CIO

APRIL 10, 2024

As with many data-hungry workloads, the instinct is to offload LLM applications into a public cloud, whose strengths include speedy time-to-market and scalability. Inferencing funneled through RAG must be efficient, scalable, and optimized to make GenAI applications useful. Inferencing and… Sherlock Holmes???

Artificial Inteligence 203

Artificial Inteligence Generative AI Storage Artificial Intelligence 203

CIO

JANUARY 6, 2023

Database developers should have experience with NoSQL databases, Oracle Database, big data infrastructure, and big data engines such as Hadoop. It requires a strong ability for complex project management and to juggle design requirements while ensuring the final product is scalable, maintainable, and efficient.

LAN 215

LAN Systems Administration How To Software Engineering 215

CIO

APRIL 27, 2023

The demand for specialized skills has boosted salaries in cybersecurity, data, engineering, development, and program management. It’s a role that not only requires technical skills, but also leadership and communication skills as well to work across departments and to manage teams of engineers. increase from 2021.

Technical Review 174

Technical Review Software Review Systems Review Software Engineering 174

CIO

JUNE 19, 2024

Platform engineering: purpose and popularity Platform engineering teams are responsible for creating and running self-service platforms for internal software developers to use. The value proposition of IT will move into providing scalable, reliable platform services as well as IT expertise into those product teams.”

Weak Development Team 208

Weak Development Team Engineering UI/UX Software Development 208

O'Reilly Media - Ideas

JANUARY 14, 2025

Building applications with RAG requires a portfolio of data (company financials, customer data, data purchased from other sources) that can be used to build queries, and data scientists know how to work with data at scale. Data engineers build the infrastructure to collect, store, and analyze data.

Trends 130

Trends Technology Security Artificial Inteligence 130

CIO

JUNE 14, 2023

Going from petabytes (PB) to exabytes (EB) of data is no small feat, requiring significant investments in hardware, software, and human resources. This can be achieved by utilizing dense storage nodes and implementing fault tolerance and resiliency measures for managing such a large amount of data. Focus on scalability.

Data 97

Data Scalability Storage Big Data 97

Cloudera

NOVEMBER 28, 2023

Cloudera Private Cloud Data Services is a comprehensive platform that empowers organizations to deliver trusted enterprise data at scale in order to deliver fast, actionable insights and trusted AI. This means you can expect simpler data management and drastically improved productivity for your business users.

Data 84

Data Scalability Analytics Quality Assurance 84

Cloudera

AUGUST 26, 2020

Informatica and Cloudera deliver a proven set of solutions for rapidly curating data into trusted information. Informatica’s comprehensive suite of Data Engineering solutions is designed to run natively on Cloudera Data Platform — taking full advantage of the scalable computing platform.

Data 84

Data Artificial Inteligence Machine Learning Disaster Recovery 84

Altexsoft

JANUARY 14, 2021

The variety of data explodes and on-premises options fail to handle it. Apart from the lack of scalability and flexibility offered by modern databases, the traditional ones are costly to implement and maintain. At the moment, cloud-based data warehouse architectures provide the most effective employment of data warehousing resources.

Backup 115

Backup Azure Software Review Architecture 115

Cloudera

APRIL 22, 2021

Storage plays one of the most important roles in the data platforms strategy, it provides the basis for all compute engines and applications to be built on top of it. Businesses are also looking to move to a scale-out storage model that provides dense storages along with reliability, scalability, and performance.

Data 101

Data Storage Architecture Big Data 101

d2iq

JANUARY 18, 2022

Kubernetes would seem to be an ideal way to address some of the obstacles to getting AI/ML workloads into production. Kubeflow has its own challenges, too, including difficulties with installation and with integrating its loosely-coupled components, as well as poor documentation.

Artificial Inteligence 72

Artificial Inteligence Machine Learning AWS Weak Development Team 72

Altexsoft

DECEMBER 4, 2020

Hardware and software become obsolete sooner than ever before. So data migration is an unavoidable challenge each company faces once in a while. Transferring data from one computer environment to another is a time-consuming, multi-step process involving such activities as planning, data profiling, testing, to name a few.

Software Review 69

Software Review Software Data Technical Review 69

Altexsoft

MARCH 18, 2021

This includes Apache Hadoop , an open-source software that was initially created to continuously ingest data from different sources, no matter its type. Cloud data warehouses such as Snowflake, Redshift, and BigQuery also support ELT, as they separate storage and compute resources and are highly scalable.

Systems Review 109

Systems Review Technical Review Software Review Big Data 109

Altexsoft

FEBRUARY 11, 2023

Data architect and other data science roles compared Data architect vs data engineer Data engineer is an IT specialist that develops, tests, and maintains data pipelines to bring together data from various sources and make it available for data scientists and other specialists.

Data 87

Data Data Engineering Big Data Architecture 87

Cloudera

APRIL 26, 2023

It offers features such as data ingestion, storage, ETL, BI and analytics, observability, and AI model development and deployment. The platform offers advanced capabilities for data warehousing (DW), data engineering (DE), and machine learning (ML), with built-in data protection, security, and governance.

Cloud 64

Cloud Data Load Balancer Storage 64

Cloudera

FEBRUARY 1, 2024

This is the stage where scalability becomes a reality, adapting to growing data and user demands while continuously fortifying security measures. Moreover, it is a period of dynamic adaptation, where documentation and operational protocols will adapt as your data and technology landscape change.

Backup 82

Backup Cloud Architecture Resources 82

Altexsoft

JANUARY 22, 2020

As a result, it became possible to provide real-time analytics by processing streamed data. 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.

