Cloudera

AUGUST 30, 2022

Data Lakehouse: Data lakehouses integrate and unify the capabilities of data warehouses and data lakes, aiming to support artificial intelligence, business intelligence, machine learning, and data engineering use cases on a single platform. Deploying modern data architectures.

Architecture 79

Architecture Data Artificial Inteligence Artificial Intelligence 79

Cloudera

JUNE 30, 2022

Today’s general availability announcement covers Iceberg running within key data services in the Cloudera Data Platform (CDP) — including Cloudera Data Warehousing ( CDW ), Cloudera Data Engineering ( CDE ), and Cloudera Machine Learning ( CML ). Read why the future of data lakehouses is open.

Data 89

Data Analytics Machine Learning Artificial Inteligence 89

Cloudera

AUGUST 17, 2021

When we announced the GA of Cloudera Data Engineering back in September of last year, a key vision we had was to simplify the automation of data transformation pipelines at scale. Let’s take a common use-case for Business Intelligence reporting. Figure 2: Example BI reporting data pipeline.

Data 115

Data Open Source Government Analytics 115

Cloudera

NOVEMBER 19, 2020

Too often, though, legacy systems cannot deliver the needed speed and scalability to make these analytic defenses usable across disparate sources and systems. For many agencies, 80 percent of the work in support of anomaly detection and fraud prevention goes into routine tasks around data management. Fraudulent Activity Detection.

Government 120

Government Artificial Inteligence Machine Learning Data 120

Perficient

APRIL 1, 2024

This data needs to be ingested on a real-time basis into the warehouse, for further usage by the data scientist & data analyst team. Terminologies Used Below are few terms used in the blog. Fabric enables integration of team of data scientist, data engineers & data analyst on a single unified platform.

Data 64

Data Azure Case Study Data Engineering 64

Cloudera

NOVEMBER 2, 2020

These are end-to-end, high volume applications that are used for general purpose data processing, Business Intelligence, operational reporting, dashboarding, and ad hoc exploration. But an important caveat is that ingest speed, semantic richness for developers, data freshness, and query latency are paramount.

Data 97

Data Analytics Storage Big Data 97

Cloudera

SEPTEMBER 9, 2018

It enables organizations to address their data warehousing reporting needs by making it quick and easy to consolidate data into a single repository yet still service your users’ needs in a scalable and cost-effective way. Using Cloudera Altus for your cloud data warehouse. Try Altus Data Warehouse today.

Data 40

Data Data Engineering Analytics Cloud 40

Cloudera

JULY 9, 2018

It is cliché to discuss IT/business solutions as people, process, and technology. Some call it the “golden triangle,” but in this blog, we refer to it as the iron triangle. Since the 1960s, technology has disrupted business through the advent of computing and information management. The New Iron Triangle. The Solution.

Machine Learning 42

Machine Learning Artificial Inteligence Data Analytics 42

Altexsoft

OCTOBER 21, 2022

It offers high throughput, low latency, and scalability that meets the requirements of Big Data. The technology was written in Java and Scala in LinkedIn to solve the internal problem of managing continuous data flows. A subscriber is a receiving program such as an end-user app or business intelligence tool.

Weak Development Team 93

Weak Development Team Technical Review Systems Review Software Review 93

Altexsoft

MAY 14, 2021

On top of that, new technologies are constantly being developed to store and process Big Data allowing data engineers to discover more efficient ways to integrate and use that data. You may also want to watch our video about data engineering: A short video explaining how data engineering works.

Big Data 78

Big Data Analytics Tools Applications 78

StubHub

SEPTEMBER 20, 2018

Analysts focus on ad-hoc, in-depth analytics to provide insightful business intelligence and product analysis. Infinite capacity and scalability The data system?—?including including data storage, pipeline, analytic platform and machine learning platform?—?is is cloud-based and scalable based on needs and usage.

Data 40

Data Scalability Analytics Sport 40

Cloudera

OCTOBER 26, 2017

Become more agile with business intelligence and data analytics. Business intelligence (BI) and analytics in the cloud is an area that has gained the attention of many organizations looking to provide a better user experience for their data analysts and engineers. Clouds (source: Pexels ).

Cloud 43

Cloud Data Storage Analytics 43

Cloudera

MARCH 2, 2021

However, in the typical enterprise, only a small team has the core skills needed to gain access and create value from streams of data. This data engineering skillset typically consists of Java or Scala programming skills mated with deep DevOps acumen. A rare breed. What do you mean by democratizing?

Data 113

Data Weak Development Team Data Engineering Scalability 113

Datavail

SEPTEMBER 8, 2022

According to an IDG survey , companies now use an average of more than 400 different data sources for their business intelligence and analytics processes. What’s more, 20 percent of these companies are using 1,000 or more sources, far too many to be properly managed by human data engineers.

Data 40

Data Nonprofit Quality Assurance Education 40