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Next Generation SageMaker Notebooks – Now with Built-in Data Preparation, Real-Time Collaboration, and Notebook Automation | Amazon Web Services

In 2019, we introduced Amazon SageMaker Studio, the first fully integrated development environment (IDE) for data science and machine learning (ML). SageMaker Studio gives you access to fully managed Jupyter Notebooks that integrate with purpose-built tools to perform all ML steps, from preparing data to training and debugging models, tracking experiments, deploying and monitoring models, and managing pipelines.

Today, I’m excited to announce the next generation of Amazon SageMaker Notebooks to increase efficiency across the ML development workflow. You can now improve data quality in minutes with the built-in data preparation capability, edit the same notebooks with your teams in real time, and automatically convert notebook code to production-ready jobs.

Let me show you what’s new!

New Notebook Capability for Simplified Data Preparation
The new built-in data preparation capability is powered by Amazon SageMaker Data Wrangler and is available in SageMaker Studio notebooks.  SageMaker Studio notebooks automatically generate key visualizations on top of Pandas data frames to help you understand data distribution and identify data quality issues, like missing values, invalid data, and outliers. You can also select the target column for ML models and generate ML-specific insights such as imbalanced class or high correlation columns. You then receive recommendations for data transformations to resolve the issues. You can apply the data transformations right in the UI, and SageMaker Studio notebooks automatically generate the corresponding transformation code in the notebook cells that you can use to replay your data preparation pipeline.

Using the Built-in Data Preparation Capability
To get started, pip install and import sagemaker_datawrangler along with the pandas Python package. Then, download the dataset you want to analyze to the notebook working directory, and read the dataset with pandas.

import pandas as pd 
import sagemaker_datawrangler 

!aws s3 cp s3:///data.csv . 

df = pd.read_csv("data.csv")

Now, when you display the data frame, it automatically shows key data visualizations at the top of each column, surfaces data insights, detects data quality issues, and suggests solutions to improve data quality. When you select a column as the target column for ML predictions, you get target-specific insights and warnings, such as mixed data types in target (for regression use cases) or too few instances per class (for classification use cases).

In this example, I’m using the Women’s E-Commerce Clothing Reviews dataset that contains customer reviews and ratings for women’s clothing. This dataset was obtained from Kaggle and has been modified by Amazon to add synthetic data quality issues.

You can review the suggested data transformations to improve the data quality and apply them right in the UI. For a list of all supported data transformations, have a look at the documentation. Once you apply a data transformation, SageMaker Studio notebooks automatically generate the code to reproduce those data preparation steps in another notebook cell.

For my example, I select Rating as my target column. Target column insights tells me in a high-priority warning that this column has too few instances per class and with a medium-priority warning that classes are too imbalanced. Let’s follow the suggestions and drop rare target values and drop missing values. I will also follow the suggestions for some of the feature columns and drop missing values in the Review Text column and drop the Division Name column.

Once I apply the transformations, the notebook generates this code for me:

# Pandas code generated by sagemaker_datawrangler
output_df = df.copy(deep=True)


# Code to Drop rare target values for column: Rating to resolve warning: Too few instances per class 
rare_target_labels_to_drop = ['-100', '100']
output_df = output_df[~output_df['Rating'].isin(rare_target_labels_to_drop)]


# Code to Drop missing for column: Rating to resolve warning: Missing values 
output_df = output_df[output_df['Rating'].notnull()]


# Code to Drop missing for column: Review Text to resolve warning: Missing values 
output_df = output_df[output_df['Review Text'].notnull()]


# Code to Drop column for column: Division Name to resolve warning: Missing values 
output_df=output_df.drop(columns=['Division Name'])

I can now review and modify the code if needed or start integrating the data transformations as part of my ML development workflow.

Introducing Shared Spaces for Team-Based Sharing and Real-Time Collaboration
SageMaker Studio now offers shared spaces that give data science and ML teams a workspace where they can read, edit, and run notebooks together in real time to streamline collaboration and communication during the development process. Shared spaces provide a shared Amazon EFS directory that you can utilize to share files within a shared space. All taggable SageMaker resources that you create in a shared space are automatically tagged to help you organize and have a filtered view of your ML resources, such as training jobs, experiments, and models, that are relevant to the business problem you work on in the space. This also helps you monitor costs and plan budgets using tools such as AWS Budgets and AWS Cost Explorer.

And that’s not all. You can now also create multiple SageMaker domains within the same AWS account to scope access and isolate resources to different teams or business units in your organization. Now, let me show you how to create a shared space for users within a SageMaker domain.

Using Shared Spaces
You can use the SageMaker console or the AWS CLI to create shared spaces for a SageMaker domain. To get started in the SageMaker console, go to Domains, select or create a new domain, and select Space management on the Domain details page. Then, select Create and give the shared space a name.

Users in this SageMaker domain can now launch and join the shared space through their SageMaker domain user profiles.

In a shared space, select the new Collaborators icon in the left navigation menu. You can now see who else is currently active in this space. The following screenshot shows user tom on the left, editing a notebook file. On the right, user antje sees the edits in real time, together with an annotation of the user name that currently edits that notebook cell.

New Notebook Capability to Automatically Convert Notebook Code to Production-Ready Jobs
You can now select a notebook and automate it as a job that can run in a production environment without the need to manage the underlying infrastructure. When you create a SageMaker Notebook Job, SageMaker Studio takes a snapshot of the entire notebook, packages its dependencies in a container, builds the infrastructure, runs the notebook as an automated job on a schedule you define, and deprovisions the infrastructure upon job completion. This notebook capability is now also available in SageMaker Studio Lab, our free ML development environment that provides the compute, storage, and security to learn and experiment with ML.

Using the Notebook Capability to Automate Notebooks
To get started, open a notebook file in SageMaker Studio. Then, right-click your notebook file and select Create Notebook Job or select the Create Notebook Job icon, as highlighted in the following screenshot.

Define a name for the Notebook Job, review the input file location, specify the compute type to use, and whether to run the job immediately or on a schedule. Then, select Create.

The Notebook Job has been created, and you can review all Notebook Job Definitions in the UI.

Now Available
The new Amazon SageMaker Studio notebook capabilities are now available in all AWS Regions where Amazon SageMaker Studio is available except for the AWS China Regions.

At launch, the built-in data preparation capability powered by SageMaker Data Wrangler is supported for SageMaker Studio notebooks and the following notebook kernel images:

  • Python 3 (Data Science) with Python 3.7
  • Python 3 (Data Science 2.0) with Python 3.8
  • Python 3 (Data Science 3.0) with Python 3.10
  • Spark Analytics 1.0 and 2.0

For more information, visit Amazon SageMaker Notebooks.

