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

AWS Big Data

JANUARY 8, 2024

It aims to provide a framework to create low-latency streaming applications on the AWS Cloud using Amazon Kinesis Data Streams and AWS purpose-built data analytics services. In this post, we will review the common architectural patterns of two use cases: Time Series Data Analysis and Event Driven Microservices.

Analytics 115

Analytics IoT Data-driven Snapshot 115

IBM Big Data Hub

OCTOBER 30, 2023

IBM, a pioneer in data analytics and AI, offers watsonx.data, among other technologies, that makes possible to seamlessly access and ingest massive sets of structured and unstructured data. The platform provides an intelligent, self-service data ecosystem that enhances data governance, quality and usability.

Insurance 79

Insurance Data Warehouse Data-driven Unstructured Data 79

CIO Business Intelligence

JUNE 29, 2022

The recent announcement of the Microsoft Intelligent Data Platform makes that more obvious, though analytics is only one part of that new brand. Azure Data Factory. Azure Data Lake Analytics. Data warehouses are designed for questions you already know you want to ask about your data, again and again.

Data Lake 105

Data Lake Analytics Data Warehouse Machine Learning 105

IBM Big Data Hub

SEPTEMBER 19, 2022

IoT systems access millions of devices that generate large amounts of streaming data. For some equipment, a single event may prove critical to understanding and responding to the health of the machine in real time, increasing the importance of accurate, reliable data. Why real-time analytics matters for IoT systems.

IoT 52

IoT Analytics Data Warehouse Manufacturing 52

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 78

Data Science Data Analytics Prescriptive Analytics Analytics 78

Smart Data Collective

OCTOBER 13, 2021

There is no disputing the fact that the collection and analysis of massive amounts of unstructured data has been a huge breakthrough. We would like to talk about data visualization and its role in the big data movement. How does Data Virtualization complement Data Warehousing and SOA Architectures?

Visualization 135

Visualization Cost-Benefit Big Data Prescriptive Analytics 135

Jet Global

NOVEMBER 21, 2023

To have any hope of generating value from growing data sets, enterprise organizations must turn to the latest technology. You’ve heard of data warehouses, and probable data lakes, but now, the data lakehouse is emerging as the new corporate buzzword. To address this, the data lakehouse was born.

Data Lake 52

Data Lake Data Warehouse Reporting Enterprise 52

AWS Big Data

FEBRUARY 21, 2023

Data warehouses play a vital role in healthcare decision-making and serve as a repository of historical data. A healthcare data warehouse can be a single source of truth for clinical quality control systems. What is a dimensional data model? What is a dimensional data model? What is a data vault?

Data Warehouse 67

Data Warehouse Data Lake Cost-Benefit Modeling 67

Jet Global

NOVEMBER 7, 2023

This growth is caused, in part, by the increasing use of cloud platforms for data storage and processing. But it is also a result of the surge in multimedia content in cloud repositories that requires tools and methods for extracting insights from rich, unstructured data formats.

Enterprise 52

Enterprise Data Warehouse Operational Reporting Reporting 52

Cloudera

AUGUST 31, 2020

Given the prohibitive cost of scaling it, in addition to the new business focus on data science and the need to leverage public cloud services to support future growth and capability roadmap, SMG decided to migrate from the legacy data warehouse to Cloudera’s solution using Hive LLAP. The case for a new Data Warehouse?

Slice and Dice 64

Slice and Dice Management Data Warehouse Analytics 64

Ontotext

DECEMBER 15, 2022

Transactional data – a subcategory of operational data consisting of recorded business events. Structured data comes in rows and columns and is neatly stored in tables. For decades it’s been the bread and butter of enterprise data teams. What’s the big deal about unstructured and semi-structured data?

Metadata 52

Metadata Unstructured Data Structured Data Enterprise 52

AWS Big Data

MARCH 7, 2024

At the same time, they need to optimize operational costs to unlock the value of this data for timely insights and do so with a consistent performance. With this massive data growth, data proliferation across your data stores, data warehouse, and data lakes can become equally challenging.

Data Lake 115

Data Lake Analytics Dashboards Metrics 115

CIO Business Intelligence

MAY 24, 2022

The survey found the mean number of data sources per organisation to be 400, and more than 20 percent of companies surveyed to be drawing from 1,000 or more data sources to feed business intelligence and analytics systems. However, more than 99 percent of respondents said they would migrate data to the cloud over the next two years.

Data-driven 76

Data-driven Data Lake Data Warehouse Cost-Benefit 76

Andrew White

DECEMBER 9, 2019

Scope could be: Data (i.e. Information (processed data). Records (files, or what you might all unstructured data). Events or transactions. Analytical stewardship is a missing link in analytics, BI and data science. Downstream in the analytics pipeline. Analytic (the analytics itself). Images (i.e.

