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

Existek

JANUARY 11, 2022

It’ll be interesting to discover how market-leading providers set new standards and alter approaches to cloud computing. Azure vs AWS market share. AZURE VS AWS MARKET SHARE. It might be challenging as it requires a detailed analysis of the business-specific needs and a deeper understanding of the overall market situation. .

Azure 52

Azure AWS Cloud How To 52

Exadel

FEBRUARY 20, 2023

Amazon For Cloud Artificial Intelligence Amazon began by making storage and virtual machines. Vertex AI leverages a combination of data engineering, data science, and ML engineering workflows with a rich set of tools for collaborative teams. Let’s race through these vendors and see what they’re up to.

Artificial Inteligence 52

Artificial Inteligence Cloud Machine Learning Azure 52

Cloudera

DECEMBER 10, 2020

Why worry about costs with cloud-native data warehousing? Have you been burned by the unexpected costs of a cloud data warehouse? If so, you know about the failed economics of some cloud-native solutions on the market today. If not, before adopting a cloud data warehouse, consider the true costs of a cloud-native data warehouse.

Data 98

Data Technical Review Storage Systems Review 98

KitelyTech

FEBRUARY 3, 2021

In this article, we discussed the most popular big data and data science development services that your company can take advantage of. User Data Collection. User data collection is data about a user who is collected for market research purposes. Database Management.

Big Data 52

Big Data Data Development Business Intelligence 52

Altexsoft

AUGUST 25, 2020

Prominent enterprises in numerous sectors including sales, marketing, research, and healthcare are actively collecting big data. 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.

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Big Data Data Engineering Engineering Data 75

d2iq

FEBRUARY 19, 2021

Components that are unique to data engineering and machine learning (red) surround the model, with more common elements (gray) in support of the entire infrastructure on the periphery. Before you can build a model, you need to ingest and verify data, after which you can extract features that power the model.

Artificial Inteligence 103

Artificial Inteligence Machine Learning Technical Review Software Review 103

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

Altexsoft

SEPTEMBER 9, 2022

These are important for SEO purposes because search engines use them to “understand” what the page is about and how it has to be displayed and ranked in search results. Besides, they have to attract users to visit the website, so it’s a powerful marketing tool. It aims at making data assets understandable and discoverable for users.

Tools 59

Tools Technical Review Software Review Systems Review 59

Altexsoft

APRIL 13, 2023

Due to extensive usage of connected IoT devices and advanced processing technologies, SCCTs not only gather data and build operational reports but also create predictions, define the impact of various macro- and microeconomic factors on the supply chain, and run “what-if” scenarios to find the best course of action. To buy or build?

Technical Review 59

Technical Review Software Review Analytics Systems Review 59

TechCrunch

NOVEMBER 4, 2022

Ashish Kakran , principal at Thomvest Ventures , is a product manager/engineer turned investor who enjoys supporting founders with a balance of technical know-how, customer insights, empathy with challenges and market knowledge. Each step of the data analysis process is ripe for disruption. More posts by this contributor.

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Data Storage Analytics Data Engineering 218

Cloudera

APRIL 26, 2023

At its core, CDP Private Cloud Data Services (“the platform”) is an end-to-end cloud native platform that provides a private open data lakehouse. It offers features such as data ingestion, storage, ETL, BI and analytics, observability, and AI model development and deployment.

Cloud 66

Cloud Data Load Balancer Storage 66

AWS Machine Learning - AI

AUGUST 8, 2024

Twilio enables companies to use communications and data to add intelligence and security to every step of the customer journey, from sales and marketing to growth, customer service, and many more engagement use cases in a flexible, programmatic way. The following diagram illustrates the solution architecture.

Artificial Inteligence 99

Artificial Inteligence Data Generative AI AWS 99

CIO

JULY 15, 2022

The forecasting systems DTN had acquired were developed by different companies, on different technology stacks, with different storage, alerting systems, and visualization layers. Working with his new colleagues, he quickly identified rebuilding those five systems around a single forecast engine as a top priority.

Systems Review 279

Systems Review Fractional CTO System Development Team Review 279

Altexsoft

JANUARY 14, 2021

Snowflake, Redshift, BigQuery, and Others: Cloud Data Warehouse Tools Compared. From simple mechanisms for holding data like punch cards and paper tapes to real-time data processing systems like Hadoop, data storage systems have come a long way to become what they are now. Is it still so?

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Backup Azure Software Review Architecture 115

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. Stream processing.

Analytics 20

Analytics Data IoT Analysis 20

Altexsoft

JUNE 6, 2019

data is analyzed. “We We help retailers with a variety of their challenges, including better inventory prediction using their point-of-sale data combined with external factors such as weather, local events, and economic uncertainty. Only the smart speaker market has grown significantly, from 21.5 Rapidly changing market.

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Artificial Inteligence Artificial Intelligence Retail Innovation 89

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 Compliance 109

Altexsoft

JULY 18, 2023

Its flexibility allows it to operate on single-node machines and large clusters, serving as a multi-language platform for executing data engineering , data science , and machine learning tasks. Before diving into the world of Spark, we suggest you get acquainted with data engineering in general. Machine learning.

Weak Development Team 52

Weak Development Team Big Data Data Artificial Inteligence 52

Cloudera

AUGUST 20, 2021

The Cloudera Data Platform comprises a number of ‘data experiences’ each delivering a distinct analytical capability using one or more purposely-built Apache open source projects such as Apache Spark for Data Engineering and Apache HBase for Operational Database workloads. Build Once, Scale Anywhere.

