CIO

JUNE 7, 2022

Data analytics is a discipline focused on extracting insights from data. It comprises the processes, tools and techniques of data analysis and management, including the collection, organization, and storage of data. Data analytics and data science are closely related.

Analytics 332

Analytics Data Analysis Business Analytics 332

Altexsoft

APRIL 16, 2021

As the topic is closely related to business intelligence (BI) and data warehousing (DW), we suggest you to get familiar with general terms first: A guide to business intelligence. An overview of data warehouse types. What is data pipeline. This could be a transactional database or any other storage we take data from.

Analytics 98

Analytics Analysis Storage Business Intelligence 98

Hu's Place - HitachiVantara

APRIL 11, 2019

According to the Harvard Business Review , " Cross-industry studies show that on average, less than half of an organization’s structured data is actively used in making decisions—and less than 1% of its unstructured data is analyzed or used at all.

Data Engineering 81

Data Engineering Machine Learning Artificial Inteligence Technical Review 81

O'Reilly Media - Data

APRIL 3, 2018

Data Science and Machine Learning sessions will cover tools, techniques, and case studies. This year’s sessions on Data Engineering and Architecture showcases streaming and real-time applications, along with the data platforms used at several leading companies. Data platforms. Telecom sessions.

Machine Learning 154

Machine Learning Artificial Inteligence Company Case Study 154

Perficient

APRIL 1, 2024

Traditionally, organizations used to provision multiple services of Azure Services, like Azure Storage, Azure Databricks, etc. Case Study A private equity organization wants to have a close eye on equity stocks it has invested in for their clients. Fabric brings all the required services into a single platform.

Data 64

Data Azure Case Study Data Engineering 64

Kentik

MARCH 7, 2023

Tenets of network observability A detailed explanation of network observability itself is out of the scope of this article, but I want to focus on its core tenets before exploring a couple of brief case studies. Network observability, when properly implemented, enables operators to: Ingest telemetry from every part of the network.

Network 99

Network Cloud Data Case Study 99

Altexsoft

MARCH 16, 2021

Omics data is heterogeneous information generated from genome, proteome, transcriptome, metabolome, and epigenome used to study complex biochemical and regulatory processes of humans and other organisms. It’s referred to as big data, since, for example, just a single complete human genome sequence produces about 200 Gb of raw data.

Big Data 116

Big Data Healthcare Applications Data 116

Altexsoft

MARCH 31, 2020

It means you must collect transactional data and move it from the database that supports transactions to another system that can handle large volumes of data. And, as is common, to transform it before loading to another storage system. But how do you move data? The simplest illustration for a data pipeline.

Data 76

Data Storage Analytics Data Center 76

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.

Weak Development Team 52

Weak Development Team Big Data Data Machine Learning 52

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. How data engineering works under the hood.

Big Data 59

Big Data Data Google Cloud Storage 59

Altexsoft

DECEMBER 9, 2020

Some studies show that about half of Americans make decisions based on their “gut-feeling”. Moreover, the MicroStrategy Global Analytics Study reports that access to data is extremely limited, taking 60 percent of employees hours or even days to get the information they need. “War is 90 percent information.”. Napoleon Bonaparte.

Analytics 102

Analytics Technical Review Technology Applications 102

Cloudera

JANUARY 15, 2021

Cloudera Data Warehouse is a highly scalable service that marries the SQL engine technologies of Apache Impala and Apache Hive with cloud-native features to deliver best-in-class price-performance for users running data warehousing workloads in the cloud. No local data loading step was required prior to query execution.

Performance 94

Performance Cloud Data Storage 94

Cloudera

APRIL 9, 2021

In this article, we look at seven challenges, explore the impacts to platform and business owners and highlight how a modern data warehouse can address them. A recent Harvard Business Review study confirmed that data is increasingly being spread across data centres, private clouds and public clouds. Multiplatform.

Data 58

Data Software Review Technical Review Architecture 58

Cloudera

NOVEMBER 22, 2021

In addition, data pipelines include more and more stages, thus making it difficult for data engineers to compile, manage, and troubleshoot those analytical workloads. benchmarking study conducted by independent 3rd party ). CRM platforms). Limited flexibility to use more complex hosting models (e.g.,

Scalability 92

Scalability Technical Review Data Analytics 92

Altexsoft

NOVEMBER 23, 2020

However, many companies treat even a massive data migration as a low-level, two-clicks task. Recent studies revealed that 55 percent of data migration projects went over budget and 62 percent appeared to be harder than expected or failed. Otherwise, let’s start from the most basic question: What is data migration?

Data 104

Data Transportation Backup Storage 104

Cloudera

OCTOBER 25, 2022

“They combine the best of both worlds: flexibility, cost effectiveness of data lakes and performance, and reliability of data warehouses.”. It allows users to rapidly ingest data and run self-service analytics and machine learning. There should be no ability of data lakehouse users to expose the environment to significant risk.