Analytics 66

Analytics Data Lake Data Governance Metadata 66

datapine

NOVEMBER 19, 2019

With the help of collaborative methods such as utilizing a business dashboard , workers are able to share information as to why certain events are unfolding in a particular way and so on. Using related data, content, and the business context behind findings, users can add their own knowledge to the results of business intelligence.

Business Intelligence 146

Business Intelligence Knowledge Discovery Dashboards Unstructured Data 146

Cloudera

OCTOBER 26, 2020

Relevance-based text search over unstructured data (text, pdf,jpg, …). Better performance for fast changing / updateable data. Time series analytics, event analytics and real time data warehouse best Querying Experience with the most intelligent autocompletes. Virtual private clusters. Encryption.

Testing 86

Testing Cost-Benefit Unstructured Data Big Data 86

Ontotext

MARCH 18, 2020

According to an article in Harvard Business Review , cross-industry studies show that, on average, big enterprises actively use less than half of their structured data and sometimes about 1% of their unstructured data. The many data warehouse systems designed in the last 30 years present significant difficulties in that respect.

Enterprise 59

Enterprise B2B Unstructured Data Machine Learning 59

IBM Big Data Hub

MAY 17, 2023

It can offer data on demand to different business units within an organization, with the help of various sensors and payloads. The services are activated through access management for data collection, analysis and event monitoring in existing drones which are managed by clients and businesses.

Data Collection 78

Data Collection Visualization Data-driven Unstructured Data 78

Cloudera

AUGUST 20, 2021

Analytical Outcome: CDP delivers multiple analytical outcomes including, to name a few, operational dashboards via the CDP Operational Database experience or ad-hoc analytics via the CDP Data Warehouse to help surface insights related to a business domain. ultimately reducing operational costs to manage the platform.

Strategy 96

Strategy Cost-Benefit Visualization Data Warehouse 96

Cloudera

SEPTEMBER 15, 2022

July brings summer vacations, holiday gatherings, and for the first time in two years, the return of the Massachusetts Institute of Technology (MIT) Chief Data Officer symposium as an in-person event. A key area of focus for the symposium this year was the design and deployment of modern data platforms. What is a data fabric?

Data Lake 81

Data Lake Data Architecture Data-driven Data Warehouse 81

Sisense

JULY 6, 2021

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. To drive this point home, Yonatan Dolan, an Analytics Specialist from AWS, introduced AWS’ new Lake House architecture.

Data Lake 52

Data Lake Data Warehouse Data-driven Unstructured Data 52

Alation

APRIL 19, 2022

Data modernization is the process of transferring data to modern cloud-based databases from outdated or siloed legacy databases, including structured and unstructured data. In that sense, data modernization is synonymous with cloud migration. Efficient Data Processing. Enhanced Accessibility.

Cost-Benefit 52

Cost-Benefit Data Governance Manufacturing Data Architecture 52

AWS Big Data

FEBRUARY 22, 2023

The initial design had some additional challenges: Diverse data source – The data source in an ecommerce platform consists of structured, semi-structured, and unstructured data, which requires flexible data storage. Other business units can now act faster after an event like a marketing campaign.

Data Lake 52

Data Lake Dashboards Cost-Benefit Metadata 52

Sisense

MAY 11, 2021

When it comes to data modeling, function determines form. Let’s say you want to subject a dataset to some form of anomaly detection; your model might take the form of a singular event stream that can be read by an anomaly detection service. This design philosophy was adapted from our friends at Fishtown Analytics.).

Modeling 84

Modeling Big Data IoT Data Warehouse 84

Domino Data Lab

JULY 2, 2019

Of course, if you use several different data management frameworks within your data science workflows—as just about everybody does these days—much of that RDBMS magic vanishes in a puff of smoke. Some may ask: “Can’t we all just go back to the glory days of business intelligence, OLAP, and enterprise data warehouses?”

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

Cloudera

MAY 16, 2018

This approach also relates to monitoring internal fiduciary risk by tying separate events together, such as a large position (relative to historic norms) being taken immediately after the risk model that would have flagged it was modified in a separate system. Market data: Coordinated trading among multiple parties.

Data Lake 42

Data Lake Risk Visualization Unstructured Data 42

AWS Big Data

JUNE 9, 2023

A modern data architecture enables companies to ingest virtually any type of data through automated pipelines into a data lake, which provides highly durable and cost-effective object storage at petabyte or exabyte scale. This process has to be scheduled separately by the user on a time or event basis.

Data Lake 115

Data Lake Metadata Optimization Statistics 115

datapine

NOVEMBER 19, 2019

With the help of collaborative methods, workers are able to share information such as why certain events are unfolding in a particular way and so on. Using related data, content, and the business context behind findings, users can add their own knowledge to the results of business intelligence. Summing Up.

Business Intelligence 20

Business Intelligence Knowledge Discovery Unstructured Data Software 20