Dataconomy

MAY 12, 2025

LlamaIndex is adept at managing and storing vectors, nodes, and indices, ensuring that data retrieval remains fast and responsive, regardless of dataset size. LlamaIndex documents Documents play a crucial role in how LlamaIndex functions, as they undergo transformation into nodes for effective data querying.

Data Warehouse 91

Data Warehouse Database Analytics Analytics 91

Flipboard

JANUARY 6, 2025

The platform helped the agency digitize and process forms, pictures, and other documents. The federal government agency Precise worked with needed to automate manual processes for document intake and image processing. For image processing, the agency does a lot of inspections and takes a lot of pictures.

AWS 65

AWS ML ML Machine Learning 65

databricks

JUNE 4, 2025

Events Data + AI Summit Data + AI World Tour Data Intelligence Days Event Calendar Blog and Podcasts Databricks Blog Explore news, product announcements, and more Databricks Mosaic Research Blog Discover the latest in our Gen AI research Data Brew Podcast Let’s talk data!

AWS 130

AWS AI AI Data Science 130

Dataconomy

MARCH 6, 2025

Testing: Various methods are used to support or refute these hypotheses, incorporating both quantitative and qualitative data. Evaluation: Finally, researchers document their findings, including potential limitations and implications. Deduction: This step involves creating testable hypotheses derived from broader explanations.

Data Mining 91

Data Mining Data Mining Data Mining Data Warehouse 91

Smart Data Collective

SEPTEMBER 26, 2021

There’s not much value in holding on to raw data without putting it to good use, yet as the cost of storage continues to decrease, organizations find it useful to collect raw data for additional processing. The raw data can be fed into a database or data warehouse. The central concept is the idea of a document.

Database 130

Database Data Visualization Big Data Big Data 130

DrivenData Labs

MAY 21, 2024

Better documentation with more examples , clearer explanations of the choices and tools, and a more modern look and feel. Find the latest at [link] (the old documentation will redirect here shortly). Project documentation ¶ As data science codebases live longer, code is often refactored into a package.

Data Science 233

Data Science Python Data Scientist Data Warehouse 233

AWS Machine Learning Blog

APRIL 3, 2025

When needed, the system can access an ODAP data warehouse to retrieve additional information. Document management Documents are securely stored in Amazon S3, and when new documents are added, a Lambda function processes them into chunks.

AWS 86

AWS Data Governance Data Silos SQL 86

Smart Data Collective

JUNE 6, 2021

Other uses may include: Maintenance checks Guides, resources, training and tutorials (all available in BigQuery documentation ) Employee efficiency reviews Machine learning Innovation advancements through the examination of trends. (1). Big data analytics advantages. What is Big Data?” What is Google BigQuery? References.

Big Data Analytics 137

Big Data Analytics Big Data Analytics Big Data Big Data 137

AWS Machine Learning Blog

FEBRUARY 12, 2025

For example, your input document might include tables within the PDF. In such cases, using an FM to parse the data will provide better results. You can use advanced parsing options supported by Amazon Bedrock Knowledge Bases for parsing non-textual information from documents using FMs.

AWS 98

AWS Machine Learning Machine Learning AI 98

Data Science 101

DECEMBER 3, 2019

Do you have a data governance document? What data do you collect? Technical Questions Before Starting a Data Strategy. How and where is your current data stored? What is the current data infrastructure? Do you have a data warehouse? Do you use any external data?

Data Warehouse 84

Data Warehouse Data Governance Business Intelligence Business Intelligence 84

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

Smart Data Collective

JULY 20, 2021

As a standalone product, this software helps professionals with rich sets of spreadsheets, charts and documents. Quip integration tool will allow teams to improve collaborations, export and import live data, enhanced visibility and outstanding device support. This tool will help you to sync and store data from multiple sources quickly.

