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 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 92

AWS Machine Learning Machine Learning AI 92

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

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

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

Alation

AUGUST 4, 2022

And where data was available, the ability to access and interpret it proved problematic. Big data can grow too big fast. Left unchecked, data lakes became data swamps. Some data lake implementations required expensive ‘cleansing pumps’ to make them navigable again.

Big Data 95

Big Data Big Data Apache Kafka Data Lakes 95

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

IBM Journey to AI blog

AUGUST 1, 2023

models are trained on IBM’s curated, enterprise-focused data lake. Fortunately, data stores serve as secure data repositories and enable foundation models to scale in both terms of their size and their training data. Foundation models focused on enterprise value IBM’s watsonx.ai All watsonx.ai

AI 70

AI AI Machine Learning Machine Learning 70

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

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

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

Pickl AI

FEBRUARY 23, 2025

Look for features such as scalability (the ability to handle growing datasets), performance (speed of processing), ease of use (user-friendly interfaces), integration capabilities (compatibility with existing systems), security measures (data protection features), and pricing models (licensing costs).

Big Data 52

Big Data Big Data Apache Hadoop Apache Kafka 52

Alation

MARCH 1, 2022

Lineage helps them identify the source of bad data to fix the problem fast. Manual lineage will give ARC a fuller picture of how data was created between AWS S3 data lake, Snowflake cloud data warehouse and Tableau (and how it can be fixed). Time is money,” said Leonard Kwok, Senior Data Analyst, ARC.

Data Warehouse 52

Data Warehouse Data Lakes Cloud Data Database 52

Flipboard

MAY 9, 2025

He is focused on big data, data lakes, streaming and batch analytics services, and generative AI technologies. Dhara Vaishnav is Solution Architecture leader at AWS and provides technical advisory to enterprise customers to use cutting-edge technologies in generative AI, data, and analytics.

AWS 91

AWS Natural Language Processing AI AI 91

IBM Journey to AI blog

OCTOBER 12, 2023

To optimize data analytics and AI workloads, organizations need a data store built on an open data lakehouse architecture. This type of architecture combines the performance and usability of a data warehouse with the flexibility and scalability of a data lake.

AI 93

AI AI Data Scientist Data Governance 93

IBM Journey to AI blog

JULY 27, 2023

Precisely conducted a study that found that within enterprises, data scientists spend 80% of their time cleaning, integrating and preparing data , dealing with many formats, including documents, images, and videos. Overall placing emphasis on establishing a trusted and integrated data platform for AI.

AI 69

AI AI Data Scientist Data Quality 69

phData

FEBRUARY 14, 2023

Data integration is essentially the Extract and Load portion of the Extract, Load, and Transform (ELT) process. Data ingestion involves connecting your data sources, including databases, flat files, streaming data, etc, to your data warehouse. Snowflake provides native ways for data ingestion.

Data Warehouse 59

Data Warehouse Azure AWS Database 59

Alation

JULY 19, 2022

For example, a new data scientist who is curious about which customers are most likely to be repeat buyers, might search for customer data only to discover an article documenting a previous project that answered their exact question. Data scientists often have different requirements for a data catalog than data analysts.

Data Scientist 59

Data Scientist Data Quality Data Science Data Analyst 59

phData

APRIL 29, 2024

Oracle – The Oracle connector, a database-type connector, enables real-time data transfer of large volumes of data from on-premises or cloud sources to the destination of choice, such as a cloud data lake or data warehouse. File – Fivetran offers several options to sync files to your destination.

SQL 52

SQL Data Warehouse Azure Cloud Data 52

Alation

FEBRUARY 13, 2020

Figure 1 illustrates the typical metadata subjects contained in a data catalog. Figure 1 – Data Catalog Metadata Subjects. Datasets are the files and tables that data workers need to find and access. They may reside in a data lake, warehouse, master data repository, or any other shared data resource.

