CIO

JUNE 14, 2023

Candidates are required to complete a minimum of 12 credits, including four required courses: Algorithms for Data Science, Probability and Statistics for Data Science, Machine Learning for Data Science, and Exploratory Data Analysis and Visualization. Candidates have 90 minutes to complete the exam.

Big Data 317

Big Data Analytics Data eLearning 317

CIO

JANUARY 19, 2024

The exam tests general knowledge of the platform and applies to multiple roles, including administrator, developer, data analyst, data engineer, data scientist, and system architect. The exam consists of 60 questions and the candidate has 90 minutes to complete it.

Artificial Inteligence 317

Artificial Inteligence Data Machine Learning Azure 317

CIO

JULY 6, 2023

OCI’s Supercluster includes OCI Compute Bare Metal, which provides an ultralow-latency remote direct access memory (RDMA) over a Converged Ethernet (RoCE) cluster for low-latency networking, and a choice of high-performance computing storage options.

Generative AI 283

Generative AI Artificial Inteligence Strategy Google Cloud 283

CIO

FEBRUARY 13, 2023

CIOs anticipate an increased focus on cybersecurity (70%), data analysis (55%), data privacy (55%), AI/machine learning (55%), and customer experience (53%). On-prem infrastructure will grow cold — with the exception of storage, Nardecchia says.

Budget 363

Budget Artificial Inteligence Technical Review VR 363

CIO

JUNE 22, 2023

There’s a demand for skills such as cybersecurity, cloud, IT project management, UX/UI design, change management, and business analysis. There’s a high demand for software engineers, data engineers, business analysts and data scientists, as finance companies move to build in-house tools and services for customers.

Industry 306

Industry UI/UX Telecommunications Quality Assurance 306

Altexsoft

FEBRUARY 11, 2023

Data architecture is the organization and design of how data is collected, transformed, integrated, stored, and used by a company. What is the main difference between a data architect and a data engineer? By the way, we have a video dedicated to the data engineering working principles.

Data 87

Data Data Engineering Big Data Architecture 87

Cloudera

JUNE 26, 2023

Here are some tips and tricks of the trade to prevent well-intended yet inappropriate data engineering and data science activities from cluttering or crashing the cluster. For data engineering and data science teams, CDSW is highly effective as a comprehensive platform that trains, develops, and deploys machine learning models.

Tools 75

Tools Data Engineering Analytics Testing 75

Kentik

APRIL 12, 2023

DataOps is a methodology that aims to streamline and automate managing and delivering data throughout its lifecycle, from ingestion to analysis and visualization. It is an extension of DevOps principles and practices to data management, enabling organizations to manage and automate data pipelines for quality, accuracy, and reliability.

Network 52

Network Data Engineering Machine Learning Artificial Inteligence 52

Altexsoft

AUGUST 29, 2023

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data.

Architecture 52

Architecture Data Storage Machine Learning 52

TechCrunch

MAY 18, 2022

When asked, Heartex says that it doesn’t collect any customer data and open sources the core of its labeling platform for inspection. “We’ve built a data architecture that keeps data private on the customer’s storage, separating the data plane and control plane,” Malyuk added.

Open Source 203

Open Source Weak Development Team Artificial Inteligence Data 203

Mobilunity

MAY 8, 2023

Key zones of an Enterprise Data Lake Architecture typically include ingestion zone, storage zone, processing zone, analytics zone, and governance zone. Ingestion zone is where data is collected from various sources and ingested into the data lake. Storage zone is where the raw data is stored in its original format.

Engineering 52

Engineering Data Storage AWS 52

Dzone - DevOps

FEBRUARY 16, 2023

So in this article, I will talk about how I improved overall data processing efficiency by optimizing the choice and usage of data warehouses. Too Much Data on My Plate The choice of data warehouses was never high on my worry list until 2021. In the company's infancy, we didn't have too much data to juggle.

Data 71

Data ChatGPT Storage Architecture 71

Hu's Place - HitachiVantara

APRIL 11, 2019

Few if any data management frameworks are business focused, to not only promote efficient use of data and allocation of resources, but also to curate the data to understand the meaning of the data as well as the technologies that are applied to the data so that data engineers can move and transform the essential data that data consumers need.