Big Data 142

Big Data Big Data ETL Analytics 142

IBM Journey to AI blog

MAY 9, 2023

is not just for data scientists and developers — business users can also access it via an easy-to-use interface that responds to natural language prompts for different tasks. With watsonx.data , businesses can quickly connect to data, get trusted insights and reduce data warehouse costs. Watsonx.ai

AI 110

AI AI Data Warehouse Machine Learning 110

Smart Data Collective

APRIL 29, 2020

The extraction of raw data, transforming to a suitable format for business needs, and loading into a data warehouse. Data transformation. This process helps to transform raw data into clean data that can be analysed and aggregated. Data analytics and visualisation. Microsoft Azure.

Data Warehouse 98

Data Warehouse Azure SQL ETL 98

Towards AI

FEBRUARY 13, 2023

How to build a chatbot that answers questions about documentation and cites its sources The tutorial was initially hosted via a live stream on our Learn AI Discord. Three 5-minute reads/videos to keep you learning 1.How

AI 98

AI AI Data Warehouse Data Lakes 98

AWS Machine Learning Blog

JUNE 7, 2023

Amazon Textract is a machine learning (ML) service that automatically extracts text, handwriting, and data from any document or image. Amazon Textract has a Tables feature within the AnalyzeDocument API that offers the ability to automatically extract tabular structures from any document.

Machine Learning 97

Machine Learning Machine Learning Data Analysis Data Analysis 97

IBM Data Science in Practice

JANUARY 13, 2025

The blog post explains how the Internal Cloud Analytics team leveraged cloud resources like Code-Engine to improve, refine, and scale the data pipelines. Background One of the Analytics teams tasks is to load data from multiple sources and unify it into a data warehouse.

ETL 100

ETL Data Pipeline Database Data Warehouse 100

phData

APRIL 28, 2025

A Matillion pipeline is a collection of jobs that extract, load, and transform (ETL/ELT) data from various sources into a target system, such as a cloud data warehouse like Snowflake. Document business rules and assumptions directly within the workflow. Data tables used and their role in the workflow.

AI 52

AI AI SQL ETL 52

phData

MARCH 8, 2023

Examples of data sources and destinations include: Shopify Google Analytics Snowflake Data Cloud Oracle Salesforce Fivetran’s mission is to, “make access to data as easy as electricity” – so for the last 10 years, they have developed their platform into a leader in the cloud-based ELT market. What is Fivetran Used For?

Data Warehouse 96

Data Warehouse Data Engineering Data Engineer Data Engineering 96

Pickl AI

OCTOBER 17, 2024

Introduction ETL plays a crucial role in Data Management. This process enables organisations to gather data from various sources, transform it into a usable format, and load it into data warehouses or databases for analysis. Loading The transformed data is loaded into the target destination, such as a data warehouse.

ETL 52

ETL Data Warehouse Data Quality Data Governance 52

Smart Data Collective

APRIL 15, 2021

You need to make sure that all departments are data-friendly and in sync with each other. Most will include documentation of data sources, the KPIs of the specific industry, the kind of reporting necessary, and whether or not the data flow will require automation. Set Up Data Integration. Develop a Strategy.

Business Intelligence 108

Business Intelligence Business Intelligence Analytics Analytics 108

phData

NOVEMBER 4, 2024

For our hypothetical car company, we will use Dataiku’s Answers application to create a personalized customer service chatbot that can pull data from warranty contracts, car spec manuals, and customer history to respond to inquiries. Dataiku and Snowflake: A Good Combo?

Machine Learning 69

Machine Learning Machine Learning Data Science ML 69

Pickl AI

MARCH 19, 2025

It is widely used for storing and managing structured data, making it an essential tool for data engineers. MongoDB MongoDB is a NoSQL database that stores data in flexible, JSON-like documents. Apache Spark Apache Spark is a powerful data processing framework that efficiently handles Big Data.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

ODSC - Open Data Science

APRIL 9, 2025

With advances in natural language processing and vision models, AI is now helping to populate and refine graph structures, especially in unstructured domains like legal documentation or scientific literature. Imagine an AI that can retrieve and relate not just documents but scenes, sounds, and visual contexts. But thats changingfast.

AI 52

AI AI Natural Language Processing Data Warehouse 52

AWS Machine Learning Blog

OCTOBER 9, 2024

To start using OpenSearch for anomaly detection you first must index your data into OpenSearch , from there you can enable anomaly detection in OpenSearch Dashboards. To learn more, see the documentation. To learn more, see the documentation. To learn more, see the documentation.