Data Lakes 64

Data Lakes Data Analysis Data Analysis Big Data 64

Pickl AI

JANUARY 28, 2025

External Data Sources: These can be market research data, social media feeds, or third-party databases that provide additional insights. 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.

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

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. Enterprises might also have petabytes, if not exabytes, of valuable proprietary data stored in their mainframe that needs to be unlocked for new insights and ML/AI models.

AI 45

AI AI Data Quality Database 45

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. The stored data is visualized in a BI dashboard using QuickSight.

AWS 126

AWS Natural Language Processing Machine Learning Machine Learning 126

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

JUNE 11, 2025

Whether you’re running small-scale analytics or managing enterprise-level data warehouses, these tips will help drive performance and meaningful business outcomes for your organization. Storage Costs Our first tip involves taking a closer look at managing how your data is stored, organized, and accessed.

Clustering 59

Clustering SQL Database Data Lakes 59

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

Alation

APRIL 14, 2022

Similar to a data warehouse schema, this prep tool automates the development of the recipe to match. For example, data science always consumes “historical” data, and there is no guarantee that the semantics of older datasets are the same, even if their names are unchanged. Scheduling. Target Matching.

Data Governance 59

Data Governance ML ML Cloud Data 59

Alation

JUNE 9, 2021

We have an explosion, not only in the raw amount of data, but in the types of database systems for storing it ( db-engines.com ranks over 340) and architectures for managing it (from operational datastores to data lakes to cloud data warehouses). Organizations are drowning in a deluge of data.

Data Governance 59

Data Governance Database Cloud Data Machine Learning 59

Alation

APRIL 6, 2023

Data governance is traditionally applied to structured data assets that are most often found in databases and information systems. This blog focuses on governing spreadsheets that contain data, information, and metadata, and must themselves be governed. There are others that consider spreadsheets to be trouble.

Data Governance 52

Data Governance Database Data Lakes Data Warehouse 52

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

Mlearning.ai

JULY 10, 2023

There are other options you can place, and as usual, I suggest you to reference the official documentation to learn more. The process of creating Scheduled Query demonstrates how intuitively Google made the user interface, since you almost don’t need a proper documentation or training to do this.

SQL 52

SQL Database Database Administration Data Lakes 52

Alation

FEBRUARY 20, 2020

Imagine if you had an app on your computer which made you type a Unix file path when you wanted to open a document. Today the MicroStrategy team announced the next step in their relationship with Alation, the embedding of Alation Data Explorer in MicroStrategy 10.

Tableau 52

Tableau Analytics Analytics Data Governance 52

phData

MAY 6, 2024

Another benefit of deterministic matching is that the process to build these identities is relatively simple, and tools your teams might already use, like SQL and dbt , can efficiently manage this process within your cloud data warehouse. Store this data in a customer data platform or data lake.

Data Lakes 52

Data Lakes Data Warehouse Cloud Data Data Quality 52

phData

SEPTEMBER 20, 2023

A data mesh is a conceptual architectural approach for managing data in large organizations. Traditional data management approaches often involve centralizing data in a data warehouse or data lake, leading to challenges like data silos, data ownership issues, and data access and processing bottlenecks.

Data Silos 52

Data Silos Database Data Quality Data Engineering 52

Pickl AI

SEPTEMBER 5, 2024

Storage Solutions: Secure and scalable storage options like Azure Blob Storage and Azure Data Lake Storage. Key features and benefits of Azure for Data Science include: Scalability: Easily scale resources up or down based on demand, ideal for handling large datasets and complex computations.

Azure 52

Azure Data Scientist Data Science Machine Learning 52

AWS Machine Learning Blog

NOVEMBER 1, 2024

Much of these greenhouse gas emissions can be attributed to travel (such as air travel, hotel, meetings), distribution associated for drugs and documents, and electricity used in coordination centers. Instead, a core component of decentralized clinical trials is a secure, scalable data infrastructure with strong data analytics capabilities.

AWS 111

AWS Data Lakes Machine Learning Machine Learning 111

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 132

AWS SQL Data Lakes Data Warehouse 132