Smart Data Collective

APRIL 2, 2019

Data Lakes are among the most complex and sophisticated data storage and processing facilities we have available to us today as human beings. Analytics Magazine notes that data lakes are among the most useful tools that an enterprise may have at its disposal when aiming to compete with competitors via innovation.

Data Lake 84

Data Lake Big Data OLAP Testing 84

datapine

APRIL 15, 2020

Your Chance: Want to test an agile business intelligence solution? No matter if you need to develop a comprehensive online data analysis process or reduce costs of operations, agile BI development will certainly be high on your list of options to get the most out of your projects. Finalize testing. Train end-users.

Business Intelligence 276

Business Intelligence Analytics Testing Dashboards 276

BizAcuity

MAY 10, 2022

Amazon strategically went with the pricing model of ‘on-demand’, allowing developers to pay only as-per their computational needs. 2011: IBM enters the cloud market with IBM SmartCloud. Google launches BigQuery, its own data warehousing tool and Microsoft introduces Azure SQL Data Warehouse and Azure Data Lake Store.

Data-driven 52

Data-driven IoT Unstructured Data Data Lake 52

AWS Big Data

APRIL 5, 2023

Showpad also struggled with data quality issues in terms of consistency, ownership, and insufficient data access across its targeted user base due to a complex BI access process, licensing challenges, and insufficient education. With QuickSight, we pay for usage. This makes it easy for us to provide access to everyone by default.

Dashboards 52

Dashboards Reporting Cost-Benefit Visualization 52

datapine

AUGUST 29, 2022

Known as the person who coined the term Lambda Architecture, co-author Nathan Marz is a well-renowned expert in the field of big data and programming. The new edition also explores artificial intelligence in more detail, covering topics such as Data Lakes and Data Sharing practices. is one of the greatest on the market.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

Domino Data Lab

APRIL 3, 2019

Instead, we must build robust ML models which take into account inherent limitations in our data and embrace the responsibility for the outcomes. How did the challenges and opportunities related to security, data management, and system architecture get braided together throughout the past ~6 decades of IT?

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84