Strategy 100

Strategy Data Technical Review Weak Development Team 100

Altexsoft

JULY 29, 2022

Apache Hadoop is an open-source Java-based framework that relies on parallel processing and distributed storage for analyzing massive datasets. 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. Source: Allied Market Research. Scalability.

Big Data 59

Big Data Data Google Cloud Open Source 59

Altexsoft

APRIL 26, 2022

With the consistent rise in data volume, variety, and velocity, organizations started seeking special solutions to store and process the information tsunami. This demand gave birth to cloud data warehouses that offer flexibility, scalability, and high performance. Each node has its own disk storage. What is Snowflake?

Weak Development Team 59

Weak Development Team Data Storage Technical Review 59

Perficient

APRIL 1, 2024

Traditionally, organizations used to provision multiple services of Azure Services, like Azure Storage, Azure Databricks, etc. They want to generate trends, predictions (using ML), and analyze data based on algorithms developed by their portfolio management team in collaboration with data scientists written in Python.

Data 64

Data Azure Case Study Data Engineering 64

Altexsoft

MARCH 16, 2021

In general, a data infrastructure is a system of hardware and software tools used to collect, store, transfer, prepare, analyze, and visualize data. Check our article on data engineering to get a detailed understanding of the data pipeline and its components. Big data infrastructure in a nutshell.

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Big Data Healthcare Applications Data 116

Altexsoft

JUNE 26, 2023

It’s the first and essential stage of data-related activities and projects, including business intelligence , machine learning , and big data analytics. Data gathering also plays a key role in different steps of product management , from product discovery to product marketing. Data engineering explained in 14 minutes.

Artificial Inteligence 52

Artificial Inteligence Machine Learning Data Systems Review 52

Cloudera

NOVEMBER 2, 2020

Correlations across data domains, even if they are not traditionally stored together (e.g. real-time customer event data alongside CRM data; network sensor data alongside marketing campaign management data). The extreme scale of “big data”, but with the feel and semantics of “small data”.

Data 100

Data Analytics Storage Big Data 100

Hu's Place - HitachiVantara

MARCH 18, 2019

The requirement was to centralize and archive the data collected from these endpoint devices while keeping with regulation requirements. Hitachi Vantara provided HCP object storage to replace traditional storage for ease of access, search and index, privacy regulations and hybrid cloud.

Healthcare 61

Healthcare Tourism Systems Review Energy 61

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 60

Data Software Review Technical Review Architecture 60

Cloudera

DECEMBER 12, 2018

Yet, there were some limitations in MPP at the time, because some of these systems running Hive were quite large, and the database community thought that instead of the future being Hive on MapReduce or something similar, that we could extend, bend, and change the MPP engines to actually operate in a more scalable manner on such large data.

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Data Marketing Storage Big Data 42

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. All data goes through the middleman — in our case, Kafka — that manages messages and ensures their security.

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Weak Development Team Technical Review Systems Review Open Source 93

Altexsoft

JULY 19, 2022

The HR team will manage all of this data and generate datasets to be consumed by other users in the company like the marketing team. The same will be done by other domains around the company, e.g., marketing domain, sales domain, logistics domain, etc. And it’s their job to guarantee data quality. It works like this.

Architecture 52

Architecture Data Analytics Data Engineering 52

Altexsoft

DECEMBER 4, 2020

Here, we’ll focus on tools that can save you the lion’s share of tedious tasks — namely, key types of data migration software, selection criteria, and some popular options available in the market. Types of data migration tools. Three types of data migration tools. Phases of the data migration process.

Software Review 69

Software Review Software Data Technical Review 69

Altexsoft

JUNE 7, 2021

Apache Hadoop is an open-source framework written in Java for distributed storage and processing of huge datasets. The keyword here is distributed since the data quantities in question are too large to be accommodated and analyzed by a single computer. scalability. Virtually, Hadoop puts no limits on the storage capacity.

Big Data 98

Big Data Tools Data Storage 98

Altexsoft

OCTOBER 2, 2020

If your business generates tons of data and you’re looking for ways to organize it for storage and further use, you’re at the right place. Read the article to learn what components data management consists of and how to implement a data management strategy in your business. Snowflake data management processes.

Strategy 64

Strategy Database Administration Data Technical Review 64

Altexsoft

OCTOBER 24, 2019

Similar to humans companies generate and collect tons of data about the past. And this data can be used to support decision making. While our brain is both the processor and the storage, companies need multiple tools to work with data. And one of the most important ones is a data warehouse. Subject-oriented data.

Architecture 50

Architecture Enterprise Data Technical Review 50

Altexsoft

AUGUST 4, 2021

A data mart is a subject-oriented relational database commonly containing a subset of DW data that is specific for a particular business department of an enterprise, e.g., a marketing department. Data marts get information from relatively few sources and are small in size — less than 100 GB. Time-limited data projects.

Data 64

Data Analytics Construction Cloud 64

Altexsoft

SEPTEMBER 20, 2019

Analyzing business information to facilitate data-driven decision making is what we call business intelligence or BI. In plain language, BI is a set of tools and methods to extract raw data from its source, transform it, load into a unified storage, and present to the user. Data visualization tools and libraries.

Business Intelligence 18

Business Intelligence Tools Data Analytics 18