CIO

DECEMBER 22, 2022

DataOps (data operations) is an agile, process-oriented methodology for developing and delivering analytics. It brings together DevOps teams with data engineers and data scientists to provide the tools, processes, and organizational structures to support the data-focused enterprise. What is DataOps?

Analytics 325

Analytics Data Engineering Machine Learning Artificial Inteligence 325

Apiumhub

AUGUST 12, 2021

An International speaker, books & video author, and writer for Java Magazine, IBM Developer, Oracle, and InfoQ. His main work is software development consulting, which combines actually writing code with advising clients on how to do that better. Michal Lukaszewski – AI Manager @Intel Software developer, mentor, manager.

Industry 59

Industry Technical Advisors CTO Coach Azure 59

Altexsoft

AUGUST 25, 2020

Big data is tons of mixed, unstructured information that keeps piling up at high speed. That’s why traditional data transportation methods can’t efficiently manage the big data flow. Big data fosters the development of new tools for transporting, storing, and analyzing vast amounts of unstructured data.

Big Data 75

Big Data Data Engineering Engineering Data 75

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. Key challenges. Performance.

Big Data 63

Big Data Data Storage Microservices 63

Abhishek Tiwari

JULY 12, 2018

Data Pipeline A data pipeline is a software that ingests data from multiple sources, transforms it and finally makes it available to internal or external products. Unfortunately, building data pipelines remains a daunting, time-consuming, and costly activity. Depending on frameworks, data processing units (a.k.a

Data 63

Data Software Review Technical Review Microservices 63

Erik Bernhardsson

OCTOBER 27, 2015

For the other camp, in my experience understanding backend development is super important. So I think for anyone who wants to build cool ML algos, they should also learn backend and data engineering. How do you respond when you hear the phrase ‘big data’? And above all, having deep empathy for user behavior.

Data 100

Data Big Data Machine Learning Artificial Inteligence 100

Erik Bernhardsson

OCTOBER 27, 2015

For the other camp, in my experience understanding backend development is super important. So I think for anyone who wants to build cool ML algos, they should also learn backend and data engineering. How do you respond when you hear the phrase ‘big data’? And above all, having deep empathy for user behavior.

Data 100

Data Big Data Machine Learning Artificial Inteligence 100

Kentik

APRIL 11, 2016

The concept of parallel processing based on a “clustered” multi-computer architecture has a long history dating back at least as far as Gene Amdahl’s work at IBM in the 1960s. The problem for Hadoop users is that the Hadoop architecture doesn’t lend itself to interactive, low latency, ad-hoc queries.

Big Data 40

Big Data Analytics Network Architecture 40

Altexsoft

JUNE 11, 2021

Besides, they hired a data scientist to further discover opportunities for process improvement and trained more people in big data. As part of their development strategy, they wanted to produce new samples and deliver them to customers within 15 days. The biggest challenge here is the increased complexity of data architecture.

Applications 69

Applications Weak Development Team Systems Review Software Review 69

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. Remember that these “units” are “viewed” by our users, who are largely professional software developers and programmers. What does this mean?

Trends 118

Trends Technical Review Technology Artificial Inteligence 118

Cloudera

APRIL 26, 2023

Can you achieve similar outcomes with your on-premises data platform? Application modernization initiatives have led to cloud native architectures gaining popularity on premises, making it a sensible choice to extend to your data platform. This is exactly where cloud native architectures excel, and why they are so popular.

Cloud 65

Cloud Data Load Balancer Storage 65

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. The role of business intelligence developer. Data visualization as a part of data representation and analytics.

Analytics 20

Analytics Data IoT Analysis 20

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

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 129

Weak Development Team Data Technical Review Survey 129

Altexsoft

DECEMBER 14, 2022

Gone are the days of a web app being developed using a common LAMP (Linux, Apache, MySQL, and PHP ) stack. 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. Hardware isn’t virtualized. Docker Architecture.

Weak Development Team 52

Weak Development Team Linux Operating System Virtualization 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. a suitable technology to implement data lake architecture. a suitable technology to implement data lake architecture. Apache Hadoop architecture. What is Hadoop?

Big Data 59

Big Data Data Google Cloud Storage 59

O'Reilly Media - Ideas

MARCH 1, 2023

Now developers are using AI to write software. Content about software development was the most widely used (31% of all usage in 2022), which includes software architecture and programming languages. A lot has happened in the past year. But the conversation cooled almost as quickly as it started. Total platform usage grew by 14.1%

Trends 136

Trends Technical Review Technology Software Review 136

Cloudera

DECEMBER 20, 2023

Those models are trained or augmented with data from a data management platform. The data management platform, models, and end applications are powered by cloud infrastructure and/or specialized hardware. This is enabled through a Ray Module in cml extension’s Python package published by our team.

