Altexsoft

JUNE 26, 2023

While today’s world abounds with data, gathering valuable information presents a lot of organizational and technical challenges, which we are going to address in this article. We’ll particularly explore data collection approaches and tools for analytics and machine learning projects. What is data collection?

Machine Learning 52

Machine Learning Artificial Inteligence Data Systems Review 52

Altexsoft

SEPTEMBER 9, 2022

Metadata management is a part of the data governance process which, in turn, is an element of the overall data management strategy. Today, such modern data management frameworks as DataOps strongly rely on effective metadata capture and management to bring order into the chaotic data flows. data cataloging).

Tools 59

Tools Technical Review Software Review Systems Review 59

CIO

AUGUST 18, 2022

Traditionally, organizations have maintained two systems as part of their data strategies: a system of record on which to run their business and a system of insight such as a data warehouse from which to gather business intelligence (BI). You can intuitively query the data from the data lake.

Data 350

Data Artificial Inteligence Technical Advisors Technical Review 350

Cloudera

AUGUST 31, 2021

Today, modern data warehousing has evolved to meet the intensive demands of the newest analytics required for a business to be data driven. Traditional data warehouse vendors may have maturity in data storage, modeling, and high-performance analysis.

Data 82

Data Analytics Cloud Technical Review 82

Cloudera

DECEMBER 10, 2020

Some data warehousing solutions such as appliances and engineered systems have attempted to overcome these problems, but with limited success. . Recently, cloud-native data warehouses changed the data warehousing and business intelligence landscape. You have a centralized data catalog and schema registry.

Data 98

Data Technical Review Storage Systems Review 98

Cloudera

AUGUST 30, 2022

Data is the fuel that drives government, enables transparency, and powers citizen services. Data quality issues deter trust and hinder accurate analytics. Citizens who have negative experiences with government services are less likely to use those services in the future. Modern data architectures. Forrester ).

Architecture 77

Architecture Data Artificial Inteligence Artificial Intelligence 77

Datavail

JUNE 8, 2023

Data Summit 2023 was filled with thought-provoking sessions and presentations that explored the ever-evolving world of data. From the technical possibilities and challenges of new and emerging technologies to using Big Data for business intelligence, analytics, and other business strategies, this event had something for everyone.

Database Administration 52

Database Administration Data Artificial Inteligence Analytics 52

O'Reilly Media - Ideas

FEBRUARY 11, 2020

Key survey results: The C-suite is engaged with data quality. Data scientists and analysts, data engineers, and the people who manage them comprise 40% of the audience; developers and their managers, about 22%. Data quality might get worse before it gets better. Adopting AI can help data quality.

Weak Development Team 127

Weak Development Team Data Technical Review Survey 127

Cloudera

JULY 9, 2018

How do I wrangle in my data science community? How do I govern these systems and development processes while also managing quality? The Business Dilemma: Data science is complex and has its own language and perceptions. Business intelligence is the glue that binds traditional BI and data science together.

Machine Learning 42

Machine Learning Artificial Inteligence Data Analytics 42

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 85

Data Analytics Machine Learning Artificial Inteligence 85

Datavail

JUNE 15, 2023

“It unifies the business model across all the different types of analytics workloads, whether they’re SQL, machine learning – whatever job you want, you can use the same compute infrastructure. Data Factory : A data integration tool with 150+ connectors to cloud and on-premises data sources.

Azure 52

Azure Analytics Storage Generative AI 52

ParkMyCloud

MAY 29, 2019

In addition, they also have a strong knowledge of cloud services such as AWS, Google or Azure, with experience on ITSM, I&O, governance, automation, and vendor management. Business Intelligence Analyst. BI Analyst can also be described as BI Developers, BI Managers, and Big Data Engineer or Data Scientist.

Trends 72

Trends Cloud IoT Artificial Inteligence 72

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 111

Data Open Source Government Analytics 111

Cloudera

FEBRUARY 8, 2021

Serving – controlling and running essential business operations (dealer operations, production monitoring) . Predictive Analytics – predictive analytics based upon AI and machine learning (predictive maintenance, demand-based inventory optimization as examples). 1 The enterprise data lifecycle. Conclusion.

Data 115

Data Technical Review Data Engineering Analysis 115

Altexsoft

FEBRUARY 10, 2021

Similar to how DevOps once reshaped the software development landscape, another evolving methodology, DataOps, is currently changing Big Data analytics — and for the better. DataOps is a relatively new methodology that knits together data engineering, data analytics, and DevOps to deliver high-quality data products as fast as possible.