AWS 96

AWS ML ML Data Quality 96

phData

MARCH 7, 2023

This SDK allows you to directly connect to your Snowflake Data Warehouse and create a mapping of values and rows that need to be inserted. Once this step is complete, you can then insert the data. So how does Snowflake do this? Snowflake provides a Streaming Ingest SDK that you can implement using Java.

Data Warehouse 98

Data Warehouse Database 98

Alation

JANUARY 17, 2023

The modern data stack is a combination of various software tools used to collect, process, and store data on a well-integrated cloud-based data platform. It is known to have benefits in handling data due to its robustness, speed, and scalability. A typical modern data stack consists of the following: A data warehouse.

Data Warehouse 52

Data Warehouse ETL Tableau Cloud Data 52

IBM Journey to AI blog

OCTOBER 16, 2023

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. How to scale AL and ML with built-in governance A fit-for-purpose data store built on an open lakehouse architecture allows you to scale AI and ML while providing built-in governance tools.

AI 88

AI AI Data Warehouse ML 88

Mlearning.ai

FEBRUARY 16, 2023

The ultimate need for vast storage spaces manifests in data warehouses: specialized systems that aggregate data coming from numerous sources for centralized management and consistency. In this article, you’ll discover what a Snowflake data warehouse is, its pros and cons, and how to employ it efficiently.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

ODSC - Open Data Science

FEBRUARY 24, 2023

Great Expectations GitHub | Website Great Expectations (GX) helps data teams build a shared understanding of their data through quality testing, documentation, and profiling. With Great Expectations , data teams can express what they “expect” from their data using simple assertions.

Exploratory Data Analysis 126

Exploratory Data Analysis Data Visualization Data Analysis Data Analysis 126

AWS Machine Learning Blog

FEBRUARY 14, 2024

Amazon Redshift has announced a feature called Amazon Redshift ML that makes it straightforward for data analysts and database developers to create, train, and apply machine learning (ML) models using familiar SQL commands in Redshift data warehouses.

AWS 141

AWS AI AI Database 141

Pickl AI

APRIL 9, 2025

By 2025, global data volumes are expected to reach 181 zettabytes, according to IDC. To harness this data effectively, businesses rely on ETL (Extract, Transform, Load) tools to extract, transform, and load data into centralized systems like data warehouses.

ETL 52

ETL Data Warehouse AWS Business Intelligence 52

phData

SEPTEMBER 19, 2023

By incorporating metadata into the data model, users can easily discover, understand, and interpret the data stored in the lake. With the amounts of data involved, this can be crucial to utilizing a data lake effectively. However, this can be time-consuming and prone to human error, leading to misinformation.

Data Lakes 52

Data Lakes Data Modeling Data Models Data Warehouse 52

phData

NOVEMBER 28, 2024

“ Vector Databases are completely different from your cloud data warehouse.” – You might have heard that statement if you are involved in creating vector embeddings for your RAG-based Gen AI applications. When documents are split into smaller chunks, search systems can find relevant sections more precisely and quickly.

Python 52

Python Database SQL Machine Learning 52

Smart Data Collective

JUNE 7, 2019

Volume – Companies gather data from different sources such as business transactions, social media, and other relevant data. Variety – It means all data can be presented in a variety of formats – from structured numeric data to the unstructured ones, which include text documents, audio, video, and email.

Big Data 104

Big Data Big Data Data Analysis Data Analysis 104

phData

DECEMBER 4, 2024

Like most Gen AI use cases, the first step to achieving customer service automation is to clean and centralize all information in a data warehouse for your AI to work from. Document Search Everyone who’s ever read a product manual knows it can be notoriously complex, and finding the information you’re looking for is difficult.

AI 52

AI AI Data Warehouse Data Quality 52

AWS Machine Learning Blog

SEPTEMBER 18, 2024

Additionally, Feast promotes feature reuse, so the time spent on data preparation is reduced greatly. It promotes a disciplined approach to data modeling, making it easier to ensure data quality and consistency across the ML pipelines.

AWS 123

AWS Machine Learning Machine Learning ML 123