Start building your ML projects with the next generation of Amazon SageMaker Notebooks today!

— Antje

New – Share ML Models and Notebooks More Easily Within Your Organization with Amazon SageMaker JumpStart | Amazon Web Services

Amazon SageMaker JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. SageMaker JumpStart gives you access to built-in algorithms with pre-trained models from popular model hubs, pre-trained foundation models to help you perform tasks such as article summarization and image generation, and end-to-end solutions to solve common use cases.

Today, I’m happy to announce that you can now share ML artifacts, such as models and notebooks, more easily with other users that share your AWS account using SageMaker JumpStart.

Using SageMaker JumpStart to Share ML Artifacts
Machine learning is a team sport. You might want to share your models and notebooks with other data scientists in your team to collaborate and increase productivity. Or, you might want to share your models with operations teams to put your models into production. Let me show you how to share ML artifacts using SageMaker JumpStart.

In SageMaker Studio, select Models in the left navigation menu. Then, select Shared models and Shared by my organization. You can now discover and search ML artifacts that other users shared within your AWS account. Note that you can add and share ML artifacts developed with SageMaker as well as those developed outside of SageMaker.

To share a model or notebook, select Add. For models, provide basic information, such as title, description, data type, ML task, framework, and any additional metadata. This information helps other users to find the right models for their use cases. You can also enable training and deployment for your model. This allows users to fine-tune your shared model and deploy the model in just a few clicks through SageMaker JumpStart.

To enable model training, you can select an existing SageMaker training job that will autopopulate all relevant information. This information includes the container framework, training script location, model artifact location, instance type, default training and validation datasets, and target column. You can also provide custom model training information by selecting a prebuilt SageMaker Deep Learning Container or selecting a custom Docker container in Amazon ECR. You can also specify default hyperparameters and metrics for model training.

To enable model deployment, you also need to define the container image to use, the inference script and model artifact location, and the default instance type. Have a look at the SageMaker Developer Guide to learn more about model training and model deployment options.

Sharing a notebook works similarly. You need to provide basic information about your notebook and the Amazon S3 location of the notebook file.

Users that share your AWS account can now browse and select shared models to fine-tune, deploy endpoints, or run notebooks directly in SageMaker JumpStart.

In SageMaker Studio, select Quick start solutions in the left navigation menu, then select Solutions, models, example notebooks to access all shared ML artifacts, together with pre-trained models from popular model hubs and end-to-end solutions.

Now Available
The new ML artifact-sharing capability within Amazon SageMaker JumpStart is available today in all AWS Regions where Amazon SageMaker JumpStart is available. To learn more, visit Amazon SageMaker JumpStart and the SageMaker JumpStart documentation.

Start sharing your models and notebooks with Amazon SageMaker JumpStart today!

— Antje

AWS Machine Learning University New Educator Enablement Program to Build Diverse Talent for ML/AI Jobs | Amazon Web Services

AWS Machine Learning University is now providing a free educator enablement program. This program provides faculty at community colleges, minority-serving institutions (MSIs), and historically Black colleges and universities (HBCUs) with the skills and resources to teach data analytics, artificial intelligence (AI), and machine learning (ML) concepts to build a diverse pipeline for in-demand jobs of today and tomorrow.

According to the National Science Foundation, Black and Hispanic or Latino students earn bachelor’s degrees in Computer Science—the dominant pathway to AI/ML—at a much lower rate than their white peers, earning less than 11 percent of computer science degrees awarded. However, research shows that having diverse perspectives among skilled practitioners and across the AI/ML lifecycle contributes to the development of AI/ML systems that are safe, trustworthy, and have less bias. 

In 2018, we announced the Machine Learning University (MLU) to share with all developers the same courses that we used to train engineers at Amazon and AWS. This platform offers self-service, self-paced, AI/ML digital courses.

And today, we add this new program to our AI/ML training offering. Although anyone could access the MLU self-paced learning, it places the burden on the learner to source prerequisite work and solutions. This educator enablement program takes the concepts and lessons developed by MLU and makes them more accessible to educators. It offers a year-round educator enablement program with lesson planning, course playbooks, and access to free compute resources.

Program Details
Educators are onboarded in small-group cohorts into bootcamps where they will learn the material and deep dive into how to teach it via instructor-led lectures and hands-on projects. Educators who complete the bootcamp can take part in different year-round development opportunities, such as a dedicated Slack channel to share teaching best practices, education topic series and virtual study sessions moderated by MLU instructors, and regional events for continued professional development. Also, they will receive continuing education credits and AWS-provided stipends.

Faculty and students get access to instructional material through Amazon SageMaker Studio Lab. SageMaker Studio Lab was announced last year and is AWS’s free (no credit card required) ML development environment. It provides computing and storage for anybody that wants to learn and experiment with ML. Institutions can unlock additional resources to support their ML programs by registering for AWS Academy. AWS Academy unlocks all the AWS services for a complete AI/ML program.

Community colleges and universities can integrate this educator enablement program into their computer science, information technology, and business curricula to create an AI/ML course, certificate, or degree. We have worked with educators and education boards such as Houston Community College to create content that is vetted for credit-worthy and degree-earning curricula.

In August 2022, we launched our first educator bootcamp in partnership with The Coding School. The bootcamp was delivered over two weeks, offering lectures, case studies, and hands-on projects. 25 educators completed the Educator Machine Learning Bootcamp, representing 22 US community colleges and universities.

Learn More and Join The Program
During 2023, AWS Machine Learning University will run six educator-enablement cohorts starting in January. The program will give priority consideration to educators at community colleges, MSIs, and HBCUs, in alignment with this program mission to increase access to AI/ML technology to historically underserved and underrepresented students.

If you are a computer science educator or part of a board of educators interested in fostering more depth in your computer science coursework, you should sign up for the educator enablement program.

Marcia

New for Amazon Redshift – Simplify Data Ingestion and Make Your Data Warehouse More Secure and Reliable | Amazon Web Services

When we talk with customers, we hear that they want to be able to harness insights from data in order to make timely, impactful, and actionable business decisions. A common pattern with data-driven organizations is that they have many different data sources they need to ingest into their analytics systems. This requires them to build manual data pipelines spanning across their operational databases, data lakes, streaming data, and data within their warehouse. As a consequence of this complex setup, it can take data engineers weeks or even months to build data ingestion pipelines. These data pipelines are costly, and the delays can lead to missed business opportunities. Additionally, data warehouses are increasingly becoming mission critical systems that require high availability, reliability, and security.

