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Cloud CISO Perspectives: Our Big Sleep agent makes a big leap, and other AI news

GCP – Cloud CISO Perspectives: Our Big Sleep agent makes a big leap, and other AI news

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Welcome to the first Cloud CISO Perspectives for July 2025. Today, Sandra Joyce, vice president, Google Threat Intelligence, talks about an incredible milestone with our Big Sleep AI agent, as well as other news from the intersection of security and AI.

As with all Cloud CISO Perspectives, the contents of this newsletter are posted to the Google Cloud blog. If you’re reading this on the website and you’d like to receive the email version, you can subscribe here.

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Our Big Sleep agent makes a big leap, and other AI news

By Sandra Joyce, vice president, Google Threat Intelligence

Sandra Joyce

Sandra Joyce, vice president, Google Threat Intelligence

Business leaders everywhere are scrambling to implement AI in a way that creates value while trying to define what that value means — at the same time. As we build on our efforts to shape AI and define AI workflows in cybersecurity, we are already really excited about using AI in the work that we do.

I spoke about some of that work at the RSA Conference in April, including how AI is reshaping cybersecurity and how Google uses data to drive our practical applications of AI in both attack and defense. We revealed Tuesday that our Big Sleep AI agent, first introduced in November 2024 by Google DeepMind and Project Zero, has taken a very significant step for defenders: We believe this is the first time an AI agent has been used to directly foil efforts to exploit a vulnerability in the wild.

Through the combination of threat intelligence from the Google Threat Intelligence Group (GTIG) and the Big Sleep AI agent, we were recently able to identify a critical SQLite vulnerability known only to threat actors that was imminently going to be used — and actually cut it off beforehand.

With Big Sleep, we’ve demonstrated how we can find vulnerabilities that defenders don’t yet know about. In this case, we found a vulnerability that the attackers knew about and had every intention of using, and we were able to detect and report it for patching before they could exploit it.

Developed by Google DeepMind and Google Project Zero, Big Sleep can help security researchers find zero-day (previously-unknown) software security vulnerabilities. Since it was introduced last year, it has continued to discover multiple flaws in widely-used software, exceeding our expectations and accelerating AI-powered vulnerability research.

With Big Sleep, we’ve demonstrated how we can find vulnerabilities that defenders don’t yet know about. In this case, we found a vulnerability that the attackers knew about and had every intention of using, and we were able to detect and report it for patching before they could exploit it.

Attackers have long had an advantage because they were taking shots at a massive goal with a lot of ground to defend, but the productivity gains from a defender’s point of view are astounding to us. If you had a human in place of Big Sleep, they would’ve had to pour over two different versions of open-source source code and manually see where the vulnerability was, all while knowing that attackers were planning on using this vulnerability soon.

Speed and accuracy made all the difference in this case, which gave us an edge over threat actors. Since defenders own and control these systems, AI has given us a very powerful development (and vulnerability remediation) advantage.

Big Sleep is also being deployed to help improve the security of other widely-used open-source projects, too — a major win for ensuring faster, more effective security across the internet more broadly.

Empowering defensive AI agents

While AI agents represent a sea change for cybersecurity, the work they do needs to be done safely and responsibly. We outlined our approach to building AI agents in June in ways that safeguard privacy, mitigate the risks of rogue actions, and ensure the agents operate with the benefit of human oversight and transparency. When deployed according to secure by design principles, agents can give defenders an edge like no other tool that came before them.

We will continue to share our agentic AI insights and report findings through our industry-standard disclosure process. You can keep tabs on all publicly-disclosed vulnerabilities from Big Sleep on our issue tracker page.

We’re seeing the impact of AI across security, from boosting threat hunting to stronger security validations to smarter red team analyses. Similarly, the speed and accuracy of AI comes to aid defenders when dealing with the ever-growing onslaught of email phishing attacks. Attackers have been using AI to improve a lot of the previous hints that a legit-looking email was actually a phishing attack, such as using colloquial language, proper slang, and tailoring the email to the recipient.

Yet if you train the AI model to look at what spearphishing emails look like, it can get better at detection, triage, and identifying phishing threats faster and at a scale that if a human has to jump in to review something, they have to review less now. Our AI-powered defenses help Gmail block all sorts of phishing, spam, and malware.

  • Gmail automatically blocks more than 99.9% of spam, phishing and malware, and protects over 1.5 billion inboxes.
  • We developed several ground-breaking AI models last year that significantly strengthened Gmail cyber defenses, including a new large language model (LLM) that we trained on phishing, malware and spam that blocks 20% more spam than before and reviews 1,000 times more user-reported spam daily.

When it comes to attackers and their use of AI, we’re still in the “before times.” As I noted at RSAC, Google Threat Intelligence Group has seen AI used to flesh out code, we’ve seen AI used for deepfakes, and to craft better spearphishing emails, but we’ve yet to see a big, game-changing incident where AI did something that humans simply couldn’t have done. We haven’t seen anything like an agentic attacker or an agentic attack, or a self-perpetuated campaign.

I fully anticipate that these types of attacks are coming, so it’s crucial that AI developers collaborate across industry and with public sector partners to prepare defenders and ensure AI’s success. As part of our efforts to build partnerships, we worked with industry partners last year to launch the Coalition for Secure AI (CoSAI), an initiative to ensure the safe implementation of AI systems.

To further this work, we announced yesterday that Google will donate data from our Secure AI Framework (SAIF) to help accelerate CoSAI’s agentic AI, cyber defense, and software supply chain security workstreams.

At Google, we’ve been investing in AI and machine learning tools for more than a decade. While we have always believed in AI’s potential to help make software more secure, over the last year we have seen real leaps in its capabilities, with AI redefining what lasting and durable cybersecurity can look like.

You can learn more about our efforts to use AI to help secure and support organizations around the world from our Office of the CISO.

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In case you missed it

Here are the latest updates, products, services, and resources from our security teams so far this month:

  • Summer of cybersecurity: Empowering defenders with AI: We’re sharing more about our latest AI innovations for security, public and private partnerships, and new initiatives to secure the digital ecosystem for everyone — including our plans for Black Hat and Def Con. Read more.
  • Engineering Deutsche Telekom’s sovereign data platform: Ashutosh Mishra, vice-president at Deutsche Telekom, explains how Google Cloud helped the company build its sovereign data platform. Read more.
  • New networking features in GDC air-gapped can power innovation: Three major advancements in Google Distributed Cloud air-gapped networking are designed to give you more control over your environment. Read more.
  • Unpacking security in Looker Conversational Analytics: Your data remains under your control when using Looker Conversational Analytics, letting you use Gemini to better understand your data. Read more.
  • Opening up Zero-Knowledge Proof technology to promote privacy in age assurance: Open sourcing these powerful cryptographic tools will make it much easier for private and public sector developers to build their own privacy-enhancing applications and digital ID solutions, meeting an urgent need. Read more.
  • Advancing protection in Chrome on Android: Android recently announced Advanced Protection, which extends our Advanced Protection Program to a device-level security setting for Android users that need heightened security. Here’s how it integrates with Chrome on Android. Read more.

