AWS Machine Learning Blog

NOVEMBER 12, 2024

You may check out additional reference notebooks on aws-samples for how to use Meta’s Llama models hosted on Amazon Bedrock. You can implement these steps either from the AWS Management Console or using the latest version of the AWS Command Line Interface (AWS CLI).

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Dataversity

MARCH 26, 2024

Writing data to an AWS data lake and retrieving it to populate an AWS RDS MS SQL database involves several AWS services and a sequence of steps for data transfer and transformation.

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AWS Machine Learning Blog

FEBRUARY 28, 2024

Structured Query Language (SQL) is a complex language that requires an understanding of databases and metadata. Today, generative AI can enable people without SQL knowledge. This generative AI task is called text-to-SQL, which generates SQL queries from natural language processing (NLP) and converts text into semantically correct SQL.

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Flipboard

DECEMBER 11, 2024

Many of these applications are complex to build because they require collaboration across teams and the integration of data, tools, and services. Data engineers use data warehouses, data lakes, and analytics tools to load, transform, clean, and aggregate data. Choose Create VPC.

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AWS Machine Learning Blog

JUNE 20, 2024

Our goal was to improve the user experience of an existing application used to explore the counters and insights data. The data is stored in a data lake and retrieved by SQL using Amazon Athena. The following figure shows a search query that was translated to SQL and run. The challenge is to assure quality.

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Flipboard

JUNE 26, 2023

Companies are faced with the daunting task of ingesting all this data, cleansing it, and using it to provide outstanding customer experience. Typically, companies ingest data from multiple sources into their data lake to derive valuable insights from the data. Run the AWS Glue ML transform job.

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AWS Machine Learning Blog

SEPTEMBER 27, 2024

Amazon Simple Storage Service (Amazon S3) stores the model artifacts and creates a data lake to host the inference output, document analysis output, and other datasets in CSV format. About the authors Scott Patterson is a Senior Solutions Architect at AWS. The sunburst graph below is a visualization of this classification.

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Smart Data Collective

FEBRUARY 23, 2022

Traditional relational databases provide certain benefits, but they are not suitable to handle big and various data. That is when data lake products started gaining popularity, and since then, more companies introduced lake solutions as part of their data infrastructure. AWS Athena and S3. Limits of Athena.

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AWS Machine Learning Blog

AUGUST 21, 2023

You can streamline the process of feature engineering and data preparation with SageMaker Data Wrangler and finish each stage of the data preparation workflow (including data selection, purification, exploration, visualization, and processing at scale) within a single visual interface.

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AWS Machine Learning Blog

JUNE 5, 2023

To simplify access to Parquet files, Amazon SageMaker Canvas has added data import capabilities from over 40 data sources , including Amazon Athena , which supports Apache Parquet. Canvas provides connectors to AWS data sources such as Amazon Simple Storage Service (Amazon S3), Athena, and Amazon Redshift.

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AWS Machine Learning Blog

JANUARY 4, 2024

One such area that is evolving is using natural language processing (NLP) to unlock new opportunities for accessing data through intuitive SQL queries. Instead of dealing with complex technical code, business users and data analysts can ask questions related to data and insights in plain language.

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Data Science 101

NOVEMBER 11, 2019

Azure Synapse Analytics This is the future of data warehousing. It combines data warehousing and data lakes into a simple query interface for a simple and fast analytics service. SQL Server 2019 SQL Server 2019 went Generally Available. It can be used to do distributed Machine Learning on AWS.

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Data Science Dojo

JULY 6, 2023

Amazon Redshift: Amazon Redshift is a cloud-based data warehousing service provided by Amazon Web Services (AWS). Amazon Redshift allows data engineers to analyze large datasets quickly using massively parallel processing (MPP) architecture. It offers extensibility and integration with various data engineering tools.

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IBM Journey to AI blog

JUNE 11, 2024

Further to the acquisition, Broadcom decided to discontinue (link resides outside ibm.com) its AWS authorization to resell VMware Cloud on AWS as of 30 April 2024. As a result, AWS will no longer be able to offer new subscriptions or additional services.

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IBM Journey to AI blog

MARCH 14, 2024

The solution: IBM databases on AWS To solve for these challenges, IBM’s portfolio of SaaS database solutions on Amazon Web Services (AWS), enables enterprises to scale applications, analytics and AI across the hybrid cloud landscape. Let’s delve into the database portfolio from IBM available on AWS. 

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Alation

FEBRUARY 20, 2020

For many enterprises, a hybrid cloud data lake is no longer a trend, but becoming reality. Due to these needs, hybrid cloud data lakes emerged as a logical middle ground between the two consumption models. AWS Propelling Hybrid Cloud Environments. The Problem with Hybrid Cloud Environments.

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AWS Machine Learning Blog

MARCH 8, 2023

This solution helps market analysts design and perform data-driven bidding strategies optimized for power asset profitability. In this post, you will learn how Marubeni is optimizing market decisions by using the broad set of AWS analytics and ML services, to build a robust and cost-effective Power Bid Optimization solution.

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phData

SEPTEMBER 19, 2023

With the amount of data companies are using growing to unprecedented levels, organizations are grappling with the challenge of efficiently managing and deriving insights from these vast volumes of structured and unstructured data. What is a Data Lake? Consistency of data throughout the data lake.

