AWS Big Data

MARCH 7, 2023

Sources Data can be loaded from multiple sources, such as systems of record, data generated from applications, operational data stores, enterprise-wide reference data and metadata, data from vendors and partners, machine-generated data, social sources, and web sources.

Analytics 71

Analytics Data Warehouse Data Lake Metadata 71

Cloudera

JUNE 7, 2022

We live in a hybrid data world. In the past decade, the amount of structured data created, captured, copied, and consumed globally has grown from less than 1 ZB in 2011 to nearly 14 ZB in 2020. Impressive, but dwarfed by the amount of unstructured data, cloud data, and machine data – another 50 ZB.

IT 112

IT Data Architecture Unstructured Data Big Data 112

CIO Business Intelligence

JUNE 23, 2022

We live in a hybrid data world. In the past decade, the amount of structured data created, captured, copied, and consumed globally has grown from less than 1 ZB in 2011 to nearly 14 ZB in 2020. Impressive, but dwarfed by the amount of unstructured data, cloud data, and machine data – another 50 ZB.

IT 77

IT Data Architecture Unstructured Data Big Data 77

AWS Big Data

JUNE 6, 2023

As the SumUp merchants user journey only starts onsite (with a Sign up or product purchase), but extends to offline card reader transactions or usage of our products from within our app and web dashboard, it is important to combine Digital Analytics data with other (backend) data sources in our Data Lake or DWH.

Analytics 55

Analytics Data Lake Testing Optimization 55

AWS Big Data

MARCH 26, 2024

Profile aggregation – When you’ve uniquely identified a customer, you can build applications in Managed Service for Apache Flink to consolidate all their metadata, from name to interaction history. Then, you transform this data into a concise format. The following diagram shows a sample C360 dashboard built on Amazon QuickSight.

Data Strategy 68

Data Strategy Strategy Data Warehouse Prescriptive Analytics 68

CIO Business Intelligence

JUNE 14, 2023

Additionally, it is vital to be able to execute computing operations on the 1000+ PB within a multi-parallel processing distributed system, considering that the data remains dynamic, constantly undergoing updates, deletions, movements, and growth. Consider data types. There’s big data, and then there’s Cloudera.

Unstructured Data 64

Unstructured Data IT Manufacturing Visualization 64

Cloudera

SEPTEMBER 6, 2018

Support machine learning (ML) algorithms and data science activities, to help with name matching, risk scoring, link analysis, anomaly detection, and transaction monitoring. Provide audit and data lineage information to facilitate regulatory reviews. Spark also enables data science at scale. Cloudera Enterprise.

Machine Learning 40

Machine Learning Big Data Risk Unstructured Data 40