Flipboard

DECEMBER 11, 2024

Data processing and SQL analytics Analyze, prepare, and integrate data for analytics and AI using Amazon Athena, Amazon EMR, AWS Glue, and Amazon Redshift. Data and AI governance Publish your data products to the catalog with glossaries and metadata forms. Big Data Architect. Zach Mitchell is a Sr.

Big Data Architect 159

Big Data Architect Big Data ML Generative AI 159

Marktechpost

JUNE 5, 2024

As a result, it’s easier to find problems with data quality, inconsistencies, and outliers in the dataset. Metadata analysis is the first step in establishing the association, and subsequent steps involve refining the relationships between individual database variables.

Data Quality 110

Data Quality Metadata Data Integration ETL 110

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.

Big Data 98

Big Data Metadata ETL Data Science 98

AWS Machine Learning Blog

MARCH 5, 2025

The examples focus on questions on chunk-wise business knowledge while ignoring irrelevant metadata that might be contained in a chunk. About the authors Samantha Stuart is a Data Scientist with AWS Professional Services, and has delivered for customers across generative AI, MLOps, and ETL engagements.

Generative AI 76

Generative AI LLM AI AI 76

Pickl AI

JULY 30, 2024

Its architecture includes FlowFiles, repositories, and processors, enabling efficient data processing and transformation. With a user-friendly interface and robust features, NiFi simplifies complex data workflows and enhances real-time data integration. Its visual interface allows users to design complex ETL workflows with ease.

Data Ingestion 52

Data Ingestion ETL Big Data Data Integration 52

Pickl AI

JULY 6, 2023

These work together to enable efficient data processing and analysis: · Hive Metastore It is a central repository that stores metadata about Hive’s tables, partitions, and schemas. Thus, making it easier for analysts and data scientists to leverage their SQL skills for Big Data analysis.

Big Data 52

Big Data Data Analysis ETL Metadata 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.

Data Science 52

Data Science ETL Data Scientist Data Quality 52

Pickl AI

NOVEMBER 15, 2023

Data scientists can explore, experiment, and derive valuable insights without the constraints of a predefined structure. This capability empowers organizations to uncover hidden patterns, trends, and correlations in their data, leading to more informed decision-making. What Is a Data Warehouse? What Is Data Lake Architecture?

ETL 52

ETL Metadata Business Intelligence Data Analysis 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

Flipboard

NOVEMBER 22, 2024

How generative AI troubleshooting for Spark works For Spark jobs, the troubleshooting feature analyzes job metadata, metrics and logs associated with the error signature of your job to generates a comprehensive root cause analysis. About the Authors Noritaka Sekiyama is a Principal Big Data Architect on the AWS Glue team.

Generative AI 143

Generative AI Software Development ETL Big Data Architect 143

Flipboard

NOVEMBER 24, 2023

The SnapLogic Intelligent Integration Platform (IIP) enables organizations to realize enterprise-wide automation by connecting their entire ecosystem of applications, databases, big data, machines and devices, APIs, and more with pre-built, intelligent connectors called Snaps.

ETL 157

ETL Prompt Engineering Prompt Engineer Generative AI 157

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.

ETL 52

ETL Data Mining Data Integration Actionable Intelligence 52

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. The table metadata is managed by Data Catalog. You can use SageMaker Lakehouse to unify the data across different data sources.

Metadata 135

Metadata ETL Data Analysis Big Data 135

Flipboard

NOVEMBER 27, 2024

Enterprises are facing challenges in accessing their data assets scattered across various sources because of increasing complexities in managing vast amount of data. Traditional search methods often fail to provide comprehensive and contextual results, particularly for unstructured data or complex queries.

Metadata 148

Metadata Auto-complete Data Discovery ML Engineer 148