Questions tagged with Amazon SageMaker Data Wrangler
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Intelligent Data platfom equivalent
Hello! I am looking for an equivalent to this solution that MIcrosoft has flaunted called IDP intelligent data platform, it is governance + operations + analytics in one. they flaunt synapse with aml and purview and other stuff they did with these to make it more integrated . I know we have RDS and Sagemaker -- but how about purview? how are we tgo make it more cohesive?
[bug report] Sagemaker data wrangler: An error occurred loading this view
Hello, I import my data from Athena, then add a new custom data transform. As soon as I click on the "Custom transform" option, the error occurs with message: **An error occurred loading this view**. There is no other useful message to find out the problem. Please tell me how to troubleshot or fix this problem. ![Enter image description here](https://repost.aws/media/postImages/original/IMT74o2F2nS8KYUj3jxJ4mjA) Thank you
Sagemaker instances keep awakening and charge the credit
I have tried Data Wrangler in Sagemaker last month and close the service. A few weeks later I have noticed the credit was charge $1 every hour and just realized that the Data Wranger auto-save the flow every minute. So, I deleted the unsaved flow and shut down all the services and instances according to advice on these two links : * https://docs.aws.amazon.com/sagemaker/latest/dg/studio-lab-use-shutdown.html * https://docs.aws.amazon.com/sagemaker/latest/dg/ex1-cleanup.html Then, I left the Sagemaker untouched for the whole month of May, and just got back to the console yesterday. This is what I found out for May's bill: Amazon SageMaker RunInstance $531.74 == | Detail | Usage | Total | | --- | --- | --- | | $0.00 for Host:ml.m5.xlarge per hour under monthly free tier | 125.000 Hrs | $0.00 | | $0.00 for Notebk:ml.t2.medium per hour under monthly free tier | 107.056 Hrs | $0.00 | | $0.00 per Data Wrangler Interactive ml.m5.4xlarge hour under monthly free tier | 25.000 Hrs | $0.00 | | $0.23 per Hosting ml.m5.xlarge hour in US East (N. Virginia) | 88.997 Hrs | $20.47 | | $0.922 per Data Wrangler Interactive ml.m5.4xlarge hour in US East (N. Virginia) | 554.521 Hrs | $511.27 | So, with another attempt, I installed an extension to automatically shut down idle kernels and set the limit to 10 min from advice here: https://aws.amazon.com/blogs/machine-learning/save-costs-by-automatically-shutting-down-idle-resources-within-amazon-sagemaker-studio/ Checked the cost in usage report, it turns out that the service was shut down after installing the extension but then it revoked itself after 5 hours later (during my sleep time). There's still cost from Studio although with less charge than previous one. | Service | Operation | UsageType | StartTime | EndTime |UsageValue | | --- | --- | --- | --- | --- | --- | | AmazonSageMaker | RunInstance | USE1-Studio_DW:KernelGateway-ml.m5.4xlarge | 5/24/2022 23:00 | 5/25/2022 0:00 | 1 | | AmazonSageMaker | RunInstance | USE1-Studio_DW:KernelGateway-ml.m5.4xlarge | 5/25/2022 0:00 | 5/25/2022 1:00 | 1 | | AmazonSageMaker | RunInstance | USE1-Studio_DW:KernelGateway-ml.m5.4xlarge | 5/25/2022 1:00 | 5/25/2022 2:00 | 1 | | AmazonSageMaker | RunInstance | USE1-Studio_DW:KernelGateway-ml.m5.4xlarge | 5/25/2022 2:00 | 5/25/2022 3:00 | 0.76484417 | | AmazonSageMaker | RunInstance | USE1-Studio_DW:KernelGateway-ml.m5.4xlarge | 5/25/2022 8:00 | 5/25/2022 9:00 | 0.36636722 | | AmazonSageMaker | RunInstance | USE1-Studio_DW:KernelGateway-ml.m5.4xlarge | 5/25/2022 9:00 | 5/25/2022 10:00 | 0.38959556 | During this time, I'm sure that there're no running instances, running apps, kernel sessions or terminal sessions. I even deleted the user profile. Last thing I haven't tried is to set up scheduled shutdown coz I think the services should not cause difficulty to our life that much. Any advice for any effective action to completely shutdown the Sagemaker instance? Thanks.