Amazon Redshift is a fully managed petabyte-scale data warehouse used by tens of thousands of customers to easily, quickly, securely, and cost-effectively analyze all their data at any scale. This year at re:Invent, Amazon Redshift has announced a number of features to help you simplify data ingestion and get to insights easily and quickly, within a secure, reliable environment.

In this blog, I introduce some of these new features that fit into two main categories:

  • Simplify data ingestion
    • Amazon Redshift now supports auto-copy from Amazon S3 (available in preview). With this new capability, Amazon Redshift automatically loads the files that arrive in an Amazon Simple Storage Service (Amazon S3) location that you specify into your data warehouse. The files can use any of the formats supported by the Amazon Redshift copy command, such as CSV, JSON, Parquet, and Avro. In this way, you don’t need to manually or repeatedly run copy procedures. Amazon Redshift automates file ingestion and takes care of data-loading steps under the hood.
    • With Amazon Aurora zero-ETL integration with Amazon Redshift, you can use Amazon Redshift for near real-time analytics and machine learning on petabytes of transactional data stored on Amazon Aurora MySQL databases (available in limited preview). With this capability, you can choose the Amazon Aurora databases containing the data you want to analyze with Amazon Redshift. Data is then replicated into your data warehouse within seconds after transactional data is written into Amazon Aurora, eliminating the need to build and maintain complex data pipelines. You can replicate data from multiple Amazon Aurora databases into the same Amazon Redshift instance to run analytics across multiple applications. With near real-time access to transactional data, you can leverage Amazon Redshift’s analytics and capabilities, such as built-in machine learning (ML), materialized views, data sharing, and federated access to multiple data stores and data lakes, to derive insights from transactional and other data.
    • With the general availability of Amazon Redshift Streaming Ingestion, you can now natively ingest hundreds of megabytes of data per second from Amazon Kinesis Data Streams and Amazon MSK into an Amazon Redshift materialized view and query it in seconds. Learn more in this post.
  • Make your data warehouse more secure and reliable
    • You can now improve the availability of your data warehouse by choosing multiple Availability Zone (AZ) deployments. Multi-AZ deployments for your Amazon Redshift clusters are available in preview and reduce recovery times to seconds through automatic recovery. In this way, you can build solutions that are more compliant with the recommendations of the Reliability Pillar of the AWS Well-Architected Framework.
    • With dynamic data masking (available in preview), you can protect sensitive information stored in your data warehouse and ensure that only the relevant data is accessible by users based on their roles. You can limit how much identifiable data is visible to users using multiple levels of policies so different users and groups can have different levels of data access without having to create multiple copies of data. Dynamic data masking complements other granular access control capabilities in Amazon Redshift including row-level and column-level security and role-based access controls. In this way, Dynamic Data Masking helps you meet requirements for GDPR, CCPA, and other privacy regulations.
    • Amazon Redshift now supports central access controls for data sharing with AWS Lake Formation (available in public preview). You can now use Lake Formation to simplify governance of data shared from Amazon Redshift and centrally manage granular access across all data-sharing consumers.

There have been other interesting news for Amazon Redshift at re:Invent you might have already heard about:

  • The general availability of Amazon Redshift integration for Apache Spark makes it easy to build and run Spark applications on Amazon Redshift and Redshift Serverless, opening up the data warehouse for a broader set of AWS analytics and machine learning solutions.
  • AWS Backup now supports Amazon Redshift. AWS Backup allows you to define a central backup policy to manage data protection of your applications and can also protect your Amazon Redshift clusters. In this way, you have a consistent experience when managing data protection across all supported services.

Availability and Pricing
Multi-AZ deployments, central access control for data sharing with AWS Lake Formation, auto-copy from Amazon S3, and dynamic data masking are available in preview in US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), Europe (Ireland), and Europe (Stockholm).

There is no additional cost for using auto-copy from Amazon S3 and near real-time analytics on transactional data. There is no extra charge for dynamic data masking and central access control for data sharing. For more information, see Amazon Redshift pricing.

These new capabilities take you one step further in analyzing all your data across data sources with simple data ingestion capabilities, while improving the security and reliability of your data warehouse.

Danilo

New — Introducing Support for Real-Time and Batch Inference in Amazon SageMaker Data Wrangler | Amazon Web Services

To build machine learning models, machine learning engineers need to develop a data transformation pipeline to prepare the data. The process of designing this pipeline is time-consuming and requires a cross-team collaboration between machine learning engineers, data engineers, and data scientists to implement the data preparation pipeline into a production environment.

The main objective of Amazon SageMaker Data Wrangler is to make it easy to do data preparation and data processing workloads. With SageMaker Data Wrangler, customers can simplify the process of data preparation and all of the necessary steps of data preparation workflow on a single visual interface. SageMaker Data Wrangler reduces the time to rapidly prototype and deploy data processing workloads to production, so customers can easily integrate with MLOps production environments.

However, the transformations applied to the customer data for model training need to be applied to new data during real-time inference. Without support for SageMaker Data Wrangler in a real-time inference endpoint, customers need to write code to replicate the transformations from their flow in a preprocessing script.

Introducing Support for Real-Time and Batch Inference in Amazon SageMaker Data Wrangler
I’m pleased to share that you can now deploy data preparation flows from SageMaker Data Wrangler for real-time and batch inference. This feature allows you to reuse the data transformation flow which you created in SageMaker Data Wrangler as a step in Amazon SageMaker inference pipelines.

SageMaker Data Wrangler support for real-time and batch inference speeds up your production deployment because there is no need to repeat the implementation of the data transformation flow. You can now integrate SageMaker Data Wrangler with SageMaker inference. The same data transformation flows created with the easy-to-use, point-and-click interface of SageMaker Data Wrangler, containing operations such as Principal Component Analysis and one-hot encoding, will be used to process your data during inference. This means that you don’t have to rebuild the data pipeline for a real-time and batch inference application, and you can get to production faster.

Get Started with Real-Time and Batch Inference
Let’s see how to use the deployment supports of SageMaker Data Wrangler. In this scenario, I have a flow inside SageMaker Data Wrangler. What I need to do is to integrate this flow into real-time and batch inference using the SageMaker inference pipeline.

First, I will apply some transformations to the dataset to prepare it for training.

I add one-hot encoding on the categorical columns to create new features.

Then, I drop any remaining string columns that cannot be used during training.

My resulting flow now has these two transform steps in it.

After I’m satisfied with the steps I have added, I can expand the Export to menu, and I have the option to export to SageMaker Inference Pipeline (via Jupyter Notebook).