Data Engineering 81

Data Engineering Machine Learning Artificial Inteligence Technical Review 81

O'Reilly Media - Ideas

JANUARY 8, 2019

The demand for data skills (“the sexiest job of the 21st century”) hasn’t dissipated. LinkedIn recently found that demand for data scientists in the US is “off the charts,” and our survey indicated that the demand for data scientists and data engineers is strong not just in the US but globally.

Trends 86

Trends Data Machine Learning Artificial Inteligence 86

Cloudera

DECEMBER 10, 2020

Generally, if five LOB users use the data warehouse on a public cloud for eight hours a day for one month, you pay for the use of the service and the associated cloud hardware resources (compute and storage) for this period. 150 for storage use = $15 / TB / month x 10 TB. 150 for storage use = $15 / TB / month x 10 TB.

Data 98

Data Technical Review Storage Systems Review 98

Confluent

FEBRUARY 6, 2019

This blog post focuses on how the Kafka ecosystem can help solve the impedance mismatch between data scientists, data engineers and production engineers. Impedance mismatch between data scientists, data engineers and production engineers. For now, we’ll focus on Kafka.

Machine Learning 107

Machine Learning Artificial Inteligence Scalability Data Engineering 107

Mentormate

OCTOBER 31, 2023

A thorough stakeholder analysis enriches the AI project by making it more comprehensive, ethical, and aligned with the overarching business needs and societal values. It’s vital to anticipate both the upfront costs, like model training, and ongoing expenses, like data storage or additional software.

Artificial Inteligence 98

Artificial Inteligence Healthcare Budget Training 98

Cloudera

JULY 24, 2023

That first step requires integrating the latest versions of all required open source projects, including not just data processing engines (e.g., Apache Impala, Apache Spark) but also all foundational services needed for storage (e.g., data engineering pipelines, machine learning models).

Open Source 65

Open Source Analytics Software Review Metrics 65

Cloudera

DECEMBER 20, 2022

For this reason, the need to have a data platform that allows for a fine-tuned analysis and prediction of supply and demand becomes a necessary tool to operate the business in a more efficient and profitable way. Intelligent network management. All of this introduces a new issue: grid management.

Energy 79

Energy Analytics Data Machine Learning 79

Altexsoft

APRIL 22, 2020

So, along with data scientists who create algorithms, there are data engineers, the architects of data platforms. In this article we’ll explain what a data engineer is, the field of their responsibilities, skill sets, and general role description. What is a data engineer?

Data Engineering 69

Data Engineering Engineering Artificial Inteligence Data 69

Altexsoft

OCTOBER 30, 2021

If you’re an executive who has a hard time understanding the underlying processes of data science and get confused with terminology, keep reading. We will try to answer your questions and explain how two critical data jobs are different and where they overlap. Data science vs data engineering.

Data Engineering 98

Data Engineering Engineering Data Machine Learning 98

Cloudera

AUGUST 31, 2021

While this “data tsunami” may pose a new set of challenges, it also opens up opportunities for a wide variety of high value business intelligence (BI) and other analytics use cases that most companies are eager to deploy. . Traditional data warehouse vendors may have maturity in data storage, modeling, and high-performance analysis.

Data 80

Data Analytics Cloud Technical Review 80

O'Reilly Media - Ideas

APRIL 9, 2019

A few months ago, I wrote about the differences between data engineers and data scientists. An interesting thing happened: the data scientists started pushing back, arguing that they are, in fact, as skilled as data engineers at data engineering. Data engineering is not in the limelight.

Data Engineering 111

Data Engineering Engineering Data Technical Review 111

KitelyTech

FEBRUARY 3, 2021

Database management is what your database administrator uses to store, organize, and access computer data. It often includes three main components like the database schema, database application, and data engine. Database management systems must be flexible enough to handle large amounts of data in a short period of time.

Big Data 52

Big Data Data Development Business Intelligence 52

Altexsoft

AUGUST 25, 2020

That’s why a data specialist with big data skills is one of the most sought-after IT candidates. Data Engineering positions have grown by half and they typically require big data skills. Data engineering vs big data engineering. This greatly increases data processing capabilities.

