Kentik

MAY 2, 2016

In part 1 of this series we introduced Kentik Data Engine™, the backend to Kentik Detect™, which is a large-scale distributed datastore that is optimized for querying IP flow records (NetFlow v5/9, sFlow, IPFIX) and related network data (GeoIP, BGP, SNMP). Want to try KDE with your own network data? Time: 0.458s.

Data Engineering 40

Data Engineering Engineering .Net Data 40

Hu's Place - HitachiVantara

JUNE 5, 2019

“ It unleashes data’s ultimate potential by automating the processes that enable the right data to get to the right place at the right time – all while ensuring it remains secure and accessible only to authorized employees. Business owners reported immediate benefits from this implementation. Fatima Hamad, Sr.

Technical Review 40

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Altexsoft

AUGUST 29, 2023

The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. This article explains what a data lake is, its architecture, and diverse use cases. Enterprise-grade security.

Architecture 52

Architecture Data Storage Machine Learning 52

Altexsoft

FEBRUARY 8, 2021

diversity of sales channels, complex structure resulting in siloed data and lack of visibility. These challenges can be addressed by intelligent management supported by data analytics and business intelligence (BI) that allow for getting insights from available data and making data-informed decisions to support company development.

Business Intelligence 87

Business Intelligence Analytics Analysis Data 87

Altexsoft

APRIL 13, 2023

To store all this diverse information, you’ll have to utilize a centralized data repository such as a data warehouse or data lake. You can also consider a cloud data lakehouse as an option since it addresses the limitations of the aforementioned repository types and works with various data workloads. Scalability.

Technical Review 59

Technical Review Software Review Analytics Systems Review 59

TechCrunch

NOVEMBER 2, 2022

When Berlin-based Y42 launched in 2020 , its focus was mostly on orchestrating data pipelines for business intelligence. “The use case for data has moved beyond ad hoc reporting to become the very lifeblood of a company. No-code business intelligence service y42 raises $2.9M seed round.

Data 203

Data Business Intelligence Open Source Government 203

Altexsoft

SEPTEMBER 9, 2022

Metadata management tools (also called enterprise metadata management or EMM systems) are software solutions that help capture and control metadata. Usually they come as modules of data governance, digital asset management, or data management platforms. It provides data consumers with. an integrated business glossary.

Tools 59

Tools Technical Review Software Review Systems Review 59

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. Watch this video to get an overview of CDW. .

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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

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Altexsoft

JANUARY 22, 2020

Please note: this topic requires some general understanding of analytics and data engineering, so we suggest you read the following articles if you’re new to the topic: Data engineering overview. A complete guide to business intelligence and analytics. The role of business intelligence developer.

Analytics 20

Analytics Data IoT Analysis 20

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. Towards Data Science ). Forrester ).

Architecture 75

Architecture Data Artificial Inteligence Artificial Intelligence 75

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 ).

Data 83

Data Analytics Machine Learning Artificial Inteligence 83

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 114

Government Artificial Inteligence Machine Learning Data 114

Altexsoft

OCTOBER 24, 2019

While our brain is both the processor and the storage, companies need multiple tools to work with data. And one of the most important ones is a data warehouse. In this article, we will discuss what an enterprise data warehouse is, its types and functions, and how it’s used in data processing.

Architecture 50

Architecture Enterprise Data Technical Review 50

Altexsoft

MARCH 18, 2021

The primary difference resides in WHERE and WHEN data transformation and loading happens. With ETL, data is transformed in a temporary staging area before it gets to a target repository (e.g As data keeps growing in volumes and types, the use of ETL becomes quite ineffective, costly, and time-consuming. ELT comes to the rescue.

Systems Review 109

Systems Review Technical Review Software Review Compliance 109

Altexsoft

JANUARY 14, 2021

A data warehouse is often abbreviated as DW or DWH. You may also find it under the name of an enterprise data warehouse (EDW). It is usually created and used primarily for data reporting and analysis purposes. The variety of data explodes and on-premises options fail to handle it. Scalability opportunities.