Please visit the Google Cloud blog for more security stories published this month.

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Threat Intelligence news

  • Why isolated recovery environments are critical in modern cyber resilience: IREs can provide a measurable, critical difference in disaster recovery strategies. Here are practical steps organizations can take to implement them effectively. Read more.
  • Securing protection relays in modern substations: Cyberattacks on digitized protection relays in substations pose a severe threat to power grid stability, risking widespread outages and infrastructure damage. With CISA warning of heightened risks from Iran-nexus groups targeting vital networks, here’s what critical infrastructure providers need to know about securing these relays. Read more.

Please visit the Google Cloud blog for more threat intelligence stories published this month.

Now hear this: Podcasts from Google Cloud

  • The SIEM Paradox: Logs, lies, and failing to detect: Svetla Yankova, founder and CEO, Citreno, joins hosts Anton Chuvakin and Tim Peacock to talk about SIEM tooling and threat detection challenges. Listen here.
  • Resilience and security with Google Product Security Engineering: How does Google balance high reliability and operational excellence with the needs of detection and response? Cristina Vintila, product security engineering manager, Google Cloud, talks with Anton and Tim about how PSE has evolved. Listen here.
  • The human element when designing privacy: From consulting with a world leader to Fuschia, Sarah Aoun, Google privacy engineer, goes deep into the nuances, challenges, and excitement of building digital privacy with Anton and Tim. Listen here.
  • The Defender’s Advantage: The rise of ClickFix: Dima Lenz, security engineer, Google Threat Intelligence Group, joins host Luke McNamara to discuss how threat actors have been using ClickFix to socially engineer users. Listen here.

To have our Cloud CISO Perspectives post delivered twice a month to your inbox, sign up for our newsletter. We’ll be back in a few weeks with more security-related updates from Google Cloud.

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GCP – Shaping the future together with our partners: The potential of agentic AI

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Partners have always been central to the Google Cloud ecosystem, becoming more and more instrumental in bringing Google’s AI innovations to enterprises. I am inspired by how partners have already built more than 1,000 agentic use cases across every domain to solve deeply entrenched pain points for our shared customers.

The emergence of agentic AI marks a true paradigm shift for technology, promising to reshape industries and redefine how businesses operate and create value. For our incredible ecosystem of partners, this represents a profound opportunity to lead customers into a more intelligent and automated future.

To chart this new territory, we’re releasing a new analysis today that provides a strategic framework for the journey ahead: Shaping the Future: The Transformative Potential of Agentic AI and the Strategic Imperative for Google Cloud Partners. 

An unprecedented opportunity for our partners

Our analysis1 reveals that agentic AI is set to create a ~$1T global market for agentic AI services for partners. To put this in context, the projected $350B to $450B opportunity in the U.S. alone is larger than the entire U.S. traditional partner services market today.

Last year, our partner ecosystem influenced nearly 80% of Google Cloud’s incremental revenue growth. This year, IDC2 expects our global system integrators to grow their Google Cloud AI practices as much as 100% as customers see increasing ROI. Now, with agentic AI, our partner ecosystem can build on this incredible momentum. Today, more than 90% of enterprises report interest in deploying agentic AI solutions within the next three years. Leading software players are also signaling their conviction, with agentic AI mentions in public filings 12x more today vs a year ago. The pace of adoption is electric and distinct value pools are fast emerging. Our ecosystem must act quickly to tap into the substantial, immediate opportunity already at stake.

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The strategic leap for partners: From implementer to transformer

Leading in the agentic era means moving beyond technology implementation. To drive fundamental business transformation with agentic AI, SIs need to secure “quick wins” to deliver immediate ROI while simultaneously building toward “big bets” on long-term innovation. By providing value on high-impact workflows, partners can build the credibility and reusable IP needed to tackle the bespoke, multi-agent systems that will define the future. This journey is guided by six core principles:

  1. Identify pain points and build prototypes: Focus on solving high-value, customer-specific pain points to drive differentiation, delivering working prototypes to prove value from day one.

  2. Reimagine core business processes: Evolve the delivery to go beyond pure technical and systems execution, with initial focus on upfront consultative AI design to help customers restructure business processes by co-designing agentic workflows.

  3. Use new tools to solve data gaps: Create opportunities to quickly overcome clients’ perceived data readiness challenges by using generative AI tools and modern inter-agent communication protocols to activate agents with less structured datasets.

  4. Deploy agents at scale: Manage change and embed agents into business workflow, and establish processes to track agent performance. Bring robust change management capabilities to accelerate deployment and deliver clear ROI.

  5. Manage the full agentic lifecycle: Deliver new forms of ongoing support, such as orchestrating agentic fleets, evaluating agent performance and refreshing agent knowledge. Create long-term competitive advantage by investing in reusable IP and reference integrations.

  6. Innovate commercially: Consider different pricing models to align with the nature of agentic AI. Explore recurring, transaction-based, or outcome-based structures that reflect the measurable impact agents deliver.

Our commitment: Building the industry’s best agentic AI ecosystem with you

We are committed to supporting our partners at every level with a partner-first approach to services and delivery. We continue to infuse partners into every layer of our agentic AI stack to enable a truly open and thriving ecosystem, including:

  • Open Innovation and thought leadership: We are committed to open-source contributions like the Agent Development Kit (ADK) and the Agent2Agent (A2A) protocol, fostering interoperability with support from over 100 industry leaders like ServiceNow and Workday. Our best-in-class infrastructure and pioneering AI research is powered by Google AI and DeepMind, who will continue to bring partners the leading technologies available.

  • A purpose-built stack for agents: We provide a comprehensive toolkit to build, deploy, scale and commercialize agentic solutions with maximum interoperability. With best in class security and governance at all layers to ensure seamless and secure rollout of many agents across an enterprise. At its foundation, partners have choice across GPUs, TPUs and 200+ AI models. Partners can solve data readiness issues using BigQuery, develop custom agents on a unified platform with Vertex AI, commercialize them through the Google Cloud Agent Marketplace and make available to users on Agentspace.

  • A thriving partner ecosystem: Our partner-first commitment extends beyond technology. We are increasing resources to help you address customer demand for AI agents, including a 2x increase in funding AI opportunities over the past year alone. We will continue to invest in your success with increased funding for critical partner training and enhanced co-selling programs to help you close larger deals, faster.

