Dataconomy

SEPTEMBER 4, 2023

Text analytics: Text analytics, also known as text mining, deals with unstructured text data, such as customer reviews, social media comments, or documents. It uses natural language processing (NLP) techniques to extract valuable insights from textual data. Poor data integration can lead to inaccurate insights.

Analytics 203

Analytics Analytics Big Data Analytics Big Data Analytics 203

AWS Machine Learning Blog

FEBRUARY 21, 2025

The agent knowledge base stores Amazon Bedrock service documentation, while the cache knowledge base contains curated and verified question-answer pairs. For this example, you will ingest Amazon Bedrock documentation in the form of the User Guide PDF into the Amazon Bedrock knowledge base. This will be the primary dataset.

AWS 130

AWS Natural Language Processing Machine Learning Machine Learning 130

Flipboard

MARCH 7, 2023

Then the transcripts of contacts become available to CSBA to extract actionable insights through millions of customer contacts for the sellers, and the data is stored in the Seller Data Lake. After the AI/ML-based analytics, all actionable insights are generated and then stored in the Seller Data Lake.

ML 123

ML ML AWS AI 123

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.

AI 132

AI AI ML ML 132

AWS Machine Learning Blog

JUNE 25, 2024

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.

AWS 132

AWS Natural Language Processing Machine Learning Machine Learning 132

Pickl AI

JANUARY 28, 2025

Data can be structured (e.g., documents and images). The diversity of data sources allows organizations to create a comprehensive view of their operations and market conditions. Data Integration Once data is collected from various sources, it needs to be integrated into a cohesive format.

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

DagsHub

OCTOBER 23, 2024

To combine the collected data, you can integrate different data producers into a data lake as a repository. A central repository for unstructured data is beneficial for tasks like analytics and data virtualization. Data Cleaning The next step is to clean the data after ingesting it into the data lake.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

DagsHub

NOVEMBER 11, 2024

So, we must understand the different unstructured data types and effectively process them to uncover hidden patterns. Textual Data Textual data is one of the most common forms of unstructured data and can be in the format of documents, social media posts, emails, web pages, customer reviews, or conversation logs.

AI 52

AI AI Data Lakes Database 52

phData

SEPTEMBER 26, 2023

A common problem solved by phData is the migration from an existing data platform to the Snowflake Data Cloud , in the best possible manner. Sources The sources involved could influence or determine the options available for the data ingestion tool(s). These could include other databases, data lakes, SaaS applications (e.g.

SQL 52

SQL Database ETL Data Modeling 52

Pickl AI

OCTOBER 10, 2024

It integrates well with cloud services, databases, and big data platforms like Hadoop, making it suitable for various data environments. Typical use cases include ETL (Extract, Transform, Load) tasks, data quality enhancement, and data governance across various industries.

Data Quality 52

Data Quality AWS Machine Learning Machine Learning 52

phData

APRIL 29, 2024

Understanding Fivetran Fivetran is a popular Software-as-a-Service platform that enables users to automate the movement of data and ETL processes across diverse sources to a target destination. For a longer overview, along with insights and best practices, please feel free to jump back to the previous blog.

SQL 52

SQL Data Warehouse Azure Cloud Data 52

IBM Journey to AI blog

JULY 17, 2023

These encoder-only architecture models are fast and effective for many enterprise NLP tasks, such as classifying customer feedback and extracting information from large documents. While they require task-specific labeled data for fine tuning, they also offer clients the best cost performance trade-off for non-generative use cases.

AI 74

AI AI Machine Learning Machine Learning 74

IBM Journey to AI blog

JULY 3, 2024

As data types and applications evolve, you might need specialized NoSQL databases to handle diverse data structures and specific application requirements. With an open data lakehouse, you can access a single copy of data wherever your data resides.

AI 45

AI AI Data Quality Database 45

phData

FEBRUARY 14, 2023

Data Collector also offers replication and Change Data Capture (CDC) to be able to accurately and efficiently get your data into Snowflake. Data Collector can use Snowflake’s native Snowpipe in its pipelines. This may result in data inconsistency when UPDATE and DELETE operations are performed on the target database.

Data Warehouse 59

Data Warehouse Azure AWS Database 59

The MLOps Blog

OCTOBER 20, 2023

References : Links to internal or external documentation with background information or specific information used within the analysis presented in the notebook. Data to explore: Outline the tables or datasets you’re exploring/analyzing and reference their sources or link their data catalog entries. documentation.

SQL 52

SQL Database Data Scientist Python 52

phData

SEPTEMBER 28, 2023

Power BI Dataflows provide vital functionalities that effectively empower users to cleanse and reshape data from various sources. These Dataflows are crucial in fostering consistency and reducing the duplication of repetitive ETL (Extract, Transform, Load) steps, achieved by reusing transformations.

Power BI 78

Power BI Data Preparation Machine Learning Machine Learning 78

Flipboard

MAY 15, 2025

These organizations have a huge demand for lakehouse solutions that combine the best of data warehouses and data lakes to simplify data management with easy access to all data from their preferred engines. EMEA Data & AI PSA, based in Madrid. Amazon SageMaker Unified Studio , Amazon EMR 7.5.0

AWS 136

AWS SQL Data Lakes Data Warehouse 136

AWS Machine Learning Blog

OCTOBER 11, 2024

Create business intelligence (BI) dashboards for visual representation and analysis of event data. A streamlined process should include steps to ensure that events are promptly detected, prioritized, acted upon, and documented for future reference and compliance purposes, enabling efficient operational event management at scale.

AWS 102

AWS AI AI Data Lakes 102