I select Export to SageMaker Inference Pipeline, and SageMaker Data Wrangler will prepare a fully customized Jupyter notebook to integrate the SageMaker Data Wrangler flow with inference. This generated Jupyter notebook performs a few important actions. First, define data processing and model training steps in a SageMaker pipeline. The next step is to run the pipeline to process my data with Data Wrangler and use the processed data to train a model that will be used to generate real-time predictions. Then, deploy my Data Wrangler flow and trained model to a real-time endpoint as an inference pipeline. Last, invoke my endpoint to make a prediction.

This feature uses Amazon SageMaker Autopilot, which makes it easy for me to build ML models. I just need to provide the transformed dataset which is the output of the SageMaker Data Wrangler step and select the target column to predict. The rest will be handled by Amazon SageMaker Autopilot to explore various solutions to find the best model.

Using AutoML as a training step from SageMaker Autopilot is enabled by default in the notebook with the use_automl_step variable. When using the AutoML step, I need to define the value of target_attribute_name, which is the column of my data I want to predict during inference. Alternatively, I can set use_automl_step to False if I want to use the XGBoost algorithm to train a model instead.

On the other hand, if I would like to instead use a model I trained outside of this notebook, then I can skip directly to the Create SageMaker Inference Pipeline section of the notebook. Here, I would need to set the value of the byo_model variable to True. I also need to provide the value of algo_model_uri, which is the Amazon Simple Storage Service (Amazon S3) URI where my model is located. When training a model with the notebook, these values will be auto-populated.

In addition, this feature also saves a tarball inside the data_wrangler_inference_flows folder on my SageMaker Studio instance. This file is a modified version of the SageMaker Data Wrangler flow, containing the data transformation steps to be applied at the time of inference. It will be uploaded to S3 from the notebook so that it can be used to create a SageMaker Data Wrangler preprocessing step in the inference pipeline.

The next step is that this notebook will create two SageMaker model objects. The first object model is the SageMaker Data Wrangler model object with the variable data_wrangler_model, and the second is the model object for the algorithm, with the variable algo_model. Object data_wrangler_model will be used to provide input in the form of data that has been processed into algo_model for prediction.

The final step inside this notebook is to create a SageMaker inference pipeline model, and deploy it to an endpoint.

Once the deployment is complete, I will get an inference endpoint that I can use for prediction. With this feature, the inference pipeline uses the SageMaker Data Wrangler flow to transform the data from your inference request into a format that the trained model can use.

In the next section, I can run individual notebook cells in Make a Sample Inference Request. This is helpful if I need to do a quick check to see if the endpoint is working by invoking the endpoint with a single data point from my unprocessed data. Data Wrangler automatically places this data point into the notebook, so I don’t have to provide one manually.

Things to Know
Enhanced Apache Spark configuration — In this release of SageMaker Data Wrangler, you can now easily configure how Apache Spark partitions the output of your SageMaker Data Wrangler jobs when saving data to Amazon S3. When adding a destination node, you can set the number of partitions, corresponding to the number of files that will be written to Amazon S3, and you can specify column names to partition by, to write records with different values of those columns to different subdirectories in Amazon S3. Moreover, you can also define the configuration in the provided notebook.

You can also define memory configurations for SageMaker Data Wrangler processing jobs as part of the Create job workflow. You will find similar configuration as part of your notebook.

Availability — SageMaker Data Wrangler supports for real-time and batch inference as well as enhanced Apache Spark configuration for data processing workloads are generally available in all AWS Regions that Data Wrangler currently supports.

To get started with Amazon SageMaker Data Wrangler supports for real-time and batch inference deployment, visit AWS documentation.

Happy building
— Donnie

New — Amazon SageMaker Data Wrangler Supports SaaS Applications as Data Sources | Amazon Web Services

Data fuels machine learning. In machine learning, data preparation is the process of transforming raw data into a format that is suitable for further processing and analysis. The common process for data preparation starts with collecting data, then cleaning it, labeling it, and finally validating and visualizing it. Getting the data right with high quality can often be a complex and time-consuming process.

This is why customers who build machine learning (ML) workloads on AWS appreciate the ability of Amazon SageMaker Data Wrangler. With SageMaker Data Wrangler, customers can simplify the process of data preparation and complete the required processes of the data preparation workflow on a single visual interface. Amazon SageMaker Data Wrangler helps to reduce the time it takes to aggregate and prepare data for ML.

However, due to the proliferation of data, customers generally have data spread out into multiple systems, including external software-as-a-service (SaaS) applications like SAP OData for manufacturing data, Salesforce for customer pipeline, and Google Analytics for web application data. To solve business problems using ML, customers have to bring all of these data sources together. They currently have to build their own solution or use third-party solutions to ingest data into Amazon S3 or Amazon Redshift. These solutions can be complex to set up and not cost-effective.

Introducing Amazon SageMaker Data Wrangler Supports SaaS Applications as Data Sources
I’m happy to share that starting today, you can aggregate external SaaS application data for ML in Amazon SageMaker Data Wrangler to prepare data for ML. With this feature, you can use more than 40 SaaS applications as data sources via Amazon AppFlow and have these data available on Amazon SageMaker Data Wrangler. Once the data sources are registered in AWS Glue Data Catalog by AppFlow, you can browse tables and schemas from these data sources using Data Wrangler SQL explorer. This feature provides seamless data integration between SaaS applications and SageMaker Data Wrangler using Amazon AppFlow.

Here is a quick preview of this new feature:

This new feature of Amazon SageMaker Data Wrangler works by using integration with Amazon AppFlow, a fully managed integration service that enables you to securely exchange data between SaaS applications and AWS services. With Amazon AppFlow, you can establish bidirectional data integration between SaaS applications, such as Salesforce, SAP, and Amplitude and all supported services, into your Amazon S3 or Amazon Redshift.

Then, with Amazon AppFlow, you can catalog the data in AWS Glue Data Catalog. This is a new feature where with Amazon AppFlow, you can create an integration with AWS Glue Data Catalog for Amazon S3 destination connector. With this new integration, customers can catalog SaaS data applications into AWS Glue Data Catalog with a few clicks, directly from the Amazon AppFlow Flow configuration, without the need to run any crawlers.

Once you’ve established a flow and inserted it into the AWS Glue Data Catalog, you can use this data inside the Amazon SageMaker Data Wrangler. Then, you can do the data preparation as you usually do. You can write Amazon Athena queries to preview data, join data from multiple sources, or import data to prepare for ML model training.