Big Data 75

Big Data Data Engineering Engineering Data 75

Cloudera

JANUARY 20, 2021

Data Enrichment – data pipeline processing, aggregation & management to ready the data for further refinement. Reporting – delivering business enterprise insight (sales analysis and forecasting, market research, budgeting as examples). STEP 3: Monitor data throughput from each factory.

Data 103

Data Artificial Inteligence Analytics Machine Learning 103

Altexsoft

SEPTEMBER 9, 2022

Metadata management is a set of activities, technologies, and policies that target metadata collection, storage, and organizing. It aims at making data assets understandable and discoverable for users. Metadata storage usually implies developing a specialized repository. Metadata storage. What is metadata management?

Tools 59

Tools Technical Review Software Review Systems Review 59

Existek

JANUARY 11, 2022

It might be challenging as it requires a detailed analysis of the business-specific needs and a deeper understanding of the overall market situation. . In 2010, they launched Windows Azure, the PaaS, positioning it as an alternative to Google App Engine and Amazon EC2. So how do companies choose the cloud service provider?

Azure 52

Azure AWS Cloud How To 52

Altexsoft

JUNE 25, 2019

If we look at the hierarchy of needs in data science implementations, we’ll see that the next step after gathering your data for analysis is data engineering. This discipline is not to be underestimated, as it enables effective data storing and reliable data flow while taking charge of the infrastructure.

Data Engineering 106

Data Engineering Engineering Data Artificial Inteligence 106

Abhishek Tiwari

DECEMBER 22, 2017

Cheap storage and on-demand compute in the cloud coupled with the emergence of new big data frameworks and tools are forcing us to rethink the whole ETL and data warehousing architecture. If the majority of your data is unstructured such as text, images, documents, etc. Classic ETL. Late transformation.

Storage 40

Storage Big Data Google Cloud Analysis 40

Hu's Place - HitachiVantara

JANUARY 3, 2019

Data curation will be a focus to understand the meaning of the data as well as the technologies that are applied to the data so that data engineers can move and transform the essential data that data consumers need to power the organization.

Trends 86

Trends Artificial Inteligence Data Center Machine Learning 86

Netflix Tech

NOVEMBER 22, 2019

4:45pm-5:45pm NFX 209 File system as a service at Netflix Kishore Kasi , Senior Software Engineer Abstract : As Netflix grows in original content creation, its need for storage is also increasing at a rapid pace. Technology advancements in content creation and consumption have also increased its data footprint.

AWS 40

AWS Open Source Linux Off-The-Shelf 40

Netflix Tech

NOVEMBER 22, 2019

4:45pm-5:45pm NFX 209 File system as a service at Netflix Kishore Kasi , Senior Software Engineer Abstract : As Netflix grows in original content creation, its need for storage is also increasing at a rapid pace. Technology advancements in content creation and consumption have also increased its data footprint.

AWS 40

AWS Open Source Linux Off-The-Shelf 40

Altexsoft

JUNE 14, 2021

You can start investing in data infrastructure and analytical pipelines to automate data collection and analysis mechanisms. You’ll need a data engineering team for that. How data engineers and data platforms work. The data you collect is only as good as the insights gained from it.

Insurance 88

Insurance Machine Learning Artificial Inteligence Policies 88

Altexsoft

AUGUST 19, 2022

That’s why any business in any industry can benefit from using the platform – from small mom-and-pop style ones that only need data visualization and basic reporting to enterprise corporations that process large masses of data across multiple departments. You get 10GB of cloud storage and can upload 1GB of data at a time.

Weak Development Team 52

Weak Development Team Data Azure Analytics 52

RapidValue

JULY 21, 2019

DataOps aids data practitioners to continuously deliver quality data to applications and business processes. The end-users of data, like the data analysts and data scientists, work closely with both data engineers and IT Ops in order to deliver continuous data movement.

Business Transformation 65

Business Transformation Enterprise DevOps Analytics 65

Altexsoft

OCTOBER 29, 2020

But before you dive in, we recommend you reviewing our more beginner-friendly articles on data transformation: Complete Guide to Business Intelligence and Analytics: Strategy, Steps, Processes, and Tools. What is Data Engineering: Explaining the Data Pipeline, Data Warehouse, and Data Engineer Role.

Testing 63

Testing Tools Software Review Technical Review 63