The future of AI will be shaped by those who lead from the front. The partners who act decisively now will not just participate in this evolution – they will define the category. I am thrilled about the journey ahead. I invite you to download the full analysis to explore these insights in greater detail and continue to share your perspectives on how best we can co-create the agentic future.

1.  Google Cloud commissioned Boston Consulting Group to analyze agentic AI TAM for services partners.
2. IDC InfoBrief, sponsored by Google Cloud, Google Cloud AI: Driving Opportunity and Growth for Global Consulting & Systems Integrator Partners, doc #US53276025, April 2025

Read More for the details.

AI/ML-ready Apache Spark with Dataproc

GCP – AI/ML-ready Apache Spark with Dataproc

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Apache Spark is the cornerstone for large-scale data processing, model training, and inference for AI/ML workloads. Yet, the complexities of environment configuration, dependency management, and MLOps integration can slow you down. To accelerate your AI/ML journey, Dataproc now delivers powerful, ML-ready capabilities for Spark. Available on both Dataproc on Compute Engine clusters and Google Cloud Serverless for Apache Spark, these enhancements are engineered to streamline development and operations, reducing setup overhead and simplifying workflows. This allows data scientists and engineers to dedicate more time to building and deploying impactful models rather than wrestling with infrastructure.

Let’s explore what’s new and how to start using these innovations today.

AI/ML-capable runtimes

Getting a Spark environment ready for ML, especially with GPU acceleration, used to involve custom scripts and manual configuration. Dataproc now streamlines this with ML Runtimes. ML Runtimes is a specialized Dataproc on Compute Engine image version, starting from 2.3 for Ubuntu-based images, designed to accelerate ML workloads. It ships with pre-packaged GPU drivers (NVIDIA Driver, CUDA, cuDNN, NCCL) and common ML libraries such as PyTorch, XGBoost, tokenizers, transformers etc, significantly cutting down cluster provisioning and setup time. 

Google Cloud Serverless for Apache Spark also benefits from runtimes with pre-installed ML libraries, bringing the same ease of use to a serverless environment. These also include libraries such as XGBoost, PyTorch, tokenizers, transformers, etc. 

“At Snap we use Spark on Dataproc for a variety of analytics and ML workloads including running GPU accelerated Spark Rapids, and model training and inference with PyTorch. The new Dataproc 2.3 ML runtime has been really helpful — reducing our cluster startup latency by 75% and eliminating toil for our ML Platform developers to build and manage environments.”– Prudhvi Vatala, Sr. Manager, Snap Inc.

It’s easy to create a Dataproc on a compute Engine cluster, specifying the ML image version and the required GPU accelerators for your workers.

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<ListValue: [StructValue([(‘code’, ‘gcloud dataproc clusters create <your-cluster-name> \rn–project=<your-project-id> \rn–region=<your-region> \rn–image-version=2.3-ml-unbuntu \rn–master-machine-type g2-standard-4 –master-accelerator=type=nvidia-l4,count=1rn–worker-machine-type=g2-standard-8 \rn–worker-accelerator type=nvidia-l4,count=1’), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3e3dee2f2340>)])]>

Additionally, Serverless Spark sessions (Generally Available) also support GPUs, and come similarly packaged with GPU drivers and common ML libraries.

Develop Spark applications in Colab or your favorite IDE

Now, you can develop and run Spark applications using Colab Enterprise notebooks in BigQuery Studio or via integrated development environments (IDEs) like VSCode and Jupyter. 

BigQuery Colab enterprise notebooks are available within BigQuery Studio with native support for Spark application development. With Colab Enterprise notebooks, you can create  Serverless Spark sessions using Spark Connect and work with your tables in BigLake metastore. 

Colab notebook provides advanced features such as gen AI code assistance and error explanation, with error correction coming soon. It also supports observability for your Spark jobs and management of Spark sessions. In addition, the Colab notebooks lets you mix BigQuery SQL with Spark code in a single notebook and interoperate on the resulting tables. Once your code is ready, you can schedule notebooks via the inbuilt scheduling functionality or use BigQuery Pipelines for more complicated DAGs.

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You can also use IDEs such as Visual Studio Code or JupyterLab for Spark application development. JupyterLab users can use the Dataproc JupyterLab plugin to simplify interactive development with Spark serverless sessions and simplify creation and submission of batch jobs via Serverless batch jobs. This plugin comes preinstalled in Vertex Workbench, so you can be productive in minutes.

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On VS Code, you can use the Cloud Code extension, which supports development against a range of Google Cloud services. After configuring the Cloud Code extension, you can browse BigQuery datasets and tables, browse and manage your Dataproc compute resources (clusters, serverless interactive sessions and batch), create Spark notebooks from available templates or start developing on your own, and then schedule your workloads all from VS Code. This choice in development tooling allows you to pick one that best suits your workflow, without sacrificing access to the power of Dataproc Spark.

Distributed training and inference with GPU support

Dataproc’s ML runtimes are built to run distributed training and inference, leveraging frameworks like XGBoost, TensorFlow, and PyTorch, all pre-configured for GPU utilization. For example, for distributed training with XGBoost on Spark, you can leverage the pre-installed xgboost.spark library. By setting parameters such as num_workers to distribute the task across Spark executors and device=”cuda”, you can effectively train your models on multiple GPUs, significantly speeding up the training process for large datasets. Here’s an example of how to configure XGBoost classifier for distributed GPU training on your Spark cluster:

code_block
<ListValue: [StructValue([(‘code’, ‘from xgboost.spark import SparkXGBClassifierrnfrom pyspark.sql import SparkSessionrnrn# Configure the XGBoost classifier for distributed GPU trainingrnxgb_classifier = SparkXGBClassifier(rn featuresCol=”features”,rn labelCol=”label”,rn num_workers=spark.sparkContext.defaultParallelism,rn device=”cuda”, # Enable GPU trainingrn # Other XGBoost parameters like max_depth etc.rn max_depth=6 rn)rnrn# Train the modelrnxgb_model = xgb_classifier.fit(train_df)rnrn# Model persistence and predictionrnxgb_model.save(“path/to/your/xgboost_spark_model”)rnpredictions = xgb_model.transform(test_df)’), (‘language’, ‘lang-py’), (‘caption’, <wagtail.rich_text.RichText object at 0x3e3ded3d9760>)])]>

Interactive environment customization for Spark Connect

When working interactively with Spark, such as in a Colab notebook using Spark Connect, ensuring Python library consistency between your client and the Spark cluster is crucial. Dataproc simplifies adding PyPI packages dynamically to a Spark session by extending the addArtifacts method. You can now specify the list of packages to install/upgrade/downgrade in version-scheme (same as pip install). This instructs the Spark Connect server to install the package and its dependencies, making them available to workers for your UDFs and other code.

code_block
<ListValue: [StructValue([(‘code’, ‘# Installs textdistance(specific version) and random2 (latest) library on the cluster. UDFs using textdistance and random2 can now run on worker nodesrnrnspark.addArtifacts(“textdistance==4.6.1”, “random2”, pypi=True)’), (‘language’, ‘lang-py’), (‘caption’, <wagtail.rich_text.RichText object at 0x3e3ded3d9790>)])]>

In addition you can also customize your Spark environment on Dataproc with Init scripts and custom images.

