Apiumhub

APRIL 1, 2021

Learning data science through books will help you get a holistic view of Data Science as data science is not just about computing, it also includes mathematics, probability, statistics, programming, machine learning, and much more. Top Data science books you should definitely read.

Artificial Inteligence 52

Artificial Inteligence Data Handbook Machine Learning 52

CIO

FEBRUARY 14, 2023

Here, I’ll focus on why these three elements and capabilities are fundamental building blocks of a data ecosystem that can support real-time AI. DataStax Real-time data and decisioning First, a few quick definitions. Real-time data involves a continuous flow of data in motion.

Artificial Inteligence 279

Artificial Inteligence Strategy Data Machine Learning 279

Altexsoft

MAY 27, 2020

But, in any case, the pipeline would provide data engineers with means of managing data for training, orchestrating models, and managing them on production. A dedicated team of data scientists or people with a business domain would define the data that will be used for training.

Machine Learning 89

Machine Learning Artificial Inteligence Architecture Training 89

CIO

AUGUST 5, 2022

Data visualization definition. Data visualization is the presentation of data in a graphical format such as a plot, graph, or map to make it easier for decision makers to see and understand trends, outliers, and patterns in data. Maps and charts were among the earliest forms of data visualization.

Data 316

Data Analytics Travel Business Intelligence 316

OpenCredo

JULY 13, 2018

The Power of the Architecture-driven Organisation. Engineers in an agile development team generally do not have much control over this scenario, but as a consultant it is something I would definitely want to highlight in order to give Project Positron the best chance of success in its organisation. – Melvin Conway.

Architecture 40

Architecture Fractional CTO CTO Coach Engineering 40

Cloudera

MARCH 17, 2023

As the use of ChatGPT becomes more prevalent, I frequently encounter customers and data users citing ChatGPT’s responses in their discussions. I love the enthusiasm surrounding ChatGPT and the eagerness to learn about modern data architectures such as data lakehouses, data meshes, and data fabrics.

ChatGPT 62

ChatGPT Education Data Comparison 62

Cloudera

JULY 27, 2021

While billing used to be one of two critical things for any successful telco (the other being the network), today’s digital service providers prioritise channels, ecosystems, payments and cloud service architectures in enterprise architecture. Edge analytics by definition require in-network deployment.

Telecommunications 62

Telecommunications Data Architecture Big Data 62

Altexsoft

MARCH 30, 2023

Shell, Adobe, Burberry, Columbia, Bayer — you definitely know the names. The answer is simple: They use the same technology to make the most of data. Along with thousands of other data-driven organizations from different industries, the above-mentioned leaders opted for Databrick to guide strategic business decisions.

Weak Development Team 64

Weak Development Team Machine Learning Artificial Inteligence Software Review 64

Netflix Tech

OCTOBER 18, 2022

Meson was based on a single leader architecture with high availability. We want users to rely on shared templates and reuse their workflow definitions across their team, saving time and effort on creating the same functionality. Figure 1 shows the high-level architecture. With the high growth of workflows in the past few years?

Data 81

Data UI/UX Systems Review Software Review 81

Altexsoft

JANUARY 14, 2021

We’ll review all the important aspects of their architecture, deployment, and performance so you can make an informed decision. Before jumping into the comparison of available products right away, it will be a good idea to get acquainted with the data warehousing basics first. Data warehouse architecture.

Backup 115

Backup Azure Software Review Systems Review 115

TIBCO - Connected Intelligence

AUGUST 10, 2020

Your data demands, like your data itself, are outpacing your data engineering methods and teams. You’ll discover that they all have identified data virtualization as a must-have addition to your data integration tooling and a critical enabler to a more modern, distributed data architecture.

Virtualization 69

Virtualization Data How To Data Engineering 69

Kentik

APRIL 4, 2023

Highly available networks are resistant to failures or interruptions that lead to downtime and can be achieved via various strategies, including redundancy, savvy configuration, and architectural services like load balancing. Resiliency. Resilient networks can handle attacks, dropped connections, and interrupted workflows.

Network 104

Network Load Balancer Cloud Backup 104

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 119

Trends Technical Review Technology Artificial Inteligence 119

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

JULY 29, 2022

a runtime environment (sandbox) for classic business intelligence (BI), advanced analysis of large volumes of data, predictive maintenance , and data discovery and exploration; a store for raw data; a tool for large-scale data integration ; and. a suitable technology to implement data lake architecture.

Big Data 59

Big Data Data Google Cloud Storage 59

Altexsoft

JUNE 26, 2023

Both data integration and ingestion require building data pipelines — series of automated operations to move data from one system to another. For this task, you need a dedicated specialist — a data engineer or ETL developer. Data engineering explained in 14 minutes. How systems exchange data.

Machine Learning 52

Machine Learning Artificial Inteligence Data Systems Review 52

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

Hu's Place - HitachiVantara

JUNE 5, 2019

This was a major accomplishment due to the lack of documentation, legacy tribal knowledge, lack of awareness of the differences between operational and analytic reporting, multiple definitions from different domains, lack of basic data discipline, and whole new technology stacks for volume and scale processing.

Technical Review 40

Technical Review Analytics Data Engineering Scalability 40

AWS Machine Learning - AI

JANUARY 26, 2024

Understanding and addressing LLM vulnerabilities, threats, and risks during the design and architecture phases helps teams focus on maximizing the economic and productivity benefits generative AI can bring. Many customers are looking for guidance on how to manage security, privacy, and compliance as they develop generative AI applications.

