Altexsoft

APRIL 16, 2021

An overview of data warehouse types. Optionally, you may study some basic terminology on data engineering or watch our short video on the topic: What is data engineering. What is data pipeline. OLTP vs OLAP: technology comparison. A comparison chart of OLTP and OLAP database features.

Analytics 98

Analytics Analysis Storage Business Intelligence 98

Altexsoft

JULY 18, 2023

These seemingly unrelated terms unite within the sphere of big data, representing a processing engine that is both enduring and powerfully effective — Apache Spark. Before diving into the world of Spark, we suggest you get acquainted with data engineering in general. How data engineering works in a nutshell.

Weak Development Team 52

Weak Development Team Big Data Data Machine Learning 52

Mobilunity

MAY 8, 2023

It is designed to store vast amounts of data from a variety of sources. Moreover an Enterprise Data Lake makes it accessible to data scientists, analysts, and other users across the enterprise. Ingestion zone is where data is collected from various sources and ingested into the data lake.

Engineering 52

Engineering Data Storage AWS 52

O'Reilly Media - Ideas

JANUARY 25, 2024

While we like to talk about how fast technology moves, internet time, and all that, in reality the last major new idea in software architecture was microservices, which dates to roughly 2015. Who wants to learn about design patterns or software architecture when some AI application may eventually do your high-level design?

Trends 115

Trends Technical Review Technology Artificial Inteligence 115

Altexsoft

APRIL 26, 2022

We’ll dive deeper into Snowflake’s pros and cons, its unique architecture, and its features to help you decide whether this data warehouse is the right choice for your company. Data warehousing in a nutshell. BTW, we have an engaging video explaining how data engineering works.

Weak Development Team 59

Weak Development Team Data Storage Technical Review 59

Mobilunity

NOVEMBER 24, 2021

The demand for specialists who know how to process and structure data is growing exponentially. In most digital spheres, especially in fintech, where all business processes are tied to data processing, a good big data engineer is worth their weight in gold. Who Is an ETL Engineer? Data modeling.

Development 52

Development Storage Recruiting Architecture 52

Agile Engine

APRIL 2, 2024

Typical roles you’ll find on dedicated teams include: Application developers Quality assurance experts and software testers UI/UX designers AI and data engineers Project managers Other specialized experts tailored to your project’s specific needs When are dedicated teams a good idea for your company?

UI/UX 52

UI/UX Development Software Development Quality Assurance 52

Netflix Tech

SEPTEMBER 10, 2019

Our data scientists often want to apply their knowledge of the business and statistics to fully understand the outcome of an experiment. Instead of relying on engineers to productionize scientific contributions, we’ve made a strategic bet to build an architecture that enables data scientists to easily contribute.

Analysis 86

Analysis Metrics Software Review Testing 86

Altexsoft

FEBRUARY 8, 2021

Machine learning techniques analyze big data from various sources, identify hidden patterns and unobvious relationships between variables, and create complex models that can be retrained to automatically adapt to changing conditions. Comparison between traditional and machine learning approaches to demand forecasting.

Business Intelligence 87

Business Intelligence Analytics Analysis Data 87

Altexsoft

OCTOBER 8, 2021

Not to mention that they require a decent level of expertise to develop, deploy, and maintain data integration flows. Now that you have a general picture of what data integration tools are, let’s move to the comparison of popular vendors. How to choose data integration software: key comparison criteria.

Tools 52

Tools Data Software Review Open Source 52

Altexsoft

MARCH 18, 2021

Transformations may include: data sorting and filtering to get rid of irrelevant items, de-duplicating and cleansing, translating and converting, removing or encrypting to protect sensitive information, splitting or joining tables, etc. These are data engineers who are responsible for implementing these processes.

Systems Review 109

Systems Review Technical Review Software Review Compliance 109

Altexsoft

OCTOBER 2, 2020

Key disciplines and roles in data management. 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. It’s very important to create a comprehensive data governance policy.

Database Administration 64

Database Administration Strategy Data Technical Review 64

Mobilunity

DECEMBER 21, 2022

Data scientists specialize in a variety of tasks that can benefit businesses in different ways. Some of the most noteworthy services they offer include: Data Science and Big Data Consulting. Big data engineer experts review existing data architecture, explore data sources, and analyze data lakes.

Data 52

Data How To Machine Learning Artificial Inteligence 52

Mobilunity

DECEMBER 28, 2022

Data wrangling and cleaning techniques. Deep learning architectures and techniques. Machine learning engineers use their skills to complete different specialized tasks, including: Designing and developing machine learning models and algorithms. Collaborating with software developers, data engineers, and other data scientists.

