AWS Machine Learning Blog

FEBRUARY 18, 2025

The objective was to use AWS to replicate and automate the current manual troubleshooting process for two candidate systems. To handle the log data efficiently, raw logs were centralized into an Amazon Simple Storage Service (Amazon S3) bucket. Amazon Bedrock Agents streamlines workflows and automates repetitive tasks.

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Pickl AI

MARCH 19, 2025

Summary: Data engineering tools streamline data collection, storage, and processing. Tools like Python, SQL, Apache Spark, and Snowflake help engineers automate workflows and improve efficiency. Learning these tools is crucial for building scalable data pipelines. How is Data Engineering Different from Data Science?

Big Data 52

Big Data Automation Data Science Python 52

Marktechpost

JULY 18, 2023

Extract, Transform, and Load are referred to as ETL. ETL is the process of gathering data from numerous sources, standardizing it, and then transferring it to a central database, data lake, data warehouse, or data store for additional analysis. Involved in each step of the end-to-end ETL process are: 1.

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ETL Data Integration Business Intelligence Automation 52

Pickl AI

OCTOBER 6, 2024

Summary: This blog explores the key differences between ETL and ELT, detailing their processes, advantages, and disadvantages. Understanding these methods helps organizations optimize their data workflows for better decision-making. What is ETL? ETL stands for Extract, Transform, and Load.

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ETL Data Quality Data Integration Big Data 52

Flipboard

NOVEMBER 24, 2023

This emergent ability in LLMs has compelled software developers to use LLMs as an automation and UX enhancement tool that transforms natural language to a domain-specific language (DSL): system instructions, API requests, code artifacts, and more. We use the following prompt: Human: Your job is to act as an expert on ETL pipelines.

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IBM Journey to AI blog

JANUARY 5, 2023

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.

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Pickl AI

JUNE 7, 2024

Summary: Choosing the right ETL tool is crucial for seamless data integration. Top contenders like Apache Airflow and AWS Glue offer unique features, empowering businesses with efficient workflows, high data quality, and informed decision-making capabilities. Choosing the right ETL tool is crucial for smooth data management.

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AWS Machine Learning Blog

FEBRUARY 21, 2025

He specializes in large language models, cloud infrastructure, and scalable data systems, focusing on building intelligent solutions that enhance automation and data accessibility across Amazons operations.

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LLM Large Language Models Natural Language Processing Machine Learning 116

Flipboard

DECEMBER 11, 2024

This allows data engineers, data scientists, business analysts, and other data practitioners working from the same tool to quickly understand how an application works, seamlessly review each others work, and make the required changes. About the Authors Noritaka Sekiyama is a Principal Big Data Architect on the AWS Glue team.

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ODSC - Open Data Science

SEPTEMBER 27, 2023

In the ever-evolving world of big data, managing vast amounts of information efficiently has become a critical challenge for businesses across the globe. Unlike traditional data warehouses or relational databases, data lakes accept data from a variety of sources, without the need for prior data transformation or schema definition.

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Big Data Metadata ETL Data Science 98

Pickl AI

JULY 30, 2024

Summary: Apache NiFi is a powerful open-source data ingestion platform design to automate data flow management between systems. Its architecture includes FlowFiles, repositories, and processors, enabling efficient data processing and transformation. What is Apache NiFi?

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Pickl AI

OCTOBER 10, 2024

Summary: Data transformation tools streamline data processing by automating the conversion of raw data into usable formats. These tools enhance efficiency, improve data quality, and support Advanced Analytics like Machine Learning. These tools automate the process, making it faster and more accurate.

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ETL Data Quality Machine Learning Business Intelligence 52

Marktechpost

JUNE 5, 2024

You may use OpenRefine for more than just data cleaning; it can also help you find mistakes and outliers that could compromise your data’s quality. Apache Griffin Apache Griffin is an open-source data quality tool that aims to enhance big data processes. It has a quick and clear grasp of data quality issues.

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

Architecture overview Our MLOps architecture is designed to automate and monitor all stages of the ML lifecycle. An example direct acyclic graph (DAG) might automate data ingestion, processing, model training, and deployment tasks, ensuring that each step is run in the correct order and at the right time.

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AWS Machine Learning Blog

MARCH 5, 2025

Scaling ground truth generation with a pipeline To automate ground truth generation, we provide a serverless batch pipeline architecture, shown in the following figure. The serverless batch pipeline architecture we presented offers a scalable solution for automating this process across large enterprise knowledge bases. 201% $12.2B

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Pickl AI

JUNE 11, 2024

Let’s delve into the key components that form the backbone of a data warehouse: Source Systems These are the operational databases, CRM systems, and other applications that generate the raw data feeding the data warehouse. Data Extraction, Transformation, and Loading (ETL) This is the workhorse of architecture.

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TransOrg Analytics

SEPTEMBER 24, 2024

Our cloud data engineering services are designed to transform your business by creating robust and scalable data foundations across any scale. We provide comprehensive solutions to assess, architect, build, deploy, and automate your data engineering landscape on the leading cloud platforms.

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ETL LLM Data Ingestion Automation 52

TransOrg Analytics

SEPTEMBER 24, 2024

Our cloud data engineering services are designed to transform your business by creating robust and scalable data foundations across any scale. We provide comprehensive solutions to assess, architect, build, deploy, and automate your data engineering landscape on the leading cloud platforms.

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ETL LLM Data Ingestion Automation 52

ODSC - Open Data Science

OCTOBER 18, 2024

The advent of big data, affordable computing power, and advanced machine learning algorithms has fueled explosive growth in data science across industries. However, research shows that up to 85% of data science projects fail to move beyond proofs of concept to full-scale deployment.