With this feature, you need to do a few simple steps to perform seamless data integration between SaaS applications into Amazon SageMaker Data Wrangler via Amazon AppFlow. This integration supports more than 40 SaaS applications, and for a complete list of supported applications, please check the Supported source and destination applications documentation.

Get Started with Amazon SageMaker Data Wrangler Support for Amazon AppFlow
Let’s see how this feature works in detail. In my scenario, I need to get data from Salesforce, and do the data preparation using Amazon SageMaker Data Wrangler.

To start using this feature, the first thing I need to do is to create a flow in Amazon AppFlow that registers the data source into the AWS Glue Data Catalog. I already have an existing connection with my Salesforce account, and all I need now is to create a flow.

One important thing to note is that to make SaaS application data available in Amazon SageMaker Data Wrangler, I need to create a flow with Amazon S3 as the destination. Then, I need to enable Create a Data Catalog table in the AWS Glue Data Catalog settings. This option will automatically catalog my Salesforce data into AWS Glue Data Catalog.

On this page, I need to select a user role with the required AWS Glue Data Catalog permissions and define the database name and the table name prefix. In addition, in this section, I can define the data format preference, be it in JSON, CSV, or Apache Parquet formats, and filename preference if I want to add a timestamp into the file name section.

To learn more about how to register SaaS data in Amazon AppFlow and AWS Glue Data Catalog, you can read Cataloging the data output from an Amazon AppFlow flow documentation page.

Once I’ve finished registering SaaS data, I need to make sure the IAM role can view the data sources in Data Wrangler from AppFlow. Here is an example of a policy in the IAM role:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": "glue:SearchTables",
            "Resource": [
                "arn:aws:glue:*:*:table/*/*",
                "arn:aws:glue:*:*:database/*",
                "arn:aws:glue:*:*:catalog"
            ]
        }
    ]
} 

By enabling data cataloging with AWS Glue Data Catalog, from this point on, Amazon SageMaker Data Wrangler will be able to automatically discover this new data source and I can browse tables and schema using the Data Wrangler SQL Explorer.

Now it’s time to switch to the Amazon SageMaker Data Wrangler dashboard then select Connect to data sources.

On the following page, I need to Create connection and select the data source I want to import. In this section, I can see all the available connections for me to use. Here I see the Salesforce connection is already available for me to use.

If I would like to add additional data sources, I can see a list of external SaaS applications that I can integrate into the Set up new data sources section. To learn how to recognize external SaaS applications as data sources, I can learn more with the select How to enable access.

Now I will import datasets and select the Salesforce connection.

On the next page, I can define connection settings and import data from Salesforce. When I’m done with this configuration, I select Connect.

On the following page, I see my Salesforce data that I already configured with Amazon AppFlow and AWS Glue Data Catalog called appflowdatasourcedb. I can also see a table preview and schema for me to review if this is the data I need.

Then, I start building my dataset using this data by performing SQL queries inside the SageMaker Data Wrangler SQL Explorer. Then, I select Import query.

Then, I define a name for my dataset.

At this point, I can start doing the data preparation process. I can navigate to the Analysis tab to run the data insight report. The analysis will provide me with a report on the data quality issues and what transform I need to use next to fix the issues based on the ML problem I want to predict. To learn more about how to use the data analysis feature, see Accelerate data preparation with data quality and insights in the Amazon SageMaker Data Wrangler blog post.

In my case, there are several columns I don’t need, and I need to drop these columns. I select Add step.

One feature I like is that Amazon SageMaker Data Wrangler provides numerous ML data transforms. It helps me to streamline the process of cleaning, transforming and feature engineering my data in one dashboard. For more about what SageMaker Data Wrangler provides for transformation data, please read this Transform Data documentation page.

In this list, I select Manage columns.

Then, in the Transform section, I select the Drop column option. Then, I select a few columns that I don’t need.

Once I’m done, the columns I don’t need are removed and the Drop column data preparation step I just created is listed in the Add step section.

I can also see the visual of my data flow inside the Amazon SageMaker Data Wrangler. In this example, my data flow is quite basic. But when my data preparation process becomes complex, this visual view makes it easy for me to see all the data preparation steps.

From this point on, I can do what I require with my Salesforce data. For example, I can export data directly to Amazon S3 by selecting Export to and choosing Amazon S3 from the Add destination menu. In my case, I specify Data Wrangler to store the data in Amazon S3 after it has processed it by selecting Add destination and then Amazon S3.

Amazon SageMaker Data Wrangler provides me flexibility to automate the same data preparation flow using scheduled jobs. I can also automate feature engineering with SageMaker Pipelines (via Jupyter Notebook) and SageMaker Feature Store (via Jupyter Notebook), and deploy to Inference end point with SageMaker Inference Pipeline (via Jupyter Notebook).

Things to Know
Related news – This feature will make it easy for you to do data aggregation and preparation with Amazon SageMaker Data Wrangler. As this feature is an integration with Amazon AppFlow and also AWS Glue Data Catalog, you might want to learn more on Amazon AppFlow now supports AWS Glue Data Catalog integration and provides enhanced data preparation page.

Availability – Amazon SageMaker Data Wrangler supports SaaS applications as data sources available in all the Regions currently supported by Amazon AppFlow.

Pricing – There is no additional cost to use SaaS applications supports in Amazon SageMaker Data Wrangler, but there is a cost to running Amazon AppFlow to get the data in Amazon SageMaker Data Wrangler.

Visit Import Data From Software as a Service (SaaS) Platforms documentation page to learn more about this feature, and follow the getting started guide to start data aggregating and preparing SaaS applications data with Amazon SageMaker Data Wrangler.

Happy building!
Donnie

Announcing Additional Data Connectors for Amazon AppFlow | Amazon Web Services

Gathering insights from data is a more effective process if that data isn’t fragmented across multiple systems and data stores, whether on premises or in the cloud. Amazon AppFlow provides bidirectional data integration between on-premises systems and applications, SaaS applications, and AWS services. It helps customers break down data silos using a low- or no-code, cost-effective solution that’s easy to reconfigure in minutes as business needs change.

Today, we’re pleased to announce the addition of 22 new data connectors for Amazon AppFlow, including:

  • Marketing connectors (e.g., Facebook Ads, Google Ads, Instagram Ads, LinkedIn Ads).
  • Connectors for customer service and engagement (e.g., MailChimp, Sendgrid, Zendesk Sell or Chat, and more).
  • Business operations (Stripe, QuickBooks Online, and GitHub).

