Altexsoft

AUGUST 29, 2023

Instead of relying on traditional hierarchical structures and predefined schemas, as in the case of data warehouses, a data lake utilizes a flat architecture. This structure is made efficient by data engineering practices that include object storage. Watch our video explaining how data engineering works.

Architecture 52

Architecture Data Storage Machine Learning 52

Hu's Place - HitachiVantara

NOVEMBER 11, 2018

This will be a blend of private and public hyperscale clouds like AWS, Azure, and Google Cloud Platform. The term “hyperscale” is used by Gartner to refer to Amazon Web Services, Microsoft Azure, and Google Cloud Platform. REAN Cloud has expertise working with the hyperscale public clouds.

Cloud 78

Cloud Google Cloud Azure AWS 78

Altexsoft

JULY 23, 2020

It facilitates collaboration between a data science team and IT professionals, and thus combines skills, techniques, and tools used in data engineering, machine learning, and DevOps — a predecessor of MLOps in the world of software development. MLOps lies at the confluence of ML, data engineering, and DevOps.

Artificial Inteligence 145

Artificial Inteligence Machine Learning DevOps Tools 145

ParkMyCloud

DECEMBER 11, 2020

With the combined knowledge from our previous blog posts on free training resources for AWS and Azure , you’ll be well on your way to expanding your cloud expertise and finding your own niche. 9 Free Azure Training Resources. Cloud Certification Guide: How to Master & Showcase Your Expertise in AWS, Azure, & Google Cloud.

Google Cloud 97

Google Cloud Training Resources Cloud 97

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

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. Data availability. Please note!

Tools 52

Tools Software Review Systems Review Testing 52

Altexsoft

MARCH 31, 2020

These can be data science teams , data analysts, BI engineers, chief product officers , marketers, or any other specialists that rely on data in their work. The simplest illustration for a data pipeline. Data pipeline components. Data pipeline components. When do you need a data pipeline?

Data 76

Data Storage Analytics Data Center 76

ParkMyCloud

JANUARY 17, 2020

Each of the ‘big three’ cloud providers (AWS, Azure, GCP) offer a number of cloud certification options that individuals can get to validate their cloud knowledge and skill set, while helping them advance in their careers and broaden the scope of their achievements. . Microsoft Azure Certifications. Azure Fundamentals.

Google Cloud 9

Google Cloud Azure AWS Cloud 9

Altexsoft

DECEMBER 4, 2020

Three types of data migration tools. Automation scripts can be written by data engineers or ETL developers in charge of your migration project. This makes sense when you move a relatively small amount of data and deal with simple requirements. Phases of the data migration process. Data sources and destinations.

Software Review 69

Software Review Software Data Technical Review 69

Altexsoft

OCTOBER 2, 2020

Data integration and interoperability: consolidating data into a single view. Specialist responsible for the area: data architect, data engineer, ETL developer. Scattered across different storages in various formats, data values don’t talk to each other. Snowflake data management processes.

Database Administration 64

Database Administration Strategy Data Technical Review 64

Altexsoft

JULY 19, 2022

Decentralized data ownership by domain. Zhamak Dehghani divides the data into the “two planes”: The operational plane presents the data from the source systems where it originates — for example, front-desk apps, IoT systems , point of sales systems , etc. And it’s their job to guarantee data quality.

Architecture 52

Architecture Data Analytics Data Engineering 52

Altexsoft

OCTOBER 21, 2022

The technology was written in Java and Scala in LinkedIn to solve the internal problem of managing continuous data flows. In other words, Kafka can serve as a messaging system, commit log, data integration tool, and stream processing platform. cloud data warehouses — for example, Snowflake , Google BigQuery, and Amazon Redshift.

Weak Development Team 93

Weak Development Team Technical Review Systems Review Software Review 93

Altexsoft

NOVEMBER 10, 2021

Unlike warehouses that can only deal with structured data, lakehouses allow for a wider choice of data formats including video, audio, text documents, PDF files, system logs, etc. Moreover, they support real-time data, e.g., streams from IoT devices. Open formats support. websites, etc.

Architecture 52

Architecture Data Storage Machine Learning 52

O'Reilly Media - Ideas

MAY 1, 2019

Data science and data tools. Practical Linux Command Line for Data Engineers and Analysts , May 20. First Steps in Data Analysis , May 20. Data Analysis Paradigms in the Tidyverse , May 30. Data Visualization with Matplotlib and Seaborn , June 4. Real-time Data Foundations: Kafka , June 11.

Course 45

Course Training Artificial Inteligence Machine Learning 45

Altexsoft

JANUARY 22, 2020

With the uprise of internet-of-things (IoT) devices, overall data volume increase, and engineering advancements in this field led to new ways of collecting, processing, and analysing data. As a result, it became possible to provide real-time analytics by processing streamed data. Azure Stream Analytics.

Analytics 20

Analytics Data IoT Analysis 20

O'Reilly Media - Ideas

MARCH 6, 2019

Data science and data tools. Practical Linux Command Line for Data Engineers and Analysts , March 13. Data Modelling with Qlik Sense , March 19-20. Foundational Data Science with R , March 26-27. What You Need to Know About Data Science , April 1. IoT Fundamentals , April 4-5.

Course 10

Course Artificial Inteligence Training Microservices 10

Cloudera

MARCH 6, 2018

Altus SDX enables companies to more easily build and deploy high-value applications for customer analytics, IoT, cyber-security, and more. Nimbly run many distinct applications against shared data. Altus SDX creates a common catalog of data, defining what is available and how it can be used by whom.

Analytics 40

Analytics Cloud Big Data AWS 40

O'Reilly Media - Ideas

SEPTEMBER 11, 2019

Machine learning, artificial intelligence, data engineering, and architecture are driving the data space. The Strata Data Conferences helped chronicle the birth of big data, as well as the emergence of data science, streaming, and machine learning (ML) as disruptive phenomena.

Conference 8

Conference Data Data Engineering Artificial Inteligence 8

Cloudera

MAY 18, 2021

That is accomplished by delivering most technical use cases through a primarily container-based CDP services (CDP services offer a distinct environment for separate technical use cases e.g., data streaming, data engineering, data warehousing etc.) data streaming, data engineering, data warehousing etc.),

Cloud 83

Cloud Technical Review Storage Backup 83