MLOps via Vertex AI

Dataproc works with Vertex AI, Google Cloud’s unified platform for AI and ML, helping to improve MLOps for your AI/ML workflows with Spark. Using the Vertex AI SDK directly within your Dataproc Spark code enables experiment tracking and model management, allowing you to:

  • Track experiments: Track log parameters, metrics, and artifacts from your Dataproc Spark training jobs to Vertex AI Experiments. This allows you to compare runs, visualize results, and reproduce experiments reliably.

  • Register models: Once training is complete, register your trained models into the Vertex AI Model Registry. This provides a central repository for model versioning, staging, and governance, simplifying the path to deployment.

code_block
<ListValue: [StructValue([(‘code’, ‘# code snippet for Dataproc Spark on GCE. Some details ommited for brevityrnrnrnfrom google.cloud import aiplatformrnrn# — Initialize Vertex AI SDK & Enable Autologging —rnaiplatform.init(project=PROJECT_ID, location=REGION, experiment=EXPERIMENT_NAME)rnrn# Start a run to log experiment metricsrnaiplatform.start_run(run=RUN_NAME)rnrnxgb_spark_estimator = SparkXGBClassifier(rn featuresCol=”features”,rn labelCol=”label” rn # Add other XGBoost parameters needed for trainingrn )rnrn# train modelrntrained_spark_model = xgb_spark_estimator.fit(train_df)rnrn# register modelrn# 1. Get the underlying XGBoost model and save itrnnative_booster = trained_spark_model.get_booster()rnnative_booster.save_model(local_path)rnrn# Log relevant metrics manually in Vertex Experiments rnmetrics={parameter_name:parameter_value}rnaiplatform.log_metrics(metrics)rnrnrn# 2. Upload to GCSrndestination_gcs_object_name = f”{GCS_MODEL_ARTIFACT_DIR_NAME}/{MODEL_FILENAME}” rnstorage.Client(project=PROJECT_ID).bucket(GCS_BUCKET_NAME).blob(destination_gcs_object_name).upload_from_filename(local_path) rnrn# 3. Register to Vertex AI Model RegistryrnPRE_BUILT_SERVING_CONTAINER_IMAGE_URI = “us-docker.pkg.dev/vertex-ai/prediction/xgboost-cpu.2-1:latest”rnrnregistered_model = aiplatform.Model.upload(rn display_name=MODEL_DISPLAY_NAME,rn artifact_uri=GCS_ARTIFACT_DIRECTORY_URI,rn serving_container_image_uri=PRE_BUILT_SERVING_CONTAINER_IMAGE_URI,rn description=”Spark XGBoost model”,rn sync=True # Wait for the model to be uploaded and registeredrn)’), (‘language’, ‘lang-py’), (‘caption’, <wagtail.rich_text.RichText object at 0x3e3ded3d9340>)])]>

This integration makes your AI/ML workloads on Spark more manageable, reproducible, and deployable, per your organization’s wider MLOps strategy.

Deploy to production

Move from interactive development to production easily.

When using BigQuery Colab notebooks for development, you get Git support to version-control your code and go through your CI/CD flow. You can also schedule your Spark notebook using BigQuery’s built-in pipeline feature, which allows you create single scheduled notebooks or more complicated DAGs, chaining multiple notebooks or queries. You can run these pipelines using the user account or a service account for production pipelines.

BigQuery Pipelines let you compose your flow into discrete tasks, so you can mix Apache Spark on Dataproc and BigQuery execution. In the following BigQuery pipeline, the first task ingests raw data via a BigQuery query, then the data is transformed via Apache Spark via a notebook task. This notebook contains the pertinent Spark transform steps. Finally, the graph splits into two parallel tasks: a notebook that produces a report based on output of the previous task, and a final query that cleans up the initial ingested data.

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When using an IDE you can achieve a similar flow by using the Git client of these IDEs to version your Spark code. You can also create and deploy pipelines using Cloud Composer, Google Cloud’s managed serverless Apache Airflow offering. You can run jobs on your existing Dataproc clusters, ephemeral job clusters, or on Serverless Batch.

code_block
<ListValue: [StructValue([(‘code’, ‘# Following code illustrates how to schedule serverless batch jobs with Cloud Composer (Airflow)rnrnrn# import statements and configurations statements omitted for brevityrnrnrn# Define the full job payload for DataprocCreateBatchOperatorrnwith models.DAG(rn “dataproc_batch_operators”, # The id you will see in the DAG airflow pagern default_args=default_args, # The interval with which to schedule the DAGrn schedule_interval=datetime.timedelta(days=1), # Override to match your needsrn) as dag:rn create_batch = DataprocCreateBatchOperator(rn task_id=”batch_create”,rn batch={rn “pyspark_batch”: {rn “main_python_file_uri”: PYTHON_FILE_LOCATION,rn “jar_file_uris”: [SPARK_BIGQUERY_JAR_FILE],rn },rn “environment_config”: {rn “peripherals_config”: {rn “spark_history_server_config”: {rn “dataproc_cluster”: PHS_CLUSTER_PATH,rn },rn },rn },rn },rn batch_id=”create-xgboost-batch”,rn )’), (‘language’, ‘lang-py’), (‘caption’, <wagtail.rich_text.RichText object at 0x3e3ded3d9910>)])]>

AI/ML-ready Apache Spark

With Dataproc, you can build your AI/ML workloads with Apache Spark more easily. By providing pre-configured ML Runtimes with GPU support, simplifying Python dependency management for interactive sessions via Spark Connect, enabling development from your preferred IDE, and offering seamless integration with Vertex AI for MLOps, Dataproc accelerates the entire ML lifecycle. Move from exploration and training to robust, production-ready Spark ML pipelines. Explore the Dataproc documentation today to start leveraging these capabilities.

Read More for the details.