Amazon SageMaker Data Wrangler now supports additional M5 and R5 instances for interactive data preparation
Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes in Amazon SageMaker Studio, the first fully integrated development environment (IDE) for ML. With SageMaker Data Wrangler, you can simplify the process of data preparation and feature engineering, and complete each step of the data preparation workflow, including data selection, cleansing, exploration, and visualization, from a single visual interface. SageMaker Data Wrangler runs on ml.m5.4xlarge by default. SageMaker Data Wrangler includes built-in data transforms and analyses written in PySpark so you can process large data sets (up to hundreds of gigabytes (GB) of data) efficiently on the default instance. Starting today, you can use additional M5 or R5 instance types with more CPU or memory in SageMaker Data Wrangler to improve performance for your data preparation workloads. Amazon EC2 M5 instances offer a balance of compute, memory, and networking resources for a broad range of workloads. Amazon EC2 R5 instances are the memory optimized instances. Both M5 and R5 instance types are well suited for CPU and memory intensive applications such as running built-in transforms for very large data sets (up to terabytes (TB) of data) or applying custom transforms written in Panda on medium data sets (up to tens of GBs). To learn more about the newly supported instances with Amazon SageMaker Data Wrangler, visit the [blog ](https://aws.amazon.com/blogs/machine-learning/process-larger-and-wider-datasets-with-amazon-sagemaker-data-wrangler/) or the [AWS document](https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler-data-flow.html), and the[ pricing page](https://aws.amazon.com/sagemaker/pricing/). To get started with SageMaker Data Wrangler, visit the [AWS documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler.html).
Data Wrangler Full Outer Join Not Working As Expected Nor Concatenate
I've got two CSV files that are loaded into Data Wrangler that are intended to augment each other. The tables have some columns that are the same (in name) and some that are not, many of the rows are missing entries for many of the columns. The two tables represent separate datasets. Consider the example below: Table 1: | Filename | LabelA | LabelB | | --- | --- | --- | | ./A/001.dat | 1 | 1 | | ./A/002.dat | 0 | 1 | Table 2: | Filename | LabelB | LabelC | | --- | --- | --- | | ./B/001.dat | | 0 | | ./B/002.dat | 0 | 1 | I am looking to merge / concatenate the two table. The problem is that neither Data Wrangler join nor concatenate seems to work (at least as expected). Desired result: | Filename | LabelA | LabelB | LabelC | | --- | --- | --- | --- | | ./A/001.dat | 1 | 1 | | | ./A/002.dat | 0 | 1 | | | ./B/001.dat | | | 0 | | ./B/002.dat | | 0 | 1 | When using a "Full Outer" join and ask to combine "Filename" and "LabelB" columns, it will take all the values from Table 1 OR Table 2 even if Table 1 does not have that entry (for example, some rows will have Filename = <nothing> rather than Filename = ./B/001.dat). When using concatenate, Data Wrangler errors on the fact that it cannot match EVERY column between the tables. Now in my example there are many columns and many rows which precludes a manual process of joining without merging columns and then going through a renaming and merging process one-by-one. How do get these tables to simply merge? I feel I must be missing something obvious. I am about to give up on Data Wrangler and do it all in a python script using pandas, but I thought I should give Data Wrangler a try while learning the MLops process.
Launch Announcement: Amazon SageMaker Data Wrangler now supports Databricks as a data source
[Amazon SageMaker Data Wrangler](https://aws-preview.aka.amazon.com/sagemaker/data-wrangler/) reduces the time it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes in Amazon SageMaker Studio, the first fully integrated development environment (IDE) for ML. With SageMaker Data Wrangler, you can simplify the process of data preparation and feature engineering, and complete each step of the data preparation workflow, including data selection, cleansing, exploration, and visualization from a single visual interface. You can import data from multiple data sources such as Amazon S3, Amazon Athena, Amazon Redshift, Snowflake. Starting today, you can now use Databricks as a data source in Amazon SageMaker Data Wrangler to easily prepare data in Databricks for machine learning. [Databricks](https://partners.amazonaws.com/partners/001E0000016WxP5IAK/Databricks), an AWS Partner, helps organizations prepare their data for analytics, empower data science and data-driven decisions across the organization, and rapidly adopt machine learning (ML). To learn more about Databricks integration with Amazon SageMaker Data Wrangler, view our [blog](https://aws.amazon.com/blogs/machine-learning/prepare-data-from-databricks-for-machine-learning-using-amazon-sagemaker-data-wrangler/) or [AWS document](https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler-import.html#data-wrangler-databricks). To get started with Amazon SageMaker Data Wrangler, visit our [AWS documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler.html) and [pricing page](https://aws.amazon.com/sagemaker/pricing/).