IBM Big Data Hub

SEPTEMBER 19, 2023

Data science is an area of expertise that combines many disciplines such as mathematics, computer science, software engineering and statistics. It focuses on data collection and management of large-scale structured and unstructured data for various academic and business applications.

Data Science 85

Data Science Data Analytics Prescriptive Analytics Analytics 85

AWS Big Data

MARCH 25, 2024

This data store provides your organization with the holistic customer records view that is needed for operational efficiency of RAG-based generative AI applications. For building such a data store, an unstructured data store would be best. This is typically unstructured data and is updated in a non-incremental fashion.

Data Lake 90

Data Lake Unstructured Data Management Modeling 90

Jedox

OCTOBER 22, 2020

Gartner defines “dark data” as the data organizations collect, process, and store during regular business activities, but doesn’t use any further. Gartner also estimates 80% of all data is “dark”, while 93% of unstructured data is “dark.”. Limited real-time analytics and visuals. Data accuracy concerns.

Data Analytics 69

Data Analytics Analytics Unstructured Data Data mining 69

AWS Big Data

JANUARY 8, 2024

Stream ingestion – The stream ingestion layer is responsible for ingesting data into the stream storage layer. It provides the ability to collect data from tens of thousands of data sources and ingest in real time. The raw data can be streamed to Amazon S3 for archiving.

Analytics 111

Analytics IoT Data-driven Snapshot 111

Sisense

OCTOBER 6, 2020

As quantitative data is always numeric, it’s relatively straightforward to put it in order, manage it, analyze it, visualize it, and do calculations with it. Spreadsheet software like Excel, Google Sheets, or traditional database management systems all mainly deal with quantitative data.

Statistics 98

Statistics Unstructured Data Data-driven Visualization 98

Cloudera

NOVEMBER 22, 2021

In legacy analytical systems such as enterprise data warehouses, the scalability challenges of a system were primarily associated with computational scalability, i.e., the ability of a data platform to handle larger volumes of data in an agile and cost-efficient way. Introduction. CRM platforms).

Data Processing 91

Data Processing Data Warehouse Enterprise Visualization 91

IBM Big Data Hub

DECEMBER 16, 2022

For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance. It uses metadata and data management tools to organize all data assets within your organization.

Metadata 52

Metadata Data Quality Data-driven Data Governance 52

Cloudera

MAY 16, 2018

All three cases require a “big picture” approach that incorporates new and alternative data sources and cross-functional collaboration throughout the organization not only to identify illegal activities, rogue traders, or personal misconduct but also to provide evidential material that demonstrates a deep understanding of the intent.

Data Lake 41

Data Lake Risk Visualization Unstructured Data 41

AWS Big Data

FEBRUARY 8, 2023

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. Amazon Redshift enables you to run complex SQL analytics at scale and performance on terabytes to petabytes of structured and unstructured data, and make the insights widely available through popular business intelligence (BI) and analytics tools.

Data Quality 74

Data Quality Testing Data Warehouse Unstructured Data 74

Sisense

JUNE 30, 2020

In a world increasingly dominated by data, users of all kinds are gathering, managing, visualizing, and analyzing data in a wide variety of ways. One of the downsides of the role that data now plays in the modern business world is that users can be overloaded with jargon and tech-speak, which can be overwhelming.

Visualization 93

Visualization Analytics Dashboards Data-driven 93

BizAcuity

MAY 10, 2022

2012: Amazon Redshift, the first of its kind cloud-based data warehouse service comes into existence. Fact: IBM built the world’s first data warehouse in the 1980’s. Microsoft also releases Power BI, a data visualization and business intelligence tool. There is Alibaba Cloud, Turbonomic, Terremark etc.

Data-driven 52

Data-driven IoT Unstructured Data Data Lake 52

Sisense

MAY 28, 2019

Two orthogonal approaches to data analytics have developed in this decade of BI: 1. Operating “in-data” to enable the direct query of unstructured data lakes, providing a visualization layer on top of them. The allure of operationalizing BI in-data is its perceived simplicity.

Data Analytics 89

Data Analytics Data Lake Analytics Unstructured Data 89

Sisense

JULY 22, 2020

The trend has been towards using cloud-based applications and tools for different functions, such as Salesforce for sales, Marketo for marketing automation, and large-scale data storage like AWS or data lakes such as Amazon S3 , Hadoop and Microsoft Azure. Sisense provides instant access to your cloud data warehouses.

Slice and Dice 98

Slice and Dice Digital Transformation Data Warehouse Data Lake 98

Sisense

MAY 11, 2021

The reasons for this are simple: Before you can start analyzing data, huge datasets like data lakes must be modeled or transformed to be usable. According to a recent survey conducted by IDC , 43% of respondents were drawing intelligence from 10 to 30 data sources in 2020, with a jump to 64% in 2021!

Modeling 84

Modeling Big Data IoT Data Warehouse 84

Sisense

JULY 6, 2021

To drive this point home, Yonatan Dolan, an Analytics Specialist from AWS, introduced AWS’ new Lake House architecture. This cutting-edge service integrates the abilities of a data lake, a data warehouse, and purpose-built stores, to enable unified governance and easy data movement.

Data Lake 52

Data Lake Data Warehouse Data-driven Unstructured Data 52

Jet Global

MAY 9, 2024

The key components of a data pipeline are typically: Data Sources : The origin of the data, such as a relational database , data warehouse, data lake , file, API, or other data store. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52