Cloud 52

Cloud Data Firewall AWS 52

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 82

Data Analytics Cloud Technical Review 82

Altexsoft

JUNE 26, 2023

Both data integration and ingestion require building data pipelines — series of automated operations to move data from one system to another. For this task, you need a dedicated specialist — a data engineer or ETL developer. Data engineering explained in 14 minutes. Find sources of relevant data.

Machine Learning 52

Machine Learning Artificial Inteligence Data Systems Review 52

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

Hu's Place - HitachiVantara

JANUARY 3, 2019

” Deployments of large data hubs have only resulted in more data silos that are not easily understood, related, or shared. More focus will be on the operational aspects of data rather than the fundamentals of capturing, storing and protecting data.

Trends 86

Trends Artificial Inteligence Data Center Machine Learning 86

Cloudera

DECEMBER 20, 2022

In addition, using data well can allow better decisions to be made, such as the possibility of bypassing the day-ahead market and going directly to the intraday market and having a better return per watt generated. . In this respect, several studies project that a proper use of advanced analytics implies savings of between 5% and 7.5%.

Energy 81

Energy Analytics Data Machine Learning 81

Altexsoft

JUNE 14, 2021

According to the Accenture study , 95 percent of new customers are ready to share their data in exchange for personalized insurance services. Accenture personalized insurance study. You can start investing in data infrastructure and analytical pipelines to automate data collection and analysis mechanisms.

Insurance 88

Insurance Machine Learning Artificial Inteligence Policies 88

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

ParkMyCloud

MAY 29, 2019

As more and more enterprises drive value from container platforms, infrastructure-as-code solutions, software-defined networking, storage, continuous integration/delivery, and AI, they need people and skills on board with ever more niche expertise and deep technological understanding. Business Intelligence Analyst.

Trends 72

Trends Cloud IoT Artificial Inteligence 72

StubHub

SEPTEMBER 20, 2018

Morin , Chief Data Officer I like to joke that I am one of the original data nerds?—?I I was into it before data was cool. In grad school, I studied operations research and convex optimization, which back then was basically a more clinical way of saying “data science.”

Data 40

Data Scalability Analytics Sport 40

Altexsoft

JANUARY 27, 2022

It’s easier to get this information from the aforementioned providers that gather data from a system of sensors, diverse third-party sources, or make use of GPS probe data. Other platforms such as Otonomo use an innovative Vehicle to Everything (V2X) technology to collect so-called connected car data from embedded modems.

Machine Learning 69

Machine Learning Artificial Inteligence Transportation Network 69

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

Kentik

MAY 23, 2017

Imagine a big data time-series datastore that unifies traffic flow records (NetFlow, sFlow, IPFIX) with related data such as BGP routing, GeoIP, network performance, and DNS logs, that retains unsummarized data for months, and that has the compute and storage power to answer ad hoc queries across billions of data points in a couple of seconds.

Network 40

Network Open Source Big Data Load Balancer 40

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. Data siloes.

Technical Review 59

Technical Review Software Review Analytics Systems Review 59

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

AUGUST 19, 2022

In our blog, we’ve been talking a lot about the importance of business intelligence (BI), data analytics, and data-driven culture for any company. Multiple studies continuously demonstrate the superiority of analytics-based organizations (e.g., 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

Altexsoft

NOVEMBER 5, 2021

To avoid errors that may threaten patient safety, AHIMA introduced the data quality management model which covers. Application, or the reason for data collection, Collection, or the process of data gathering, Warehousing, or systems and activities related to data storage and archiving, and. Supported data formats.

Technical Review 59

Technical Review Technology Software Review Healthcare 59

Altexsoft

NOVEMBER 9, 2021

Many people believe that shopping is a therapy (and as a matter of fact, studies prove it). In addition, having accurate, data-based forecasts can be a great help at the initial negotiating stage. Meanwhile, we’ll describe the process of turning raw data around you into actionable insights. Extract data.

Analytics 52

Analytics Software Review Systems Review Technical Review 52

Altexsoft

MAY 27, 2020

Machine learning is a subset of data science , a field of knowledge studying how we can extract value from data. ML in its turn suggests methods and practices to train algorithms on this data to solve problems like object classification on the image, without providing rules and programming patterns.

Machine Learning 89

Machine Learning Artificial Inteligence Architecture Training 89

Kentik

MAY 31, 2017

It’s high time to move away from this legacy paradigm to a unified, scalable, real-time solution built on the power of big data. Today’s siloed network management tools can be traced back to an earlier era, when design was constrained by the limited computing, memory, and storage capacity of appliances or single-server software deployments.

Network 40

Network Tools Big Data Metrics 40

Kentik

OCTOBER 25, 2016

These issues are rooted in the inherent compute and storage limitations of scale-up detection architectures. For more detail, read our PenTeleData case study. The big data approach that Kentik uses to deliver more accurate DDoS detection also makes possible long-term retention of raw flow records and related data.

Scalability 40

Scalability Architecture Big Data Network 40