Rocket-Powered Data Science

JULY 19, 2023

The results showed that (among those surveyed) approximately 90% of enterprise analytics applications are being built on tabular data. The ease with which such structured data can be stored, understood, indexed, searched, accessed, and incorporated into business models could explain this high percentage.

Data-driven 186

Data-driven Enterprise Analytics Machine Learning 186

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 110

IT Data Architecture Unstructured Data Big Data 110

AWS Big Data

JUNE 6, 2023

This is a guest blog post by Mira Daniels and Sean Whitfield from SumUp. The Data Science teams also use this data for churn prediction and CLTV modeling. Given that the only source to access all raw data is by exporting it to BigQuery (first), data accessibility becomes challenging if BigQuery isn’t your DWH solution.

Analytics 56

Analytics Data Lake Testing Optimization 56

The Unofficial Google Data Science Blog

SEPTEMBER 9, 2015

Sawzall is a programming language developed at Google for performing aggregation over the result of complex operations on structured data. Record-level program scope As a data scientist, you write a Sawzall script to operate at the level of a single record. However, it turns out to be quite useful for data science applications.

Data Science 56

Data Science Statistics Testing Metadata 56

Cloudera

MAY 17, 2021

Modernizing your data warehousing experience with the cloud means moving from dedicated, on-premises hardware focused on traditional relational analytics on structured data to a modern platform. The post Key considerations when making a decision on a Cloud Data Warehouse appeared first on Cloudera Blog.

Data Warehouse 89

Data Warehouse Measurement Reporting Testing 89

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 Risk Big Data Unstructured Data 40

Cloudera

JANUARY 22, 2019

Today’s data landscape is characterized by exponentially increasing volumes of data, comprising a variety of structured, unstructured, and semi-structured data types originating from an expanding number of disparate data sources located on-premises, in the cloud, and at the edge.

Big Data 44

Big Data Data Lake Internet of Things Enterprise 44