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Flipboard

DECEMBER 6, 2023

needed to address some of these challenges in one of their many AI use cases built on AWS. In this post, we discuss a Q&A bot use case that Q4 has implemented, the challenges that numerical and structured datasets presented, and how Q4 concluded that using SQL may be a viable solution.

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Data Science 101

NOVEMBER 7, 2019

Azure Synapse Analytics can be seen as a merge of Azure SQL Data Warehouse and Azure Data Lake. Synapse allows one to use SQL to query petabytes of data, both relational and non-relational, with amazing speed. Azure Synapse. I think this announcement will have a very large and immediate impact.

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ODSC - Open Data Science

OCTOBER 13, 2024

Be sure to check out her talk, “ Don’t Go Over the Deep End: Building an Effective OSS Management Layer for Your Data Lake ,” there! Managing a data lake can often feel like being lost at sea — especially when dealing with both structured and unstructured data.

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Flipboard

AUGUST 17, 2023

Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and ML to deliver the best price-performance at any scale. If you want to do the process in a low-code/no-code way, you can follow option C.

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AWS Machine Learning Blog

JUNE 13, 2023

The natural language capabilities allow non-technical users to query data through conversational English rather than complex SQL. The AI and language models must identify the appropriate data sources, generate effective SQL queries, and produce coherent responses with embedded results at scale.

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Database SQL AWS AI 101

AWS Machine Learning Blog

JUNE 25, 2024

In addition, the generative business intelligence (BI) capabilities of QuickSight allow you to ask questions about customer feedback using natural language, without the need to write SQL queries or learn a BI tool. In the first step, an AWS Lambda function reads and validates the file, and extracts the raw data.

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phData

NOVEMBER 8, 2024

Note : Cloud Data warehouses like Snowflake and Big Query already have a default time travel feature. However, this feature becomes an absolute must-have if you are operating your analytics on top of your data lake or lakehouse. It can also be integrated into major data platforms like Snowflake. Contact phData Today!

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AWS Machine Learning Blog

MAY 31, 2024

With the Amazon Bedrock serverless experience, you can get started quickly, privately customize FMs with your own data, and integrate and deploy them into your applications using the Amazon Web Services (AWS) tools without having to manage infrastructure. The following screenshot shows what the upload looks like when it’s complete.

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AWS Machine Learning Blog

NOVEMBER 27, 2023

Configure the following scopes on your connected app: Manage user data via APIs ( api ). Perform ANSI SQL queries on Salesforce Data Cloud data (Data Cloud_query_api ). Manage Data Cloud profile data ( Data Cloud_profile_api ). Drag and drop the file, then choose Edit in SQL.

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AWS Machine Learning Blog

APRIL 18, 2023

It includes sensor devices to capture vibration and temperature data, a gateway device to securely transfer data to the AWS Cloud, the Amazon Monitron service that analyzes the data for anomalies with ML, and a companion mobile app to track potential failures in your machinery.

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ODSC - Open Data Science

APRIL 25, 2025

One of the main drivers for this problem is that most analytic systems are built within the context of a unified SQL environment. Getting all the data together, in one place, and integrated is generally the main goal of ourwork. Unfortunately in analytic systems, we have been doing little other than tight coupling for decades.

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IBM Journey to AI blog

JULY 15, 2024

Explore VMware Modernization Assessment In this blog, we will share how IBM Consulting can help organizations with a preference for AWS-based cloud-native technologies, leveraging the contemporary tools and modern cloud services that AWS has to offer.

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IBM Journey to AI blog

OCTOBER 15, 2024

IBM watsonx.data is a hybrid, governed data lake house optimized for data, analytics and AI workloads. Additionally, watsonx.data provides a flexible approach and a unified view of your data across hybrid cloud environments. Key highlights include driving business analytics with engines like Presto and Spark.

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Women in Big Data

NOVEMBER 27, 2024

How to Choose a Data Warehouse for Your Big Data Choosing a data warehouse for big data storage necessitates a thorough assessment of your unique requirements. Begin by determining your data volume, variety, and the performance expectations for querying and reporting.

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ODSC - Open Data Science

FEBRUARY 2, 2023

Computer Science and Computer Engineering Similar to knowing statistics and math, a data scientist should know the fundamentals of computer science as well. While knowing Python, R, and SQL are expected, you’ll need to go beyond that. Big Data As datasets become larger and more complex, knowing how to work with them will be key.

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ODSC - Open Data Science

JULY 27, 2023

Choosing a Data Lake Format: What to Actually Look For The differences between many data lake products today might not matter as much as you think. When choosing a data lake, here’s something else to consider. When choosing a data lake, here’s something else to consider.

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AWS Machine Learning Blog

AUGUST 4, 2023

The following steps give an overview of how to use the new capabilities launched in SageMaker for Salesforce to enable the overall integration: Set up the Amazon SageMaker Studio domain and OAuth between Salesforce and the AWS account s. The endpoint will be exposed to Salesforce Data Cloud as an API through API Gateway.

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Alation

NOVEMBER 18, 2021

External Tables Create a Shared View of the Data Lake. We’ve seen external tables become popular with our customers, who use them to provide a normalized relational schema on top of their data lake. Essentially, external tables create a shared view of the data lake, a single pane of glass everyone can reference.

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phData

MARCH 22, 2024

Snowflake-managed Iceberg table’s performance is at par with Snowflake native tables while storing the data in public cloud storage. They are Ideal for situations where the data is already stored in data lakes and do not intend to load into Snowflake but need to use the features and performance of Snowflake.

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