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Backup Azure Software Review Systems Review 115

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. The main idea of any data warehouse (DW) is to integrate data from multiple disjointed sources (e.g.,

Weak Development Team 59

Weak Development Team Data Storage Technical Review 59

Altexsoft

OCTOBER 2, 2020

Data architecture: aligning technologies with business goals. Specialist responsible for the area: data architect. Data architecture is a starting point for any data management model. Fitting into wider enterprise architecture, it outlines how data is collected, integrated, transformed, stored, and used.

Database Administration 64

Database Administration Strategy Data Technical Review 64

Altexsoft

OCTOBER 8, 2021

This often includes such procedures as data profiling (searching for errors and inconsistencies), cleansing (removing duplicates, corrupted items, etc.), Data integration process. On the enterprise level, data integration may cover a wider array of data management tasks including. high security. Source: Oracle.

Tools 52

Tools Data Software Review Open Source 52

Altexsoft

JULY 19, 2022

Through all these shifts, data mesh is called to solve the problems of centralized data platforms by giving more flexibility and independence, agility and scalability, cost-effectiveness, and cross-functionality. Data mesh principles and architecture. And it’s their job to guarantee data quality.

Architecture 52

Architecture Data Analytics Data Engineering 52

Altexsoft

AUGUST 4, 2021

Instead of combing through the vast amounts of all organizational data stored in a data warehouse, you can use a data mart — a repository that makes specific pieces of data available quickly to any given business unit. What is a data mart? Key differences between data marts, data warehouses, and data lakes.

Data 64

Data Analytics Construction Cloud 64

Cloudera

JULY 9, 2018

Most organizations struggle to unlock data science in the enterprise. To that end, Cloudera offers the Data Science Workbench, a collaborative, scalable, and highly extensible platform for data exploration, analysis, modeling, and visualization. Being able to scale up and scale down rapidly is key.

Machine Learning 41

Machine Learning Artificial Inteligence Data Analytics 41

Cloudera

NOVEMBER 2, 2020

Data streamed in is queryable immediately, in an optimal manner. Data streamed in is queryable in conjunction with historical data, avoiding need for Lambda Architecture. Data Model. Conventional enterprise data types. Figure 1 below shows a standard architecture for a Real-Time Data Warehouse.

Data 90

Data Analytics Storage Big Data 90

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. Altus Data Warehouse for SQL and business intelligence reporting and analytics.

Data 40

Data Data Engineering Analytics Cloud 40

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

JUNE 26, 2023

Data collection is a methodical practice aimed at acquiring meaningful information to build a consistent and complete dataset for a specific business purpose — such as decision-making, answering research questions, or strategic planning. For this task, you need a dedicated specialist — a data engineer or ETL developer.

Machine Learning 52

Machine Learning Artificial Inteligence Data Systems Review 52

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. Data lake architecture example. Poor data quality, reliability, and integrity.

Architecture 52

Architecture Data Storage Machine Learning 52

Cloudera

OCTOBER 26, 2017

Become more agile with business intelligence and data analytics. Many of us are all too familiar with the traditional way enterprises operate when it comes to on-premises data warehousing and data marts: the enterprise data warehouse (EDW) is often the center of the universe.

Cloud 42

Cloud Data Storage Analytics 42

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. A rare breed. The difficulty with querying streams.

Data 110

Data Weak Development Team Data Engineering Scalability 110

Altexsoft

MARCH 30, 2023

But what do the gas and oil corporation, the computer software giant, the luxury fashion house, the top outdoor brand, and the multinational pharmaceutical enterprise have in common? The answer is simple: They use the same technology to make the most of data. 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

In recent years, cloud-based data warehouses have revolutionized data processing with their advanced massively parallel processing (MPP) capabilities and SQL support. This development has paved the way for a suite of cloud-native data tools that are user-friendly, scalable, and affordable. What is a modern data stack?

Data 59

Data Technical Review Software Review Artificial Inteligence 59

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

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