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AWS – Amazon MemoryDB now supports an AWS FIS action to pause multi-Region cluster replication

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Amazon MemoryDB now supports an AWS Fault Injection Service action to pause replication for multi-Region clusters. FIS is a fully managed service for running controlled fault injection experiments to improve an application’s performance, observability, and resilience. Amazon MemoryDB Multi-Region is a fully managed, active-active, multi-Region database that lets you build multi-Region applications with up to 99.999% availability and microsecond read and single-digit millisecond write latencies. Customers can use the new FIS action to observe how their application responds to a disruption in regional replication, and tune their monitoring and recovery process to improve resiliency and application availability.

MemoryDB Multi-Region enables you to build multi-Region applications that need high availability, increased application resiliency, and improved business continuity. This new FIS action reproduces the real-world behavior when replication in a multi-Region cluster is interrupted and resumed. This lets you test and build confidence that your application responds as intended when resources in a Region are not accessible. You can create an experiment template in FIS to integrate the experiment with continuous integration and release testing and to combine with other FIS actions. For example, MemoryDB Pause Replication is combined with other actions in the Cross-Region: Connectivity scenario to isolate a Region.

MemoryDB Multi-Region Pause Replication is now available in all AWS Regions where MemoryDB Multi-Region is available. To learn more, visit the MemoryDB FIS actions documentation.

Read More for the details.

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AWS – Amazon S3 Multi-Region Access Points are now available in 12 additional AWS Regions

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Amazon S3 Multi-Region Access Points are now available in 12 additional AWS opt-in Regions: Asia Pacific (Jakarta, Hong Kong, Hyderabad, and Melbourne), Europe (Zurich, Spain, and Milan), Middle East (Bahrain and UAE), Canada West (Calgary), Africa (Cape Town) and Israel (Tel Aviv) Regions.

To get started, you need to first enable the AWS opt-in Region for your account by using the steps outlined here. Next, you can use the AWS CLI or AWS SDK to create an S3 Multi-Region Access Point in an AWS opt-in Region. For pricing information, visit the Amazon S3 pricing page. To learn more about S3 Multi-Region Access Points, visit the feature page, S3 User Guide, or S3 FAQs.

Read More for the details.

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GCP – Tzafon selects Google Cloud to build next generation agentic machine intelligence

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Tzafon, a San Francisco-based startup and AI R&D lab, is partnering with Google Cloud to utilize Google’s AI-optimized infrastructure and cloud services, which will help Tzafon deliver automation at large scale.

The Tzafon team aims to do this by building systems and models that can support multiple, autonomous AI agents that are capable of working together and interacting with common interfaces like applications, operating systems, and web browsers.

Now, Tzafon will partner with Google Cloud to access the compute resources and cloud services it needs to train its new multi-agent models and to develop new automation frameworks that will allow Tzafon’s agents to collaborate more quickly and seamlessly. 

Through its partnership with Google Cloud, Tzafon will:

  • Utilize NVIDIA GPUs through Google Cloud to train new machine intelligence models capable of managing multiple AI agents.

  • Develop individual agents capable of interacting with operating systems, web browsers, and applications on a person’s behalf.

  • Scale workloads up or down quickly using Google Kubernetes Engine.

  • Use BigQuery to effectively manage the large volumes of data underpinning its systems.

Today, more than 60% of the world’s generative AI startups are using Google Cloud. Now, Tzafon joins them in gaining access to Google Cloud’s AI stack, with its reliable compute capacity,strong price performance, robust data infrastructure, and elasticity to scale quickly, among many other features that are essential in the emerging field of AI.

You can read more about Tzafon’s mission in their white paper and learn more about how thousands of AI startups are building with Google Cloud here.

Read More for the details.

Securely deploy ChromeOS Flex – from anywhere

GCP – Securely deploy ChromeOS Flex – from anywhere

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Just three years ago, ChromeOS Flex was born with a mission to breathe new life into existing hardware, offering a modern, sustainable, and secure experience in the process. Today, we’re proud to have over 600 certified devices, and millions of ChromeOS Flex devices being used around the world every day. But we aren’t resting on our laurels. I’m happy to share some much anticipated features coming to ChromeOS Flex that will further empower businesses to modernize their fleet.

You may have seen stories like Strawberry Hotels, who recovered from a ransomware attack in less than 48 hours by repurposing their existing Windows devices with ChromeOS Flex, or Foundations Health Solutions, who saved $1 million while improving patient care by adopting ChromeOS and ChromeOS Flex. These stories highlight how ChromeOS Flex is helping organizations of all sizes extend the lifespan of their hardware, reduce IT costs, and enhance security. And now with tools like remote deployment and expanded network security and VPN application support, it’s even easier for businesses to take advantage of ChromeOS Flex.

Upgrade your fleet with ease

The first advancement we’re introducing is ChromeOS Flex remote deployment. We understand that for large organizations with many globally distributed devices, relying solely on USB drives to migrate to ChromeOS Flex can be cumbersome and time-consuming. This new tool changes that, allowing most managed Windows devices to automatically convert to ChromeOS Flex, from anywhere, with no complex IT requirements.

This tool is designed to offer significant efficiency gains, and streamline ChromeOS Flex deployment across your organization. The best part? It’s simple:

  1. Configure: Create a ChromeOS Flex package
  2. Deploy: Push the package with your preferred Windows management tool, leveraging your existing infrastructure
  3. Convert: On execution, the package will automatically convert your devices to ChromeOS Flex, join predefined Wi-Fi networks, and automatically enroll your device in the Google Admin console

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To ensure the security and stability of your existing environment, we’ve designed this tool with administrator-level access in mind, giving IT admins complete control throughout the migration process. For a deep dive into the technical details and how to get started, click here to learn more about ChromeOS Flex remote deployment.

Enhanced security with Android network security and VPN application support

We recognize the critical need for businesses to secure data and access to remote resources, corporate networks, and sensitive information, no matter where work happens. Given that VPN platforms are already the most popular Android applications for our enterprise ChromeOS customers, we’re thrilled to share that Android network security applications from leading providers are now also supported on ChromeOS Flex.

This enhancement boosts ChromeOS Flex’s security capabilities, making it an even more robust solution for repurposing legacy hardware. Businesses and end-users alike will benefit from the seamless compatibility with leading enterprise network security providers such as Cisco, Zscaler, and Fortinet. This enables consistent security policies and a unified approach to network connectivity and data protection across both ChromeOS and ChromeOS Flex devices, ensuring your data is protected at all times, no matter where you get work done.

Three years in, ChromeOS Flex is not just a solution for repurposing hardware–it’s a strategic tool for modernizing your IT infrastructure and strengthening security as well. With ChromeOS Flex remote deployment and Android network security application support, ChromeOS Flex is ready to meet the demands of large organizations, hybrid work environments, and unique security needs.

