Altexsoft

DECEMBER 23, 2022

Whether your goal is data analytics or machine learning , success relies on what data pipelines you build and how you do it. But even for experienced data engineers, designing a new data pipeline is a unique journey each time. Data engineering in 14 minutes. ELT vs ETL. Order of process phases.

Tools 52

Tools Software Review Systems Review Testing 52

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. By the type of deployment, data warehouses can be categorized into. Pricing page.

Weak Development Team 59

Weak Development Team Data Storage Technical Review 59

Altexsoft

MARCH 18, 2021

From the late 1980s, when data warehouses came into view, and up to the mid-2000s, ETL was the main method used in creating data warehouses to support business intelligence (BI). As data keeps growing in volumes and types, the use of ETL becomes quite ineffective, costly, and time-consuming. What is ELT?

Systems Review 109

Systems Review Technical Review Software Review Compliance 109

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. An additional 7% are data engineers.

Weak Development Team 125

Weak Development Team Data Technical Review Survey 125

Altexsoft

SEPTEMBER 20, 2019

Not only technological companies are concerned about data analysis, but any kind of business is. Analyzing business information to facilitate data-driven decision making is what we call business intelligence or BI. How is data visualized in BI? Business intelligence data processing in a nutshell.

Business Intelligence 18

Business Intelligence Tools Data Analytics 18

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. 2,300 / month for the cloud hardware costs.

Data 98

Data Technical Review Storage Systems Review 98

Altexsoft

JULY 18, 2023

Its flexibility allows it to operate on single-node machines and large clusters, serving as a multi-language platform for executing data engineering , data science , and machine learning tasks. Before diving into the world of Spark, we suggest you get acquainted with data engineering in general.

Weak Development Team 52

Weak Development Team Big Data Data Artificial Inteligence 52

Apiumhub

AUGUST 12, 2021

Laurent Picard – Developer Advocate @Google Laurent is a developer passionate about software, hardware, science, and everything shaping the future. Also, he serves as the Program Director for Data science/Data Engineering Educational Program at Skillbox. Twitter: [link] Linkedin: [link].

Industry 59

Industry Technical Advisors CTO Coach Azure 59

Altexsoft

JANUARY 14, 2021

It is usually created and used primarily for data reporting and analysis purposes. Thanks to the capability of data warehouses to get all data in one place, they serve as a valuable business intelligence (BI) tool, helping companies gain business insights and map out future strategies.

Backup 115

Backup Azure Architecture Software Review 115

Altexsoft

FEBRUARY 22, 2022

Integration with a business intelligence tool is important to receive a holistic analysis of your maintenance processes, track costs, visualize trends, and get actionable insights. At the same time, those novel approaches require much more data and data engineering efforts than more traditional ML methods.

Software Review 52

Software Review Technical Review Software Technology 52

Cloudera

NOVEMBER 2, 2020

Having a live view of all aspects of their network lets them identify potentially faulty hardware in real time so they can avoid impact to customer call/data service. Ingest 100s of TB of network event data per day . Data Hub – . Data integration, distribution, and routing engine. Data Hub – .

Data 95

Data Analytics Storage Big Data 95

Altexsoft

DECEMBER 14, 2022

What’s more, this software may run either partly or completely on top of different hardware – from a developer’s computer to a production cloud provider. Thus, the guest operating system can be installed on this virtual hardware, and from there, applications can be installed and run in the same way as in the host operating system.

Weak Development Team 52

Weak Development Team Linux Operating System Virtualization 52

Altexsoft

OCTOBER 24, 2019

With a data warehouse, an enterprise is able to manage huge data sets, without administering multiple databases. Such practice is a futureproof way of storing data for business intelligence (BI) , which is a set of methods/technologies of transforming raw data into actionable insights. Subject-oriented data.

Architecture 50

Architecture Enterprise Data Technical Review 50

Altexsoft

MAY 14, 2021

The concept of Big Data isn’t new: It has been the desired fruit for several decades already as the capabilities of software and hardware have made it possible for companies to successfully manage vast amounts of complex data. Big Data analytics processes and tools. Data ingestion. Data storage and processing.

Big Data 78

Big Data Analytics Tools Applications 78

Altexsoft

OCTOBER 21, 2022

The technology was written in Java and Scala in LinkedIn to solve the internal problem of managing continuous data flows. A publisher (say, telematics or Internet of Medical Things system) produces data units, also called events or messages , and directs them not to consumers but to a middleware platform — a broker.

Weak Development Team 93

Weak Development Team Technical Review Systems Review Open Source 93

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

Altexsoft

OCTOBER 8, 2021

It’s worth remembering though that open-source projects may entail hidden costs related to purchasing hardware, building networks, training staff, etc. Not to mention that they require a decent level of expertise to develop, deploy, and maintain data integration flows. Xplenty: convenient low-code environment for data integration.

Tools 52

Tools Data Software Review Open Source 52

Altexsoft

NOVEMBER 9, 2021

Traditionally, analytics is associated with business intelligence and data visualization that are focused on studying past events and current processes. Also known as predictive analytics, it’s about discovering hidden patterns and dependencies, forecasting future events, and supporting decisions with data. Extract data.

Analytics 52

Analytics Software Review Systems Review Technical Review 52

Altexsoft

DECEMBER 9, 2020

We will describe each level from the following perspectives: differences on the operational level; analytics tools companies use to manage and analyze data; business intelligence applications in real life; challenges to overcome and key changes that lead to transition. Introducing data engineering and data science expertise.

Analytics 102

Analytics Technical Review Technology Applications 102

Altexsoft

MARCH 14, 2023

Modern data stack vs traditional data stack Traditional data stacks are typically on-premises solutions based on hardware and software infrastructure managed by the organization itself. This means that companies don’t necessarily need a large data engineering team. Data democratization.

Data 59

Data Technical Review Software Review Open Source 59