In total, Amazon AppFlow now supports over 50 integrations with various different SaaS applications and AWS services. This growing set of connectors can be combined to enable you to achieve 360 visibility across the data your organization generates. For instance, you could combine CRM (Salesforce), e-commerce (Stripe), and customer service (ServiceNow, Zendesk) data to build integrated analytics and predictive modeling that can guide your next best offer decisions and more. Using web (Google Analytics v4) and social surfaces (Facebook Ads, Instagram Ads) allows you to build comprehensive reporting for your marketing investments, helping you understand how customers are engaging with your brand. Or, sync ERP data (SAP S/4HANA) with custom order management applications running on AWS. For more information on the current range of connectors and integrations, visit the Amazon AppFlow integrations page.

Amazon AppFlow and AWS Glue Data Catalog
Amazon AppFlow has also recently announced integration with the AWS Glue Data Catalog to automate the preparation and registration of your SaaS data into the AWS Glue Data Catalog. Previously, customers using Amazon AppFlow to store data from supported SaaS applications into Amazon Simple Storage Service (Amazon S3) had to manually create and run AWS Glue Crawlers to make their data available to other AWS services such as Amazon Athena, Amazon SageMaker, or Amazon QuickSight. With this new integration, you can populate AWS Glue Data Catalog with a few clicks directly from the Amazon AppFlow configuration without the need to run any crawlers.

To simplify data preparation and improve query performance when using analytics engines such as Amazon Athena, Amazon AppFlow also now enables you to organize your data into partitioned folders in Amazon S3. Amazon AppFlow also automates the aggregation of records into files that are optimized to the size you specify. This increases performance by reducing processing overhead and improving parallelism.

You can find more information on the AWS Glue Data Catalog integration in the recent What’s New post.

Getting Started with Amazon AppFlow
Visit the Amazon AppFlow product page to learn more about the service and view all the available integrations. To help you get started, there’s also a variety of videos and demos available and some sample integrations available on GitHub. And finally, should you need a custom integration, try the Amazon AppFlow Connector SDK, detailed in the Amazon AppFlow documentation. The SDK enables you to build your own connectors to securely transfer data between your custom endpoint, application, or other cloud service to and from Amazon AppFlow‘s library of managed SaaS and AWS connectors.

— Steve

New ML Governance Tools for Amazon SageMaker – Simplify Access Control and Enhance Transparency Over Your ML Projects | Amazon Web Services

As companies increasingly adopt machine learning (ML) for their business applications, they are looking for ways to improve governance of their ML projects with simplified access control and enhanced visibility across the ML lifecycle. A common challenge in that effort is managing the right set of user permissions across different groups and ML activities. For example, a data scientist in your team that builds and trains models usually requires different permissions than an MLOps engineer that manages ML pipelines. Another challenge is improving visibility over ML projects. For example, model information, such as intended use, out-of-scope use cases, risk rating, and evaluation results, is often captured and shared via emails or documents. In addition, there is often no simple mechanism to monitor and report on your deployed model behavior.

That’s why I’m excited to announce a new set of ML governance tools for Amazon SageMaker.

As an ML system or platform administrator, you can now use Amazon SageMaker Role Manager to define custom permissions for SageMaker users in minutes, so you can onboard users faster. As an ML practitioner, business owner, or model risk and compliance officer, you can now use Amazon SageMaker Model Cards to document model information from conception to deployment and Amazon SageMaker Model Dashboard to monitor all your deployed models through a unified dashboard.

Let’s dive deeper into each tool, and I’ll show you how to get started.

Introducing Amazon SageMaker Role Manager
SageMaker Role Manager lets you define custom permissions for SageMaker users in minutes. It comes with a set of predefined policy templates for different personas and ML activities. Personas represent the different types of users that need permissions to perform ML activities in SageMaker, such as data scientists or MLOps engineers. ML activities are a set of permissions to accomplish a common ML task, such as running SageMaker Studio applications or managing experiments, models, or pipelines. You can also define additional personas, add ML activities, and your managed policies to match your specific needs. Once you have selected the persona type and the set of ML activities, SageMaker Role Manager automatically creates the required AWS Identity and Access Management (IAM) role and policies that you can assign to SageMaker users.

A Primer on SageMaker and IAM Roles
A role is an IAM identity that has permissions to perform actions with AWS services. Besides user roles that are assumed by a user via federation from an Identity Provider (IdP) or the AWS Console, Amazon SageMaker requires service roles (also known as execution roles) to perform actions on behalf of the user. SageMaker Role Manager helps you create these service roles:

  • SageMaker Compute Role – Gives SageMaker compute resources the ability to perform tasks such as training and inference, typically used via PassRole. You can select the SageMaker Compute Role persona in SageMaker Role Manager to create this role. Depending on the ML activities you select in your SageMaker service roles, you will need to create this compute role first.
  • SageMaker Service Role – Some AWS services, including SageMaker, require a service role to perform actions on your behalf. You can select the Data Scientist, MLOps, or Custom persona in SageMaker Role Manager to start creating service roles with custom permissions for your ML practitioners.

Now, let me show you how this works in practice.

There are two ways to get to SageMaker Role Manager, either through Getting started in the SageMaker console or when you select Add user in the SageMaker Studio Domain control panel.

I start in the SageMaker console. Under Configure role, select Create a role. This opens a workflow that guides you through all required steps.

Let’s assume I want to create a SageMaker service role with a specific set of permissions for my team of data scientists. In Step 1, I select the predefined policy template for the Data Scientist persona.

I can also define the network and encryption settings in this step by selecting Amazon Virtual Private Cloud (Amazon VPC) subnets, security groups, and encryption keys.

In Step 2, I select what ML activities data scientists in my team need to perform.

Some of the selected ML activities might require you to specify the Amazon Resource Name (ARN) of the SageMaker Compute Role so SageMaker compute resources have the ability to perform the tasks.

In Step 3, you can attach additional IAM policies and add tags to the role if needed. Tags help you identify and organize your AWS resources. You can use tags to add attributes such as project name, cost center, or location information to a role. After a final review of the settings in Step 4, select Submit, and the role is created.

In just a few minutes, I set up a SageMaker service role, and I’m now ready to onboard data scientists in SageMaker with custom permissions in place.

Introducing Amazon SageMaker Model Cards
SageMaker Model Cards helps you streamline model documentation throughout the ML lifecycle by creating a single source of truth for model information. For models trained on SageMaker, SageMaker Model Cards discovers and autopopulates details such as training jobs, training datasets, model artifacts, and inference environment. You can also record model details such as the model’s intended use, risk rating, and evaluation results. For compliance documentation and model evidence reporting, you can export your model cards to a PDF file and easily share them with your customers or regulators.

To start creating SageMaker Model Cards, go to the SageMaker console, select Governance in the left navigation menu, and select Model cards.

