Remove Data Analysis Remove ETL Remove Metadata
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A Beginner’s Guide to Data Warehousing

Unite.AI

These can include structured databases, log files, CSV files, transaction tables, third-party business tools, sensor data, etc. The pipeline ensures correct, complete, and consistent data. Metadata: Metadata is data about the data. Metadata: Metadata is data about the data.

Metadata 159
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An integrated experience for all your data and AI with Amazon SageMaker Unified Studio (preview)

Flipboard

SageMaker Unied Studio is an integrated development environment (IDE) for data, analytics, and AI. Discover your data and put it to work using familiar AWS tools to complete end-to-end development workflows, including data analysis, data processing, model training, generative AI app building, and more, in a single governed environment.

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18 Data Profiling Tools Every Developer Must Know

Marktechpost

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.

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How Kakao Games automates lifetime value prediction from game data using Amazon SageMaker and AWS Glue

AWS Machine Learning Blog

In addition to the challenge of defining the features for the ML model, it’s critical to automate the feature generation process so that we can get ML features from the raw data for ML inference and model retraining. The ETL pipeline, MLOps pipeline, and ML inference should be rebuilt in a different AWS account.

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Build an automated insight extraction framework for customer feedback analysis with Amazon Bedrock and Amazon QuickSight

AWS Machine Learning Blog

When the automated content processing steps are complete, you can use the output for downstream tasks, such as to invoke different components in a customer service backend application, or to insert the generated tags into metadata of each document for product recommendation.

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Data Lakes Vs. Data Warehouse: Its significance and relevance in the data world

Pickl AI

What Is a Data Warehouse? On the other hand, a Data Warehouse is a structured storage system designed for efficient querying and analysis. It involves the extraction, transformation, and loading (ETL) process to organize data for business intelligence purposes. It often serves as a source for Data Warehouses.

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Unfolding the Details of Hive in Hadoop

Pickl AI

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.