Interested in learning more about ChromeOS Flex remote deployment? Visit our website.

Read More for the details.

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AWS – Introducing AI agents and tools in AWS Marketplace

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AWS Marketplace now offers AI agents and tools from AWS Partners, allowing customers to find and buy third-party AI agent solutions with streamlined procurement and multiple deployment options. Customers can accelerate their discovery of AI agents and agent tools in a centralized catalog while enjoying the benefits of purchasing through AWS Marketplace and Partners can quickly bring their AI agent solutions to market.

Customers can explore AI agent products on the new “AI Agent & Tools” solution page. Using natural language, customers can search and receive results that match their specific use cases. When evaluating solutions, customers can review listings that support model context protocol (MCP) and agent-to-agent (A2A) standard protocols, along with various deployment options, to determine the best-fit solution for their needs. Customers can then purchase and deploy their chosen solutions through various paths, including Amazon Bedrock AgentCore Runtime, or add tools to AgentCore Gateway to accelerate agent development.

For AWS Partners, AI Agents and Tools in AWS Marketplace accelerates customer reach and adoption for agentic solutions. By listing their AI agents and tools, Partners can leverage established AWS Marketplace channels to streamline sales, offer flexible pricing, and provide secure AWS deployment options. Partners can categorize their offerings and highlight MCP and A2A protocol support, enhancing discoverability through advanced search and filtering in the AWS Marketplace catalog. Integration with Amazon Bedrock AgentCore services further simplifies deployment for customers, reducing time to value and providing a secure, scalable environment for customers building innovative agentic solutions.

Start exploring AI agent solutions in AWS Marketplace. Learn how AWS Partners can start selling by accessing the AWS Marketplace Seller Guide. 

Read More for the details.

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AWS – AWS API MCP Server now available

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Today, AWS announces the developer preview of the AWS API model context protocol (MCP) server, a new tool that enables foundation models (FMs) to interact with any AWS API through natural language by creating and executing syntactically correct and valid CLI commands.

With the AWS API MCP Server, customers using popular MCP clients can streamline tasks like troubleshooting workloads, managing application deployments, and exploring AWS services and capabilities more easily, by issuing natural language requests that the host FM can translate into API calls.

The AWS API MCP Server allows MCP clients to discover supported AWS APIs and make calls to them through the host FM, enabling actions such as inspecting, creating, and modifying AWS resources. The server provides secure access control through AWS Identity and Access Management (IAM) credentials and pre-configured API permissions, ensuring that FMs can only access or perform authorized actions on permitted AWS APIs.

The AWS API MCP Server is released as an open source project and available now. Visit the AWS Labs GitHub repository to download, deploy, and start experimenting with natural language interaction with AWS APIs today.

Read More for the details.

GCP – Build with more flexibility: New open models arrive in the Vertex AI Model Garden

In our ongoing effort to provide businesses with the flexibility and choice needed to build innovative AI applications, we are expanding the catalog of open models available as Model-as-a-Service (MaaS) offerings in Vertex AI Model Garden. Following the addition of Llama 4 models earlier this year, we are announcing DeepSeek R1 is available for everyone through our Model-as-a-Service (MaaS) offering. This expansion reinforces our commitment to an open AI ecosystem, ensuring our customers can access a diverse range of powerful models to find the one best suited for their specific use case.

Deploying and managing today’s large-scale models presents operational and financial challenges. For instance, a large model such as DeepSeek R1 can require an infrastructure of eight advanced H200 GPUs to run inference. For many organizations, procuring and managing such resources is a major undertaking that can divert focus from core application development.

Vertex AI’s MaaS offering is designed to remove this complexity. By providing these models as fully managed, serverless APIs, we eliminate the need for customers to provision or manage the underlying infrastructure. This allows your teams to bypass the complexities of GPU management and focus directly on building and innovating. With Vertex AI, you benefit from a secure, enterprise-grade platform with built-in data privacy and compliance, all under a flexible, pay-as-you-go pricing model that scales with your needs.

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Getting started

Below we provide a step-by-step guide on how you can use open models available on MaaS. We have used DeepSeek R1 on Vertex AI as an example. It can be accessed both via the UI and API.

1. Enable the DeepSeek API Service

Navigate to the DeepSeek API Service from the Vertex AI Model Garden and click on the title to open the model card. Then, enable access to the DeepSeek API Service. It may take a few minutes for permissions to propagate after enablement.

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DeepSeek API Service from the Vertex AI Model Garden

2. Try out the model via the UI

Navigate to the DeepSeek API Service from the Vertex AI Model Garden and click on the tile to open the model card. You can use the UI in the sidebar to test the service.

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DeepSeek API Service with UI sidebar to test the service

3. Try out the model via Vertex AI API

To integrate DeepSeek R1 within your applications, you can use either REST API or OpenAI Python API Client Library. Note: For security of your data, DeepSeek MaaS endpoint does not have any outbound internet access. 

Get Predictions via the REST API

You can make API requests via curl from the Cloud Shell or your machine with gcloud credentials configured. Remember to replace the placeholders with this code:

code_block
<ListValue: [StructValue([(‘code’, ‘export PROJECT_ID=<ENTER_PROJECT_ID>rnexport REGION_ID=<ENTER_REGION_ID> rnrncurl \rn-X POST \rn-H “Authorization: Bearer $(gcloud auth print-access-token)” \rn-H “Content-Type: application/json” \rn”https://${REGION_ID}-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/${REGION_ID}/endpoints/openapi/chat/completions” \rn-d ‘{rn “model”: “deepseek-ai/deepseek-r1-0528-maas”,rn “max_tokens”: 200,rn “stream”: true,rn “messages”: [rn {rn “role”: “user”,rn “content”: “which is bigger – 9.11 or 9.9″rn }rn ]rn}”), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3ee4edba2cd0>)])]>

Get Predictions via the OpenAI Python API Client Library 

Install the OpenAI Python API Library:

code_block
<ListValue: [StructValue([(‘code’, ‘pip install openai’), (‘language’, ”), (‘caption’, <wagtail.rich_text.RichText object at 0x3ee4edba2550>)])]>

Initialize the client and configure the endpoint URL. To get the access token to use as an API key, you can read more here. If run from a local machine, GOOGLE_APPLICATION_CREDENTIALS will authenticate your requests.