Artificial Inteligence 73

Artificial Inteligence Artificial Intelligence Enterprise Machine Learning 73

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

Altexsoft

FEBRUARY 8, 2021

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. Develop business-specific analytics platform. Supply chain management process.

Business Intelligence 87

Business Intelligence Analytics Analysis Data 87

Altexsoft

JUNE 6, 2019

Let’s travel overseas and check out how Chinese tech giants have been developing in the same field. GPUs provide a huge computational power, so systems that run on such hardware can efficiently analyze vast amounts of data and generate more accurate forecasting results. JD.com’s chain of unmanned stores.

Artificial Inteligence 89

Artificial Inteligence Artificial Intelligence Retail Innovation 89

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. No matter how you slice it, additional instances, hardware, etc., Resiliency. Costs Redundancy isn’t cheap.

Network 104

Network Load Balancer Cloud Backup 104

Altexsoft

MARCH 31, 2020

Only after these actions can you analyze data with dedicated software (a so-called online analytical processing or OLAP system). But how do you move data? You need to have infrastructure, hardware and/or software, that will allow you to do that. You need an efficient data pipeline. What is a data pipeline?

Data 76

Data Storage Analytics Data Center 76

Cloudera

JUNE 14, 2021

How to optimize an enterprise data architecture with private cloud and multiple public cloud options? Not only that, but service providers have been deploying their own clouds, some developing IaaS offerings, and partnering with cloud native content providers like Netflix and Spotify to enhance core telco bundles.

Telecommunications 96

Telecommunications Cloud Data Virtualization 96

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

Altexsoft

FEBRUARY 22, 2022

Just note that developing customized infrastructure requires investment and assistance from an experienced integration provider. So, if you feel you’ve outgrown your preventive strategy and have a budget to develop, we’re here to tell you how it works. Collecting data from connected sensors and external sources.

Software Review 52

Software Review Technical Review Software Technology 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. Data warehousing in a nutshell. By the type of deployment, data warehouses can be categorized into.

Weak Development Team 59

Weak Development Team Data Storage Technical Review 59

Altexsoft

MARCH 16, 2021

Let’s take a quick look at the big data infrastructure. Big Data infrastructure in healthcare. In general, a data infrastructure is a system of hardware and software tools used to collect, store, transfer, prepare, analyze, and visualize data. Big data infrastructure in a nutshell. Apple Watch ECG feature.

Big Data 116

Big Data Healthcare Applications Data 116

Altexsoft

SEPTEMBER 28, 2021

web development, data analysis. Source: Python Developers Survey 2020 Results. This distinguishes Python from domain-specific languages like HTML and CSS limited to web design or SQL created for accessing data in relational database management systems. many others. How Python is used. Object-oriented. Dynamic semantics.

Weak Development Team 94

Weak Development Team Programming Software Review Systems Review 94

Abhishek Tiwari

DECEMBER 27, 2017

A common theme across all these trends is to remove the complexity by simplifying data management as a whole. In 2018, we anticipate that ETL will either lose relevance or the ETL process will disintegrate and be consumed by new data architectures. Unified data management architecture.

Trends 49

Trends Artificial Inteligence Data Big Data 49

O'Reilly Media - Ideas

JULY 15, 2020

Software engineers comprise the survey audience’s single largest cluster, over one quarter (27%) of respondents (Figure 1). If you combine the different architectural roles—i.e., Adding architects and engineers, we see that roughly 55% of the respondents are directly involved in software development.

Microservices 141

Microservices Weak Development Team Survey Architecture 141

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

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

Cloudera

APRIL 9, 2021

However, legacy data warehouses have limited support for public and private cloud deployment architectures. Managing disparate solutions is operationally inefficient, provides an inconsistent user experience and often leads to increased risk due to a lack of common security, governance and lineage of data. . Efficiency.

Data 59

Data Software Review Technical Review Architecture 59

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. Data modeling: creating useful and meaningful data entities.

Database Administration 64

Database Administration Strategy Data Technical Review 64

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 streamed in is queryable in conjunction with historical data, avoiding need for Lambda Architecture.

Data 96

Data Analytics Storage Big Data 96

O'Reilly Media - Ideas

MARCH 18, 2020

Whether it’s controlling for common risk factors—bias in model development, missing or poorly conditioned data, the tendency of models to degrade in production—or instantiating formal processes to promote data governance, adopters will have their work cut out for them as they work to establish reliable AI production lines.

Enterprise 134

Enterprise Survey Technical Review Weak Development Team 134