IBM Big Data Hub

SEPTEMBER 19, 2023

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

Data Science 78

Data Science Data Analytics Prescriptive Analytics Analytics 78

Peter James Thomas

AUGUST 18, 2018

That was the Science, here comes the Technology… A Brief Hydrology of Data Lakes. Even back then, these were used for activities such as Analytics , Dashboards , Statistical Modelling , Data Mining and Advanced Visualisation. This is the essence of Convergent Evolution. In Closing.

Data Lake 63

Data Lake Data Warehouse Data mining Statistics 63

Cloudera

DECEMBER 21, 2020

Company data exists in the data lake. Data Catalog profilers have been run on existing databases in the Data Lake. A Cloudera Data Warehouse virtual warehouse with Cloudera Data Visualisation enabled exists. A Cloudera Data Engineering service exists. Model building.

Dashboards 97

Dashboards Visualization Data Warehouse Data Lake 97

Sisense

APRIL 26, 2020

Read on to explore more about structured vs unstructured data, why the difference between structured and unstructured data matters, and how cloud data warehouses deal with them both. Structured vs unstructured data. However, both types of data play an important role in data analysis.

Unstructured Data 52

Unstructured Data Data Warehouse Structured Data Data mining 52

Cloudera

MAY 12, 2021

We welcome organizations that have built and deployed use cases for enterprise-scale machine learning and have industrialized AI to automate, secure, and optimize data-driven decision-making and/or applications to enter this category. HYBRID & MULTI-CLOUD INNOVATION. SECURITY AND GOVERNANCE LEADERSHIP. PEOPLE FIRST.

Digital Transformation 68

Digital Transformation Machine Learning Optimization Data Lake 68

Jet Global

SEPTEMBER 19, 2023

Data Access What insights can we derive from our cloud ERP? What are the best practices for analyzing cloud ERP data? How can we respond in real time to the company’s analytic needs? Data Management How do we create a data warehouse or data lake in the cloud using our cloud ERP?

Cost-Benefit 52

Cost-Benefit Data Warehouse Reporting Enterprise 52

Jet Global

SEPTEMBER 30, 2022

What are the best practices for analyzing cloud ERP data? How can we respond in real time to the company’s analytic needs? Data Management. How do we create a data warehouse or data lake in the cloud using our cloud ERP? How do I access the legacy data from my previous ERP? Self-service BI.

Reporting 52

Reporting Data Warehouse Operational Reporting Enterprise 52

datapine

AUGUST 29, 2022

The new edition also explores artificial intelligence in more detail, covering topics such as Data Lakes and Data Sharing practices. 6) Lean Analytics: Use Data to Build a Better Startup Faster, by Alistair Croll and Benjamin Yoskovitz. The subsequent chapters focus on predictive and descriptive analysis.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

Peter James Thomas

MAY 3, 2019

The objective here is to use a variety of techniques to tease out findings from available data (both internal and external) that go beyond the explicit purpose for which it was captured. Data Architecture / Infrastructure. When I first started focussing on the data arena, Data Warehouses were state of the art.

Strategy 55

Strategy Data Architecture Data Quality Data Strategy 55

Andrew White

MARCH 22, 2019

I jest here; what I mean to say is that economics and rational, smart models should trump perfectly designed ideas. This ties into the failure of data governance and MDM (see first item in this list). This ties into the failure of data governance and MDM (see first item in this list). Economics over architecture.

Analytics 68

Analytics Metadata Data Governance Management 68