code_block
<ListValue: [StructValue([(‘code’, ‘import osrnimport openairnrnPROJECT_ID = “ENTER_PROJECT_ID”rnLOCATION = “us-central1″rnMODEL_ID = “deepseek-ai/deepseek-r1-0528-maas”rnAPI_KEY = os.environ[“GOOGLE_APPLICATION_CREDENTIALS”] # or add output from gcloud auth print-access-token rnrndeepseek_vertex_endpoint_url = (rn f”https://{LOCATION}-aiplatform.googleapis.com/v1beta1/”rn f”projects/{PROJECT_ID}/locations/{LOCATION}/endpoints/openapi”rn)rnrnclient = openai.OpenAI(rn base_url=deepseek_vertex_endpoint_url,rn api_key=API_KEYrn)’), (‘language’, ‘lang-py’), (‘caption’, <wagtail.rich_text.RichText object at 0x3ee4edba2970>)])]>

Make completions requests via the client:

code_block
<ListValue: [StructValue([(‘code’, ‘response = client.chat.completions.create(rn model=”deepseek-ai/deepseek-r1-0528-maas”,rn messages=[rn {“role”: “system”, “content”: “You are a helpful assistant”},rn {“role”: “user”, “content”: “How many r’s are in strawberry ?”},rn ],rn stream=False,rn)rnrnprint(response.choices[0].message.content)rnrn# ChatCompletion(“id=”””,rn# “choices=”[rn# “Choice(finish_reason=””length”,rn# index=0,rn# “logprobs=None”,rn# “message=ChatCompletionMessage(content=””<think>\nFirst, the question is: \”How many r\\’s are in strawberry?\” I need to count the number of times the letter \\’r\\’ appears in the word \”strawberry\”.\n\nLet me write down the word: S-T-R-A”,rn# “refusal=None”,rn# “role=””assistant”,rn# “annotations=None”,rn# “audio=None”,rn# “function_call=None”,rn# “tool_calls=None))”rn# ],rn# created=,rn# “model=””deepseek-ai/deepseek-r1-0528-maas”,rn# “object=””chat.completion”,rn# “service_tier=None”,rn# “system_fingerprint=”””,rn# usage=CompletionUsage(completion_tokens=50,rn# prompt_tokens=18,rn# total_tokens=68,rn# “completion_tokens_details=None”,rn# “prompt_tokens_details=None))”‘), (‘language’, ‘lang-py’), (‘caption’, <wagtail.rich_text.RichText object at 0x3ee4edba2a30>)])]>

What’s next?

Vertex AI Model Garden opens up new possibilities for building applications that require state-of-the-art foundation models. Here are some next steps:

  • Review documentation guide for DeepSeek R1 MaaS here and Llama MaaS here

  • Review pricing here for both models 

  • Explore the Model Garden: Discover other models available as managed services

  • Build a proof-of-concept: Start with a small project to understand the model’s capabilities

  • Join the community: Share your experiences and learn from others in the Google Cloud AI Community

Read More for the details.

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AWS – AWS Transform for mainframe introduces enhanced code refactoring and business logic capabilities

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AWS Transform for mainframe now offers enhanced reforge and business logic extraction functionality to further streamline mainframe modernization. These new capabilities help organizations reduce modernization time, improve code quality and maintainability, and optimize modernization and migration costs.

The reforge capability in AWS Transform for mainframe is now generally available, enhancing transformed Java code by restructuring complex methods, adding descriptive comments, optimizing variable usage, and improving code flow. This results in more readable and maintainable code for developers. Additionally, AWS Transform for mainframe’s business logic extraction capability now provides application-level insights, from high-level summaries to detailed business function analysis, complementing the existing file-level business logic extraction, to help users better understand their legacy applications.

These capabilities are now available in all AWS Regions where AWS Transform is offered. To learn more, visit the AWS Transform for mainframe product page, read the user guide, or get started in the AWS Transform web experience.

Read More for the details.

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AWS – Amazon S3 Tables now supports Model Context Protocol (MCP) Server

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Today, AWS announces the general availability of Model Context Protocol (MCP) Server for Amazon S3 Tables as a part of AWS MCP Servers. Now, you can use natural language to interact with S3 Tables through your preferred large language model (LLM), eliminating the need to write SQL queries.

With the MCP Server for Amazon S3 Tables, you can safely create, populate, and query S3 Tables with natural language. The MCP Server provides AWS tools, resources, and contextual information that your AI assistants can use to operate on S3 Tables. To get started, install the latest version of AWS MCP Servers with an AI client software like Cline, Claude Code, or Cursor and configure your AWS account and permissions to start operating on S3 Tables.

To learn more about MCP Server for Amazon S3 Tables, read our blog. To download and try out MCP Server for Amazon S3 Tables, visit the aws-labs GitHub repository.

Read More for the details.

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AWS – Customize Amazon Nova in Amazon SageMaker AI

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Today, Amazon Nova is introducing the most comprehensive suite of model customization capabilities made available for any proprietary model family. Available as ready-to-use recipes on SageMaker AI, these capabilities allow customers to adapt Nova Micro, Nova Lite, and Nova Pro across the model training lifecycle, including pre-training, supervised fine-tuning, and alignment.

Using these customization techniques, you can adapt Nova models to accurately reflect your proprietary knowledge, workflows, and brand in your generative AI applications while maintaining Nova’s industry-leading price performance and low latency. The techniques include Continued Pre-Training, Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), Proximal Policy Optimization, and Knowledge Distillation — with support for both parameter-efficient and full-model training options across SFT, DPO and Distillation.

Nova customization recipes are available in SageMaker training jobs and SageMaker HyperPod, giving you flexibility to select the environment that best fits your infrastructure and scale requirements. You can deploy your customized models on Amazon Bedrock and invoke them via on-demand inference or Provisioned Throughput. On-demand inference is available only with parameter efficient training techniques.

Recipes for Amazon Nova on Amazon SageMaker AI are available in US East (N. Virginia).

To get started read Amazon Nova user guide and visit the GitHub repository to browse Nova specific SageMaker training recipes. 

Read More for the details.

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AWS – Amazon Nova Sonic adds language support for French, Italian, German

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Amazon Nova Sonic—a speech-to-speech foundation model—now supports French, Italian, and German, expanding on its existing coverage of English and Spanish. This update includes six additional expressive voices, offering both masculine and feminine-sounding options, to help developers create more natural and inclusive conversational AI experiences across a wider range of languages.

In addition, Amazon Nova Sonic now integrates with LiveKit, an open-source WebRTC platform, and Pipecat, an open-source framework for building voice and multimodal AI agents. These integrations simplify the development of low-latency, real-time voice applications by removing the need to manage complex audio pipelines and streaming infrastructure. As an added capability, Nova Sonic now also supports integrations with Vonage and Twilio, extending deployment flexibility for telephony and communications use cases.

Amazon Nova Sonic is a speech-to-speech foundation model that delivers real-time, human-like voice conversations with low latency. Available in Amazon Bedrock via the bidirectional streaming API, the model understands streaming speech in various speaking styles and generates expressive speech responses that dynamically adapt to the prosody of input speech.