Select Create model card to document your model information.

Introducing Amazon SageMaker Model Dashboard
SageMaker Model Dashboard lets you monitor all your models in one place. With this bird’s-eye view, you can now see which models are used in production, view model cards, visualize model lineage, track resources, and monitor model behavior through an integration with SageMaker Model Monitor and SageMaker Clarify. The dashboard automatically alerts you when models are not being monitored or deviate from expected behavior. You can also drill deeper into individual models to troubleshoot issues.

To access SageMaker Model Dashboard, go to the SageMaker console, select Governance in the left navigation menu, and select Model dashboard.

Note: The risk rating shown above is for illustrative purposes only and may vary based on input provided by you.

Now Available
Amazon SageMaker Role Manager, SageMaker Model Cards, and SageMaker Model Dashboard are available today at no additional charge in all the AWS Regions where Amazon SageMaker is available except for the AWS GovCloud and AWS China Regions.

To learn more, visit ML governance with Amazon SageMaker and check the developer guide.

Start building your ML projects with our new governance tools for Amazon SageMaker today

— Antje

Join the Preview – AWS Glue Data Quality | Amazon Web Services

Back in 1980, at my second professional programming job, I was working on a project that analyzed driver’s license data from a bunch of US states. At that time data of that type was generally stored in fixed-length records, with values carefully (or not) encoded into each field. Although we were given schemas for the data, we would invariably find that the developers had to resort to tricks in order to represent values that were not anticipated up front. For example, coding for someone with heterochromia, eyes of different colors. We ended up doing a full scan of the data ahead of our actual time-consuming and expensive analytics run in order to make sure that we were dealing with known data. This was my introduction to data quality, or the lack thereof.

AWS makes it easier for you to build data lakes and data warehouses at any scale. We want to make it easier than ever before for you to measure and maintain the desired quality level of the data that you ingest, process, and share.

Introducing AWS Glue Data Quality
Today I would like to tell you about AWS Glue Data Quality, a new set of features for AWS Glue that we are launching in preview form. It can analyze your tables and recommend a set of rules automatically based on what it finds. You can fine-tune those rules if necessary and you can also write your own rules. In this blog post I will show you a few highlights, and will save the details for a full post when these features progress from preview to generally available.

Each data quality rule references a Glue table or selected columns in a Glue table and checks for specific types of properties: timeliness, accuracy, integrity, and so forth. For example, a rule can indicate that a table must have the expected number of columns, that the column names match a desired pattern, and that a specific column is usable as a primary key.

Getting Started
I can open the new Data quality tab on one of my Glue tables to get started. From there I can create a ruleset manually, or I can click Recommend ruleset to get started:

Then I enter a name for my Ruleset (RS1), choose an IAM Role that has permission to access it, and click Recommend ruleset:

My click initiates a Glue Recommendation task (a specialized type of Glue job) that scans the data and makes recommendations. Once the task has run to completion I can examine the recommendations:

I click Evaluate ruleset to check on the quality of my data.

The data quality task runs and I can examine the results:

In addition to creating Rulesets that are attached to tables, I can use them as part of a Glue job. I create my job as usual and then add an Evaluate Data Quality node:

Then I use the Data Quality Definition Language (DDQL) builder to create my rules. I can choose between 20 different rule types:

For this blog post, I made these rules more strict than necessary so that I could show you what happens when the data quality evaluation fails.

I can set the job options, and choose the original data or the data quality results as the output of the transform. I can also write the data quality results to an S3 bucket:

After I have created my Ruleset, I set any other desired options for the job, save it, and then run it. After the job completes I can find the results in the Data quality tab. Because I made some overly strict rules, the evaluation correctly flagged my data with a 0% score:

There’s a lot more, but I will save that for the next blog post!

Things to Know
Preview Regions – This is an open preview and you can access it today the US East (Ohio, N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and Europe (Ireland) AWS Regions.

Pricing – Evaluating data quality consumes Glue Data Processing Units (DPU) in the same manner and at the same per-DPU pricing as any other Glue job.

Jeff;

New – Trusted Language Extensions for PostgreSQL on Amazon Aurora and Amazon RDS | Amazon Web Services

PostgreSQL has become the preferred open-source relational database for many enterprises and start-ups with its extensible design for developers. One of the reasons developers use PostgreSQL is it allows them to add database functionality by building extensions with their preferred programming languages.

You can already install and use PostgreSQL extensions in Amazon Aurora PostgreSQL-Compatible Edition and Amazon Relational Database Service for PostgreSQL. We support more than 85 PostgreSQL extensions in Amazon Aurora and Amazon RDS, such as the pgAudit extension for logging your database activity. While many workloads use these extensions, we heard our customers asking for flexibility to build and run the extensions of their choosing for their PostgreSQL database instances.

Today, we are announcing the general availability of Trusted Language Extensions for PostgreSQL (pg_tle), a new open-source development kit for building PostgreSQL extensions. With Trusted Language Extensions for PostgreSQL, developers can build high-performance extensions that run safely on PostgreSQL.

Trusted Language Extensions for PostgreSQL provides database administrators control over who can install extensions and a permissions model for running them, letting application developers deliver new functionality as soon as they determine an extension meets their needs.

To start building with Trusted Language Extensions, you can use trusted languages such as JavaScript, Perl, and PL/pgSQL. These trusted languages have safety attributes, including restricting direct access to the file system and preventing unwanted privilege escalations. You can easily install extensions written in a trusted language on Amazon Aurora PostgreSQL-Compatible Edition 14.5 and Amazon RDS for PostgreSQL 14.5 or a newer version.

Trusted Language Extensions for PostgreSQL is an open-source project licensed under Apache License 2.0 on GitHub. You can comment or suggest items on the Trusted Language Extensions for PostgreSQL roadmap and help us support this project across multiple programming languages, and more. Doing this as a community will help us make it easier for developers to use the best parts of PostgreSQL to build extensions.

Let’s explore how we can use Trusted Language Extensions for PostgreSQL to build a new PostgreSQL extension for Amazon Aurora and Amazon RDS.

Setting up Trusted Language Extensions for PostgreSQL
To use pg_tle with Amazon Aurora or Amazon RDS for PostgreSQL, you need to set up a parameter group that loads pg_tle in the PostgreSQL shared_preload_libraries setting. Choose Parameter groups in the left navigation pane in the Amazon RDS console and Create parameter group to make a new parameter group.

Choose Create after you select postgres14 with Amazon RDS for PostgreSQL in the Parameter group family and pg_tle in the Group Name. You can select aurora-postgresql14 for an Amazon Aurora PostgreSQL-Compatible cluster.

