The summary of essential information about Google Cloud in February 2022.
We're pleased to announce the general availability of Cloud Client Libraries for Compute Engine, now available in Java, Go, Python, NodeJS, Ruby, PHP, and C#. These new libraries provide enhanced developer productivity and ease of use by enabling a more idiomatic style for each programming language that we support.
We'll continue to support the current Google API Client Libraries for Compute Engine so existing applications that are built with them will continue to work. Cloud Client Libraries and Google API Client Libraries both access the same Compute Engine API. However, we recommend the new Google Cloud Client Libraries for new development projects. Read More.
Most businesses today choose Apache Spark for their data engineering, data exploration and machine learning use cases because of its speed, simplicity and programming language flexibility. However, managing clusters and tuning infrastructure have been highly inefficient, and a lack of an integrated experience for different use cases are draining productivity gains, opening up governance risks and reducing the potential value that businesses could achieve with Spark.
Today, we are announcing the general availability of Serverless Spark, industry's first autoscaling serverless Spark. We are also announcing the private preview of Spark through BigQuery, empowering BigQuery users to use serverless Spark for their data analytics, along with BigQuery SQL. With these, you can effortlessly power ETL, data science, and data analytics use cases at scale. Read More.
When you build highly resilient data analytics pipelines, difficult trade-offs are common. Increasing reliability can come at a cost, but then pursuing low cost can mean more hands-on, operational management. The Cloud Pub/Sub team wants to give you the flexibility to optimize for cost, reliability, and level of management given your workload. So, we’re introducing an additional messaging option that balances cost with high availability. This new Pub/Sub Lite offering is called regional topics.
If achieving very low cost is a priority, but protecting yourself from zonal outages is a requirement, regional Lite topics are for you. Regional Lite topics strike a balance between zonal Lite topics and the more robust Pub/Sub (which is replicated across three zones) in terms of availability and pricing. Read More.
Investing in Artificial Intelligence (AI) can bring a competitive advantage to your organization. If you’re in charge of an AI or Data Science team, you’ll want to measure and maximize the value that you’re providing. Here is some advice from our years of experience in the field.
As you start the work, what measures and indicators can you use to show that your team’s work is useful for your organization? Read More.
Google Cloud’s ability to raise the bar on SAP application productivity and performance is an important source of value for our enterprise customers who run SAP environments. And now, we've got another success story to share from Google Cloud’s focus on SAP: giving customers the ability to leverage Google Cloud Filestore Enterprise as a file-sharing solution for their SAP deployments.
Today, we're going to take a closer look at what makes Filestore Enterprise such a unique and important new offering for our SAP customers, and at how it fits into the big picture of our SAP enterprise storage services. Read More.
Over the last six months, we launched 3rd Gen AMD EPYC™ CPUs (formerly code-named “Milan”) across our Compute Engine virtual machine (VM) families. We introduced the Tau VM family, targeting scale-out workloads. Tau VMs are the leader both in terms of performance and workload total cost of ownership (TCO) from any leading provider available today.
Today, we’re excited to announce the General Availability of the newest instance series in our Compute Optimized family, C2D, also powered by 3rd Gen AMD EPYC processors. Read more.
Reliably executing tasks on a schedule is critical for everything from data engineering, to infrastructure management, and application maintenance. Today, we are thrilled to announce that Google Cloud Scheduler, our enterprise-grade scheduling service, is now available in more GCP regions and multiple regions can now be used from a single project removing the prior limit of a single region per project.
With many enterprise customers deploying complex distributed cloud systems, Cloud Scheduler has helped solve the problem of single-server cron scheduling being a single point of failure. With this update you are now able to create Scheduler jobs across distinct cloud regions that can help satisfy cross-regional availability and fail-over scenarios. Read More.
Google Cloud introduced Document AI to automate document processing and to streamline workflows with state-of-the-art machine learning models. With the deep neural networks, the models generalize the learning from seeing hundreds of thousands variations of the documents.
With Document AI, we are bringing the power of this “Google search” to help customers understand their documents. This means that the same Google knowledge graph technology that helps you find the name, address or phone number of your favorite restaurant can now enrich your document extraction with the right name, fully qualified address, and updated phone number. Read More.
Digital technology promises transformative results. Yet, it’s not uncommon to encounter potholes and speed bumps along the way. One area that frequently trips up businesses is putting data into action.
It can be extraordinarily difficult to take advantage of the right data at exactly the right time — in real time — to drive decision-making. For SAP customers wanting to maximize the value of their data, Google Cloud offers a number of capabilities. Read More.
It’s 2022 and nanosatellites, NFTs, and autonomous cars that deliver your pizza are in full force. In a world where people rely on simple technology to untangle complex problems, companies must deliver simple experiences to be successful in today’s landscape.
For many cloud providers this means enabling tightly integrated data offerings that simplify the data delivery process without losing sight of the sophisticated needs of the modern data consumer. Read More.
When a deployed ML model produces poor predictions, it can be due to a wide range of problems. It can be the result of bugs that are typical in any program—but it can also be the result of ML-specific problems. Perhaps data skews and anomalies are causing model performance to degrade over time.
When a model is embedded into an application, issues like this can create poor user experiences. If the model is part of an internal process, the issues can negatively impact business decision-making. Read more.
Want to know the latest from Google Cloud? Find it here in one handy location. Check back regularly for our newest updates, announcements, resources, events, learning opportunities, and more.
Tip: Not sure where to find what you’re looking for on the Google Cloud blog? Start here: Google Cloud blog 101: Full list of topics, links, and resources. Read More.
Source: https://cloud.google.com/blog/products/gcp