Amazon Nova Sonic is now available globally on Amazon Bedrock in three AWS Region. To learn more, read the AWS News Blog, Amazon Nova Sonic product page, and User Guide. To get started, visit the Amazon Bedrock Console.

Read More for the details.

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AWS – AWS Deadline Cloud now supports Unreal Engine in Service-Managed Fleets

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AWS Deadline Cloud has expanded its support for Unreal Engine in its Service-Managed Fleets. AWS Deadline Cloud is a fully managed service that simplifies render management for teams creating computer-generated graphics and visual effects using industry-standard tools for gaming, film, television, web content, and more.

With this new feature, you can submit Unreal Engine 5.4, 5.5 or 5.6 projects to Deadline Cloud for rendering, without needing to configure or manage compute infrastructure. Getting started is simple with an easy-to-install plugin, available on the AWS Console or in GitHub. Once installed, you can easily submit jobs directly from Unreal Engine’s Movie Render Queue. AWS Deadline Cloud automatically handles the provisioning and elastic scaling of compute resources required for rendering your projects.

Deadline Cloud Unreal Engine support is available in all AWS Regions where Deadline Cloud is offered.

For more information, please visit the Deadline Cloud product page and our Deadline Cloud for Unreal Engine GitHub repository.

Read More for the details.

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AWS – AWS Knowledge MCP Server now available (Preview)

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Today, AWS announces the preview release of AWS Knowledge Model Context Protocol (MCP) Server, a new tool that surfaces authoritative AWS knowledge in an LLM-compatible format, including documentation, blog posts, What’s New announcements, and Well-Architected best practices.

AWS Knowledge MCP Server enables clients and foundation models (FMs) that support MCP to ground their responses in trusted AWS context, guidance, and best practices, providing the guidance needed for accurate reasoning and consistent execution, while reducing manual context management. Customers can now focus on business problems instead of searching for information manually.

The server is publicly accessible at no cost and does not require an AWS account. Usage is subject to rate limits. Give your developers and agents access to the most up-to-date AWS information today by configuring your MCP clients to use the AWS Knowledge MCP Server endpoint, and follow the Getting Started guide for setup instructions.

Read More for the details.

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AWS – AWS DataSync now supports IPv6

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AWS DataSync announces Internet Protocol version 6 (IPv6) support for storage resources. With this launch, customers can now use DataSync to connect to storage resources located on premises or in other clouds, using either IPv4 or IPv6 addresses.

AWS DataSync is a secure, high-speed file transfer service that simplifies moving data over networks. Customers can now use DataSync to transfer data to and from NFS, SMB, and Object Storage servers configured with IPv6 addresses. With dual-stack (IPv4 and IPv6) support, customers can continue to use DataSync in their environments as they transition their networks from IPv4 to IPv6.

IPv6 support is available in all AWS Regions where AWS DataSync is available. To learn more about configuring IPv6 connectivity with AWS DataSync, visit the AWS DataSync User Guide.

Read More for the details.

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AWS – Amazon Bedrock Data Automation is now available in 5 additional AWS Regions

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Amazon Bedrock Data Automation (BDA) is now generally available in Europe (Frankfurt), Europe (London), Europe (Ireland), Asia Pacific (Mumbai) and Asia Pacific (Sydney).

BDA is a feature of Amazon Bedrock that enables developers to automate the generation of valuable insights from unstructured multimodal content such as documents, images, video, and audio to build GenAI-based applications. By leveraging BDA, developers can reduce development time and effort, making it easier to build intelligent document processing, media analysis, and other multimodal data-centric automation solutions. BDA can be used as a standalone feature or as a parser in Amazon Knowledge Bases RAG workflows.

With this launch, BDA is now available in a total of 7 AWS Regions, including US West (Oregon) and US East (N. Virginia) Regions. To learn more, visit the Bedrock Data Automation product page and the Amazon Bedrock Pricing page.

Read More for the details.

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AWS – Amazon SageMaker streamlines S3 Tables workflow experience

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Amazon SageMaker has simplified the process of creating, querying, and joining Amazon S3 Tables with data in Amazon S3 general purpose buckets, Amazon Redshift data warehouses, and third-party data sources by allowing customers to create S3 table buckets and the related catalogs without having to navigate between multiple AWS consoles.

Users can now create tables, load data, and run queries using the Query Editor or Jupyter Notebook within SageMaker Unified Studio. For administrators, the update includes the ability to enable analytics integration with S3 for their AWS account and create custom profiles. Project owners can use these profiles to set up projects with pre-configured catalogs and S3 Tables support, reducing the manual configuration steps required to get started.

This updated S3 Tables experience in SageMaker Unified Studio is available in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Canada(Central), South America (São Paulo), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Tokyo),Asia Pacific (Sydney), Europe (Paris), Europe (Stockholm), Europe (London), and Europe (Frankfurt).

To get started with the updated S3 Tables workflow in SageMaker Unified Studio, see the Amazon SageMaker documentation.

Read More for the details.

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AWS – AWS Glue now supports zero-ETL integrations from Amazon DynamoDB and eight applications to S3 Tables

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AWS Glue now supports zero-ETL integration (managed ingestion) from Amazon DynamoDB and eight applications to Amazon S3 Tables, automating the extraction and loading of data into S3 Tables from DynamoDB and applications like Salesforce, SAP, ServiceNow, and Zendesk.

S3 Tables are purpose-built for storing tabular data at scale, with built-in Apache Iceberg support. You can enable S3 Tables to work with AWS Lake Formation to support various analytics services, including Amazon Athena, Amazon EMR, Amazon Redshift, and AWS Glue. Zero-ETL integrations are fully managed by AWS and minimize the need to build and manage ETL data pipelines. With this new zero-ETL integration, you can efficiently extract and load data from DynamoDB tables or from your customer support, relationship management, and ERP applications into your S3 Table-backed data lake for analysis. Zero-ETL integration reduces users’ operational burden and saves weeks of engineering effort needed to design, build, and test data pipelines.

Zero-ETL integration from DynamoDB and eight applications to S3 Tables is now available in the US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Tokyo), Asia Pacific (Hong Kong), Asia Pacific (Singapore), Asia Pacific (Sydney), Europe (Stockholm), Europe (Frankfurt), Europe (Ireland), South America (Sao Paulo), Asia Pacific (Seoul), Europe (London), and Canada (Central) AWS Regions.

You can create and manage integrations using either the AWS Glue console, the AWS Command Line Interface (AWS CLI), or the AWS Glue APIs. To learn more, visit What is zero-ETL and Glue zero-ETL documentation.

Read More for the details.

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