Analytics 20

Analytics Data IoT Analysis 20

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.

Artificial Inteligence 71

Artificial Inteligence Artificial Intelligence Enterprise Machine Learning 71

Abhishek Tiwari

JULY 12, 2018

Unfortunately, building data pipelines remains a daunting, time-consuming, and costly activity. Not everyone is operating at Netflix or Spotify scale data engineering function. Often companies underestimate the necessary effort and cost involved to build and maintain data pipelines.

Data 63

Data Software Review Technical Review Microservices 63

Cloudera

DECEMBER 10, 2020

Drawing on more than a decade of experience in building and deploying massive scale data platforms on economical budgets, Cloudera has designed and delivered a cost-cutting cloud-native solution – Cloudera Data Warehouse (CDW), part of the new Cloudera Data Platform (CDP). 2,300 / month for the cloud hardware costs.

Data 98

Data Technical Review Storage Systems Review 98

Altexsoft

JUNE 7, 2021

scalability. Hadoop allows you to leverage data from multiple sources and in different formats, both structured and unstructured. You don’t need to archive or clean data before loading. Hadoop works on low-cost, commodity hardware which makes it relatively cheap to maintain. No real-time data processing.

Big Data 98

Big Data Tools Data Storage 98

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 . By doing so the benefits to ingest speed, query latency, and scalability can be huge. Data Hub – .

Data 97

Data Analytics Storage Big Data 97

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. But for high availability and data loss prevention, it’s recommended that you have at least three brokers.

Weak Development Team 93

Weak Development Team Technical Review Systems Review Open Source 93

Mobilunity

DECEMBER 27, 2024

” Cyril Samovskiy, Founder of Mobilunity Tech Stack Proficiency AI-proficient engineers must write clean, efficient, and scalable code, ensuring their AI frameworks run effectively in various environments.

Artificial Inteligence 52

Artificial Inteligence Technical Review Engineering Systems Review 52

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. Native frameworks.

Big Data 63

Big Data Data Storage Microservices 63

Altexsoft

OCTOBER 2, 2020

A data architect focuses on building a robust infrastructure so that data brings business value. Data modeling: creating useful and meaningful data entities. Data integration and interoperability: consolidating data into a single view. Snowflake provides computing resources scalable for different workloads.

Strategy 64

Strategy Database Administration Data Technical Review 64

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. Apache Kafka.

Big Data 78

Big Data Analytics Tools Applications 78

Altexsoft

OCTOBER 24, 2019

And this is what makes a data warehouse different from a Data Lake. Data Lakes are used to store unstructured data for analytical purposes. But unlike warehouses, data lakes are used more by data engineers/scientists to work with big sets of raw data. Subject-oriented data.

Architecture 50

Architecture Enterprise Data Technical Review 50

Altexsoft

JULY 29, 2022

What happens, when a data scientist, BI developer , or data engineer feeds a huge file to Hadoop? Under the hood, the framework divides a chunk of Big Data into smaller, digestible parts and allocates them across multiple commodity machines to be processed in parallel. Scalability. Apache Hadoop architecture.

Big Data 59

Big Data Data Google Cloud Open Source 59

Cloudera

APRIL 9, 2021

Legacy data warehouse solutions are often inefficient due to their scale-up architecture, attempting to serve multiple phases of the data lifecycle with a single monolithic architecture, ineffective management and performance tuning tools. . ETL jobs and staging of data often often require large amounts of resources.

Data 58

Data Software Review Technical Review Architecture 58

Mobilunity

DECEMBER 9, 2024

ML algorithms for predictions and data-based decisions; Deep Learning expertise to analyze unstructured data, such as images, audio, and text; Mathematics and statistics. Google Professional Machine Learning Engineer implies developers knowledge of design, building, and deployment of ML models using Google Cloud tools.

Engineering 52

Engineering Artificial Inteligence Machine Learning Artificial Intelligence 52

O'Reilly Media - Ideas

JULY 15, 2020

Technical roles represented in the “Other” category include IT managers, data engineers, DevOps practitioners, data scientists, systems engineers, and systems administrators. Combined, technology verticals—software, computers/hardware, and telecommunications—account for about 35% of the audience (Figure 2).

Microservices 145

Microservices Weak Development Team Survey Architecture 145

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. Staffing problems.

Big Data 116

Big Data Healthcare Applications Data 116

Kentik

MAY 31, 2023

For many enterprises, applications represent only a portion of a much larger reliability mandate, including offices, robotics, hardware, and IoT, and the complex networking, data, and observability infrastructure required to facilitate such a mandate.

Network 52

Network Cloud Load Balancer Systems Review 52

Altexsoft

SEPTEMBER 20, 2019

Now we are moving to the section, where we will discuss tools that you can use to perform data visualization. Data visualization tools and libraries. There are a lot of products on the market of data visualization. GB, Sisense about 5 GB), and require modern hardware to operate without burning your computer.

Business Intelligence 18

Business Intelligence Tools Data Analytics 18

Capgemini

OCTOBER 10, 2024

As a Cloud Infrastructure Manager, I want to be able to easily scale up or down the compute resources in my cloud infrastructure to meet changing demands, without having to worry about the underlying hardware. Scalability: It can handle large volumes of data with ease, making it ideal for complex projects4. Thanks to Gemini 1.5

Artificial Inteligence 52

Artificial Inteligence Analysis Google Cloud Infrastructure 52

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. Scalability. Please note!

Tools 52

Tools Software Review Systems Review Testing 52