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Data Science ETL Data Scientist Data Quality 52

ODSC - Open Data Science

APRIL 3, 2023

Data Wrangling: Data Quality, ETL, Databases, Big Data The modern data analyst is expected to be able to source and retrieve their own data for analysis. Competence in data quality, databases, and ETL (Extract, Transform, Load) are essential.

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Data Science Big Data ETL Deep Learning 52

Pickl AI

JANUARY 28, 2025

Data Integration Once data is collected from various sources, it needs to be integrated into a cohesive format. Data Quality Management : Ensures that the integrated data is accurate, consistent, and reliable for analysis. They store structured data in a format that facilitates easy access and analysis.

Business Intelligence 52

Business Intelligence ETL Data Integration Big Data 52

Marktechpost

JULY 17, 2023

Data gathering, pre-processing, modeling, and deployment are all steps in the iterative process of predictive analytics that results in output. We can automate the procedure to deliver forecasts based on new data continuously fed throughout time. This tool’s user-friendly UI consistently receives acclaim from users.

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Machine Learning Data Mining Data Scientist Data Science 98

The MLOps Blog

MARCH 15, 2023

As the volume of data keeps increasing at an accelerated rate, these data tasks become arduous in no time leading to an extensive need for automation. This is what data processing pipelines do for you. Let’s understand how the other aspects of a data pipeline help the organization achieve its various objectives.

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AWS Machine Learning Blog

AUGUST 2, 2024

About the authors Vicente Cruz Mínguez is the Head of Data & Advanced Analytics at Cepsa Química. He has more than 8 years of experience with big data and machine learning projects in financial, retail, energy, and chemical industries. The following diagram illustrates this architecture.

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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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Pickl AI

NOVEMBER 4, 2024

Introduction Data Engineering is the backbone of the data-driven world, transforming raw data into actionable insights. As organisations increasingly rely on data to drive decision-making, understanding the fundamentals of Data Engineering becomes essential. ETL is vital for ensuring data quality and integrity.

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Data Quality ETL Data Integration Machine Learning 52

Artificial Corner

JUNE 6, 2023

Timeline of data engineering — Created by the author using canva In this post, I will cover everything from the early days of data storage and relational databases to the emergence of big data, NoSQL databases, and distributed computing frameworks. MongoDB, developed by MongoDB Inc.,

Data Mining 52

Data Mining Big Data ETL Machine Learning 52

Marktechpost

JULY 23, 2023

The pay-per-use cloud-based data warehousing technologies are quick, effective, and highly scalable. Importance of Data Warehouse To meet the continuously shifting needs of business, modern data warehousing solutions automate the repetitive tasks of designing, developing, and putting in place a data warehouse architecture.

Machine Learning 52

Machine Learning Big Data ETL Data Integration 52

ODSC - Open Data Science

OCTOBER 9, 2024

By automating repetitive tasks and generating boilerplate code, these tools free up time for engineers to focus on more complex, creative aspects of software development. Well, it is offering a way to automate the time-consuming process of writing and running tests. Just keep in mind, that this shouldn’t replace the human element.

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Software Engineer Software Development DevOps Machine Learning 40

TransOrg Analytics

AUGUST 21, 2024

Such success stories have largely depended on Data Engineering processes. This article explores how data engineering can improve Customer 360 initiatives for AWS data engineering , big data engineering, and data analytics companies. Data Storage: Keeping altered data within Azure Synapse’s enrichment layer.

Big Data Engineer 52

Big Data Engineer ETL Data Ingestion Data Integration 52

Mlearning.ai

JULY 10, 2023

[link] Tables The table in GCP BigQuery is a collection of rows and columns that can store and manage massive amounts of data. It’s a managed, cloud-based service that’s designed to handle big data processing with ease. You can use stored procedures to handle complex ETL processes, make API calls, and perform data validation.

Data Science 52

Data Science Big Data ETL Automation 52

Pickl AI

MARCH 10, 2025

Power Query Power Query is a powerful ETL (Extract, Transform, Load) tool within Power BI that helps users clean and transform raw data into usable formats. Key Features Data Cleaning Functions: Remove duplicates, fill missing values, or standardise formats. Automation of Tasks: Save time by automating repetitive query steps.

Business Intelligence 52

Business Intelligence ETL Data Analysis Natural Language Processing 52

AWS Machine Learning Blog

SEPTEMBER 6, 2024

Experimenting with LLMs to automate fact generation from QA ground truth using LLMs can help. Automate, but verify, with LLMs – Use LLMs to generate initial ground truth answers and facts, with a human review and curation to align with the desired assistant output standards.

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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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Flipboard

NOVEMBER 22, 2024

This is a new capability that enables data engineers and scientists to quickly identify and resolve issues in their Spark applications. This feature uses ML and generative AI technologies to provide automated root cause analysis for failed Spark applications, along with actionable recommendations and remediation steps. Choose your job.

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Pickl AI

JULY 18, 2024

In contrast, MongoDB uses a more straightforward query language that works well with JSON data structures. MongoDB’s horizontal scaling capabilities surpass relational databases’ typical vertical scaling limitations, making it suitable for big data applications. What Is a MongoDB Atlas?

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AWS Machine Learning Blog

JANUARY 7, 2025

The answer lay in using generative AI through Amazon Bedrock Flows, enabling them to build an automated, intelligent request handling system that would transform their client service operations. Path to the solution When evaluating solutions for email triage automation, several approaches appeared viable, each with its own pros and cons.

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