Artificial Inteligence 117

Artificial Inteligence Generative AI Security Applications 117

Altexsoft

AUGUST 21, 2019

Data obsession is all the rage today, as all businesses struggle to get data. But, unlike oil, data itself costs nothing, unless you can make sense of it. Dedicated fields of knowledge like data engineering and data science became the gold miners bringing new methods to collect, process, and store data.

Development 44

Development Software Engineering Data Engineering Architecture 44

Altexsoft

OCTOBER 21, 2022

The technology was written in Java and Scala in LinkedIn to solve the internal problem of managing continuous data flows. The number of possible applications tends to grow due to the rise of IoT , Big Data analytics , streaming media, smart manufacturing, predictive maintenance , and other data-intensive technologies.

Weak Development Team 93

Weak Development Team Technical Review Systems Review Software Review 93

Netflix Tech

MARCH 25, 2019

We adopted the following mission statement to guide our investments: “Provide a complete and accurate data lineage system enabling decision-makers to win moments of truth.” Nonetheless, Netflix data landscape (see below) is complex and many teams collaborate effectively for sharing the responsibility of our data system management.

Infrastructure 87

Infrastructure Data Technical Review Systems Review 87

Altexsoft

NOVEMBER 23, 2021

To break data silos and speed up access to all enterprise information, organizations can opt for an advanced data integration technique known as data virtualization. This post is a perfect place to learn about this approach, its architecture components, differences, benefits, tools, and more. What is data virtualization?

Virtualization 69

Virtualization Tools Data Architecture 69

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

Cloudera

DECEMBER 7, 2017

At Cloudera, we call that better way the Shared Data Experience (SDX.) Now we are releasing the reference architecture for you build your own self-managed SDX foundation for all your cloud-based data and analytics applications. We announced SDX at Strata New York in September 2017.

Data 40

Data Machine Learning Artificial Inteligence Analytics 40

Erik Bernhardsson

OCTOBER 27, 2015

There’s no clear problem formulation, no clear loss function, lots of various data sets to use. Learning stuff is what matters and kind of by definition you have to do stupid s**t before you learned it. What do you wish you knew earlier about being a data scientist? I don’t consider myself a data scientist so not sure :).

Data 100

Data Big Data Machine Learning Artificial Inteligence 100

Erik Bernhardsson

OCTOBER 27, 2015

There’s no clear problem formulation, no clear loss function, lots of various data sets to use. Learning stuff is what matters and kind of by definition you have to do stupid s**t before you learned it. What do you wish you knew earlier about being a data scientist? I don’t consider myself a data scientist so not sure :).

Data 100

Data Big Data Machine Learning Artificial Inteligence 100

Altexsoft

MARCH 14, 2023

Known as the Modern Data Stack (MDS) , this suite of tools and technologies has transformed how businesses approach data management and analysis. What is a modern data stack? A data stack, in turn, focuses on data : It helps businesses manage data and make the most out of it. Modern data stack architecture.

Data 59

Data Technical Review Software Review Artificial Inteligence 59

OpenCredo

MARCH 14, 2018

To clarify, let’s venture some simple definitions. Definitions & Typical Scenarios. Following these definitions, events are essential to building an event log for each entity and therefore to track the state of the system. Commands, organised in a command log, are an account of all the things the system has been asked to do.

System 45

System Architecture Examples Storage 45

RapidValue

JULY 21, 2019

From DevOps to DataOps DataOps can be simply stated as “DevOps for data”. It is a set of practices and technologies that integrate the development and operation of data movement architectures into a continuous process. DataOps aids data practitioners to continuously deliver quality data to applications and business processes.

Business Transformation 65

Business Transformation Enterprise DevOps Analytics 65

Cloudera

SEPTEMBER 1, 2020

Although not elaborated on in this blog post, it is possible to use a CDP Data Hub Data Engineering cluster for pre-processing data via Spark, and then post to Solr on DDE for indexing and serving. If you rather want to create your own cluster definition, you can read how to in our product documentation.

Data 68

Data Backup Disaster Recovery Storage 68

Abhishek Tiwari

DECEMBER 27, 2017

Kubernetes has emerged as go to container orchestration platform for data engineering teams. In 2018, a widespread adaptation of Kubernetes for big data processing is anitcipated. Organisations are already using Kubernetes for a variety of workloads [1] [2] and data workloads are up next.

Big Data 63

Big Data Data Storage Microservices 63

Netflix Tech

DECEMBER 21, 2020

This article will list some of the use cases of AutoOptimize, discuss the design principles that help enhance efficiency, and present the high-level architecture. Some of the optimizations are prerequisites for a high-performance data warehouse. We store the MSE and N for each partition in Redis for later use.

Storage 81

Storage Data Resources Data Engineering 81

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

Apiumhub

AUGUST 12, 2021

about Mutation Testing, ACRUMEN (his new definition of software quality), some differences between Functional and Object Oriented programming,etc. His current technical expertise focuses on integration platform implementations, Azure DevOps, and Cloud Solution Architectures. He has spoken at many conferences, user groups, etc.,

Industry 59

Industry Technical Advisors CTO Coach Azure 59

Cloudera

OCTOBER 26, 2017

Become more agile with business intelligence and data analytics. The ease that one can acquire elastic computational resources of different configurations (CPU, RAM, and so on) to run analytics on data combined with the utility-based cost model where you pay for only what you use. Architecture patterns for the cloud.

Cloud 43

Cloud Data Storage Analytics 43

Cloudera

AUGUST 24, 2020

You need to have consistent security and governance that allows you to not only control who has access to data, but also have full insights into lineage, metadata and cataloging throughout your environment. By ignoring this piece now, you will definitely add more work for yourself later.

Cloud 79

Cloud Data Government Innovation 79