App Engine is a fully managed serverless compute option in Google Cloud that you can use to build and deploy low-latency, highly scalable applications. App Engine makes it easy to host and run your applications. It scales them from zero to planet scale without you having to manage infrastructure. App Engine is recommended for a wide variety of applications including web traffic that requires low-latency responses, web frameworks that support routes, HTTP methods, and APIs.

Environments

App Engine offers two environments; here’s how to choose one for your application:

• App Engine Standard – Supports specific runtime environments where applications run in a sandbox. It is ideal for apps with sudden and extreme traffic spikes because it can scale from zero to many requests as needed. Applications deploy in a matter of seconds. If your required runtime is supported and it’s an HTTP application, then App Engine Standard is the way to go.
• App Engine Flex – Is open and flexible and supports custom runtimes because the application instances run within Docker containers on Compute Engine. It is ideal for apps with consistent traffic and regular fluctuations because the instances scale from one to many. Along with HTTP applications it also supports applications requiring WebSockets. The max request timeout is 60 minutes. 

How does it work

No matter which App Engine environment you choose, the app creation and deployment process is the same. First write your code, next specify the app.yaml file with runtime configuration, and finally deploy the app on App Engine using a single command: gcloud app deploy.

Notable features

• Developer friendly – A fully managed environment lets you focus on code while App Engine manages infrastructure. 
• Fast responses – App Engine integrates seamlessly with Memorystore for Redis enabling distributed in-memory data cache for your apps.
• Powerful application diagnostics – Cloud Monitoring and Cloud Logging help monitor the health and performance of your app and Cloud Debugger and Error Reporting help diagnose and fix bugs quickly. 
• Application versioning – Easily host different versions of your app, and easily create development, test, staging, and production environments.
• Traffic splitting – Route incoming requests to different app versions for A/B tests incremental feature rollouts, and similar use cases.
• Application security – Helps safeguard your application by defining access rules with App Engine firewall and leverage managed SSL/TLS certificates by default on your custom domain at no additional cost.

Conclusion

Whether you need to build a modern web application or a scalable mobile backend App Engine has you covered. For a more in-depth look, check out the documentation. Click here for demos on how to use serverless technology and free hands-on training.https://www.youtube.com/embed/Xuf3J6SKVV0?enablejsapi=1&

For more #GCPSketchnote, follow the GitHub repo. For similar cloud content follow me on Twitter @pvergadia and keep an eye out on thecloudgirl.dev.

With the rise of digital assistants and conversational interfaces, people have grown accustomed to hearing and speaking to synthetic voices. But what do those voices sound like? Often, pretty repetitive. We’re all familiar with the Google Assistant voice, for example.

That’s why we are excited to announce the general availability of Custom Voice in our Cloud Text-to-Speech (TTS) API, a new feature that lets you train custom voice models with your own audio recordings to create unique experiences.

For businesses looking to build a strong brand identity, establishing a unique voice can help turn mobile app interactions or customer service based on interactive voice responses (IVR) into differentiated customer experiences. Our TTS API has included a speech synthesis service with a static list of voices for some time, but now, with Custom Voice, moving beyond these predefined options is easier than ever.

Custom Voice lets you simply submit your audio recordings to get access to the new voice directly in the TTS API. Custom Voice TTS includes guidance on the audio requirements to help make sure you generate a high quality custom TTS voice model. Once this new model is trained, all you have to do to start using the newly trained voice is reference the model ID in your calls to the Cloud TTS API.

At Google, we are committed to building safe and accountable AI products, not only because it’s the right thing to do, but because it is a critical step in ensuring successful use in production. As part of Google Cloud’s Responsible AI governance process, we conducted a deep ethical evaluation of Custom Voice TTS, and its relation to synthetic media, in order to surface and mitigate potential harms that it may create. If you are interested in Custom Voice TTS, there is a review process to help ensure each use case is aligned with our AI Principles and adequate voice actor consent is given.

Additionally, to verify that voice actors are actually the ones producing the audio, you will need to submit an audio file producing a sentence that Google Cloud chooses (for example: “I agree that my voice will be used to create a synthetic custom Text-to-Speech voice).

