information to explain how the forecast is made.  

First, it shows how the input time series is adjusted by removing the spikes and dips anomalies, and by compensating the level changes. That is:

time_series_adjusted_data = time_series_data - spikes_and_dips - step_changes

Second, it shows how the adjusted input time series is decomposed into different components such as both weekly and yearly seasonal components, holiday effect component and trend component. That is

time_series_adjusted_data = trend + seasonal_period_yearly + seasonal_period_weekly + holiday_effect + residual

Finally, it shows how these components are forecasted separately to compose the final forecasting results. That is:

time_series_data = trend + seasonal_period_yearly + seasonal_period_weekly + holiday_effect

For more information on these time series components, please see the documentation here.

Conclusion

With the GA of BigQuery Explainable AI, we hope you will now be able to interpret your machine learning models with ease. 

Thanks to the BigQuery ML team, especially Lisa Yin, Jiashang Liu, Amir Hormati, Mingge Deng, Jerry Ye and Abhinav Khushraj. Also thanks to the Vertex Explainable AI team, especially David Pitman and Besim Avci.