Machine Learning 52

Machine Learning Artificial Inteligence Engineering How To 52

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

Coforge

AUGUST 31, 2020

A deep understanding of the legacy and potentially viable digital systems is crucial to establish a sustainable architecture to work upon. Mapping the customer journeys must be the elementary step followed by creating interdisciplinary teams to facilitate faster decision-making.

Insurance 40

Insurance Technology Artificial Inteligence Policies 40

Altexsoft

DECEMBER 14, 2022

While you definitely saw the Docker vs Kubernetes comparison, these two systems cannot be compared directly. Docker architecture core components. Docker Architecture. Docker uses a client-server architecture where the Docker client communicates with the Docker daemon via a RESTful API, UNIX sockets, or a network interface.

Weak Development Team 52

Weak Development Team Linux Operating System Virtualization 52

O'Reilly Media - Ideas

JANUARY 25, 2022

What are the bigger changes shaping the future of software development and software architecture? We used data from the first nine months (January through September) of 2021. When doing year-over-year comparisons, we used the first nine months of 2020. This report is about those transitions. Software Development.

Trends 109

Trends Technical Review Technology Artificial Inteligence 109

Netflix Tech

JANUARY 11, 2022

To learn about Analytics and Viz Engineering, have a look at Analytics at Netflix: Who We Are and What We Do by Molly Jackman & Meghana Reddy and How Our Paths Brought Us to Data and Netflix by Julie Beckley & Chris Pham. Curious to learn about what it’s like to be a Data Engineer at Netflix?

Data 97

Data Metrics Testing Analysis 97

Altexsoft

SEPTEMBER 28, 2021

Particularly, it facilitates the work of researchers, data scientists, data engineers , QA engineers , and DevOps specialists. Besides that, throwing out the GIL would degrade the performance of software with single-threaded architecture. Versatility plus extensive toolset for almost everything.

Weak Development Team 94

Weak Development Team Programming Software Review Systems Review 94

Altexsoft

NOVEMBER 28, 2019

The project scope defines the degree of involvement for a certain role, as engineers with similar technology stacks and domain knowledge can be interchangeable. Developing BI interfaces requires a deep experience in software engineering, databases, and data analysis. Document contents in a data warehouse and meta-data storage.

Business Intelligence 22

Business Intelligence Technical Review Development Storage 22

Altexsoft

SEPTEMBER 20, 2019

Depending on the type of logical connection and data itself, visualization can be done in a suitable format. So it’s dead simple, any analytical report contains examples of data interpretations like pie charts, comparison bars, demographic maps, and many more. Architecture of your database/data warehouse.

Business Intelligence 18

Business Intelligence Tools Data Analytics 18

Cloudera

AUGUST 20, 2021

The Cloudera Data Platform comprises a number of ‘data experiences’ each delivering a distinct analytical capability using one or more purposely-built Apache open source projects such as Apache Spark for Data Engineering and Apache HBase for Operational Database workloads.

Strategy 94

Strategy Data Technical Review Weak Development Team 94

John Snow Labs

MAY 24, 2023

The two important functions of this tool are: – Performing different types of labeling with various data formats. LabelBox LabelBox is an efficient AI Data Engine platform for AI assisted labeling, data curation, model training, and more. It annotates images, videos, text documents, audio, and HTML, etc.

Tools 52

Tools Machine Learning Artificial Inteligence Software Review 52

O'Reilly Media - Ideas

JUNE 19, 2019

Some early systems allow for the comparison of an “incumbent model” against “challenger models,” including having challengers in “dark launch” or “offline” mode (this means challenger models are evaluated on production traffic but haven’t been deployed to production). Harish Doddi: “Simplifying machine learning lifecycle management”.

Government 79

Government Artificial Inteligence Machine Learning Testing 79

Cloudera

NOVEMBER 22, 2021

In addition, data pipelines include more and more stages, thus making it difficult for data engineers to compile, manage, and troubleshoot those analytical workloads. different analytical frameworks) for complex use cases that span different stages across the data lifecycle? CRM platforms).

Scalability 88

Scalability Technical Review Data Analytics 88

Cloudera

MAY 18, 2021

The intent of this article is to articulate and quantify the value proposition of CDP Public Cloud versus legacy IaaS deployments and illustrate why Cloudera technology is the ideal cloud platform to migrate big data workloads off of IaaS deployments. Experience configuration / use case deployment: At the data lifecycle experience level (e.g.,

Cloud 83

Cloud Technical Review Storage Backup 83

O'Reilly Media - Ideas

JANUARY 25, 2021

While we can’t compare in-person conference data with virtual event data, we can make a few observations. The most successful superstream series focused on software architecture and infrastructure and operations. The in-person O’Reilly Software Architecture Conference was small but growing. The horse race?

Programming 145

Programming Cloud Artificial Inteligence Machine Learning 145