We’re looking forward to seeing this API help businesses solve problems in an easy, fast, and scalable way. TTS Custom Voice is now GA in these languages:

English (US)

English (AU)

English (UK)

Spanish (US)

Spanish (Spain)

French (France)

French (Canada)

Italian (Italy)

German (Germany)

Portugues (Brazil)

Japanese (Japan)

We plan to continue expanding this lineup in order to meet your needs. Ready to try for yourself? Contact your seller to get started on your use case evaluation today!

Today, we’re announcing Datastream, a serverless change data capture (CDC) and replication service, available now in preview. Datastream allows enterprises to synchronize data across heterogeneous databases, storage systems, and applications reliably and with minimal latency to support real-time analytics, database replication, and event-driven architectures. You can now easily and seamlessly deliver change streams from Oracle and MySQL databases into Google Cloud services such as BigQuery, Cloud SQL, Google Cloud Storage, and Cloud Spanner, saving time and resources and ensuring your data is accurate and up-to-date.

“Global companies are demanding change data capture to provide replication capabilities across disparate data sources, and provide a real-time source of streaming data for real-time analytics and business operations,” says Stewart Bond, Director, Data Integration and Intelligence Software Research at IDC.

However, companies are finding it difficult to realize these capabilities because commonly used data replication offerings are costly, cumbersome to set up, and require significant management and monitoring overhead to run flexibly or at scale. This leaves customers with a difficult-to-maintain and fragmented architecture. 

Datastream’s differentiated approach 

Datastream is taking on these challenges with a differentiated approach. Its serverless architecture seamlessly and transparently scales up or down as data volumes shift in real time, freeing teams to focus on delivering up-to-date insights instead of managing infrastructure. It also provides the streamlined customer experience, ease of use, and security that our customers have come to expect from Google Cloud, with private connectivity options built into the guided setup experience. 

Datastream integrates with purpose-built and extensible Dataflow templates to pull the change streams written to Cloud Storage, and create up-to-date replicated tables in BigQuery for analytics. It also leverages Dataflow templates to replicate and synchronize databases into Cloud SQL or Cloud Spanner for database migrations and hybrid cloud configurations. 

Datastream also powers a Google-native Oracle connector in Cloud Data Fusion’s new replication feature for easy ETL/ELT pipelining. And by delivering change streams directly into Cloud Storage, customers can leverage Datastream to implement modern, event-driven architectures.

Customers tell us about the benefits they’ve found using Datastream. That includes Schnuck Markets, Inc., “Leveraging Datastream, we’ve been able to replicate data from our on-premises databases to BigQuery reliably and with little impact to our production workloads. This new method replaced our batch processing and allowed for insights to be leveraged from BigQuery quicker,” says Caleb Carr, principal technologist from Schnuck Markets. “Furthermore, implementing Datastream removed the need for our analytics group to reference on-premises databases to do their work and support our business users.”

Cogeco Communications, Inc. used Datastream to also realize the value of low-latency data access. “Datastream unlocked new customer interaction opportunities not previously possible by enabling low-latency access in BigQuery to our operational Oracle data.” says Jean-Lou Dupont, Senior Director, Enterprise Architecture, Cogeco Communications, Inc. “This streamlined integration process brings data from hundreds of disparate Oracle tables into a unified data hub. Datastream enabled us to achieve this with 10X time and effort efficiency.”

In addition, Major League Baseball (MLB) used Datastream’s replication capabilities to migrate their data from Oracle to Cloud SQL for PostgreSQL. “As we’re modernizing our applications, replicating the database data reliably out of Oracle and into Cloud SQL for PostgreSQL is a critical component of that process,” says Shawn O’Rourke, manager of technology at MLB. “Using Datastream’s CDC capabilities, we were able to replicate our database securely and with low latency, resulting in minimal downtime to our application. We can now standardize on this process and repeat it for our next databases, regardless of scale.”

Our partner HCL has worked with many organizations looking to get more out of their data and plan for the future. “HCL customers across every industry are looking for ways to extract more value out of their vast amounts of data,” says Siva G. Subramanian, Global Head for Data & Analytics at HCL Google Business Unit. “CDC plays a big part in the solutions we offer to our customers using Google Cloud. Datastream enables us to deliver a secure and reliable solution to our customers that’s easy to set up and maintain. CDC is a key and integrated part of Google Cloud Data Solutions.”