Choose a created pgtle parameter group and Edit in the Parameter group actions dropbox menu. You can search shared_preload_library in the search box and choose Edit parameter. You can add your preferred values, including pg_tle, and choose Save changes.

You can also do the same job in the AWS Command Line Interface (AWS CLI).

$ aws rds create-db-parameter-group 
  --region us-east-1 
  --db-parameter-group-name pgtle 
  --db-parameter-group-family aurora-postgresql14 
  --description "pgtle group"

$ aws rds modify-db-parameter-group 
  --region us-east-1 
  --db-parameter-group-name pgtle 
  --parameters "ParameterName=shared_preload_libraries,ParameterValue=pg_tle,ApplyMethod=pending-reboot"

Now, you can add the pgtle parameter group to your Amazon Aurora or Amazon RDS for PostgreSQL database. If you have a database instance called testing-pgtle, you can add the pgtle parameter group to the database instance using the command below. Please note that this will cause an active instance to reboot.

$ aws rds modify-db-instance 
  --region us-east-1 
  --db-instance-identifier testing-pgtle 
  --db-parameter-group-name pgtle-pg 
  --apply-immediately

Verify that the pg_tle library is available on your Amazon Aurora or Amazon RDS for PostgreSQL instance. Run the following command on your PostgreSQL instance:

SHOW shared_preload_libraries;

pg_tle should appear in the output.

Now, we need to create the pg_tle extension in your current database to run the command:

 CREATE EXTENSION pg_tle;

You can now create and install Trusted Language Extensions for PostgreSQL in your current database. If you create a new extension, you should grant the pgtle_admin role to your primary user (e.g., postgres) with the following command:

GRANT pgtle_admin TO postgres;

Let’s now see how to create our first pg_tle extension!

Building a Trusted Language Extension for PostgreSQL
For this example, we are going to build a pg_tle extension to validate that a user is not setting a password that’s found in a common password dictionary. Many teams have rules around the complexity of passwords, particularly for database users. PostgreSQL allows developers to help enforce password complexity using the check_password_hook.

In this example, you will build a password check hook using PL/pgSQL. In the hook, you can check to see if the user-supplied password is in a dictionary of 10 of the most common password values:

SELECT pgtle.install_extension (
  'my_password_check_rules',
  '1.0',
  'Do not let users use the 10 most commonly used passwords',
$_pgtle_$
  CREATE SCHEMA password_check;
  REVOKE ALL ON SCHEMA password_check FROM PUBLIC;
  GRANT USAGE ON SCHEMA password_check TO PUBLIC;

  CREATE TABLE password_check.bad_passwords (plaintext) AS
  VALUES
    ('123456'),
    ('password'),
    ('12345678'),
    ('qwerty'),
    ('123456789'),
    ('12345'),
    ('1234'),
    ('111111'),
    ('1234567'),
    ('dragon');
  CREATE UNIQUE INDEX ON password_check.bad_passwords (plaintext);

  CREATE FUNCTION password_check.passcheck_hook(username text, password text, password_type pgtle.password_types, valid_until timestamptz, valid_null boolean)
  RETURNS void AS $$
    DECLARE
      invalid bool := false;
    BEGIN
      IF password_type = 'PASSWORD_TYPE_MD5' THEN
        SELECT EXISTS(
          SELECT 1
          FROM password_check.bad_passwords bp
          WHERE ('md5' || md5(bp.plaintext || username)) = password
        ) INTO invalid;
        IF invalid THEN
          RAISE EXCEPTION 'password must not be found on a common password dictionary';
        END IF;
      ELSIF password_type = 'PASSWORD_TYPE_PLAINTEXT' THEN
        SELECT EXISTS(
          SELECT 1
          FROM password_check.bad_passwords bp
          WHERE bp.plaintext = password
        ) INTO invalid;
        IF invalid THEN
          RAISE EXCEPTION 'password must not be found on a common password dictionary';
        END IF;
      END IF;
    END
  $$ LANGUAGE plpgsql SECURITY DEFINER;

  GRANT EXECUTE ON FUNCTION password_check.passcheck_hook TO PUBLIC;

  SELECT pgtle.register_feature('password_check.passcheck_hook', 'passcheck');
$_pgtle_$
);

You need to enable the hook through the pgtle.enable_password_check configuration parameter. On Amazon Aurora and Amazon RDS for PostgreSQL, you can do so with the following command:

$ aws rds modify-db-parameter-group 
    --region us-east-1 
    --db-parameter-group-name pgtle 
    --parameters "ParameterName=pgtle.enable_password_check,ParameterValue=on,ApplyMethod=immediate"

It may take several minutes for these changes to propagate. You can check that the value is set using the SHOW command:

SHOW pgtle.enable_password_check;

If the value is on, you will see the following output:

 pgtle.enable_password_check
-----------------------------
 on

Now you can create this extension in your current database and try setting your password to one of the dictionary passwords and observe how the hook rejects it:

CREATE EXTENSION my_password_check_rules;

CREATE ROLE test_role PASSWORD '123456';
ERROR:  password must not be found on a common password dictionary

CREATE ROLE test_role;
SET SESSION AUTHORIZATION test_role;
SET password_encryption TO 'md5';
password
-- set to "password"
ERROR:  password must not be found on a common password dictionary

To disable the hook, set the value of pgtle.enable_password_check to off:

$ aws rds modify-db-parameter-group 
    --region us-east-1 
    --db-parameter-group-name pgtle 
    --parameters "ParameterName=pgtle.enable_password_check,ParameterValue=off,ApplyMethod=immediate"

You can uninstall this pg_tle extension from your database and prevent anyone else from running CREATE EXTENSION on my_password_check_rules with the following command:

DROP EXTENSION my_password_check_rules;
SELECT pgtle.uninstall_extension('my_password_check_rules');

You can find more sample extensions and give them a try. To build and test your Trusted Language Extensions in your local PostgreSQL database, you can build from our source code after cloning the repository.

Join Our Community!
The Trusted Language Extensions for PostgreSQL community is open to everyone. Give it a try, and give us feedback on what you would like to see in future releases. We welcome any contributions, such as new features, example extensions, additional documentation, or any bug reports in GitHub.

To learn more about using Trusted Language Extensions for PostgreSQL in the AWS Cloud, see the Amazon Aurora PostgreSQL-Compatible Edition and Amazon RDS for PostgreSQL documentation.

Give it a try, and please send feedback to AWS re:Post for PostgreSQL or through your usual AWS support contacts.

Channy