Sisense

OCTOBER 6, 2020

Most commonly, we think of data as numbers that show information such as sales figures, marketing data, payroll totals, financial statistics, and other data that can be counted and measured objectively. This is quantitative data. It’s “hard,” structured data that answers questions such as “how many?”

Statistics 98

Statistics Unstructured Data Data-driven Visualization 98

AWS Big Data

JULY 19, 2023

We’ve seen that there is a demand to design applications that enable data to be portable across cloud environments and give you the ability to derive insights from one or more data sources. With this connector, you can bring the data from Google Cloud Storage to Amazon S3.

Big Data 79

Big Data Software Consulting Unstructured Data 79

AWS Big Data

OCTOBER 20, 2023

We’ve seen a demand to design applications that enable data to be portable across cloud environments and give you the ability to derive insights from one or more data sources. With these connectors, you can bring the data from Azure Blob Storage and Azure Data Lake Storage separately to Amazon S3. Learn more in README.

Data Lake 80

Data Lake Big Data Consulting Data Warehouse 80

Domino Data Lab

AUGUST 9, 2021

With the right tools, your data science teams can focus on what they do best – testing, developing and deploying new models while driving forward-thinking innovation. In general terms, a model is a series of algorithms that can solve problems when given appropriate data. It’s most helpful in analyzing structured data.

Modeling 111

Modeling Deep Learning Machine Learning Statistics 111

IBM Big Data Hub

JANUARY 10, 2024

Data: AI systems learn and make decisions based on data, and they require large quantities of data to train effectively, especially in the case of machine learning (ML) models. For optimal performance, AI models should receive data from a diverse datasets (e.g.,

Unstructured Data 82

Unstructured Data Deep Learning Cost-Benefit Modeling 82

IBM Big Data Hub

JULY 6, 2023

The development of generative AI —which uses powerful foundation models that train on large amounts of unlabeled data—can be adapted to new use cases and bring flexibility and scalability that is likely to accelerate the adoption of AI significantly. It can ingest unstructured data in its raw form (e.g.,

Deep Learning 60

Deep Learning Machine Learning Unstructured Data Modeling 60

Ontotext

APRIL 27, 2021

Compared with other industries, healthcare has a fair amount of structured data, which is helpful. They also developed a large-scale knowledge graph for an early hypothesis testing tool. Tried and Tested. They have to because people’s lives are at stake.

Knowledge Discovery 59

Knowledge Discovery Unstructured Data Insurance Testing 59

FineReport

DECEMBER 27, 2021

Usually, enterprise BI incorporates relatively rigid, well-structured data models on data warehouses or data marts. The data sources are enterprise-class and monolithic, requiring long read times and IT engagement to adjust to changes in business requirements. The data is managed, secure and tested for accuracy.

Enterprise 59

Enterprise Dashboards Business Intelligence Visualization 59

Cloudera

JULY 26, 2021

A common pitfall in the development of data platforms is that they are built around the boundaries of point solutions and are constrained by the technological limitations (e.g., a technology choice such as Spark Streaming is overly focused on throughput at the expense of latency) or data formats (e.g.,

Strategy 115

Strategy Data Science Marketing Unstructured Data 115

Cloudera

OCTOBER 7, 2021

Those decentralization efforts appeared under different monikers through time, e.g., data marts versus data warehousing implementations (a popular architectural debate in the era of structured data) then enterprise-wide data lakes versus smaller, typically BU-Specific, “data ponds”.

Metadata 123

Metadata Cost-Benefit Enterprise Interactive 123