“Google Cloud’s new CDC offering, Datastream, is a differentiator for Google among hyperscale cloud service providers, by supporting replication of data from Oracle and MySQL databases into the Google Cloud environment using a serverless cloud-native architecture, which removes the burden of infrastructure management for organizations, and provides elastic scalability to handle real-time workloads,” says Stewart Bond, Director, Data Integration and Intelligence Software Research at IDC.

Datastream under the hood

Datastream reads CDC events (inserts, updates, and deletes) from source databases, and writes those events with minimal latency to a data destination. It leverages the fact that each database source has its own CDC log—for MySQL it’s the binlog, for Oracle it’s LogMiner—which it uses for its own internal replication and consistency purposes. Using Google-native, agentless, high-scale log reader technology, Datastream can quickly and efficiently generate change streams populated by events based on the database’s CDC log while minimizing performance impact on the source database.

Each generated event includes the entire row of data from the database, with the data type and value of each column. The original source data types, whether it’s, for example, an Oracle NUMBER type or a MySQL NUMERIC type, are normalized into Datastream unified types. The unified types represent a lossless superset of all possible source types, and the normalization means data from different sources can easily be processed and queried downstream in a source-agnostic way. Should a downstream system need to know the original source data type, it can perform a quick API call to Datastream’s Schema Registry, which stores up-to-date, versioned schemas for every data source. This also allows for in-flight downstream schema drift resolution as source database schemas change. 

The generated streams of events, referred to as “change streams,” are then written as files, either in JSON or Avro format during preview or in other formats like Parquet in the future, into a Cloud Storage bucket organized by source table and event times. Files are rotated as table schemas change, so events in a single file always have the same schema, as well as on a configurable file size or rotation frequency setting. This way customers can find the best balance between the speed of data availability and the file size that makes the most sense for their business use case.

Through its integration with Dataflow, Datastream powers up-to-date, replicated tables for analytics over BigQuery, and for data replication and synchronization to Cloud SQL and Spanner. Datastream refers to these constantly updated tables as “materialized views.” They are kept up-to-date via Dataflow template-based upserts into Cloud SQL or Spanner, or through consolidations into BigQuery. The consolidations, performed as part of the Dataflow template, take the change streams that are written into a log table in BigQuery, and push those changes into a final table, which mirrors the table from the source.

Datastream offers a variety of secure connectivity methods to sources, so your data is always safe in transit. And with its serverless architecture, Datastream can scale up and down readers and processing power to seamlessly keep up with the speed of data and ensure minimal latency end to end. As data volumes decrease, Datastream automatically scales back down—the result is a “pay for what you use” pricing model, where you never have to pay for idle machines or worry about bottlenecks and delays during data peaks.

Get started with Datastream 

Datastream, now available in preview, supports streaming change data from Oracle and MySQL sources, hosted either on-premises or in the cloud, into Cloud Storage. You can start streaming your data today for $2 per GB of data processed by Datastream. 

To get started, head over to the Datastream area of your Google Cloud console, under Big Data, and click Create Stream. There you can:

1. Initiate stream creation, and see what actions you need to take to set up your source and destination for successful streaming.
2. Define your source and destination, whose connectivity information is saved as connection profiles you can re-use for other streams. Sources support multiple connectivity options, with both private and public connectivity options to suit your business needs.
3. Select the source data you’d like to stream, and which you’d like to exclude.
4. Test your stream to ensure it will be successful when you’re ready to go.

Start your stream and your database’s CDC data will start to flow to your Cloud Storage bucket! From there you can integrate with Dataflow templates to load data into BigQuery, Spanner, or Cloud SQL. Datastream’s preview is supported in us-central1, europe-west1, and asia-east1, with additional regions coming soon.https://www.youtube.com/embed/FZG4w4Vbj38?enablejsapi=1&

Datastream will become generally available later this year, and will soon expand its support to also include PostgreSQL and SQL Server as sources, as well as out-of-the-box integration with BigQuery for easy delivery of up-to-date replicated tables for analytics, and message queues like Pub/Sub for real-time change stream access. 

For more resources to help get you started with change streaming, check out the Datastream documentation.