Analytics 75

Analytics DevOps Development Software Review 75

Altexsoft

NOVEMBER 10, 2021

At the same time, it brings structure to data and empowers data management features similar to those in data warehouses by implementing the metadata layer on top of the store. Traditional data warehouse platform architecture. Another type of data storage — a data lake — tried to address these and other issues.

Architecture 52

Architecture Data Storage Machine Learning 52

Cloudera

DECEMBER 7, 2017

Cloud promises many advantages as an environment for machine learning and analytics. Cloud offers elasticity of those resources to efficiently support transient analytics workloads and data pipelines. That data may be stored multiple times in different pools, each multiplying storage resource costs. It’s time for SDX.

Data 40

Data Machine Learning Artificial Inteligence Analytics 40

Altexsoft

APRIL 26, 2022

Not long ago setting up a data warehouse — a central information repository enabling business intelligence and analytics — meant purchasing expensive, purpose-built hardware appliances and running a local data center. BTW, we have an engaging video explaining how data engineering works.

Weak Development Team 59

Weak Development Team Data Storage Technical Review 59

Altexsoft

NOVEMBER 9, 2021

Traditional statistical methods use mainly internal, historical data to predict trends within relatively stable markets. Meanwhile, machine learning (ML) techniques are capable of processing a wide range of both historical and current data from multiple external and internal sources. Extract data. Fraud detection.

Analytics 52

Analytics Software Review Systems Review Technical Review 52

Altexsoft

JULY 29, 2022

Developed in 2006 by Doug Cutting and Mike Cafarella to run the web crawler Apache Nutch, it has become a standard for Big Data analytics. According to the study by the Business Application Research Center (BARC), Hadoop found intensive use as. a suitable technology to implement data lake architecture. Robust community.

Big Data 59

Big Data Data Google Cloud Storage 59

Cloudera

NOVEMBER 2, 2020

On the technical side, it is cheaper and easier than ever to instrument everything and send that data in real-time through a messaging system. On the business side, companies and governments are digitizing and automating as many of their operations as possible so decision making and asset management can be more effective.

Data 93

Data Analytics Storage Big Data 93

Altexsoft

OCTOBER 3, 2019

Predictive analytics requires numerous statistical techniques, including data mining (detecting patterns in data) and machine learning. Organizations already use predictive analytics to optimize operations and learn how to improve the employee experience. Let’s explore several popular areas of its application.

Analytics 16

Analytics Company Off-The-Shelf How To 16

TIBCO - Connected Intelligence

DECEMBER 20, 2021

TIBCO DQ will become the new data quality product family, through an evolution of our current data quality offerings, significantly enhancing current capabilities available throughout the TIBCO data fabric with built-in AI and ML to automate quality, detection, monitoring, and anomaly resolution.

Innovation 26

Innovation Analytics Virtualization Cloud 26

CIO

MAY 11, 2023

We leverage artificial intelligence, machine learning, and analytics to offer a hyperpersonalized, immersive, and entertaining gameplay experience to our users at every stage of their gaming journey in real-time. Depending on the use cases, we are using two platforms for data management.

Games 173

Games Artificial Inteligence Data Artificial Intelligence 173

Apiumhub

APRIL 8, 2021

Kirk Borne is a Principal Data Scientist at Booz Allen Hamilton. As an astrophysicist formerly working at NASA, Borne was the expert called upon to brief the President of the United States on data mining post 9/11, as the government explored how to use data mining to detect and prevent another terrorist attack.

Artificial Inteligence 110

Artificial Inteligence Technical Advisors Data Machine Learning 110

Altexsoft

DECEMBER 9, 2020

Diagnostic analytics identifies patterns and dependencies in available data, explaining why something happened. Predictive analytics creates probable forecasts of what will happen in the future, using machine learning techniques to operate big data volumes. Analytics maturity model. Ground level of analytics.

Analytics 102

Analytics Technical Review Technology Applications 102

Agile Engine

DECEMBER 27, 2023

A data analytics consultancy has a team of specialists and engineers who perform data analytics for companies that don’t have the capacity to do it in-house. Data augmentation 
We’ll bring you a 360-degree view of the customer landscape by augmenting assets with third-party data and predictive analytics.

Big Data 52

Big Data Analytics Data Analysis 52

Altexsoft

MARCH 30, 2023

What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. Besides that, it’s fully compatible with various data ingestion and ETL tools. How data engineering works in 14 minutes.

Weak Development Team 64

Weak Development Team Machine Learning Artificial Inteligence Software Review 64

Altexsoft

MARCH 14, 2023

That’s why some MDS tools are commercial distributions designed to be low-code or even no-code, making them accessible to data practitioners with minimal technical expertise. This means that companies don’t necessarily need a large data engineering team. Data democratization.

Data 59

Data Technical Review Software Review Artificial Inteligence 59