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 106

Data Lake Big Data OLAP Testing 106

datapine

APRIL 15, 2020

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. The term “agile” was originally conceived in 2011 as a software development methodology.

Business Intelligence 276

Business Intelligence Analytics Testing Dashboards 276

Cloudera

AUGUST 9, 2022

The first, ISO 27031:2011, helps describe the process and procedures involved in incident response. Standby systems can be designed to meet storage requirements during typical periods with burstable compute for failover scenarios using new features such as Data Lake Scaling. Conclusion.

Data Lake 78

Data Lake Data Warehouse Data-driven IoT 78

Data Virtualization

APRIL 7, 2022

As far back as 2011 Gartner proposed the concept of a logical data warehouse as a way to overcome some of the challenges organizations. The post Does Data Always Need to End Up in a Centralized Repository? Reading Time: 3 minutes This is an age-old question, and one that has been asked many times over the years.

Data Warehouse 52

Data Warehouse Data Integration Management Data Lake 52

BizAcuity

MAY 10, 2022

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 management solutions will need to keep up with the data demands of the next few years.

Data-driven 52

Data-driven IoT Unstructured Data Data Lake 52

Peter James Thomas

AUGUST 12, 2018

In our modern architectures, replete with web-services, APIs, cloud-based components and the quasi-instantaneous transmission of new transactions, it is perhaps not surprising that occasionally some data gets lost in translation [5] along the way. See in particular my trilogy: Using historical data to justify BI investments – Part I (2011).

Metrics 49

Metrics Statistics Data Quality Measurement 49

Domino Data Lab

APRIL 3, 2019

So much data is flowing through the other parts, but that’s not the concern of DG solutions. Fun fact: in 2011 Google bought remnants of what had previously been Motorola. Data coming from machines tends to land (aka, data at rest ) in durable stores such as Amazon S3, then gets consumed by Hadoop, Spark, etc.

Machine Learning 84

Machine Learning Data Governance Metadata Data Science 84