AWS Machine Learning Blog

JANUARY 6, 2023

TR used AWS Glue DataBrew and AWS Batch jobs to perform the extract, transform, and load (ETL) jobs in the ML pipelines, and SageMaker along with Amazon Personalize to tailor the recommendations. TR wanted to take advantage of AWS managed services where possible to simplify operations and reduce undifferentiated heavy lifting.

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AWS Machine Learning Blog

AUGUST 21, 2023

Amazon SageMaker Data Wrangler reduces the time it takes to collect and prepare data for machine learning (ML) from weeks to minutes. We are happy to announce that SageMaker Data Wrangler now supports using Lake Formation with Amazon EMR to provide this fine-grained data access restriction.

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phData

JUNE 26, 2023

While both these tools are powerful on their own, their combined strength offers a comprehensive solution for data analytics. In this blog post, we will show you how to leverage KNIME’s Tableau Integration Extension and discuss the benefits of using KNIME for data preparation before visualization in Tableau.

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phData

FEBRUARY 9, 2023

Welcome to our AWS Redshift to the Snowflake Data Cloud migration blog! In this blog, we’ll walk you through the process of migrating your data from AWS Redshift to the Snowflake Data Cloud. One popular route is leveraging third-party ETL tools like Fivetran to ensure a smooth and successful migration.

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

Hosted on Amazon ECS with tasks run on Fargate, this platform streamlines the end-to-end ML workflow, from data ingestion to model deployment. This blog post delves into the details of this MLOps platform, exploring how the integration of these tools facilitates a more efficient and scalable approach to managing ML projects.

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AWS Machine Learning Blog

AUGUST 4, 2023

Benefits of the SageMaker and Data Cloud Einstein Studio integration Here’s how using SageMaker with Einstein Studio in Salesforce Data Cloud can help businesses: It provides the ability to connect custom and generative AI models to Einstein Studio for various use cases, such as lead conversion, case classification, and sentiment analysis.

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phData

SEPTEMBER 28, 2023

Dataflows allow users to establish source connections and retrieve data, and subsequent data transformations can be conducted using the online Power Query Editor. In this blog, we will provide insights into the process of creating Dataflows and offer guidance on when to choose them to address real-world use cases effectively.

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Alation

FEBRUARY 20, 2020

Data lakes, while useful in helping you to capture all of your data, are only the first step in extracting the value of that data. We recently announced an integration with Trifacta to seamlessly integrate the Alation Data Catalog with self-service data prep applications to help you solve this issue.

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phData

SEPTEMBER 25, 2024

With the importance of data in various applications, there’s a need for effective solutions to organize, manage, and transfer data between systems with minimal complexity. While numerous ETL tools are available on the market, selecting the right one can be challenging.

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Alation

FEBRUARY 20, 2020

However, most are only deployed over one data store (Hadoop or other various backends). In 2016, these will increasingly be deployed to query multiple data sources. The implication will be doing away with some (if not all) of the ETL work required to gather all of the data in one data warehouse.

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phData

OCTOBER 11, 2023

Both tools serve distinct phases within the data analytics process, making their integration a highly advantageous proposition. In this blog, we will focus on integrating Power BI within KNIME for enhanced data analytics. This phase demands meticulous customization to optimize data for analysis.

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IBM Journey to AI blog

JULY 17, 2023

In this blog, I will cover: What is watsonx.ai? sales conversation summaries, insurance coverage, meeting transcripts, contract information) Generate: Generate text content for a specific purpose, such as marketing campaigns, job descriptions, blogs or articles, and email drafting support. What capabilities are included in watsonx.ai?

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AWS Machine Learning Blog

SEPTEMBER 1, 2023

These teams are as follows: Advanced analytics team (data lake and data mesh) – Data engineers are responsible for preparing and ingesting data from multiple sources, building ETL (extract, transform, and load) pipelines to curate and catalog the data, and prepare the necessary historical data for the ML use cases.

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AWS Machine Learning Blog

NOVEMBER 29, 2023

For instance, a notebook that monitors for model data drift should have a pre-step that allows extract, transform, and load (ETL) and processing of new data and a post-step of model refresh and training in case a significant drift is noticed.

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AWS Machine Learning Blog

MAY 30, 2023

However, preparing raw data for ML training and evaluation is often a tedious and demanding task in terms of compute resources, time, and human effort. Data preparation commonly needs to be integrated from different sources and deal with missing or noisy values, outliers, and so on.

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phData

FEBRUARY 7, 2024

The Snowflake Data Cloud is a leading cloud data platform that provides various features and services for data storage, processing, and analysis. A new feature that Snowflake offers is called Snowpark, which provides an intuitive library for querying and processing data at scale in Snowflake. What is Snowpark?

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AWS Machine Learning Blog

APRIL 16, 2024

These connections are used by AWS Glue crawlers, jobs, and development endpoints to access various types of data stores. You can use these connections for both source and target data, and even reuse the same connection across multiple crawlers or extract, transform, and load (ETL) jobs.

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The MLOps Blog

OCTOBER 20, 2023

Placing functions for plotting, data loading, data preparation, and implementations of evaluation metrics in plain Python modules keeps a Jupyter notebook focused on the exploratory analysis | Source: Author Using SQL directly in Jupyter cells There are some cases in which data is not in memory (e.g.,

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The MLOps Blog

MAY 31, 2023

The objective of an ML Platform is to automate repetitive tasks and streamline the processes starting from data preparation to model deployment and monitoring. In this section, I will talk about best practices around building the Data Processing platform. How to set up an ML Platform in eCommerce?

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Flipboard

MARCH 21, 2025

Traditionally, answering this question would involve multiple data exports, complex extract, transform, and load (ETL) processes, and careful data synchronization across systems. Users can write data to managed RMS tables using Iceberg APIs, Amazon Redshift, or Zero-ETL ingestion from supported data sources.

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Kaggle

JULY 29, 2020

In August 2019, Data Works was acquired and Dave worked to ensure a successful transition. David: My technical background is in ETL, data extraction, data engineering and data analytics. An ETL process was built to take the CSV, find the corresponding text articles and load the data into a SQLite database.

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IBM Journey to AI blog

OCTOBER 30, 2024

IBM watsonx.data facilitates scalable analytics and AI endeavors by accommodating data from diverse sources, eliminating the need for migration or cataloging through open formats. This approach enables centralized access and sharing while minimizing extract, transform and load (ETL) processes and data duplication.

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AWS Machine Learning Blog

FEBRUARY 18, 2025

To handle the log data efficiently, raw logs were centralized into an Amazon Simple Storage Service (Amazon S3) bucket. An Amazon EventBridge schedule checked this bucket hourly for new files and triggered log transformation extract, transform, and load (ETL) pipelines built using AWS Glue and Apache Spark.

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Dataconomy

MARCH 7, 2025

Jasper AI Content marketing teams swear by Jasper AI for generating blog posts, social media captions, and even ad copy in seconds. These AI-powered platforms enhance decision-making, automate reporting, and simplify complex data operations.

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